From d8d6c8a7e6fa8457e5251e10ab1d697997320a1f Mon Sep 17 00:00:00 2001 From: p6 Date: Mon, 30 May 2022 16:27:12 +0530 Subject: [PATCH 01/76] init --- README.md | 25 +--- _config.yml | 82 ++++++++--- _layouts/default.html | 22 +-- _posts/2018-01-11-customer-service.md | 31 ----- _posts/2018-01-11-quick-start-guide.md | 27 ---- _posts/2018-01-12-is-intelligence-enough.md | 19 --- ...never-stopped-worrying-never-loved-bomb.md | 13 -- ...tions-for-creating-new-site-with-jekyll.md | 15 -- _posts/2018-01-12-press-and-education.md | 20 --- _posts/2018-01-12-red-riding.md | 16 --- _posts/2018-01-12-tree-of-codes.md | 18 --- _posts/2018-01-12-we-all-wait-for-summer.md | 11 -- _posts/2018-05-12-about-bundler.md | 17 --- _posts/2018-06-12-acumulated-experience.md | 16 --- _posts/2018-06-12-education.md | 51 ------- _posts/2018-06-12-first-mass-produced.md | 16 --- ...8-06-12-powerful-things-markdown-editor.md | 104 -------------- _posts/2021-04-01-The-Platformatory-Way.md | 129 ++++++++++++++++++ assets/blog-images/platform-arch-model.svg | 1 + 19 files changed, 199 insertions(+), 434 deletions(-) delete mode 100644 _posts/2018-01-11-customer-service.md delete mode 100644 _posts/2018-01-11-quick-start-guide.md delete mode 100644 _posts/2018-01-12-is-intelligence-enough.md delete mode 100644 _posts/2018-01-12-never-stopped-worrying-never-loved-bomb.md delete mode 100644 _posts/2018-01-12-options-for-creating-new-site-with-jekyll.md delete mode 100644 _posts/2018-01-12-press-and-education.md delete mode 100644 _posts/2018-01-12-red-riding.md delete mode 100644 _posts/2018-01-12-tree-of-codes.md delete mode 100644 _posts/2018-01-12-we-all-wait-for-summer.md delete mode 100644 _posts/2018-05-12-about-bundler.md delete mode 100644 _posts/2018-06-12-acumulated-experience.md delete mode 100644 _posts/2018-06-12-education.md delete mode 100644 _posts/2018-06-12-first-mass-produced.md delete mode 100644 _posts/2018-06-12-powerful-things-markdown-editor.md create mode 100644 _posts/2021-04-01-The-Platformatory-Way.md create mode 100644 assets/blog-images/platform-arch-model.svg diff --git a/README.md b/README.md index da71f9d5f0..c6c44f7fe4 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,6 @@ -# Mediumish - Jekyll Theme +# Platformatory Blog -[Live Demo](https://wowthemesnet.github.io/mediumish-theme-jekyll/)   |   [Download](https://github.com/wowthemesnet/mediumish-theme-jekyll/archive/master.zip)   |   [Documentation](https://bootstrapstarter.com/template-mediumish-bootstrap-jekyll/)   |   [Buy me a coffee](https://www.wowthemes.net/donate/) +# Contributing -![mediumish](assets/images/mediumish-jekyll-template.png) - - -### Copyright - -Copyright (C) 2019 Sal, https://www.wowthemes.net - -**Mediumish for Jekyll** is designed and developed by [Sal](https://www.wowthemes.net) and it is *free* under MIT license. - -Buy Me A Coffee - -### Contribute - -1. [Fork the repo](https://github.com/wowthemesnet/mediumish-theme-jekyll). -2. Clone a copy of your fork on your local -3. Create a branch off of master and give it a meaningful name (e.g. my-new-mediumish-feature). -4. Make necessary changes, commit, push and open a pull request on GitHub. - -Thank you! +- Fork the repo +- Make a PR diff --git a/_config.yml b/_config.yml index 8f192fabcd..abc29eac52 100644 --- a/_config.yml +++ b/_config.yml @@ -1,35 +1,81 @@ # Site -name: "Mediumish" -title: "Mediumish" -description: "Jekyll template, Medium styled, free for bloggers." +name: "Platformatory Blog" +title: "Platformatory Blog" +description: "Platform Engineering in 2022" logo: 'assets/images/logo.png' favicon: 'assets/images/logo.png' -baseurl: /mediumish-theme-jekyll +baseurl: /blog google_analytics: 'UA-46278016-1' -disqus: 'demowebsite' -mailchimp-list: 'https://wowthemes.us11.list-manage.com/subscribe/post?u=8aeb20a530e124561927d3bd8&id=8c3d2d214b' +#disqus: 'demowebsite' +mailchimp-list: 'https://platformatory.us17.list-manage.com/subscribe/post?u=8cc2e44e80300ae193f198bcc&id=6246b12a61' include: ["_pages"] permalink: /:title/ # Authors authors: - sal: - name: Sal - display_name: Sal + p6: + name: pavan + display_name: Pavan Keshavamurthy gravatar: e56154546cf4be74e393c62d1ae9f9d4 - email: wowthemesnet@gmail.com - web: https://www.wowthemes.net + email: pavan@platformatory.io + web: https://grahana.net + twitter: https://twitter.com/p6 + description: "Cofounder of Platformatory. Aspiring Farmer; Love programming, distributed systems and random walks" + badri: + name: badri + display_name: Lakshminarasimhan Parthasarathy + avatar: 'assets/images/avatar.png' + gravatar: b1cc14991db7a456fcd761680bbc8f81 + email: lakshmi@platformatory.io + web: https://www.platformatory.io + twitter: https://twitter.com/wowthemesnet + description: "Principal Engineer at Platformatory; Musician" + srikanth: + name: srikanth + display_name: Srikanth Pai + avatar: 'assets/images/avatar.png' + gravatar: b1cc14991db7a456fcd761680bbc8f81 + email: srikanth@platformatory.io + web: https://www.platformatory.io + twitter: https://twitter.com/wowthemesnet + description: "Data Engineer at Platformatory;" + dasa: + name: dasa + display_name: Dasa Sathyan + avatar: 'assets/images/avatar.png' + gravatar: b1cc14991db7a456fcd761680bbc8f81 + email: srikanth@platformatory.io + web: https://www.platformatory.io twitter: https://twitter.com/wowthemesnet - description: "Author of Mediumish, a Bootstrap Medium styled template available for WordPress, HTML, Ghost and Jekyll. You are currently previewing Jekyll template demo." - john: - name: John - display_name: John + description: "Data Engineer at Platformatory;" + ashwin: + name: ashwin + display_name: Ashwin Venkatesan avatar: 'assets/images/avatar.png' gravatar: b1cc14991db7a456fcd761680bbc8f81 - email: wowthemesnet@gmail.com - web: https://www.wowthemes.net + email: srikanth@platformatory.io + web: https://www.platformatory.io twitter: https://twitter.com/wowthemesnet - description: "This is the author box. Write a short description of the author here. You are currently previewing Mediumish demo, a Jekyll template compatible with Github pages." + description: "Data Engineer at Platformatory;" + venkatesh: + name: venkatesh + display_name: Venkatesh Keshavamurthy + avatar: 'assets/images/avatar.png' + gravatar: b1cc14991db7a456fcd761680bbc8f81 + email: srikanth@platformatory.io + web: https://www.platformatory.io + twitter: https://twitter.com/wowthemesnet + description: "Data Engineer at Platformatory;" + kiran: + name: kiran + display_name: Kiran Sreevastav + avatar: 'assets/images/avatar.png' + gravatar: b1cc14991db7a456fcd761680bbc8f81 + email: srikanth@platformatory.io + web: https://www.platformatory.io + twitter: https://twitter.com/wowthemesnet + description: "Data Engineer at Platformatory;" + # Plugins plugins: diff --git a/_layouts/default.html b/_layouts/default.html index 337b917db3..3eeaa69336 100644 --- a/_layouts/default.html +++ b/_layouts/default.html @@ -71,26 +71,6 @@ Blog - - - - - - - - - - {% include search-lunr.html %} @@ -175,7 +155,7 @@

Explore diff --git a/_posts/2018-01-11-customer-service.md b/_posts/2018-01-11-customer-service.md deleted file mode 100644 index 902b51bfc7..0000000000 --- a/_posts/2018-01-11-customer-service.md +++ /dev/null @@ -1,31 +0,0 @@ ---- -layout: post -title: "Inception Movie" -author: john -categories: [ Jekyll, tutorial ] -tags: [red, yellow] -image: assets/images/11.jpg -description: "My review of Inception movie. Acting, plot and something else in this short description." -featured: true -hidden: true -rating: 4.5 ---- - -Review products, books, movies, restaurant and anything you like on your Jekyll blog with Mediumish! JSON-LD ready for review property. - -#### How to use? - -It's actually really simple! Add the rating in your YAML front matter. It also supports halfs: - -```html ---- -layout: post -title: "Inception Movie" -author: john -categories: [ Jekyll, tutorial ] -tags: [red, yellow] -image: assets/images/11.jpg -description: "My review of Inception movie. Actors, directing and more." -rating: 4.5 ---- -``` diff --git a/_posts/2018-01-11-quick-start-guide.md b/_posts/2018-01-11-quick-start-guide.md deleted file mode 100644 index 178e04003c..0000000000 --- a/_posts/2018-01-11-quick-start-guide.md +++ /dev/null @@ -1,27 +0,0 @@ ---- -layout: post -title: "Let's test spoilers" -author: sal -categories: [ Jekyll, tutorial ] -image: assets/images/12.jpg -featured: true -hidden: true ---- - -Director Roland Suso Richter's enigmatic psychological thriller (direct to video/DVD) was based upon screenwriter Michael Cooney's own play "Point of Death" - a title that gave away the film's entire plot twist premise. - -As in many similar films, such as Jacob's Ladder (1990), Soul Survivors (2001), and The Butterfly Effect (2004), events and people were thoroughly distorted and confused because the protagonist was at the point of death. The tagline was misleading: - -"When You Don't Have a Memory, How Can You Remember Who to Trust?" - -The mind-warping film opened with a hospital patient Simon Cable (Ryan Phillippe) awakening in a hospital with little knowledge (amnesia perhaps?) of what had happened, and why he was there, etc. He was told by attending Dr. Jeremy Newman (Stephen Rea) that it was July 29, 2002 (Simon thought it was the year 2000 - he was confused - he heard a doctor say 20:00 hours!) and that he had died for two minutes from cardiac arrest following the near-fatal accident -- but he had been revived ("You're as good as new"). Dr. Newman: "Simon, this is the 29th of July. The year is 2002. And your wife, whose name is Anna, is waiting outside." - -(The doctor left off four crucial additional words, revealed in the film's ending.) (Spoiler: Simon had died and was not resuscitated!). - -A major clue to everything that truly happened was the scene that played next under the credits - hospital staff failed to bring a patient back to life with a defibrillator after a car accident. Chest compressions failed and there was no pulse. A second major clue was provided by hospital orderly Travis (Stephen Graham): Everybody dies. No mystery there. But why and how everyone dies. Now, there's a mystery worth solving. Probably the biggest mystery there is. - -#### So how do we do spoilers? - -```html -My hidden paragraph here. -``` diff --git a/_posts/2018-01-12-is-intelligence-enough.md b/_posts/2018-01-12-is-intelligence-enough.md deleted file mode 100644 index 707f8d97af..0000000000 --- a/_posts/2018-01-12-is-intelligence-enough.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -layout: post -title: "External Featured Image" -author: sal -categories: [ Jekyll, tutorial, web development ] -image: "https://images.unsplash.com/photo-1541544537156-7627a7a4aa1c?ixlib=rb-0.3.5&ixid=eyJhcHBfaWQiOjEyMDd9&s=a20c472bc23308e390c8ffae3dd90c60&auto=format&fit=crop&w=750&q=80" ---- -Education must also train one for quick, resolute and effective thinking. To think incisively and to think for one's self is very difficult. - -> We are prone to let our mental life become invaded by legions of half truths, prejudices, and propaganda. At this point, I often wonder whether or not education is fulfilling its purpose. A great majority of the so-called educated people do not think logically and scientifically. - -Even the press, the classroom, the platform, and the pulpit in many instances do not give us objective and unbiased truths. To save man from the morass of propaganda, in my opinion, is one of the chief aims of education. Education must enable one to sift and weigh evidence, to discern the true from the false, the real from the unreal, and the facts from the fiction. - -The function of education, therefore, is to teach one to think intensively and to think critically. But education which stops with efficiency may prove the greatest menace to society. The most dangerous criminal may be the man gifted with reason, but with no morals. - -The late Eugene Talmadge, in my opinion, possessed one of the better minds of Georgia, or even America. Moreover, he wore the Phi Beta Kappa key. By all measuring rods, Mr. Talmadge could think critically and intensively; yet he contends that I am an inferior being. Are those the types of men we call educated? - -We must remember that intelligence is not enough. Intelligence plus character--that is the goal of true education. The complete education gives one not only power of concentration, but worthy objectives upon which to concentrate. The broad education will, therefore, transmit to one not only the accumulated knowledge of the race but also the accumulated experience of social living. - diff --git a/_posts/2018-01-12-never-stopped-worrying-never-loved-bomb.md b/_posts/2018-01-12-never-stopped-worrying-never-loved-bomb.md deleted file mode 100644 index e790de4793..0000000000 --- a/_posts/2018-01-12-never-stopped-worrying-never-loved-bomb.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -layout: post -title: "Never stopped worrying or loving the bomb" -author: sal -categories: [ Jekyll ] -image: assets/images/14.jpg ---- -> I’ve been through fire and water, I tell you! From my earliest pebblehood the wildest things you could imagine have been happening to this world of ours, and I have been right in the midst of them. - -So begins Hallam Hawksworth’s The Strange Adventures of a Pebble. Written in the 1920s, the book was part of a series which also included The Adventures of a Grain of Dust and A Year in the Wonderland of Trees, all of which were supposed to introduce children to the world of Natural Sciences. In each of them, Hawksworth personifies the natural object he is exploring, and using a mixture of folk tales, scientific facts and colloquial, friendly explanations guides the reader through the history of the natural world. It’s a real thrill of a ride, dramatizing the life cycle of supposedly dull things. The Adventures of a Grain of Dust begins even more loudly than Pebble: - -I don’t want you to think that I’m boasting, but I do believe I’m one of the greatest travellers that ever was; and if anybody, living or dead, has ever gone through with more than I have I’d like to hear about it. -Hallam Hawksworth was the pen-name of teacher Francis Blake Atkinson. He was married to the author Eleanor Stackhouse Atkinson, author of the children’s classic Greyfriars Bobby, which was based on the (supposedly) true story of a Scottish dog who spent fourteen years guarding his masters grave. The couple were both committed to education and published a weekly magazine for Chicago high school students called The Little Chronicle, as well as working for Encyclopaedia companies later in life. diff --git a/_posts/2018-01-12-options-for-creating-new-site-with-jekyll.md b/_posts/2018-01-12-options-for-creating-new-site-with-jekyll.md deleted file mode 100644 index c71c09e484..0000000000 --- a/_posts/2018-01-12-options-for-creating-new-site-with-jekyll.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -layout: post -title: "Options for creating a new site with Jekyll" -author: john -categories: [ Jekyll, tutorial ] -image: assets/images/13.jpg ---- - -`jekyll new ` installs a new Jekyll site at the path specified (relative to current directory). In this case, Jekyll will be installed in a directory called `myblog`. Here are some additional details: - -- To install the Jekyll site into the directory you're currently in, run `jekyll new` . If the existing directory isn't empty, you can pass the --force option with jekyll new . --force. -- `jekyll new` automatically initiates `bundle install` to install the dependencies required. (If you don't want Bundler to install the gems, use `jekyll new myblog --skip-bundle`.) -- By default, the Jekyll site installed by `jekyll new` uses a gem-based theme called Minima. With gem-based themes, some of the directories and files are stored in the theme-gem, hidden from your immediate view. -- We recommend setting up Jekyll with a gem-based theme but if you want to start with a blank slate, use `jekyll new myblog --blank` -- To learn about other parameters you can include with `jekyll new`, type `jekyll new --help`. \ No newline at end of file diff --git a/_posts/2018-01-12-press-and-education.md b/_posts/2018-01-12-press-and-education.md deleted file mode 100644 index 98e8c70d58..0000000000 --- a/_posts/2018-01-12-press-and-education.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -layout: post -title: "Press and education" -author: sal -categories: [ Jekyll, tutorial ] -image: assets/images/7.jpg ---- -Even the press, the classroom, the platform, and the pulpit in many instances do not give us objective and unbiased truths. To save man from the morass of propaganda, in my opinion, is one of the chief aims of education. Education must enable one to sift and weigh evidence, to discern the true from the false, the real from the unreal, and the facts from the fiction. - -Education must also train one for quick, **resolute and effective thinking**. To think incisively and to think for one's self is very difficult. - -> We are prone to let our mental life become invaded by legions of half truths, prejudices, and propaganda. At this point, I often wonder whether or not education is fulfilling its purpose. A great majority of the so-called educated people do not think logically and scientifically. - - -The function of education, therefore, is to teach one to think intensively and to think critically. But education which stops with efficiency may prove the greatest menace to society. The most dangerous criminal may be the man gifted with reason, but with no morals. - -The late Eugene Talmadge, in my opinion, possessed one of the better minds of Georgia, or even America. Moreover, he wore the Phi Beta Kappa key. By all measuring rods, Mr. Talmadge could think critically and intensively; yet he contends that I am an inferior being. Are those the types of men we call educated? - -We must remember that intelligence is not enough. Intelligence plus character--that is the goal of true education. The complete education gives one not only power of concentration, but worthy objectives upon which to concentrate. The broad education will, therefore, transmit to one not only the accumulated knowledge of the race but also the accumulated experience of social living. - diff --git a/_posts/2018-01-12-red-riding.md b/_posts/2018-01-12-red-riding.md deleted file mode 100644 index 66ad47606a..0000000000 --- a/_posts/2018-01-12-red-riding.md +++ /dev/null @@ -1,16 +0,0 @@ ---- -layout: post -title: "Red Riding Hood" -author: sal -categories: [ Jekyll, tutorial ] -image: assets/images/1.jpg ---- -The first mass-produced book to deviate from a rectilinear format, at least in the United States, is thought to be this 1863 edition of Red Riding Hood, cut into the shape of the protagonist herself with the troublesome wolf curled at her feet. Produced by the Boston-based publisher Louis Prang, this is the first in their “Doll Series”, a set of five “die-cut” books, known also as shape books — the other titles being Robinson Crusoe, Goody Two-Shoes (also written by Red Riding Hood author Lydia Very), Cinderella, and King Winter. - -An 1868 Prang catalogue would later claim that such “books in the shape of a regular paper Doll… originated with us”. - -> It would seem the claim could also extend to die cut books in general, as we can’t find anything sooner, but do let us know in the comments if you have further light to shed on this! Such books are, of course, still popular in children’s publishing today, though the die cutting is not now limited to mere outlines, as evidenced in a beautiful 2014 version of the same Little Red Riding Hood story. - -The die cut has also been employed in the non-juvenile sphere as well, a recent example being Jonathan Safran Foer’s ambitious Tree of Codes. - -As for this particular rendition of Charles Perrault’s classic tale, the text and design is by Lydia Very (1823-1901), sister of Transcendentalist poet Jones Very. The gruesome ending of the original — which sees Little Red Riding Hood being gobbled up as well as her grandmother — is avoided here, the gore giving way to the less bloody aims of the morality tale, and the lesson that one should not disobey one’s mother. diff --git a/_posts/2018-01-12-tree-of-codes.md b/_posts/2018-01-12-tree-of-codes.md deleted file mode 100644 index e5ccc4fbab..0000000000 --- a/_posts/2018-01-12-tree-of-codes.md +++ /dev/null @@ -1,18 +0,0 @@ ---- -layout: post -title: "Tree of Codes" -author: sal -categories: [ Jekyll, tutorial ] -image: assets/images/9.jpg ---- -The first mass-produced book to deviate from a rectilinear format, at least in the United States, is thought to be this 1863 edition of Red Riding Hood, cut into the shape of the protagonist herself with the troublesome wolf curled at her feet. Produced by the Boston-based publisher Louis Prang, this is the first in their “Doll Series”, a set of five “die-cut” books, known also as shape books — the other titles being Robinson Crusoe, Goody Two-Shoes (also written by Red Riding Hood author Lydia Very), Cinderella, and King Winter. - -As for this particular rendition of Charles Perrault’s classic tale, the text and design is by Lydia Very (1823-1901), sister of Transcendentalist poet Jones Very. The gruesome ending of the original — which sees Little Red Riding Hood being gobbled up as well as her grandmother — is avoided here, the gore giving way to the less bloody aims of the morality tale, and the lesson that one should not disobey one’s mother. - -> It would seem the claim could also extend to die cut books in general, as we can’t find anything sooner, but do let us know in the comments if you have further light to shed on this! Such books are, of course, still popular in children’s publishing today, though the die cutting is not now limited to mere outlines, as evidenced in a beautiful 2014 version of the same Little Red Riding Hood story. - - -An 1868 Prang catalogue would later claim that such “books in the shape of a regular paper Doll… originated with us”. - -The die cut has also been employed in the non-juvenile sphere as well, a recent example being Jonathan Safran Foer’s ambitious Tree of Codes. - diff --git a/_posts/2018-01-12-we-all-wait-for-summer.md b/_posts/2018-01-12-we-all-wait-for-summer.md deleted file mode 100644 index a8683bc721..0000000000 --- a/_posts/2018-01-12-we-all-wait-for-summer.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -layout: post -title: "We all wait for summer" -author: john -categories: [ Jekyll, tutorial ] -image: assets/images/5.jpg -rating: .5 ---- -As I engage in the so-called "bull sessions" around and about the school, I too often find that most college men have a misconception of the purpose of education. Most of the "brethren" think that education should equip them with the proper instruments of exploitation so that they can forever trample over the masses. Still others think that education should furnish them with noble ends rather than means to an end. - -It seems to me that education has a two-fold function to perform in the life of man and in society: the one is utility and the other is culture. Education must enable a man to become more efficient, to achieve with increasing facility the ligitimate goals of his life. \ No newline at end of file diff --git a/_posts/2018-05-12-about-bundler.md b/_posts/2018-05-12-about-bundler.md deleted file mode 100644 index 5b289c0374..0000000000 --- a/_posts/2018-05-12-about-bundler.md +++ /dev/null @@ -1,17 +0,0 @@ ---- -layout: post -title: "About Bundler" -author: sal -categories: [ Jekyll ] -image: assets/images/2.jpg -rating: 3 ---- -`gem install bundler` installs the bundler gem through RubyGems. You only need to install it once - not every time you create a new Jekyll project. Here are some additional details: - -`bundler` is a gem that manages other Ruby gems. It makes sure your gems and gem versions are compatible, and that you have all necessary dependencies each gem requires. - -The `Gemfile` and `Gemfile.lock` files inform `Bundler` about the gem requirements in your site. If your site doesn’t have these Gemfiles, you can omit `bundle exec` and just `run jekyll serve`. - -When you run `bundle exec jekyll serve`, `Bundler` uses the gems and versions as specified in `Gemfile.lock` to ensure your Jekyll site builds with no compatibility or dependency conflicts. - -For more information about how to use `Bundler` in your Jekyll project, this tutorial should provide answers to the most common questions and explain how to get up and running quickly. diff --git a/_posts/2018-06-12-acumulated-experience.md b/_posts/2018-06-12-acumulated-experience.md deleted file mode 100644 index 40b880569e..0000000000 --- a/_posts/2018-06-12-acumulated-experience.md +++ /dev/null @@ -1,16 +0,0 @@ ---- -layout: post -title: "Accumulated experience of social living" -author: sal -categories: [ Jekyll, tutorial ] -image: assets/images/15.jpg ---- -The die cut has also been employed in the non-juvenile sphere as well, a recent example being Jonathan Safran Foer’s ambitious Tree of Codes. - -As for this particular rendition of Charles Perrault’s classic tale, the text and design is by Lydia Very (1823-1901), sister of Transcendentalist poet Jones Very. The gruesome ending of the original - which sees Little Red Riding Hood being gobbled up as well as her grandmother - is avoided here, the gore giving way to the less bloody aims of the morality tale, and the lesson that one should not disobey one’s mother. - -The first mass-produced book to deviate from a rectilinear format, at least in the United States, is thought to be this 1863 edition of Red Riding Hood, cut into the shape of the protagonist herself with the troublesome wolf curled at her feet. Produced by the Boston-based publisher Louis Prang, this is the first in their “Doll Series”, a set of five “die-cut” books, known also as shape books — the other titles being Robinson Crusoe, Goody Two-Shoes (also written by Red Riding Hood author Lydia Very), Cinderella, and King Winter. - -An 1868 Prang catalogue would later claim that such “books in the shape of a regular paper Doll... originated with us”. - -> It would seem the claim could also extend to die cut books in general, as we can’t find anything sooner, but do let us know in the comments if you have further light to shed on this! Such books are, of course, still popular in children’s publishing today, though the die cutting is not now limited to mere outlines, as evidenced in a beautiful 2014 version of the same Little Red Riding Hood story. \ No newline at end of file diff --git a/_posts/2018-06-12-education.md b/_posts/2018-06-12-education.md deleted file mode 100644 index 50e19abe61..0000000000 --- a/_posts/2018-06-12-education.md +++ /dev/null @@ -1,51 +0,0 @@ ---- -layout: post -title: "Education must also train one for quick, resolute and effective thinking." -author: john -categories: [ Jekyll, tutorial ] -image: assets/images/3.jpg -beforetoc: "Markdown editor is a very powerful thing. In this article I'm going to show you what you can actually do with it, some tricks and tips while editing your post." -toc: true ---- -There are lots of powerful things you can do with the Markdown editor - -If you've gotten pretty comfortable with writing in Markdown, then you may enjoy some more advanced tips about the types of things you can do with Markdown! - -As with the last post about the editor, you'll want to be actually editing this post as you read it so that you can see all the Markdown code we're using. - - -## Special formatting - -As well as bold and italics, you can also use some other special formatting in Markdown when the need arises, for example: - -+ ~~strike through~~ -+ ==highlight== -+ \*escaped characters\* - - -## Writing code blocks - -There are two types of code elements which can be inserted in Markdown, the first is inline, and the other is block. Inline code is formatted by wrapping any word or words in back-ticks, `like this`. Larger snippets of code can be displayed across multiple lines using triple back ticks: - -``` -.my-link { - text-decoration: underline; -} -``` - -If you want to get really fancy, you can even add syntax highlighting using Rouge. - - -![walking]({{ site.baseurl }}/assets/images/8.jpg) - -## Reference lists - -The quick brown jumped over the lazy. - -Another way to insert links in markdown is using reference lists. You might want to use this style of linking to cite reference material in a Wikipedia-style. All of the links are listed at the end of the document, so you can maintain full separation between content and its source or reference. - -## Full HTML - -Perhaps the best part of Markdown is that you're never limited to just Markdown. You can write HTML directly in the Markdown editor and it will just work as HTML usually does. No limits! Here's a standard YouTube embed code as an example: - -

\ No newline at end of file diff --git a/_posts/2018-06-12-first-mass-produced.md b/_posts/2018-06-12-first-mass-produced.md deleted file mode 100644 index 605b9519df..0000000000 --- a/_posts/2018-06-12-first-mass-produced.md +++ /dev/null @@ -1,16 +0,0 @@ ---- -layout: post -title: "The first mass-produced book to deviate from a rectilinear format" -author: sal -categories: [ Jekyll, tutorial ] -image: assets/images/17.jpg ---- -The first mass-produced book to deviate from a rectilinear format, at least in the United States, is thought to be this 1863 edition of Red Riding Hood, cut into the shape of the protagonist herself with the troublesome wolf curled at her feet. Produced by the Boston-based publisher Louis Prang, this is the first in their “Doll Series”, a set of five “die-cut” books, known also as shape books — the other titles being Robinson Crusoe, Goody Two-Shoes (also written by Red Riding Hood author Lydia Very), Cinderella, and King Winter. - -An 1868 Prang catalogue would later claim that such “books in the shape of a regular paper Doll... originated with us”. - -> It would seem the claim could also extend to die cut books in general, as we can’t find anything sooner, but do let us know in the comments if you have further light to shed on this! Such books are, of course, still popular in children’s publishing today, though the die cutting is not now limited to mere outlines, as evidenced in a beautiful 2014 version of the same Little Red Riding Hood story. - -The die cut has also been employed in the non-juvenile sphere as well, a recent example being Jonathan Safran Foer’s ambitious Tree of Codes. - -As for this particular rendition of Charles Perrault’s classic tale, the text and design is by Lydia Very (1823-1901), sister of Transcendentalist poet Jones Very. The gruesome ending of the original - which sees Little Red Riding Hood being gobbled up as well as her grandmother - is avoided here, the gore giving way to the less bloody aims of the morality tale, and the lesson that one should not disobey one’s mother. diff --git a/_posts/2018-06-12-powerful-things-markdown-editor.md b/_posts/2018-06-12-powerful-things-markdown-editor.md deleted file mode 100644 index 2d0f3f594f..0000000000 --- a/_posts/2018-06-12-powerful-things-markdown-editor.md +++ /dev/null @@ -1,104 +0,0 @@ ---- -layout: post -title: "Powerful things you can do with the Markdown editor" -author: sal -categories: [ Jekyll, tutorial ] -image: assets/images/16.jpg ---- -There are lots of powerful things you can do with the Markdown editor. If you've gotten pretty comfortable with writing in Markdown, then you may enjoy some more advanced tips about the types of things you can do with Markdown! - -As with the last post about the editor, you'll want to be actually editing this post as you read it so that you can see all the Markdown code we're using. - - -## Special formatting - -As well as bold and italics, you can also use some other special formatting in Markdown when the need arises, for example: - -+ ~~strike through~~ -+ ==highlight== -+ \*escaped characters\* - - -## Writing code blocks - -There are two types of code elements which can be inserted in Markdown, the first is inline, and the other is block. Inline code is formatted by wrapping any word or words in back-ticks, `like this`. Larger snippets of code can be displayed across multiple lines using triple back ticks: - -``` -.my-link { - text-decoration: underline; -} -``` - -#### HTML - -```html -
  • - - - -
  • -``` - -#### CSS - -```css -.highlight .c { - color: #999988; - font-style: italic; -} -.highlight .err { - color: #a61717; - background-color: #e3d2d2; -} -``` - -#### JS - -```js -// alertbar later -$(document).scroll(function () { - var y = $(this).scrollTop(); - if (y > 280) { - $('.alertbar').fadeIn(); - } else { - $('.alertbar').fadeOut(); - } -}); -``` - -#### Python - -```python -print("Hello World") -``` - -#### Ruby - -```ruby -require 'redcarpet' -markdown = Redcarpet.new("Hello World!") -puts markdown.to_html -``` - -#### C - -```c -printf("Hello World"); -``` - - - - -![walking]({{ site.baseurl }}/assets/images/8.jpg) - -## Reference lists - -The quick brown jumped over the lazy. - -Another way to insert links in markdown is using reference lists. You might want to use this style of linking to cite reference material in a Wikipedia-style. All of the links are listed at the end of the document, so you can maintain full separation between content and its source or reference. - -## Full HTML - -Perhaps the best part of Markdown is that you're never limited to just Markdown. You can write HTML directly in the Markdown editor and it will just work as HTML usually does. No limits! Here's a standard YouTube embed code as an example: - -

    \ No newline at end of file diff --git a/_posts/2021-04-01-The-Platformatory-Way.md b/_posts/2021-04-01-The-Platformatory-Way.md new file mode 100644 index 0000000000..16e672dd66 --- /dev/null +++ b/_posts/2021-04-01-The-Platformatory-Way.md @@ -0,0 +1,129 @@ +--- +layout: post +title: "North star platform architecture" +author: p6 +categories: [ Platform Engineering, Data, Infrastructure, Kafka, Kong, Kubernetes ] +image: assets/blog-images/platform-arch-model.svg +featured: true +hidden: true +--- + +## Strategic Underpinnings + +In the last year or so, we've been spending a lot of time thinking about how organizations can run IT/Digital better. Clearly, IT has changed over the years from being a services centric organization that administers servers & infrastructure to modern organizations that operate a heterogenous, multi-cloud stack. In practice, many (including those who claim to be leaders) are actually laggards at running operations and we feel this is a "mission to enable" that is worthy of catering to. + +Don't get us wrong. In 2022, DevOps is ubiquitous; Most organizations claim to be Agile enabled (albeit embrace it with a liberal sprinkling of "two-speed IT" amongst other tropes); However, gaps remain at large, across the IT – Digital continuum. + +We feel that the most important trend IT / Digital organizations need to subscribe to is to adopt platforms. Why platforms? primarily because in the business that you run, your efficiency is best directed at running the business, rather than being in the business of platform software itself. This can’t be stressed enough; We see digital platform teams go down the rabbit hole of attempting to be pure tech, sometimes out of aspiration and other times out of misguided hubris. Operating platforms is a way to increase efficiencies at getting stuff done, and decrease the operational toil. Platforms should be the foundation of the Digital IT operating model. + +This is easier said than done. Embracing platforms is not without problems. + +- Enterprise software comes with nuances, feature based differentiation, hiring/skilling challenges and lock-in risks +- Even cloud platforms come with lock-in risks + +We feel it makes a lot of sense for companies to walk not only the cloud native path – which is that everything in the longer run must be built in the cloud, for the cloud, but also adopt a multi-cloud posture so as to hedge your bets and preserve flexibility towards aligning contracts that meet the bottomline. + +We also have one noteworthy addition, and that is to adopt an open-source first policy. + +This is rooted in solid fundamentals: + +- OSS rules the roost today. Server/OS market has been won long ago, and particularly since the advent of the cloud. +- Cloud providers majorly embrace OSS - providing managed services for popular OSS projects +- A whole class of PaaS software deploys to any cloud (run it yourself model) - supporting use-cases where conventional SaaS cannot be supported +- Finally, OSS foundations (CNCF, Apache Foundation, amongst others) are providing ecosystems where software development thrives, and are well funded typically by big tech that are invested in these technologies. + +As a digital/tech/IT leader, if you've sought active risk management against vendor lock in and navigating the potential consequences of choosing the wrong tech (particularly proprietary), you will find long term risk management success in the principled approach that we propose. + +We will summarize this as follows: + +1. Choose the best OSS technology catering to your problems and evaluate for project activity, stability and quality of APIs / abstractions +2. Investigate if a (cloud agnostic) PaaS vendor offers it - and can support multi-cloud deployments +3. As a next resort, does your preferred public cloud vendor provide a managed service? +4. As a final resort, does your second preference public cloud vendor provide a managed service? +5. Can you run it yourself? (but combined with tooling - particularly K8S operators?read further) + +In doing so, you will end up choosing best-in-breed (or prospectively best-in-breed) platforms such that even in the worst case scenario, you will have portability and bargaining power with no significant replatforming or migration cost. This is a winning strategy. + +------------------------ + +## The Key Components + +It is important to think deeply through what it means to run platform(s) at central IT scale; There are two main points here: + +Almost no platforms are turnkey. It is imminent that you need some extendability; The platform should provide the hooks for it. +Ideally, the platform should offer a plugin ecosystem that is community supported. +Digital platform IT runs for the sake of product teams: Platforms should offer configurable multi tenancy primitives out of the box, so as for platform teams to provide tooling (ex: Infrastructure as Code, CI/CD pipelines for DevOps automation, starter kits), security, governance and other cross cutting concerns, while relinquishing control to enable product teams to craft their own solutions with full flexibility within the bounds of their tenancy. +Based on the DevOps/SRE maturity, enable flexible operations with some shared responsibility model (such as delineating operations of the platform and operations in the platform) that gels well with the culture of the organization + +Even if we hate central IT structures as being inefficient in the longer run, centralization invariably creates short term efficiency centers for shared services atleast at a BU level. + +------------------------ + +From our experience, as diverse as the platform landscape can get, there are 3 important pillars that stand out. This must be at the very heart of your Digital Platform Strategy + +1. Hybrid Cloud Infrastructure +2. Real time core for data & service integration +3. APIs and API Management + + +## Hybrid Cloud Infrastructure: + +As detailed previously, in the ever evolving cloud landscape, hardware comes cheap and commoditized. While cloud vendors will still seek to differentiate, it is now amply clear that containers are pretty much the standard level of atomicity for any workload. Therefore, the underlying IaaS, at least in theory, is very fungible. + +This also signals the rise of Kubernetes as a great leveler and making multi-cloud – hybrid-cloud strategies actually viable without rocket surgery. + +As Kelsey Hightower puts it "Kubernetes is the interface to the cloud". If there's one thing K8S has cracked, it is the declarative control plane API model, and by that it ensures that even if a competitor were to emerge, it would begin by baselining itself against the same API standard. + +We will go out on a limb and make another prediction: in the same spirit of Atwood's law: Any sufficiently complicated distributed system that can be natively run (with the operator pattern or full blown controllers) on Kubernetes will eventually be run as ... exactly that. + +That means, for those who may need to steer away from managed services for whatever reason, running an operator is very literally the next best thing. We expect K8S will be the chassis for building literally just about anything cloud native in the next 10 years; PaaS vendors will come up with even better and specialized Developer and operator experiences to run K8S-at-the-core apps with lesser hassle. + +There are naysayers who think K8S is just too complex (there maybe a hint of truth in this even in 2022), but the sheer rate of increasing production workloads going into k8s is obviously data based testimony staring at you in the face that this is hardly as complex as they say it is. This is definitely led by the cloud providers themselves, but there is also a long tail of hybrid cloud compatible K8S distributions (such as VMWare Tanzu, RedHat OpenShift, Rancher, Nirmata, amongst others) that are enabling this. There are discretionary choices available for which flavor of Kubernetes suits you best and the vendor posture is towards K8S not as infrastructure modernization but as the foundational need for modernizing apps in itself; + +Even capabilities that were seemingly a point of proprietary leverage for public cloud, such as serverless functions have now gained a first class foothold in Kubernetes, allowing product teams to build towards a deployment target that may run on any IaaS, with Kubernetes. + +> Our advice - if you are willing to make some investments towards Kubernetes, it is about time. Do it. + +## Real time core for data & service integration + +As DB sizes grow, it is inevitable that megaliths and monoliths must die; Shared state and data just has practical limits; Services must own data and therefore, enable themselves towards decomposition into smaller parts and distribution. When that happens, the needs for integrations arise. + +The platform opportunities in integration are no recent discovery. Messaging middleware in the order of ESB -> iPaaS -> API Management have been around for decades. In the cloud native world, Enterprise integration patterns may still hold (give or take), but the aforementioned integration technologies no longer make sense for microservices communication. The services get smaller, chattier and desire eventual consistency patterns which these systems suck at, not to mention that they barely lend themselves to distributed, hybrid cloud deployments without being bottlenecks in themselves. + +There's a second, larger problem: Data integration. Analytical problem space increasingly converges into the operational (this is a great thing) and therefore real time data integration is a much bigger problem than ever. Traditional big data approaches fail to fulfill the needs of business expectations where time-to-useful-data (whether be it insight, reporting or other analytics) has to be optimized to its bounds. + +The best way to solve for both,is through embracing Event Driven, Reactive architecture as the de-facto way to architect applications. In this model: + +Choreography must be preferred over orchestration. +Applications and services exchange data through streams: Event stream processing becomes the mechanism to synchronize and process data continuously and consistently. +Most integrations can be handled through the same eventing backbone: An event store holds the logstream of events that can be applied (and replayed if necessary) to derive the state of an application + +Note that this problem has large-scale distributed system underpinnings to it. As it emerges, it is clear that Apache Kafka is the standout winner for event streaming. Its use-cases are myriad: Everything from (S)EDA, event-sourcing, stream processing to data movement. Kafka solves core problems, such as that of exactly once Processing, a brokered, pub-sub API model that offers the highest number of tuning knobs and customizability to fit purpose, first class data persistence for reliable processing and lends itself to global scale, high throughput, ow latency, fault tolerant deployments. + +Apart from these, Kafka also offers an ecosystem of connectors, stream processing bolt ons and schema management. + +Most of all, similar to K8S, Apache Kafka's data plane protocol has emerged as the defacto API standard for brokered eventing. This is evident in that even with Kafka's emergent competition, such as Azure event hubs, Apache pulsar or the new kid on the block, RedPanda (formerly vectorized.io) – that are innovating in the path of Kafka (in arguably superior ways), there is still conformance and compatibility to the same API protocol. + +>The Kafka based pub-sub streaming model is clearly emerging as the integration & data backbone of the enterprise. Data mesh like architectures will evolve on top of Kafka as the data plane standard and enable Data Productization that will be owned and stewarded by domains. App and services integrations will on the other hand, leverage changelog streams and increasingly lead to event-sourced architectures with newer Kafka centric API standards (such as AsyncAPI, CloudEvents) being the guardrails. + +## APIs (everything as a service) + +Many may have heard of Jeff Bezos’s API mandate, towards making service interfaces the only mechanism for integration. The dictum seems obvious in 2022 but at that time goes as testament to the visionary nature of leaders and the importance of APIs as socio-technical contracts that enables teams to have bounded responsibilities and ownership – an important aspect of organizational design that is required in order to scale. + +The explosion of services in the SOA era has resulted in the emergence of API Management as a formal discipline, over and above classic integration middleware platforms (such as ESBs); API Platforms focus on API based mediation, composition patterns and policy based controls on security, traffic management and quotas. + +While API Management has traditionally applied in SOA context, the microservices landscape brings in a different set of challenges than the Northbound-Southbound communication that API gateways traditionally addressed. This is the challenge of inter-service communication or East-West traffic. This is the emerging solution space of service meshes, where the opportunities include the same as that of API gateways and API management (albeit with more intelligent proxy or agent sidecars), but also extending to routing, load balancing, circuit breaking, traffic shifting, mirroring and most of all, observability through tracing across a graph of services. + +Till date, there have been attempts to converge gateways and service meshes, but in our opinion, the most complete vision towards this is by Kong (and to a lesser extent, Solo.io). Kong offers a service mesh (Kuma - albeit not SMI compatible, uses Envoy as the data plane), an API gateway (nginx in the data plane) and a programmable plugin layer that supports Lua, Golang, Python and JS. + +While it is unfair to say Kong is in the de-facto standard of it’s category class as Kafka and K8S, we find it the most plausible candidate worthy of that status eventually; Kong’s core concepts reduce to services and upstreams, routes and consumers, which maybe declaratively targeted by plugins. Kong boasts a large ecosystem of plugins and forks (such as mentionable Apisix), support for HTTP2, gRPC on both VMs, as well as K8S (with K8s ingress); Most of all, Kong provides typically an order of magnitude lift in performance vis-a-vis others and this constitutes a very compelling proposition. + +The coalescence of API gateways and Service meshes has only begun. We predict Envoy based data-planes will become better and more efficient; We will increasingly look at architectures that not only externalize all network level cross cutting concerns (such as security, traffic management, routing) to the ubiquitous Envoy-at-the-core mesh layer, but also see much “smarter” wire level interception capabilities for different technologies, thus enabling sophisticated capabilities around resilience, load balancing and much more. This is only the beginning, but there are several interesting possibilities just in the networking world with eBPF at the core. On the other hand, HTTP/2 is emerging and there is a rebound of RPC protocols (gRPC, Thrift, Dubbo) that provide consistent typing and deep, idiomatic programming language support; Finally, we also recognize heterogeneous API standards emerging (GraphQL, AsyncAPI, CloudEvents to name some) for a variety of use-cases beyond the core request-response oriented REST/SOAP standards that have been prevalent; To address these and other possibilities, if we had to make a choice today - it would almost certainly be Kong. + +> Finally, we must stress that APIs are just endpoints unless bundled together or built to suit use-cases of their audiences (ie, developers); For API Management to mature in an organization (regardless of API gateway or service mesh infrastructure), the curation of API products (or in an even more abstract sense, integration products) is the most important goal. This enables a transformation from APIs as integration endpoints towards APIs being literally, your business as a service. + +------------------ + +## Conclusion + +In this lengthy exploration, we've attempted to provide you a view of the emergent "k3" platform stack, consisting of Kubernetes, Kafka, Kong. These address respectively, the three most important pillars of an enterprise digital platform strategy - namely, infrastructure, data and APIs. For the discerning CT(I)O or platform engineering leader, the opportunities are vast and the OSS community has a lot to offer. It is important to take note and strategize accordingly. diff --git a/assets/blog-images/platform-arch-model.svg b/assets/blog-images/platform-arch-model.svg new file mode 100644 index 0000000000..26b5a5d269 --- /dev/null +++ b/assets/blog-images/platform-arch-model.svg @@ -0,0 +1 @@ + \ No newline at end of file From 5dacdf03fdca999fba57f41e3d0d1a72ffac0d16 Mon Sep 17 00:00:00 2001 From: p6 Date: Wed, 1 Jun 2022 18:45:10 +0530 Subject: [PATCH 02/76] blog changes --- _config.yml | 12 +- _layouts/default.html | 4 +- assets/css/screen.css | 2 +- assets/images/jumbotron.jpg | Bin 381204 -> 572987 bytes assets/images/jumbotron3.jpg | Bin 0 -> 381204 bytes assets/images/plf-logo.svg | 316 +++++++++++++++++++++++++++++++++++ index.html | 2 +- 7 files changed, 326 insertions(+), 10 deletions(-) create mode 100644 assets/images/jumbotron3.jpg create mode 100644 assets/images/plf-logo.svg diff --git a/_config.yml b/_config.yml index abc29eac52..1b49008c8e 100644 --- a/_config.yml +++ b/_config.yml @@ -1,11 +1,11 @@ # Site -name: "Platformatory Blog" -title: "Platformatory Blog" -description: "Platform Engineering in 2022" -logo: 'assets/images/logo.png' +name: "> RightAheadLog" +title: "> RightAheadLog" +description: "The Platform and Product Engineering Journal" +logo: 'assets/images/plf-logo.svg' favicon: 'assets/images/logo.png' baseurl: /blog -google_analytics: 'UA-46278016-1' +google_analytics: 'G-PWW2W71JX1' #disqus: 'demowebsite' mailchimp-list: 'https://platformatory.us17.list-manage.com/subscribe/post?u=8cc2e44e80300ae193f198bcc&id=6246b12a61' include: ["_pages"] @@ -20,7 +20,7 @@ authors: email: pavan@platformatory.io web: https://grahana.net twitter: https://twitter.com/p6 - description: "Cofounder of Platformatory. Aspiring Farmer; Love programming, distributed systems and random walks" + description: "Cofounder @ Platformatory; Random walks @ Bangalore" badri: name: badri display_name: Lakshminarasimhan Parthasarathy diff --git a/_layouts/default.html b/_layouts/default.html index 3eeaa69336..9562f2c491 100644 --- a/_layouts/default.html +++ b/_layouts/default.html @@ -49,7 +49,7 @@ - {{ site.name }} + {{ site.name }} @@ -108,7 +108,7 @@

    {{ site.name }}

    ================================================== -->
    - {{site.title}}   Never miss a story from us, subscribe to our newsletter +   Subscribe to the Right ahead log newsletter
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fcf06c0e08..e1e0e505ed 100644 --- a/index.html +++ b/index.html @@ -34,7 +34,7 @@

    Featured

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    All Stories

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    All Posts

    From db0244d878bd29454e0f0c5514b2491a8c6eed65 Mon Sep 17 00:00:00 2001 From: p6 Date: Thu, 2 Jun 2022 00:38:05 +0530 Subject: [PATCH 03/76] minor --- _config.yml | 32 +++++----- _includes/featuredbox.html | 4 +- _posts/2021-04-01-The-Platformatory-Way.md | 14 ++-- assets/images/plf-logo.svg | 74 +++++++++++----------- 4 files changed, 63 insertions(+), 61 deletions(-) diff --git a/_config.yml b/_config.yml index 1b49008c8e..969d31510a 100644 --- a/_config.yml +++ b/_config.yml @@ -1,7 +1,7 @@ # Site -name: "> RightAheadLog" -title: "> RightAheadLog" -description: "The Platform and Product Engineering Journal" +name: "The Write Ahead Log" +title: "The Write Ahead Log" +description: "Platform and Product Engineering Journal" logo: 'assets/images/plf-logo.svg' favicon: 'assets/images/logo.png' baseurl: /blog @@ -18,7 +18,7 @@ authors: display_name: Pavan Keshavamurthy gravatar: e56154546cf4be74e393c62d1ae9f9d4 email: pavan@platformatory.io - web: https://grahana.net + web: https://platformatory.io twitter: https://twitter.com/p6 description: "Cofounder @ Platformatory; Random walks @ Bangalore" badri: @@ -28,7 +28,7 @@ authors: gravatar: b1cc14991db7a456fcd761680bbc8f81 email: lakshmi@platformatory.io web: https://www.platformatory.io - twitter: https://twitter.com/wowthemesnet + twitter: https://twitter.com/platformatory description: "Principal Engineer at Platformatory; Musician" srikanth: name: srikanth @@ -37,8 +37,8 @@ authors: gravatar: b1cc14991db7a456fcd761680bbc8f81 email: srikanth@platformatory.io web: https://www.platformatory.io - twitter: https://twitter.com/wowthemesnet - description: "Data Engineer at Platformatory;" + twitter: https://twitter.com/platformatory + description: "Consultant at Platformatory;" dasa: name: dasa display_name: Dasa Sathyan @@ -46,8 +46,8 @@ authors: gravatar: b1cc14991db7a456fcd761680bbc8f81 email: srikanth@platformatory.io web: https://www.platformatory.io - twitter: https://twitter.com/wowthemesnet - description: "Data Engineer at Platformatory;" + twitter: https://twitter.com/platformatory + description: "Sr Consultant at Platformatory;" ashwin: name: ashwin display_name: Ashwin Venkatesan @@ -55,8 +55,8 @@ authors: gravatar: b1cc14991db7a456fcd761680bbc8f81 email: srikanth@platformatory.io web: https://www.platformatory.io - twitter: https://twitter.com/wowthemesnet - description: "Data Engineer at Platformatory;" + twitter: https://twitter.com/platformatory + description: "Sr Consultant at Platformatory;" venkatesh: name: venkatesh display_name: Venkatesh Keshavamurthy @@ -64,7 +64,7 @@ authors: gravatar: b1cc14991db7a456fcd761680bbc8f81 email: srikanth@platformatory.io web: https://www.platformatory.io - twitter: https://twitter.com/wowthemesnet + twitter: https://twitter.com/platformatory description: "Data Engineer at Platformatory;" kiran: name: kiran @@ -73,7 +73,7 @@ authors: gravatar: b1cc14991db7a456fcd761680bbc8f81 email: srikanth@platformatory.io web: https://www.platformatory.io - twitter: https://twitter.com/wowthemesnet + twitter: https://twitter.com/platformatory description: "Data Engineer at Platformatory;" @@ -108,9 +108,9 @@ kramdown: line_numbers: true # Adsense (change to "enabled" to activate, also your client id and ad slot. Create a new ad unit from your Adsense account to get the slot.) -adsense: "disabled" -adsense-data-ad-client: "ca-pub-3412143450191416" -adsense-data-ad-slot: "1363087678" +#adsense: "disabled" +#adsense-data-ad-client: "ca-pub-3412143450191416" +#adsense-data-ad-slot: "1363087678" # Lazy Images ("enabled" or "disabled") lazyimages: "disabled" diff --git a/_includes/featuredbox.html b/_includes/featuredbox.html index 90f1b2902d..950ef78e9b 100644 --- a/_includes/featuredbox.html +++ b/_includes/featuredbox.html @@ -29,7 +29,7 @@

    {% endif %}

    -

    {{ post.excerpt | strip_html | truncatewords:25 }}

    +

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    - \ No newline at end of file + diff --git a/_posts/2021-04-01-The-Platformatory-Way.md b/_posts/2021-04-01-The-Platformatory-Way.md index 16e672dd66..a20623d0ea 100644 --- a/_posts/2021-04-01-The-Platformatory-Way.md +++ b/_posts/2021-04-01-The-Platformatory-Way.md @@ -6,9 +6,11 @@ categories: [ Platform Engineering, Data, Infrastructure, Kafka, Kong, Kubernete image: assets/blog-images/platform-arch-model.svg featured: true hidden: true +teaser: A technology leader's guide to modern platform engineering +toc: true --- -## Strategic Underpinnings +# Strategic Underpinnings In the last year or so, we've been spending a lot of time thinking about how organizations can run IT/Digital better. Clearly, IT has changed over the years from being a services centric organization that administers servers & infrastructure to modern organizations that operate a heterogenous, multi-cloud stack. In practice, many (including those who claim to be leaders) are actually laggards at running operations and we feel this is a "mission to enable" that is worthy of catering to. @@ -46,7 +48,7 @@ In doing so, you will end up choosing best-in-breed (or prospectively best-in-br ------------------------ -## The Key Components +# The 3 Key Pillars It is important to think deeply through what it means to run platform(s) at central IT scale; There are two main points here: @@ -82,7 +84,7 @@ There are naysayers who think K8S is just too complex (there maybe a hint of tru Even capabilities that were seemingly a point of proprietary leverage for public cloud, such as serverless functions have now gained a first class foothold in Kubernetes, allowing product teams to build towards a deployment target that may run on any IaaS, with Kubernetes. -> Our advice - if you are willing to make some investments towards Kubernetes, it is about time. Do it. +Our advice - if you are willing to make some investments towards Kubernetes, it is about time. Do it. ## Real time core for data & service integration @@ -104,7 +106,7 @@ Apart from these, Kafka also offers an ecosystem of connectors, stream processin Most of all, similar to K8S, Apache Kafka's data plane protocol has emerged as the defacto API standard for brokered eventing. This is evident in that even with Kafka's emergent competition, such as Azure event hubs, Apache pulsar or the new kid on the block, RedPanda (formerly vectorized.io) – that are innovating in the path of Kafka (in arguably superior ways), there is still conformance and compatibility to the same API protocol. ->The Kafka based pub-sub streaming model is clearly emerging as the integration & data backbone of the enterprise. Data mesh like architectures will evolve on top of Kafka as the data plane standard and enable Data Productization that will be owned and stewarded by domains. App and services integrations will on the other hand, leverage changelog streams and increasingly lead to event-sourced architectures with newer Kafka centric API standards (such as AsyncAPI, CloudEvents) being the guardrails. +The Kafka based pub-sub streaming model is clearly emerging as the integration & data backbone of the enterprise. Data mesh like architectures will evolve on top of Kafka as the data plane standard and enable Data Productization that will be owned and stewarded by domains. App and services integrations will on the other hand, leverage changelog streams and increasingly lead to event-sourced architectures with newer Kafka centric API standards (such as AsyncAPI, CloudEvents) being the guardrails. ## APIs (everything as a service) @@ -120,10 +122,10 @@ While it is unfair to say Kong is in the de-facto standard of it’s category cl The coalescence of API gateways and Service meshes has only begun. We predict Envoy based data-planes will become better and more efficient; We will increasingly look at architectures that not only externalize all network level cross cutting concerns (such as security, traffic management, routing) to the ubiquitous Envoy-at-the-core mesh layer, but also see much “smarter” wire level interception capabilities for different technologies, thus enabling sophisticated capabilities around resilience, load balancing and much more. This is only the beginning, but there are several interesting possibilities just in the networking world with eBPF at the core. On the other hand, HTTP/2 is emerging and there is a rebound of RPC protocols (gRPC, Thrift, Dubbo) that provide consistent typing and deep, idiomatic programming language support; Finally, we also recognize heterogeneous API standards emerging (GraphQL, AsyncAPI, CloudEvents to name some) for a variety of use-cases beyond the core request-response oriented REST/SOAP standards that have been prevalent; To address these and other possibilities, if we had to make a choice today - it would almost certainly be Kong. -> Finally, we must stress that APIs are just endpoints unless bundled together or built to suit use-cases of their audiences (ie, developers); For API Management to mature in an organization (regardless of API gateway or service mesh infrastructure), the curation of API products (or in an even more abstract sense, integration products) is the most important goal. This enables a transformation from APIs as integration endpoints towards APIs being literally, your business as a service. +Finally, we must stress that APIs are just endpoints unless bundled together or built to suit use-cases of their audiences (ie, developers); For API Management to mature in an organization (regardless of API gateway or service mesh infrastructure), the curation of API products (or in an even more abstract sense, integration products) is the most important goal. This enables a transformation from APIs as integration endpoints towards APIs being literally, your business as a service. ------------------ -## Conclusion +# Conclusion In this lengthy exploration, we've attempted to provide you a view of the emergent "k3" platform stack, consisting of Kubernetes, Kafka, Kong. These address respectively, the three most important pillars of an enterprise digital platform strategy - namely, infrastructure, data and APIs. For the discerning CT(I)O or platform engineering leader, the opportunities are vast and the OSS community has a lot to offer. It is important to take note and strategize accordingly. diff --git a/assets/images/plf-logo.svg b/assets/images/plf-logo.svg index 96d695896c..5c1454747c 100644 --- a/assets/images/plf-logo.svg +++ b/assets/images/plf-logo.svg @@ -5,95 +5,95 @@ viewBox="0 0 499.6 146.8" enable-background="new 0 0 499.6 146.8" xml:space="preserve"> - - - + - - - - + - - - - + - - + - - - - - - - - - - - - - - - - + @@ -204,7 +204,7 @@ - + @@ -214,7 +214,7 @@ 365.4,4.8 365.3,-0.1 "/> - + @@ -223,7 +223,7 @@ - + @@ -239,7 +239,7 @@ - + @@ -260,7 +260,7 @@ - + @@ -269,10 +269,10 @@ - + - + @@ -288,7 +288,7 @@ - + From dc3ceb7740fecc91297989074abfc3693d22160f Mon Sep 17 00:00:00 2001 From: Ashwin Venkatesan Date: Fri, 17 Mar 2023 20:48:10 +0530 Subject: [PATCH 04/76] Added Faust 101 and Kafka Batch Processing with Airflow --- _posts/2023-03-17-Faust_101.md | 367 ++++++++++++++ ...-17-Kafka_batch_processing_with_Airflow.md | 452 ++++++++++++++++++ assets/images/airflow_blog_1.png | Bin 0 -> 289878 bytes assets/images/airflow_blog_2.png | Bin 0 -> 10174 bytes assets/images/airflow_blog_3.png | Bin 0 -> 166680 bytes assets/images/airflow_blog_4.png | Bin 0 -> 115970 bytes assets/images/faust_blog_1.png | Bin 0 -> 120546 bytes assets/images/faust_blog_2.png | Bin 0 -> 135353 bytes 8 files changed, 819 insertions(+) create mode 100644 _posts/2023-03-17-Faust_101.md create mode 100644 _posts/2023-03-17-Kafka_batch_processing_with_Airflow.md create mode 100644 assets/images/airflow_blog_1.png create mode 100644 assets/images/airflow_blog_2.png create mode 100644 assets/images/airflow_blog_3.png create mode 100644 assets/images/airflow_blog_4.png create mode 100644 assets/images/faust_blog_1.png create mode 100644 assets/images/faust_blog_2.png diff --git a/_posts/2023-03-17-Faust_101.md b/_posts/2023-03-17-Faust_101.md new file mode 100644 index 0000000000..c903e914e9 --- /dev/null +++ b/_posts/2023-03-17-Faust_101.md @@ -0,0 +1,367 @@ +# Faust 101 + +### **Introduction** + +Stream processing is a data processing technique to analyze, aggregate, filter or enhance a continuously ingested data as they come. Stream processing is used synonymously with real time data processing as the input data is processed as they are ingested with minimal latency. Some of its applications include fraud detection, IoT device data streaming, event streaming etc. Stream processing involves a publisher / source which generates data to a stream processing application where the data is processed, and the results sent to a subscriber / sink. + +Kafka Streams is one of the best streaming applications to do distributed real time stream processing. Although it's a great tool to do real time stream processing, actually setting up a production level stream processing pipeline is not an easy task. There is a learning curve associated with designing effective Kafka Streams Applications. Kafka Streams API is mainly written in Java and Scala but there are python implementations like Faust which can also be used. + +### **What is Faust?** + +Faust is the stream processing library written in Python which uses the capabilities of Kafka Streams underneath. Popular libraries like Pandas, NumPy, SQL Alchemy etc. can be used in conjunction with Faust. It uses a superfast embedded database (written in C++) called RocksDB to store states during stream processing. + +Following are the concepts used by Faust, + +- App - an instance of the Faust application. +- Agent - an asynchronous, distributed processor to process continuous data. +- Table - sharded dictionaries to enable stateful stream processing. +- Channel - a buffer/queue used to send and receive messages. +- Sensors - To monitor and record events in the Faust application. + +Faust uses Apache Kafka internally to store application configurations, table data etc. to facilitate distributed, fault-tolerant and durable data processing across machines. Faust also uses topics to input data from and write data to. Faust supports all the operations like group by, map, filter, joins etc. which are supported by Kafka Streams. + +### **Prerequisites:** + +Our objective here is to write a simple Kafka streams application in python using the Faust package. We will leverage the Confluent cloud infrastructure to create some topics. Confluent provides a fully managed cloud service for Apache Kafka which is super easy to deploy and use. You can sign up for Confluent cloud [here](https://www.confluent.io/confluent-cloud/tryfree/) and earn free credits worth $400 for the first 60 days. + +For this tutorial purposes, we will be using an open-source time-series based [power consumption dataset](https://www.kaggle.com/datasets/uciml/electric-power-consumption-data-set) which is available in Kaggle. Measurements in the dataset are of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. We will look at only the voltage parameter and find its average for every 1-hour windows. These windowed averages can tell us if there is a power spike which needs to be looked at. + +### **Step 1: Create a topic in Confluent Cloud** + +Once you have signed up for a confluent cloud account, you can create a basic cluster with default configuration at the region of your liking. + +![Image-1](../assets/images/faust_blog_1.png) + +Navigate to topics and create a topic with default configurations called `voltage_input`. + +### **Step 2: Downloading and installing requirements** + +Let’s create a python client to produce data to the topic. Navigate to ‘Clients’ under the ‘Data integration’ option on the cluster dashboard and create a new python client. + +![Image-2](../assets/images/faust_blog_2.png) + +Generate api key and copy the configuration snippet. Save it in the following path, + +``` +$HOME/.confluent/python.config +``` + +We will use the `confluent-kafka` python package to produce data to the input topic from local. Create a virtual environment with the following packages, + +``` +requestscertifi +confluent-kafka[avro,json,protobuf]>=1.4.2 +faust[fast] +``` + +Let’s download the python file which will read the config file and parse it for the application to use. + +``` +wget https://raw.githubusercontent.com/confluentinc/examples/6.1.1-post/clients/cloud/python/ccloud_lib.py +``` + +Download the dataset file `household_power_consumption.txt` from the Kaggle [link](https://www.kaggle.com/datasets/uciml/electric-power-consumption-data-set) and save it in our working directory. + +### **Step 3: Produce to the input topic** + +We will use the `ccloud_lib` python module to parse the config file and also the input arguments for the producer module. + + + +``` +#!/usr/bin/env python + +import json +import ccloud_lib + +delivered_records = 0 + +# Callback function when a message has been successfully delivered +def acked(err, msg): + global delivered_records + """Delivery report handler called on + successful or failed delivery of message + """ + if err is not None: + print("Failed to deliver message: {}".format(err)) + else: + delivered_records += 1 + print("Produced record to topic {} partition [{}] @ offset {}" + .format(msg.topic(), msg.partition(), msg.offset())) + +def fetch_time_series_data(count=1): + data_split = data[count].split(";") + try: + new_message = {"datetime": datetime.strptime(data_split[0] + " " + data_split[1], "%d/%m/%Y %H:%M:%S"), "voltage": float(data_split[4])} + except ValueError: + new_message = {"datetime": datetime.strptime(data_split[0] + " " + data_split[1], "%d/%m/%Y %H:%M:%S"), "voltage": 0.0} + return count+1,new_message + +if __name__ == '__main__': + + # Read arguments and configurations and initialize + args = ccloud_lib.parse_args() + config_file = args.config_file + topic = args.topic + conf = ccloud_lib.read_ccloud_config(config_file) + + # Create topic if needed + # ccloud_lib.create_topic(conf, topic) + + # Initialize the producer configuration + producer_conf = ccloud_lib.pop_schema_registry_params_from_config(conf) + producer_conf['key.serializer'] = StringSerializer('utf-8') + producer_conf['value.serializer'] = StringSerializer('utf-8') + p = SerializingProducer(producer_conf) + + with open("./household_power_consumption.txt", "r") as f: + data = f.readlines() + + no_of_records = 10000 + start_index = 0 + while no_of_records: + # Call poll to send any queued record + p.poll(0) + start_index,base_message = fetch_time_series_data(start_index) + no_of_records -= 1 + + value = base_message + + record_key = "power" + record_value = json.dumps(value) + + p.produce(topic, key=record_key, value=record_value, on_delivery=acked) + + # Call flush to send any queued record at the end + p.flush() +``` + +We are producing the voltage data with a datetime column to the input topic. + +`poll()` or `flush()` needs to be called to send the queued data to the Kafka broker. + +### **Step 4: Creating a Faust app** + +Let’s create a simple Faust app which will read from the `voltage_input` topic and do a windowed aggregation of the voltage values. + +First, we need to define the input and output data models for the Faust app. + +``` +# Input model +class PowerModel(faust.Record, coerce=True, date_parser=parse_date): + datetime: datetime + voltage: float + +#Output model +class AverageVoltage(faust.Record): + start_time: datetime + count: int + mean: float +``` + +Let’s initialize the faust app with the kafka broker connection details. We need to pass in the SASL credentials which were created previously and stored in the config file. + +``` +ssl_context = ssl.create_default_context() +ssl_context.load_verify_locations(cafile=certifi.where()) + +app = faust.App('myapp', broker='kafka://BROKER_HOST_NAME:9092', broker_credentials=faust.SASLCredentials(username=SASL_USERNAME, password=SASL_PASSWORD, ssl_context=ssl_context), topic_replication_factor=3, topic_partitions=3) +``` + +Define the input topic stream and output sink topic for the aggregated average voltages. + +``` +voltage_stream = app.topic('voltage_input', key_type=str, value_type=PowerModel) +sink = app.topic('voltage_avg', value_type=AverageVoltage) +``` + +Next, we need to define the aggregation logic for calculating the hourly average voltage. + +``` +def hourly_window_processor(key, events): + timestamp = key[1][0] + timestamp = datetime.fromtimestamp(timestamp) + values = [event.voltage for event in events] + count = len(values) + mean = sum(values) / count + + logger.info( + f'processing window:' + f'{len(values)} events,' + f'mean: {mean:.2f},' + f'timestamp {timestamp}', + ) + + sink.send_soon(key='avg_voltage_hourly', value=AverageVoltage(start_time=timestamp, count=count, mean=mean)) + +hourly_window_voltage = app.Table('hourly_average_voltage', default=list, partitions=3, on_window_close=hourly_window_processor)\ + .tumbling(timedelta(hours=1), expires=30)\ + .relative_to_field(PowerModel.datetime) +``` + +We are using a tumbling window here as we need to calculate the average every hour. Values are stored in the transition table `hourly_average_voltage` as it comes. Once the time window closes, the callback function calculates the average for the values in that window and sinks it to the output topic. + +Now, we need to define an app agent to append the input data stream to the hourly window voltage table. + +``` +@app.agent(voltage_stream) +async def processor(records): + async for record in records: + value_list_hour = hourly_window_voltage['events'].value() + value_list_hour.append(record) + hourly_window_voltage['events'] = value_list_hour +``` + +Following is the final aggregate code of the faust app, + + + +``` +import faust +from datetime import datetime, timedelta +from dateutil.parser import parse as parse_date +import ssl +import certifi +import logging + +logger = logging.getLogger(__name__) + +ssl_context = ssl.create_default_context() +ssl_context.load_verify_locations(cafile=certifi.where()) + +class AverageVoltage(faust.Record): + start_time: datetime + count: int + mean: float + +class PowerModel(faust.Record, coerce=True, date_parser=parse_date): + datetime: datetime + voltage: float + +app = faust.App('myapp', broker='kafka://pkc-7prvp.centralindia.azure.confluent.cloud:9092', broker_credentials=faust.SASLCredentials(username="3TCIKXFSRGTFZCMV", password="7OUQQoTN6zIA2gS5LK/hrAxQjpRjIhw+w9+/gdoUJZT+8vlPxEtKkJ+n+X1cPkpm", ssl_context=ssl_context), topic_replication_factor=3, topic_partitions=3) +app.conf.table_cleanup_interval = 1.0 + +voltage_stream = app.topic('test3', key_type=str, value_type=PowerModel) +sink = app.topic('voltage_avg', value_type=AverageVoltage) + +def hourly_window_processor(key, events): + timestamp = key[1][0] + timestamp = datetime.fromtimestamp(timestamp) + values = [event.voltage for event in events] + count = len(values) + mean = sum(values) / count + + logger.info( + f'processing window:' + f'{len(values)} events,' + f'mean: {mean:.2f},' + f'timestamp {timestamp}', + ) + + sink.send_soon(key='avg_voltage_hourly', value=AverageVoltage(start_time=timestamp, count=count, mean=mean)) + +hourly_window_voltage = app.Table('hourly_average_voltage', default=list, partitions=3, on_window_close=hourly_window_processor)\ + .tumbling(timedelta(hours=1), expires=30)\ + .relative_to_field(PowerModel.datetime) + +@app.agent(voltage_stream) +async def processor(records): + async for record in records: + value_list_hour = hourly_window_voltage['events'].value() + value_list_hour.append(record) + hourly_window_voltage['events'] = value_list_hour + +if __name__ == '__main__': + app.main() +``` + +### **Step 5: Running the app and viewing the results** + +We can start the Faust worker in a terminal by executing the following command, + +``` +python app.py worker -l info +``` + +Let’s open another terminal and start producing records to the `voltage_input` topic by executing the following command. + +``` +./producer.py -f ~/.confluent/python.config -t test1 +``` + +Let’s write a simple `consumer.py` code to consume the aggregated results from our output topic `voltage_avg`. + + + +``` +from confluent_kafka import Consumer +import json +import ccloud_lib +import sys +import datetime + +if __name__ == '__main__': + + # Read arguments and configurations and initialize + args = ccloud_lib.parse_args() + config_file = args.config_file + topic = args.topic + conf = ccloud_lib.read_ccloud_config(config_file) + + # Create Consumer instance with a unique group id + # 'auto.offset.reset=earliest' to start reading from the beginning of the + # topic if no committed offsets exist + consumer_conf = ccloud_lib.pop_schema_registry_params_from_config(conf) + consumer_conf['group.id'] = 'python_example_group_1' + consumer_conf['auto.offset.reset'] = 'earliest' + consumer = Consumer(consumer_conf) + + # Subscribe to topic + consumer.subscribe([topic]) + + # Process messages + total_count = 0 + try: + while True: + msg = consumer.poll(1.0) + if msg is None: + print("Waiting for message or event/error in poll()") + continue + elif msg.error(): + print('error: {}'.format(msg.error())) + else: + # Check for Kafka message + record_key = msg.key() + record_value = msg.value() + data = json.loads(record_value) + total_count += 1 + print(data) + except KeyboardInterrupt: + pass + finally: + # Leave group and commit final offsets + consumer.close() +``` + +Run the following command to start the consumer in terminal, + +``` +./consumer.py -f ~/.confluent/python.config -t voltage_avg +``` + +Now, you can view the magic by opening three terminals and running the producer, Faust app worker and consumer simultaneously in them. + +### **Conclusion:** + +In this tutorial, we were able to accomplish the following, + +1. Create a simple Faust application to calculate hourly windowed average of voltages from our input dataset. +2. Create a simple python producer and consumer to interact with a Confluent cloud topic. + +Faust is great way to leverage the capabilities of Kafka Streams in conjunction with popular python libraries like Pandas, NumPy, Scikit-learn etc. to write distributed, fault-tolerant streaming applications. Faust is simple and can be used to build real-time applications involving machine learning, deep learning etc. + +### **References:** + +1. [https://faust.readthedocs.io/en/latest/userguide/tables.html](https://faust.readthedocs.io/en/latest/userguide/tables.html) +2. [https://github.com/robinhood/faust/blob/master/examples/windowed_aggregation.py](https://github.com/robinhood/faust/blob/master/examples/windowed_aggregation.py) +3. [https://github.com/confluentinc/examples/tree/7.1.0-post/clients/cloud/python](https://github.com/confluentinc/examples/tree/7.1.0-post/clients/cloud/python) \ No newline at end of file diff --git a/_posts/2023-03-17-Kafka_batch_processing_with_Airflow.md b/_posts/2023-03-17-Kafka_batch_processing_with_Airflow.md new file mode 100644 index 0000000000..06e0145350 --- /dev/null +++ b/_posts/2023-03-17-Kafka_batch_processing_with_Airflow.md @@ -0,0 +1,452 @@ +# Kafka batch processing with Airflow + +## Introduction + +In the world of big data, managing large datasets is crucial. Batch processing is the process of running a large set of data through a particular program or pipeline at one time. In this blog post, we will discuss how Kafka and Airflow can be used for batch processing. + +Kafka is a distributed streaming platform which uses logs as the unit of storage for messages passed within the system. Kafka has a unique architecture that allows it to handle high volume data streams in real-time. Event streaming enables real time data interpretation and processing to effect near real time decisions to improve efficiency and optimize costs in businesses. Kafka offers a distributed, highly scalable, elastic, fault-tolerant, and secure infrastructure for real time event streams. Kafka is used in a wide variety of use cases like processing real time transactions in banks, real time analysis of sensor data from IoT devices etc. + +Airflow is an open source orchestration tool to manage big data workflows. Workflows are programmatically authored as Directed Acyclic Graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Airflow pipelines are defined as Python code, which means that anything that you can do with Python you can do with Airflow. It also provides a variety of operators and hooks which helps us to integrate seamlessly with most of the cloud and open-source services. Airflow is designed as a configuration-as-a-code system and it can be heavily customized with plugins. + +## ****Why Airflow in Batch processing pipeline?**** + +Although real time stream processing is used with Kafka mostly, there are a lot of instances where batch processing is required. For example, + +- Online stores might be interested in finding out the hourly or daily aggregate of the number of unique users arriving through a particular advertising channel to optimise their ad spends. +- Banks settling the accumulated bills to vendors at the end of day based on daily transactions. + +![Image-1](../assets/images/airflow_blog_1.png) + +Airflow is an ideal tool to orchestrate and manage batch processing of logs from Kafka topics as it run on schedules, so the resource usage is significantly lower when compared to Kafka streams or any streaming application. Airflow allows us to decouple big tasks into smaller ones and manage the dependencies between them using DAGs. It also allows us to monitor the status of tasks, re-run or restart tasks from any given point in a workflow in case of failure. Airflow also lets you integrate with tools like Apache Spark etc. to execute the task. + +## Kafka consumer partition assignment strategy + +In general, Kafka consumers are identified by a consumer group id i.e. all the consumer having the same group id will share the subscribed `TopicPartitions` among them. When a consumer is added or removed from the group, a consumer re-balance is triggered which completely reassigns `TopicPartitions` among the new set of consumers. There is no guarantee that the consumer-partition mapping will remain the same before and after re-balance. To read about more on the different partition assignment strategies, please visit this [link](https://www.conduktor.io/blog/kafka-partition-assignment-strategy/). + +To control the consumer-partition mapping, we need to manually assign a `TopicPartition` to a consumer in the group. This can be achieved by using `consumer.assign()` command. Manual partition assignment can be useful in cases where different processing logic are applied for different partitions, assigning dedicated consumers to heavy throughput partitions etc. In this example, we will use `consumer.assign` to consume from a specific partition, process the data and produce the values to the same output topic partition as input topic. + +## **Airflow Dynamic Task Mapping** + +Dynamic task mapping enables us to create parallel tasks in DAG at run time. It facilitates running parallel tasks equal to the number of input Kafka topic partitions. In Kafka, maximum parallelisation is defined by the number of topic partitions. The expand() command is used to dynamically map tasks in Airflow. For more information, visit this [link](https://airflow.apache.org/docs/apache-airflow/2.3.0/concepts/dynamic-task-mapping.html). + +![Image-2](../assets/images/airflow_blog_2.png) + +We will use manual partition assignment with consumers rather than group based partition assignment. This is to ensure that data can be consumed from an input topic partition, processed and produced to an output topic partition same as the input topic partition in parallel tasks. For the above logic to hold true, the input and output topic should be created with the same number of partitions. Consumer re-balancing in group based partition assignment can lead to mix up while processing data in parallel tasks. + +## **Step 1: Download and setup Airflow** + +For this tutorial purposes, we will run airflow in docker. For Production, Airflow supports helm charts for Kubernetes deployment. We will be following this [link](https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html) to set up Airflow in docker. + +1. Open a terminal window and navigate to the working directory. + + ```bash + cd airflow_kafka + ``` + +2. Fetch the `docker-compose.yml` file, + + ```bash + curl -LfO   'https://airflow.apache.org/docs/apache-airflow/2.3.2/docker-compose.yaml' + ``` + +3. Create the required folders for docker compose and set the airflow uid (for Linux), + + ```bash + mkdir -p ./dags ./logs ./plugins ./results + echo -e "AIRFLOW_UID=$(id -u)" > .env + ``` + + +We have created an extra folder called results to store topic offset locally. + +Next, we need Kafka python libraries which are not available in the default airflow image. So, let’s build our custom image with the required dependencies, + +1. Create a `requirements.txt` file with the required packages, + + ``` + confluent-kafka + ``` + +2. Create a `Dockerfile` to build custom image, + + ```docker + FROM apache/airflow:2.3.0 + USER root + RUN apt-get update \ + && apt-get install -y --no-install-recommends \ + vim \ + && apt-get autoremove -yqq --purge \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + USER airflow + COPY requirements.txt . + RUN pip install --no-cache-dir -r requirements.txt + ``` + +3. In `docker-compose.yml`, comment the image option and comment out the build option, + + ```yaml + version: '3' + x-airflow-common: + &airflow-common + # In order to add custom dependencies or upgrade provider packages you can use your extended image. + # Comment the image line, place your Dockerfile in the directory where you placed the docker-compose.yaml + # and uncomment the "build" line below, Then run `docker-compose build` to build the images. + # image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.3.0} + build: . + environment: + ...... + ``` + + +1. Also, add results to the docker container volumes, + + ```yaml + volumes: + - ./dags:/opt/airflow/dags + - ./logs:/opt/airflow/logs + - ./plugins:/opt/airflow/plugins + - ./results:/opt/airflow/results + ``` + +2. Run database migrations and create the first user account, + + ```bash + docker-compose up airflow-init + ``` + +3. Now the Airflow is set up and can be started by executing, + + ```bash + docker-compose up -d + ``` + + +Airflow web UI can be accessed at [http://localhost:8080/](http://localhost:8080/) + +## **Step 2: Setup the Kafka Server config file** + +Before we start writing dags, let’s create the config file to access the input topic in a Kafka cluster. This tutorial assumes you have basic Kafka knowledge and already have an input Kafka topic set up in a remote or local Kafka cluster. Here, we will use an existing topic in the Confluent Cloud as the input topic. The client config file to connect to Kafka cluster will look like the following, + + + +``` +bootstrap.servers=: +security.protocol=SASL_SSL +sasl.mechanisms=PLAIN +sasl.username= +sasl.password= +``` + +In addition to the above, we can also define producer and consumer specific properties. It would be ideal to have separate config files for producer and consumer. Place the `kafka_server.config` file inside the plugins folder to make it available inside the container. + +The input topic here contains the following as message value, + +```json +{  + "datetime": "23/12/2006 16:52:00", + "voltage": 239.94 +} +``` + +## **Step 3: Define DAG parameters** + +Let’s create the `dag.py` inside the /dags folder. Airflow automatically reads dags defined in the /dags folder. + +1. Import the required modules, + + ```python + from datetime import datetime, timedelta + import os + import pendulum + import pandas as pd + import csv + import json + from confluent_kafka import Consumer, TopicPartition, Producer + from confluent_kafka.serialization import StringSerializer + from confluent_kafka.admin import AdminClient + from airflow.decorators import dag, task + ``` + +2. Function to read and parse the config file, + + ```python + def read_ccloud_config(config_file): + """Read Confluent Cloud configuration for librdkafka clients""" + + conf = {} + with open(config_file) as fh: + for line in fh: + line = line.strip() + if len(line) != 0 and line[0] != "#": + parameter, value = line.strip().split('=', 1) + conf[parameter] = value.strip() + + return conf + ``` + +3. A producer callback function to print the delivered status, + + ```python + delivered_records = 0 + + # Callback function when a message has been successfully delivered + def acked(err, msg): + global delivered_records + """Delivery report handler called on + successful or failed delivery of message + """ + if err is not None: + print("Failed to deliver message: {}".format(err)) + else: + delivered_records += 1 + print("Produced record to topic {} partition [{}] @ offset {}" + .format(msg.topic(), msg.partition(), msg.offset())) + ``` + +4. Define the global variables to be used in the dag tasks and the dag configurations. The input values are hard-coded in this example. The input values can be given while triggering the dag as input parameters as well. + + ```python + # Input Values + topic_config_file = "/opt/airflow/plugins/kafka_server.config" + topic_name = "airflow-input-topic" + output_topic_name = "airflow-output-topic" + # Local file to save latest offsets for topic partitions + local_offset_file = "/opt/airflow/results/topic-offsets-latest.csv" + consumer_conf = read_ccloud_config(topic_config_file) + producer_conf = read_ccloud_config(topic_config_file) + # Create an empty csv file with headers if not available + if not os.path.exists(local_offset_file): + with open(local_offset_file, "w+") as file: + csvwriter = csv.writer(file) + csvwriter.writerow(["id", "topic", "partition", "last_offset"]) + + # Define the DAG configuration for the DAG() function with the @dag decorator, + @dag( + dag_id="{}_batch_processor".format(topic_name), + schedule_interval='@daily', + start_date=pendulum.datetime(2022, 5, 1), + catchup=False, + dagrun_timeout=timedelta(minutes=60), + ) + def DAG(): + ``` + + +The config `catchup=False` is set up to avoid DAG to run missed scheduled runs. + +## **Step 4: Fetch partition ids for the given topic** + +Let’s start building the DAG by defining each sequential task. All the tasks should be defined inside the `DAG()` function with `@task` decorator. + +Initial task is to fetch the partition ids for the given topic, + +```python +def DAG(): + @task + def get_topic_partitions(): + kadmin = AdminClient(conf) + partitions = kadmin.list_topics(topic=topic_name).topics[topic_name].partitions + partition_ids = [] + for partition in partitions: + partition_ids.append(partition) + # print(partition_ids) + return partition_ids +``` + +Kafka Admin Client is used to fetch the topic partitions. + +## **Step 5: Consume new data for each partition** + +Next task is to consume the message for each given topic partition till the latest offset from the previously committed offset. + +```python +@task +def consume_from_topic_partition(partition_id): + consume_skip = False + # Transient file to store the consumed messages + filename = "/opt/airflow/results/raw/%s_%s.csv" % (topic_name, partition_id) + if not os.path.exists(filename): + with open(filename, 'a+') as csvfile: + csvwriter = csv.writer(csvfile) + csvwriter.writerow(["key", "datetime", "voltage", "offset"]) + # Setup consumer related config + consumer_conf["auto.offset.reset"] = "earliest" + consumer_conf["group.id"] = "airflow-group" + consumer = Consumer(consumer_conf) + + # Get the earliest and latest offset for a topic partition + low, high = consumer.get_watermark_offsets(TopicPartition(topic_name, partition_id)) + + high_offset = high - 1 + + print(high_offset) + + # Load the local offset file as dataframe + local_offset_db = pd.read_csv(local_offset_file, index_col=[0]) + + last_offset_row = local_offset_db.loc[(local_offset_db["topic"] == topic_name) & (local_offset_db["partition"] == partition_id), ["last_offset"]] + + if len(last_offset_row) == 0: + # If the row for the topic partition does not exist, create one. + with open(local_offset_file, "a+") as csvfile: + csvwriter = csv.writer(csvfile) + csvwriter.writerow(["%s_%s" % (topic_name, partition_id) , topic_name, partition_id, 0]) + consumer.assign([TopicPartition(topic_name, partition_id, low)]) + else: + last_offset_value = last_offset_row["last_offset"].values[0] + # Skip consumer if last committed offset is the same as latest offset as now new messages to consume + if last_offset_value == high_offset: + consume_skip = True + consumer.assign([TopicPartition(topic_name, partition_id, last_offset_value + 1)]) + + if consume_skip: + print("Already consumed till latest offset") + consumer.close() + else: + total_count = 0 + try: + while True: + msg = consumer.poll(1.0) + if msg is None: + # No message available within timeout. + # Initial message consumption may take up to + # `session.timeout.ms` for the consumer group to + # rebalance and start consuming + print("Waiting for message or event/error in poll()") + continue + elif msg.error(): + print('error: {}'.format(msg.error())) + else: + total_count += 1 + value = json.loads(msg.value()) + value["datetime"] = datetime.strptime(value["datetime"], "%d/%m/%Y %H:%M:%S") + value["voltage"] = float(value["voltage"]) + with open(filename, 'a') as csvfile: + csvwriter = csv.writer(csvfile) + csvwriter.writerow([msg.key(), value["datetime"], value["voltage"], msg.offset()]) + # Break loop if message offset is the latest offset + if msg.offset() == high_offset: + break + except KeyboardInterrupt: + pass + finally: + print(total_count) + # Leave group and commit final offsets + consumer.close() + return partition_id, high_offset +``` + +## **Step 6: Commit latest offset for each topic partition locally** + +Next task is to commit the latest consumed offset to the local offset file. Partition Id and latest offset are taken as input from the previous task. Partition ids are passed to the next task. + +```python +@task +def commit_offsets_locally(partition_offset_values): + partition_ids = [] + local_offset_db = pd.read_csv(local_offset_file, index_col=[0]) + for value in partition_offset_values: + print(value) + partition_ids.append(value[0]) + local_offset_db.loc[(local_offset_db["topic"] == topic_name) & (local_offset_db["partition"] == value[0]), "last_offset"] = value[1] + local_offset_db = local_offset_db.sort_index() + local_offset_db.to_csv(local_offset_file) + return partition_ids +``` + +## **Step 7: Aggregate values for each partition** + +Next task is to aggregate the values for the consumed messages. We will calculate the average voltage for each unique date and store them in a transient processed-csv file. + +```python +@task +def process_topic_messages(partition_id): + messages_db = pd.read_csv("/opt/airflow/results/raw/%s_%s.csv" % (topic_name, partition_id), parse_dates=["datetime"]) + daily_voltage_avg = messages_db[["datetime", "voltage"]].set_index("datetime").groupby(pd.Grouper(freq='D')).mean().reset_index() + daily_voltage_avg.columns = ["date", "avg_voltage"] + # Store in a transient processed file + daily_voltage_avg.to_csv("/opt/airflow/results/processed/%s_%s.csv" % (topic_name, partition_id)) + return partition_id +``` + +## **Step 8: Produce aggregated value to output topic** + +Next task is to produce the aggregated values to the output topic. Date wise average voltage values are produced to the same partition as the input topic. So, the input and output topics are co-partitioned. + +```python +@task +def produce_processed_data_to_output_topic(partition_id): + # Add producer related configuration + producer_conf['key.serializer'] = StringSerializer('utf-8') + producer_conf['value.serializer'] = StringSerializer('utf-8') + p = Producer(producer_conf) + with open("/opt/airflow/results/processed/%s_%s.csv" % (topic_name, partition_id), 'r') as file: + reader = csv.reader(file) + for row in reader: + if 'date' in row: + continue + p.poll(0) + record_key = row[1] + record_value = json.dumps({"date": str(row[1]), "voltage": row[2]}) + p.produce(output_topic_name, key=record_key, value=record_value, partition=partition_id, on_delivery=acked) + p.flush() + return "Finished producing" +``` + +## **Step 9: Define the task flow for the DAG** + +Next step is to connect the defined tasks in the appropriate order for the dag to execute. The `expand()` command is used to execute tasks in parallel and simultaneously. + +```python +partitions_latest_offset_list = consume_from_topic_partition.expand(partition_id=get_topic_partitions()) +partitions_list = commit_offsets_locally(partitions_latest_offset_list) +partitions_list = process_topic_messages.expand(partition_id=partitions_list) +produce_processed_data_to_output_topic.expand(partition_id=partitions_list) +``` + +Finally, call the `DAG()` function to execute the dag. + +```python +dag = DAG() +``` + +Executing the dag does not trigger the dag. A dag is triggered based on the defined schedule and can also be triggered manually in web UI and rest API endpoint. + +Now, run the `docker-compose up -d`  command and head to the link [http://localhost:8080](http://localhost:8080/)/. + +Default username and password for the airflow web UI are, + +``` +Username: airflow +Password: airflow +``` + +Once you log in, you should be able to see the defined DAG in the web UI which should have the same name as the dag_id we defined in the DAG configuration. + +![Image-3](../assets/images/airflow_blog_3.png) + +We can now trigger the **`airflow-input-topic_batch_processor`** by clicking the play button under the `Actions` section. + +We can track the dag status in Graph or Grid mode in the dag page. Also, we can see the Logs, XCom etc. for each dag task like below. + +![Image-4](../assets/images/airflow_blog_4.png) + +## **Conclusion** + +In this tutorial, we were able to do the following, + +1. Build custom Airflow docker image with required python dependencies. +2. Run Airflow in docker compose with a custom docker image. +3. Dynamic task mapping in Airflow to run parallel tasks. +4. Produce to and consume from a Kafka topic per partition. +5. Input and Output Kafka topics are co-partitioned. + +In conclusion, Kafka and Airflow are two of the most popular open-source tools that are used for batch processing. Kafka is designed to handle high-volume data streams in real-time, while Airflow is designed to orchestrate complex workflows and data processing pipelines. Using Kafka and Airflow together can provide a scalable and fault-tolerant solution for batch processing large datasets. + +### **References** + +1. [https://airflow.apache.org/docs/apache-airflow/2.3.0/start/docker.html](https://airflow.apache.org/docs/apache-airflow/2.3.0/start/docker.html) +2. [https://airflow.apache.org/docs/apache-airflow/2.3.0/concepts/dynamic-task-mapping.html](https://airflow.apache.org/docs/apache-airflow/2.3.0/concepts/dynamic-task-mapping.html) +3. [https://airflow.apache.org/docs/docker-stack/build.html](https://airflow.apache.org/docs/docker-stack/build.html) +4. [https://github.com/confluentinc/examples/tree/7.1.0-post/clients/cloud/python](https://github.com/confluentinc/examples/tree/7.1.0-post/clients/cloud/python) \ No newline at end of file diff --git a/assets/images/airflow_blog_1.png b/assets/images/airflow_blog_1.png new file mode 100644 index 0000000000000000000000000000000000000000..3506850f3392d0c39710318bfb0c63cb90d3a256 GIT binary patch literal 289878 zcmdSAg;!Nu8!x;G5foty2%82;rMpu?TEIKmDEQuB?tsX7y@~ghl&XPGJ_ea1%4no zib{P(MMa%kQCNmR$RJW;BA;E;_7_}SwN#V2AFt*)9SiU-4Ehs{)T^lG9FEBC_q(V< zrDo(rMTdhRt)}lZr;WcZcX~X{al~1>+k_i!4#QEQsTp&ute;@M0 zJo5jYKiq9+#{MXp9g{`$)`LPI2$ZN+N-vK24b;AWM?}9H%Zz8tHm?fOM)WC`|GzFo zLk9z@9>Uv7q{i$~@9+>J191pq8^ji+3j$q^XgldBskd^+%}njxz^%A(R8z%T<)%dS znVeSnsQA=ksFz`OU7W^Fk|nf+{ogC8$aRE^Tmc=N6TDFMo!*vNqUb|Je-80QgChD0 zhaz=hacq3TN8-Uy6c*9jn!WCx3y62Ica(Nb44094e)vH?_C@imkjpE9EzGBRGcwY{ ztatypb3ZU4uN^EqIHtL9-8Z<#5?fx1&VF};Bcno8Lf4<`qO-Iuxe2wN+Suz9>Mwd4 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ze?BshF8KqA$B;6S6>hnkmJ|M4)BzKNb?10YDPX5n!}&gLf(6S+bQZYOv9;pv5~2=5?W|LEABZX8#nFrc`RoU Date: Fri, 31 Mar 2023 11:52:50 +0530 Subject: [PATCH 05/76] Added Command Processing with Airflow blog --- .gitignore | 2 + _config.yml | 4 +- _posts/2023-03-17-Faust_101.md | 87 ++-- ...24-Kafka_batch_processing_with_Airflow.md} | 52 +- ...Workflows_with_Apache_Kafka_and_Airflow.md | 451 ++++++++++++++++++ .../KafkaBatchProcessing.png | Bin 0 -> 26958 bytes .../batch_processing_blog}/airflow_blog_1.png | Bin .../batch_processing_blog}/airflow_blog_2.png | Bin .../batch_processing_blog}/airflow_blog_3.png | Bin .../batch_processing_blog}/airflow_blog_4.png | Bin .../Dispatcher_Architecture.png | Bin 0 -> 65883 bytes .../command_processing_blog/EmailDAG.png | Bin 0 -> 69347 bytes .../EventDrivenArchitecture.png | Bin 0 -> 155351 bytes .../blog-images/faust_101_blog/FaustLogo.png | Bin 0 -> 3733 bytes .../faust_101_blog}/faust_blog_1.png | Bin .../faust_101_blog}/faust_blog_2.png | Bin 16 files changed, 541 insertions(+), 55 deletions(-) rename _posts/{2023-03-17-Kafka_batch_processing_with_Airflow.md => 2023-03-24-Kafka_batch_processing_with_Airflow.md} (93%) create mode 100644 _posts/2023-03-30-Command_Processing_and_Event_Driven_Workflows_with_Apache_Kafka_and_Airflow.md create mode 100644 assets/blog-images/batch_processing_blog/KafkaBatchProcessing.png rename assets/{images => blog-images/batch_processing_blog}/airflow_blog_1.png (100%) rename assets/{images => blog-images/batch_processing_blog}/airflow_blog_2.png (100%) rename assets/{images => blog-images/batch_processing_blog}/airflow_blog_3.png (100%) rename assets/{images => blog-images/batch_processing_blog}/airflow_blog_4.png (100%) create mode 100644 assets/blog-images/command_processing_blog/Dispatcher_Architecture.png create mode 100644 assets/blog-images/command_processing_blog/EmailDAG.png create mode 100644 assets/blog-images/command_processing_blog/EventDrivenArchitecture.png create mode 100644 assets/blog-images/faust_101_blog/FaustLogo.png rename assets/{images => blog-images/faust_101_blog}/faust_blog_1.png (100%) rename assets/{images => blog-images/faust_101_blog}/faust_blog_2.png (100%) diff --git a/.gitignore b/.gitignore index 8312df086d..1b78d53126 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,5 @@ _site *.swp .sass-cache *~ +_posts/2023-03-29-Ksql_and_Kafka_Connect_101.md +assets/blog-images/ksql_connect_blog \ No newline at end of file diff --git a/_config.yml b/_config.yml index 969d31510a..278028aa85 100644 --- a/_config.yml +++ b/_config.yml @@ -4,7 +4,7 @@ title: "The Write Ahead Log" description: "Platform and Product Engineering Journal" logo: 'assets/images/plf-logo.svg' favicon: 'assets/images/logo.png' -baseurl: /blog +baseurl: '' google_analytics: 'G-PWW2W71JX1' #disqus: 'demowebsite' mailchimp-list: 'https://platformatory.us17.list-manage.com/subscribe/post?u=8cc2e44e80300ae193f198bcc&id=6246b12a61' @@ -53,7 +53,7 @@ authors: display_name: Ashwin Venkatesan avatar: 'assets/images/avatar.png' gravatar: b1cc14991db7a456fcd761680bbc8f81 - email: srikanth@platformatory.io + email: ashwin@platformatory.io web: https://www.platformatory.io twitter: https://twitter.com/platformatory description: "Sr Consultant at Platformatory;" diff --git a/_posts/2023-03-17-Faust_101.md b/_posts/2023-03-17-Faust_101.md index c903e914e9..f0c4a171f6 100644 --- a/_posts/2023-03-17-Faust_101.md +++ b/_posts/2023-03-17-Faust_101.md @@ -1,12 +1,22 @@ -# Faust 101 - -### **Introduction** +--- +layout: post +title: "Faust 101 - Stream processing" +author: ashwin +categories: [ Faust, Stream processing, Data, Kafka, Kafka Streams ] +image: assets/blog-images/faust_101_blog/FaustLogo.png +featured: true +hidden: true +teaser: A quick beginners walkthorugh on Faust - python stream processing library +toc: true +--- + +## Introduction Stream processing is a data processing technique to analyze, aggregate, filter or enhance a continuously ingested data as they come. Stream processing is used synonymously with real time data processing as the input data is processed as they are ingested with minimal latency. Some of its applications include fraud detection, IoT device data streaming, event streaming etc. Stream processing involves a publisher / source which generates data to a stream processing application where the data is processed, and the results sent to a subscriber / sink. Kafka Streams is one of the best streaming applications to do distributed real time stream processing. Although it's a great tool to do real time stream processing, actually setting up a production level stream processing pipeline is not an easy task. There is a learning curve associated with designing effective Kafka Streams Applications. Kafka Streams API is mainly written in Java and Scala but there are python implementations like Faust which can also be used. -### **What is Faust?** +## What is Faust? Faust is the stream processing library written in Python which uses the capabilities of Kafka Streams underneath. Popular libraries like Pandas, NumPy, SQL Alchemy etc. can be used in conjunction with Faust. It uses a superfast embedded database (written in C++) called RocksDB to store states during stream processing. @@ -18,37 +28,50 @@ Following are the concepts used by Faust, - Channel - a buffer/queue used to send and receive messages. - Sensors - To monitor and record events in the Faust application. -Faust uses Apache Kafka internally to store application configurations, table data etc. to facilitate distributed, fault-tolerant and durable data processing across machines. Faust also uses topics to input data from and write data to. Faust supports all the operations like group by, map, filter, joins etc. which are supported by Kafka Streams. +Faust stores internal data like application configurations, table data etc. in Kafka topics to facilitate distributed, fault-tolerant and durable data processing across machines. In general, Faust inputs data from topics, process it and write output to topic again. Faust supports all the operations like group by, map, filter, joins etc. which are supported by Kafka Streams. + +## Faust vs Kafka Streams + +Some of the key differences between Faust and Kafka Streams are as follows: + +- Faust is written in Python, while Kafka Streams is written in Java. +- Python has better support for data engineering and machine learning libraries like Pandas, Scikit-learn etc. which can be effectively utilized by Faust. +- Kafka Streams uses a Domain Specific Language(DSL) whereas Faust is just Python. +- Faust provides a simpler, higher-level API that is easier to use than Kafka Streams' lower-level API. If you are new to stream processing or prefer a simpler API, Faust may be a better choice. + +Both Kafka Streams and Faust are tightly integrated with Kafka and can leverage Kafka's features such as availability, concurrency and fault tolerance. -### **Prerequisites:** +In essence, Faust is a good starting point for anyone who wants to learn how Kafka stream processing systems work. It is a better option for data analytics or data science use cases. + +## Prerequisites Our objective here is to write a simple Kafka streams application in python using the Faust package. We will leverage the Confluent cloud infrastructure to create some topics. Confluent provides a fully managed cloud service for Apache Kafka which is super easy to deploy and use. You can sign up for Confluent cloud [here](https://www.confluent.io/confluent-cloud/tryfree/) and earn free credits worth $400 for the first 60 days. For this tutorial purposes, we will be using an open-source time-series based [power consumption dataset](https://www.kaggle.com/datasets/uciml/electric-power-consumption-data-set) which is available in Kaggle. Measurements in the dataset are of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. We will look at only the voltage parameter and find its average for every 1-hour windows. These windowed averages can tell us if there is a power spike which needs to be looked at. -### **Step 1: Create a topic in Confluent Cloud** +## Step 1: Create a topic in Confluent Cloud Once you have signed up for a confluent cloud account, you can create a basic cluster with default configuration at the region of your liking. -![Image-1](../assets/images/faust_blog_1.png) +![Image-1](../assets/blog-images/faust_101_blog/faust_blog_1.png) Navigate to topics and create a topic with default configurations called `voltage_input`. -### **Step 2: Downloading and installing requirements** +## Step 2: Downloading and installing requirements Let’s create a python client to produce data to the topic. Navigate to ‘Clients’ under the ‘Data integration’ option on the cluster dashboard and create a new python client. -![Image-2](../assets/images/faust_blog_2.png) +![Image-2](../assets/blog-images/faust_101_blog/faust_blog_2.png) Generate api key and copy the configuration snippet. Save it in the following path, -``` +```bash $HOME/.confluent/python.config ``` We will use the `confluent-kafka` python package to produce data to the input topic from local. Create a virtual environment with the following packages, -``` +```plain-text requestscertifi confluent-kafka[avro,json,protobuf]>=1.4.2 faust[fast] @@ -56,19 +79,19 @@ faust[fast] Let’s download the python file which will read the config file and parse it for the application to use. -``` +```bash wget https://raw.githubusercontent.com/confluentinc/examples/6.1.1-post/clients/cloud/python/ccloud_lib.py ``` Download the dataset file `household_power_consumption.txt` from the Kaggle [link](https://www.kaggle.com/datasets/uciml/electric-power-consumption-data-set) and save it in our working directory. -### **Step 3: Produce to the input topic** +## Step 3: Produce to the input topic We will use the `ccloud_lib` python module to parse the config file and also the input arguments for the producer module. - +`producer.py` -``` +```python #!/usr/bin/env python import json @@ -140,13 +163,13 @@ We are producing the voltage data with a datetime column to the input topic. `poll()` or `flush()` needs to be called to send the queued data to the Kafka broker. -### **Step 4: Creating a Faust app** +## Step 4: Creating a Faust app Let’s create a simple Faust app which will read from the `voltage_input` topic and do a windowed aggregation of the voltage values. First, we need to define the input and output data models for the Faust app. -``` +```python # Input model class PowerModel(faust.Record, coerce=True, date_parser=parse_date): datetime: datetime @@ -161,7 +184,7 @@ class AverageVoltage(faust.Record): Let’s initialize the faust app with the kafka broker connection details. We need to pass in the SASL credentials which were created previously and stored in the config file. -``` +```python ssl_context = ssl.create_default_context() ssl_context.load_verify_locations(cafile=certifi.where()) @@ -170,14 +193,14 @@ app = faust.App('myapp', broker='kafka://BROKER_HOST_NAME:9092', broker_credenti Define the input topic stream and output sink topic for the aggregated average voltages. -``` +```python voltage_stream = app.topic('voltage_input', key_type=str, value_type=PowerModel) sink = app.topic('voltage_avg', value_type=AverageVoltage) ``` Next, we need to define the aggregation logic for calculating the hourly average voltage. -``` +```python def hourly_window_processor(key, events): timestamp = key[1][0] timestamp = datetime.fromtimestamp(timestamp) @@ -203,7 +226,7 @@ We are using a tumbling window here as we need to calculate the average every ho Now, we need to define an app agent to append the input data stream to the hourly window voltage table. -``` +```python @app.agent(voltage_stream) async def processor(records): async for record in records: @@ -214,9 +237,9 @@ async def processor(records): Following is the final aggregate code of the faust app, - +`app.py` -``` +```python import faust from datetime import datetime, timedelta from dateutil.parser import parse as parse_date @@ -275,25 +298,25 @@ if __name__ == '__main__': app.main() ``` -### **Step 5: Running the app and viewing the results** +## Step 5: Running the app and viewing the results We can start the Faust worker in a terminal by executing the following command, -``` +```bash python app.py worker -l info ``` Let’s open another terminal and start producing records to the `voltage_input` topic by executing the following command. -``` +```bash ./producer.py -f ~/.confluent/python.config -t test1 ``` Let’s write a simple `consumer.py` code to consume the aggregated results from our output topic `voltage_avg`. - +`consumer.py` -``` +```python from confluent_kafka import Consumer import json import ccloud_lib @@ -345,13 +368,13 @@ if __name__ == '__main__': Run the following command to start the consumer in terminal, -``` +```bash ./consumer.py -f ~/.confluent/python.config -t voltage_avg ``` Now, you can view the magic by opening three terminals and running the producer, Faust app worker and consumer simultaneously in them. -### **Conclusion:** +## Conclusion In this tutorial, we were able to accomplish the following, @@ -360,7 +383,7 @@ In this tutorial, we were able to accomplish the following, Faust is great way to leverage the capabilities of Kafka Streams in conjunction with popular python libraries like Pandas, NumPy, Scikit-learn etc. to write distributed, fault-tolerant streaming applications. Faust is simple and can be used to build real-time applications involving machine learning, deep learning etc. -### **References:** +## References 1. [https://faust.readthedocs.io/en/latest/userguide/tables.html](https://faust.readthedocs.io/en/latest/userguide/tables.html) 2. [https://github.com/robinhood/faust/blob/master/examples/windowed_aggregation.py](https://github.com/robinhood/faust/blob/master/examples/windowed_aggregation.py) diff --git a/_posts/2023-03-17-Kafka_batch_processing_with_Airflow.md b/_posts/2023-03-24-Kafka_batch_processing_with_Airflow.md similarity index 93% rename from _posts/2023-03-17-Kafka_batch_processing_with_Airflow.md rename to _posts/2023-03-24-Kafka_batch_processing_with_Airflow.md index 06e0145350..b0d8db3ca2 100644 --- a/_posts/2023-03-17-Kafka_batch_processing_with_Airflow.md +++ b/_posts/2023-03-24-Kafka_batch_processing_with_Airflow.md @@ -1,4 +1,14 @@ -# Kafka batch processing with Airflow +--- +layout: post +title: "Kafka batch processing with Airflow" +author: ashwin +categories: [ Airflow, Batch processing, Data, Kafka, Kafka Consumer, Docker] +image: assets/blog-images/batch_processing_blog/KafkaBatchProcessing.png +featured: true +hidden: true +teaser: Batch processing from Kafka Topics by using Apache Airlfow DAG +toc: true +--- ## Introduction @@ -8,14 +18,14 @@ Kafka is a distributed streaming platform which uses logs as the unit of storage Airflow is an open source orchestration tool to manage big data workflows. Workflows are programmatically authored as Directed Acyclic Graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Airflow pipelines are defined as Python code, which means that anything that you can do with Python you can do with Airflow. It also provides a variety of operators and hooks which helps us to integrate seamlessly with most of the cloud and open-source services. Airflow is designed as a configuration-as-a-code system and it can be heavily customized with plugins. -## ****Why Airflow in Batch processing pipeline?**** +## Why Airflow in Batch processing pipeline? Although real time stream processing is used with Kafka mostly, there are a lot of instances where batch processing is required. For example, - Online stores might be interested in finding out the hourly or daily aggregate of the number of unique users arriving through a particular advertising channel to optimise their ad spends. - Banks settling the accumulated bills to vendors at the end of day based on daily transactions. -![Image-1](../assets/images/airflow_blog_1.png) +![Image-1](../assets/blog-images/batch_processing_blog/airflow_blog_1.png) Airflow is an ideal tool to orchestrate and manage batch processing of logs from Kafka topics as it run on schedules, so the resource usage is significantly lower when compared to Kafka streams or any streaming application. Airflow allows us to decouple big tasks into smaller ones and manage the dependencies between them using DAGs. It also allows us to monitor the status of tasks, re-run or restart tasks from any given point in a workflow in case of failure. Airflow also lets you integrate with tools like Apache Spark etc. to execute the task. @@ -25,15 +35,15 @@ In general, Kafka consumers are identified by a consumer group id i.e. all the c To control the consumer-partition mapping, we need to manually assign a `TopicPartition` to a consumer in the group. This can be achieved by using `consumer.assign()` command. Manual partition assignment can be useful in cases where different processing logic are applied for different partitions, assigning dedicated consumers to heavy throughput partitions etc. In this example, we will use `consumer.assign` to consume from a specific partition, process the data and produce the values to the same output topic partition as input topic. -## **Airflow Dynamic Task Mapping** +## Airflow Dynamic Task Mapping Dynamic task mapping enables us to create parallel tasks in DAG at run time. It facilitates running parallel tasks equal to the number of input Kafka topic partitions. In Kafka, maximum parallelisation is defined by the number of topic partitions. The expand() command is used to dynamically map tasks in Airflow. For more information, visit this [link](https://airflow.apache.org/docs/apache-airflow/2.3.0/concepts/dynamic-task-mapping.html). -![Image-2](../assets/images/airflow_blog_2.png) +![Image-2](../assets/blog-images/batch_processing_blog/airflow_blog_2.png) We will use manual partition assignment with consumers rather than group based partition assignment. This is to ensure that data can be consumed from an input topic partition, processed and produced to an output topic partition same as the input topic partition in parallel tasks. For the above logic to hold true, the input and output topic should be created with the same number of partitions. Consumer re-balancing in group based partition assignment can lead to mix up while processing data in parallel tasks. -## **Step 1: Download and setup Airflow** +## Step 1: Download and setup Airflow For this tutorial purposes, we will run airflow in docker. For Production, Airflow supports helm charts for Kubernetes deployment. We will be following this [link](https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html) to set up Airflow in docker. @@ -124,11 +134,11 @@ Next, we need Kafka python libraries which are not available in the default airf Airflow web UI can be accessed at [http://localhost:8080/](http://localhost:8080/) -## **Step 2: Setup the Kafka Server config file** +## Step 2: Setup the Kafka Server config file Before we start writing dags, let’s create the config file to access the input topic in a Kafka cluster. This tutorial assumes you have basic Kafka knowledge and already have an input Kafka topic set up in a remote or local Kafka cluster. Here, we will use an existing topic in the Confluent Cloud as the input topic. The client config file to connect to Kafka cluster will look like the following, - +`kafka_server.config` ``` bootstrap.servers=: @@ -149,9 +159,9 @@ The input topic here contains the following as message value, } ``` -## **Step 3: Define DAG parameters** +## Step 3: Define DAG parameters -Let’s create the `dag.py` inside the /dags folder. Airflow automatically reads dags defined in the /dags folder. +Let’s create the `dag.py` inside the `/dags` folder. Airflow automatically reads dags defined in the `/dags` folder. 1. Import the required modules, @@ -235,7 +245,7 @@ Let’s create the `dag.py` inside the /dags folder. Airflow automatically reads The config `catchup=False` is set up to avoid DAG to run missed scheduled runs. -## **Step 4: Fetch partition ids for the given topic** +## Step 4: Fetch partition ids for the given topic Let’s start building the DAG by defining each sequential task. All the tasks should be defined inside the `DAG()` function with `@task` decorator. @@ -256,7 +266,7 @@ def DAG(): Kafka Admin Client is used to fetch the topic partitions. -## **Step 5: Consume new data for each partition** +## Step 5: Consume new data for each partition Next task is to consume the message for each given topic partition till the latest offset from the previously committed offset. @@ -337,7 +347,7 @@ def consume_from_topic_partition(partition_id): return partition_id, high_offset ``` -## **Step 6: Commit latest offset for each topic partition locally** +## Step 6: Commit latest offset for each topic partition locally Next task is to commit the latest consumed offset to the local offset file. Partition Id and latest offset are taken as input from the previous task. Partition ids are passed to the next task. @@ -355,7 +365,7 @@ def commit_offsets_locally(partition_offset_values): return partition_ids ``` -## **Step 7: Aggregate values for each partition** +## Step 7: Aggregate values for each partition Next task is to aggregate the values for the consumed messages. We will calculate the average voltage for each unique date and store them in a transient processed-csv file. @@ -370,7 +380,7 @@ def process_topic_messages(partition_id): return partition_id ``` -## **Step 8: Produce aggregated value to output topic** +## Step 8: Produce aggregated value to output topic Next task is to produce the aggregated values to the output topic. Date wise average voltage values are produced to the same partition as the input topic. So, the input and output topics are co-partitioned. @@ -394,7 +404,7 @@ def produce_processed_data_to_output_topic(partition_id): return "Finished producing" ``` -## **Step 9: Define the task flow for the DAG** +## Step 9: Define the task flow for the DAG Next step is to connect the defined tasks in the appropriate order for the dag to execute. The `expand()` command is used to execute tasks in parallel and simultaneously. @@ -422,17 +432,17 @@ Username: airflow Password: airflow ``` -Once you log in, you should be able to see the defined DAG in the web UI which should have the same name as the dag_id we defined in the DAG configuration. +Once you log in, you should be able to see the defined DAG in the web UI which should have the same name as the `dag_id` we defined in the DAG configuration. -![Image-3](../assets/images/airflow_blog_3.png) +![Image-3](../assets/blog-images/batch_processing_blog/airflow_blog_3.png) We can now trigger the **`airflow-input-topic_batch_processor`** by clicking the play button under the `Actions` section. We can track the dag status in Graph or Grid mode in the dag page. Also, we can see the Logs, XCom etc. for each dag task like below. -![Image-4](../assets/images/airflow_blog_4.png) +![Image-4](../assets/blog-images/batch_processing_blog/airflow_blog_4.png) -## **Conclusion** +## Conclusion In this tutorial, we were able to do the following, @@ -444,7 +454,7 @@ In this tutorial, we were able to do the following, In conclusion, Kafka and Airflow are two of the most popular open-source tools that are used for batch processing. Kafka is designed to handle high-volume data streams in real-time, while Airflow is designed to orchestrate complex workflows and data processing pipelines. Using Kafka and Airflow together can provide a scalable and fault-tolerant solution for batch processing large datasets. -### **References** +## References 1. [https://airflow.apache.org/docs/apache-airflow/2.3.0/start/docker.html](https://airflow.apache.org/docs/apache-airflow/2.3.0/start/docker.html) 2. [https://airflow.apache.org/docs/apache-airflow/2.3.0/concepts/dynamic-task-mapping.html](https://airflow.apache.org/docs/apache-airflow/2.3.0/concepts/dynamic-task-mapping.html) diff --git a/_posts/2023-03-30-Command_Processing_and_Event_Driven_Workflows_with_Apache_Kafka_and_Airflow.md b/_posts/2023-03-30-Command_Processing_and_Event_Driven_Workflows_with_Apache_Kafka_and_Airflow.md new file mode 100644 index 0000000000..5010bba266 --- /dev/null +++ b/_posts/2023-03-30-Command_Processing_and_Event_Driven_Workflows_with_Apache_Kafka_and_Airflow.md @@ -0,0 +1,451 @@ +--- +layout: post +title: "Command Processing and Event Driven Workflows with Apache Kafka and Airflow" +author: ashwin +categories: [ Airflow, Command processing, Event Driven Workflows, Kafka, Kafka Consumer, Docker] +image: assets/blog-images/command_processing_blog/EventDrivenArchitecture.png +featured: true +hidden: true +teaser: Reactive Command processing in real time using Apache Kafka and by utilising Workflow architecture in Apache Airflow +toc: true +--- + +## Introduction + +In today's world, data processing has become an essential part of businesses. With the rise of big data, companies are looking for efficient ways to process data quickly and accurately. Apache Kafka and Apache Airflow are two popular technologies used for this purpose. + +Apache Kafka is a distributed messaging system designed to handle large amounts of data efficiently. It can be used as a real-time stream processing platform, and it is widely used for its ability to handle high throughput and low latency. Kafka is a popular choice for building event-driven architectures because of its persistence and scalability. + +Apache Airflow, on the other hand, is an open-source platform used for managing and scheduling complex data pipelines. Airflow allows users to define and execute workflows, making it easier to manage and monitor data pipelines. Airflow boasts a wide range of operators for various types of operations and integration with third party applications. + +## Command Processing and Event Driven Workflows + +Command processing refers to a software design pattern in which an application receives and executes commands from users or other systems. In general, the application receives a command and then executes it in a synchronous manner, meaning that it waits for the command to complete before executing the next one. Command processing is simpler to implement and provides greater control over the order in which tasks are executed, making it well-suited for applications where the user is in direct control of the system. + +On the other hand, event-driven workflows are based on the idea of reacting to events that occur within a system, rather than waiting for a command to be executed. This pattern is often used in complex systems, such as distributed systems, to ensure that actions are taken in response to specific events, without the need for manual intervention. Event-driven workflows are more flexible and can adapt to changing conditions in real-time, making them ideal for systems that require a high degree of automation or respond to external events. + +But in reality, in most cases,the commands need to processed in real-time to quickly inform the dependent systems to effect the appropriate action derived from the command. By combining command processing with the event driven architecture, we can process commands asynchronously in real-time and trigger actions derived from the command. Let’s see how to achieve this with the help of Apache Kafka and Apache Airflow. + +## Command Processing in Apache Kafka + +Kafka is designed to handle high volumes of data in real-time, making it well-suited for use in complex systems where events are monitored continuously. Some of the capabilities of Kafka which makes it ideal for command processing, + +1. **Persistent:** Kafka is designed to be highly persistent, ensuring that messages are not lost even if there are failures or crashes in the system. +2. **Low latency:** Kafka is optimized for low latency, enabling it to deliver real-time data streams with minimal delay. +3. **System of record:** Kafka can be a centralised system of record for all the events occurred. This also gives us the flexibility to reprocess events in the arrived order in case of failure or requirement. +4. **Fan Out/Parallelism:** Many event-driven apps need fan out = "write once, read many"- that is, multiple applications may be interested in subscribing to and reacting to a single event + +Kafka topics can act as the medium for passing commands and events between different components of the system. In this architecture, commands are sent to a specific topic by a user or external system, and then consumed by a component responsible for processing that particular command. This architecture also allows for the separation of concerns between different components, making it easier to manage and maintain the system over time. + +For example, consider an e-commerce platform where customers can place orders for products. When a customer places an order, a command is sent to a specific Kafka topic, which is consumed by an order processing component. The component then executes the command by creating an order record in a database and generating an event indicating that a new order has been placed. This event is then sent to another Kafka topic, which is consumed by a shipping component responsible for initiating the shipping process. + +## Orchestrating Event Workflows in Apache Airflow + +Many applications process events in step-wise workflows (also known as Staged Event Driven Architecture) -- these workflows need control on the individual steps (including sequencing, dependencies on previous steps, handling failures, parallel processing etc). Airflow to this end is the best orchestration platform for performing these with a purely Python interface (something that developers love). Airflow allows users to define workflows as Directed Acyclic Graphs (DAGs), where nodes represent individual tasks and edges represent dependencies between those tasks. + +Airflow provides a number of key features to support workflow management, including: + +1. **Scheduling and automation:** Airflow allows users to define schedules and automate workflow execution, ensuring that tasks are executed in the correct order and at the right time. It also supports passing on variables from upstream tasks to downstream tasks making dynamic conditional execution possible. +2. **Monitoring and logging:** Airflow provides rich monitoring and logging capabilities, allowing users to track the progress of workflows, diagnose issues, and optimize performance. +3. **Extensibility:** Airflow supports custom operators, sensors, and hooks, allowing users to interact with a wide range of systems and technologies. +4. **Alerting:** Airflow provides built-in alerting capabilities, allowing you to track the progress of your pipeline and receive notifications if anything goes wrong. +5. **Compatibility:** Airflow supports a wide range of data processing tools and frameworks, including Apache Kafka, Apache Spark, and TensorFlow, making it a highly compatible choice for event processing use cases. + +So, Apache Airflow is not just a great tool for both classic data integration & ETL/ELT oriented processing needs (in the analytics plane) but also orchestration of event driven workflows (in the operational plane). + +## Architecture + +A general architecture is as follows, + +1. Initially, the command stream is produced to a Kafka topic from the user or external systems. +2. A command dispatcher service (like a Kafka consumer) handles reading & committing offsets including hand off to Airflow by invoking the concerned DAG (via it's REST interface). By doing this, we therefore separate consuming from processing. +3. Airflow DAG itself is parametrized to process one command / event at a time. This therefore provides granular observability and auditable controls of what happened, thus enabling retrying/re-processing or dead-letter queue. + +![Dispatcher_Architecture.png](/assets/blog-images/command_processing_blog/Dispatcher_Architecture.png) + +This pattern could thus be used for: + +- Analytical data processing closely tied to events (such as CDC) +- Handle patterns such as CQRS with projections (Airflow DAG computes projections) +- Just general event workflows with chained actions (when this happens, the following chain of other events must occur) + +We must note that this architecture while useful, may not suit high concurrency because of the nature of DAG processing for the following reasons, + +- Each DAG run processes only one event at a time (as opposed to a batch) and thus has a large cost. +- There are also consequent impacts on the dispatcher's ability to maintain strict order in the face of DAG run failures. Therefore, processing order cannot be guaranteed in high concurrency mode. + +This architecture is more suited for low-concurrency scenarios requiring granular execution level observability of command processes. + +Now, lets see an example of how to implement the above defined architecture. + +## Prerequisites + +- Kafka cluster running remotely or locally. We will be using a Confluent cloud cluster in this demo. You can easily set up a Confluent cloud cluster by [signing up](https://confluent.cloud/signup) if you have not already. +- Python environment with following packages, + + ```python + confluent-kafka[avro] + ``` + + +## Generate Command Stream + +For the purposes of this demo, we will send the events to a topic using a Kafka Producer python client. A topic called `events_topic` has already been created in advance. + +The client config file to connect to Kafka cluster will look like the following, + +`kafka_server.config` + +``` +bootstrap.servers={{ CLUSTER_LISTENER_URL }}:{{ CLUSTER_LISTENER_PORT }} +security.protocol=SASL_SSL +sasl.mechanisms=PLAIN +sasl.username={{ CLUSTER_API_KEY }} +sasl.password={{ CLUSTER_SECRET_KEY }} +# Required connection configs for Confluent Cloud Schema Registry +schema.registry.url={{ SR_URL }} +basic.auth.credentials.source=USER_INFO +basic.auth.user.info={{ SR_API_KEY }}:{{ SR_API_SECRET }} +``` + +An example message for the `event_topic` is as follows, + +```json +{ + "command" : "app.send_email_alert", + "params": "{\"email\": \"test@email.com\"}" +} +``` + +where, + +`command` - The command which needs to be executed. This will also help us determine the dag which needs to be called in case of multiple dags. + +`params` - A JSON string which contains all the required arguments for a DAG run. This can vary based on the type of event or command. + +Following is the sample producer code to produce events to the `event_topic` , + +```python +from confluent_kafka import Producer +from confluent_kafka.serialization import StringSerializer +from confluent_kafka.schema_registry import SchemaRegistryClient +from confluent_kafka.schema_registry.avro import AvroSerializer +from confluent_kafka.serialization import SerializationContext, MessageField +import json +import argparse + +def read_ccloud_config(config_file): + conf = {} + with open(config_file) as fh: + for line in fh: + line = line.strip() + if len(line) != 0 and line[0] != "#": + parameter, value = line.strip().split('=', 1) + conf[parameter] = value.strip() + return conf + +def pop_schema_registry_params_from_config(conf): + """Remove potential Schema Registry related configurations from dictionary""" + + conf.pop('schema.registry.url', None) + conf.pop('basic.auth.user.info', None) + conf.pop('basic.auth.credentials.source', None) + + return conf + +if __name__ == '__main__': + + # Read arguments and configurations and initialize + parser = argparse.ArgumentParser(description='Kafka Producer') + parser.add_argument('--config_file', required=True,help='Absolute path to configurations file that contains properties used to configure a Kafka producer') + parser.add_argument('--topic', required=False, help='Kafka topic to produce messages to.') + config_file = args.config_file + topic = args.topic + conf = read_ccloud_config(config_file) + + schema_registry_conf = { + 'url': conf['schema.registry.url'], + 'basic.auth.user.info': conf['basic.auth.user.info']} + + schema_registry_client = SchemaRegistryClient(schema_registry_conf) + + event_schema = { + "type" : "record", + "name" : "EventSchema", + "namespace" : "event", + "fields" : [ { + "name" : "command", + "type" : "string", + "doc" : "Command to process and the DAG to trigger" + }, + { + "name" : "params", + "type" : "string", + "doc" : "Arguments for event workflow specific DAG" + } ], + "doc:" : "Schema of example command" + } + + schema_str = json.dumps(event_schema) + + event_avro_serializer = AvroSerializer(schema_registry_client = schema_registry_client, + schema_str = schema_str) + + string_serializer = StringSerializer('utf_8') + + producer_conf = pop_schema_registry_params_from_config(conf) + producer = Producer(producer_conf) + + delivered_records = 0 + + # Optional per-message on_delivery handler (triggered by poll() or flush()) + # when a message has been successfully delivered or + # permanently failed delivery (after retries). + def acked(err, msg): + global delivered_records + """Delivery report handler called on + successful or failed delivery of message + """ + if err is not None: + print("Failed to deliver message: {}".format(err)) + else: + delivered_records += 1 + print("Produced record to topic {} partition [{}] @ offset {}" + .format(msg.topic(), msg.partition(), msg.offset())) + + event_json = { + "event" : "app.send_email_alert", + "params": "{\"email\": \"test@email.com\"}" + } + producer.produce(topic=topic, + key=string_serializer("event"), + value=avro_serializer(event_json, SerializationContext(topic, MessageField.VALUE)), + on_delivery=acked) + + print("\nFlushing records...") + producer.flush() +``` + +Run the above python code with topic and config file as arguments, + +```bash +python producer.py --topic event_topic --config_file kafka_server.config +``` + +## Download and setup Airflow + +For this tutorial purposes, we will run airflow in docker. For Production, Airflow supports helm charts for Kubernetes deployment. We will be following this [link](https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html) to set up Airflow in docker. + +1. Open a terminal window and navigate to the working directory. + + ```bash + cd airflow_kafka + ``` + +2. Fetch the `docker-compose.yml` file, + + ```bash + curl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.3.2/docker-compose.yaml' + ``` + +3. Create the required folders for docker compose and set the airflow uid (for Linux), + + ```bash + mkdir -p ./dags ./logs ./plugins + echo -e "AIRFLOW_UID=$(id -u)" > .env + ``` + +4. Run database migrations and create the first user account, + + ```bash + docker-compose up airflow-init + ``` + +5. Now the Airflow is set up and can be started by executing, + + ```bash + docker-compose up -d + ``` + + +Airflow web UI can be accessed at [http://localhost:8080/](http://localhost:8080/) + +## Define Event Workflow DAG + +Event-workflow dags should be defined in the Airflow `dags` folder for the airflow to recognize it. A dag can be a sequence of tasks to be executed to process an incoming event. In this example, a DAG to send email alerts on trigger will be used. This concept can be extended to any type of DAGs with different operators. + +`/opt/airflow/dags/send_email.py` + +```python +from datetime import datetime, timedelta +from airflow import DAG +from airflow.operators.email_operator import EmailOperator + +default_args = { + 'owner': 'airflow', + 'depends_on_past': False, + 'start_date': datetime(2023, 3, 1), + 'email_on_failure': True, + 'email_on_retry': False, + 'retries': 1, + 'retry_delay': timedelta(minutes=5), +} + +dag = DAG( + 'send_email_alert', + default_args=default_args, + schedule_interval='@once', +) + +send_email_task = EmailOperator( + task_id='send_alert_task', + to='{{ dag_run.conf["email"] if dag_run else "admin@email.com" }}', + subject='Alert Email from Airflow', + html_content='

    This is a alert email sent from Airflow.

    ', + dag=dag, +) + +send_email_task + +``` + +In this example, we've defined a DAG called `send_email_alert` that runs once (**`schedule_interval='@once'`**) and has a single task called **`send_email_task`**. The **`EmailOperator`** is used to send the email, and we've defined the recipient, subject, and body of the email as arguments to the operator. The recipient parameter is passed from dag run conf parameter. We've also specified some default arguments for the DAG like the number of times to retry the task if it fails etc. + +![EmailDAG.png](/assets/blog-images/command_processing_blog/EmailDAG.png) + +To run this DAG, you'll need to have a working SMTP server set up to send the email. You can configure the SMTP server settings in your Airflow configuration file. + +`/opt/airflow/ansible.cfg` + +``` +# If you want airflow to send emails on retries, failure, and you want to use +# the airflow.utils.email.send_email_smtp function, you have to configure an +# smtp server here +smtp_host = smtp.gmail.com +smtp_starttls = True +smtp_ssl = False +smtp_user = your gmail id +# smtp_password = your gmail password +smtp_port = 25 +smtp_mail_from = give the email, from which email id you want send the mails(your mail id ) +``` + +## Process Command Stream and trigger DAG + +Event streams need to be consumed by a Dispatcher (Kafka Consumer) which knows how to process an incoming event or action. In this case, a dispatcher decides which event workflow DAG needs to be triggered based on the incoming events. The dispatcher should be able to talk to Airflow and trigger the appropriate DAG. We will use the Airflow HTTP API to trigger the DAG run. + +Following is an example python dispatcher code, + +```python +from confluent_kafka import Consumer +from confluent_kafka.serialization import StringDeserializer +from confluent_kafka.schema_registry import SchemaRegistryClient +from confluent_kafka.schema_registry.avro import AvroDeserializer +import json +import argparse + +def read_ccloud_config(config_file): + conf = {} + with open(config_file) as fh: + for line in fh: + line = line.strip() + if len(line) != 0 and line[0] != "#": + parameter, value = line.strip().split('=', 1) + conf[parameter] = value.strip() + return conf + +def pop_schema_registry_params_from_config(conf): + """Remove potential Schema Registry related configurations from dictionary""" + + conf.pop('schema.registry.url', None) + conf.pop('basic.auth.user.info', None) + conf.pop('basic.auth.credentials.source', None) + + return conf + +if __name__ == '__main__': + + # Read arguments and configurations and initialize + parser = argparse.ArgumentParser(description='Kafka Consumer') + parser.add_argument('--config_file', required=True,help='Absolute path to configurations file that contains properties used to configure a Kafka consumer') + parser.add_argument('--topic', required=False, help='Kafka topic to consume messages from.') + config_file = args.config_file + topic = args.topic + conf = read_ccloud_config(config_file) + + schema_registry_conf = { + 'url': conf['schema.registry.url'], + 'basic.auth.user.info': conf['basic.auth.user.info']} + + schema_registry_client = SchemaRegistryClient(schema_registry_conf) + + event_schema = { + "type" : "record", + "name" : "EventSchema", + "namespace" : "event", + "fields" : [ { + "name" : "command", + "type" : "string", + "doc" : "Command to process and the DAG to trigger" + }, + { + "name" : "params", + "type" : "string", + "doc" : "Arguments for event workflow specific DAG" + } ], + "doc:" : "Schema of example command" + } + + schema_str = json.dumps(event_schema) + + event_avro_deserializer = AvroDeserializer(schema_registry_client = schema_registry_client, + schema_str = schema_str) + + string_deserializer = StringDeserializer('utf_8') + + consumer_conf = pop_schema_registry_params_from_config(conf) + consumer = Consumer(consumer_conf) + + while True: + try: + # SIGINT can't be handled when polling, limit timeout to 1 second. + msg = consumer.poll(1.0) + if msg is None: + continue + key = string_deserializer(msg.key()) + event_json = avro_deserializer(msg.value(), SerializationContext(msg.topic(), MessageField.VALUE)) + if event_json is not None: + event_type = event_json["event"] + params = json.loads(event_json["params"]) + # DAG ID is inferred from the event type value + dag_id = event_type.split(".")[1] + payload = {"conf": params} + # The Authorization is the base64 encoded value of Airflow username and password + headers = { + "Content-Type": "application/json", + "Authorization": "Basic YWRtaW46YWRtaW4=" + } + dag_run = requests.post(f'http://localhost:8080/api/v1/dags/{dag_id}/dagRuns', data=json.dumps(payload), headers=headers) + dag_response = dag_run.json() + print(dag_response) + except KeyboardInterrupt: + pass + finally: + # Leave group and commit final offsets + consumer.close() + +``` + +The `dag_id` should be dynamically inferred from the event message based on the schema followed to maximise the range of dags covered using a single dispatcher and automate the process. In this example, the `dag_id` is inferred from the `command` column of the event message. For example, if `command` is `app.send_email_alert`, the DAG id is assumed to be `send_email_alert` . + +Run the above python code with topic and config file as arguments, + +```bash +python dispatcher.py --topic event_topic --config_file kafka_server.config +``` + +The triggered DAG and its status can be viewed in the UI. + +## Conclusion + +In conclusion, we can build robust, scalable, and flexible event workflows in Apache Airflow and trigger them by processing commands from Apache Kafka in real time. Kafka allows you to stream events from various sources and distribute them to multiple consumers, while Airflow provides a flexible framework for orchestrating the processing of these events using event-specific DAGs. With Airflow's built-in monitoring and alerting capabilities, you can easily track the progress of your pipelines and receive notifications if anything goes wrong. 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zmde0xVXvp{=VSl)fAudoGTwWwwJ}rU>?dF66+jyE#7#RABlaXlI`jCEaHH8WZP!^b z*IBJ&2HV&<+pKf8v;Tm}bY?OmwtYYHUk74hBlbn5{QiM=)Wdecft9~~LgK!d$b`f_ YF>$|t#`ArT!;0p7Hu-xsc!nMR4;ZCcEdT%j literal 0 HcmV?d00001 diff --git a/assets/images/faust_blog_1.png b/assets/blog-images/faust_101_blog/faust_blog_1.png similarity index 100% rename from assets/images/faust_blog_1.png rename to assets/blog-images/faust_101_blog/faust_blog_1.png diff --git a/assets/images/faust_blog_2.png b/assets/blog-images/faust_101_blog/faust_blog_2.png similarity index 100% rename from assets/images/faust_blog_2.png rename to assets/blog-images/faust_101_blog/faust_blog_2.png From fbe097f6c571ac1c83dba141b80fe6b43cc00e22 Mon Sep 17 00:00:00 2001 From: p6 Date: Sat, 15 Jul 2023 23:34:17 +0530 Subject: [PATCH 06/76] get blogposts out --- _config.yml | 6 +- _layouts/default.html | 2 +- _posts/2021-04-01-The-Platformatory-Way.md | 131 ------------------ ...21-05-30-platform-engineering-manifesto.md | 63 +++++++++ ...5-31-three-pillars-of-platform-strategy.md | 84 +++++++++++ _posts/2023-03-03-SaaS-architecture-primer.md | 73 ++++++++++ _posts/platform-strategy-back.md | 91 ++++++++++++ assets/blog-images/50-shades.jpg | Bin 0 -> 83515 bytes assets/blog-images/mainfesto.jpg | Bin 0 -> 82099 bytes assets/blog-images/platform-strategy.svg | 1 + assets/blog-images/wget-log | 11 ++ 11 files changed, 327 insertions(+), 135 deletions(-) delete mode 100644 _posts/2021-04-01-The-Platformatory-Way.md create mode 100644 _posts/2021-05-30-platform-engineering-manifesto.md create mode 100644 _posts/2021-05-31-three-pillars-of-platform-strategy.md create mode 100644 _posts/2023-03-03-SaaS-architecture-primer.md create mode 100644 _posts/platform-strategy-back.md create mode 100644 assets/blog-images/50-shades.jpg create mode 100644 assets/blog-images/mainfesto.jpg create mode 100644 assets/blog-images/platform-strategy.svg create mode 100644 assets/blog-images/wget-log diff --git a/_config.yml b/_config.yml index 969d31510a..b0ded27c4f 100644 --- a/_config.yml +++ b/_config.yml @@ -16,11 +16,11 @@ authors: p6: name: pavan display_name: Pavan Keshavamurthy - gravatar: e56154546cf4be74e393c62d1ae9f9d4 + gravatar: a92e3c1140c93b65879c69ecf47bb1b5 email: pavan@platformatory.io web: https://platformatory.io - twitter: https://twitter.com/p6 - description: "Cofounder @ Platformatory; Random walks @ Bangalore" + twitter: https://twitter.com/platformatory + description: "Pavan is a cofounder and principal consultant at Platformatory. While not being a handyman for everything at Platformatory, he is likely indluging in random walks (around Bangalore), music or herding kids" badri: name: badri display_name: Lakshminarasimhan Parthasarathy diff --git a/_layouts/default.html b/_layouts/default.html index 9562f2c491..b0f53b516e 100644 --- a/_layouts/default.html +++ b/_layouts/default.html @@ -108,7 +108,7 @@

    {{ site.name }}

    ================================================== -->
    -   Subscribe to the Right ahead log newsletter +   Subscribe to the Write ahead log newsletter
    diff --git a/_posts/2021-04-01-The-Platformatory-Way.md b/_posts/2021-04-01-The-Platformatory-Way.md deleted file mode 100644 index a20623d0ea..0000000000 --- a/_posts/2021-04-01-The-Platformatory-Way.md +++ /dev/null @@ -1,131 +0,0 @@ ---- -layout: post -title: "North star platform architecture" -author: p6 -categories: [ Platform Engineering, Data, Infrastructure, Kafka, Kong, Kubernetes ] -image: assets/blog-images/platform-arch-model.svg -featured: true -hidden: true -teaser: A technology leader's guide to modern platform engineering -toc: true ---- - -# Strategic Underpinnings - -In the last year or so, we've been spending a lot of time thinking about how organizations can run IT/Digital better. Clearly, IT has changed over the years from being a services centric organization that administers servers & infrastructure to modern organizations that operate a heterogenous, multi-cloud stack. In practice, many (including those who claim to be leaders) are actually laggards at running operations and we feel this is a "mission to enable" that is worthy of catering to. - -Don't get us wrong. In 2022, DevOps is ubiquitous; Most organizations claim to be Agile enabled (albeit embrace it with a liberal sprinkling of "two-speed IT" amongst other tropes); However, gaps remain at large, across the IT – Digital continuum. - -We feel that the most important trend IT / Digital organizations need to subscribe to is to adopt platforms. Why platforms? primarily because in the business that you run, your efficiency is best directed at running the business, rather than being in the business of platform software itself. This can’t be stressed enough; We see digital platform teams go down the rabbit hole of attempting to be pure tech, sometimes out of aspiration and other times out of misguided hubris. Operating platforms is a way to increase efficiencies at getting stuff done, and decrease the operational toil. Platforms should be the foundation of the Digital IT operating model. - -This is easier said than done. Embracing platforms is not without problems. - -- Enterprise software comes with nuances, feature based differentiation, hiring/skilling challenges and lock-in risks -- Even cloud platforms come with lock-in risks - -We feel it makes a lot of sense for companies to walk not only the cloud native path – which is that everything in the longer run must be built in the cloud, for the cloud, but also adopt a multi-cloud posture so as to hedge your bets and preserve flexibility towards aligning contracts that meet the bottomline. - -We also have one noteworthy addition, and that is to adopt an open-source first policy. - -This is rooted in solid fundamentals: - -- OSS rules the roost today. Server/OS market has been won long ago, and particularly since the advent of the cloud. -- Cloud providers majorly embrace OSS - providing managed services for popular OSS projects -- A whole class of PaaS software deploys to any cloud (run it yourself model) - supporting use-cases where conventional SaaS cannot be supported -- Finally, OSS foundations (CNCF, Apache Foundation, amongst others) are providing ecosystems where software development thrives, and are well funded typically by big tech that are invested in these technologies. - -As a digital/tech/IT leader, if you've sought active risk management against vendor lock in and navigating the potential consequences of choosing the wrong tech (particularly proprietary), you will find long term risk management success in the principled approach that we propose. - -We will summarize this as follows: - -1. Choose the best OSS technology catering to your problems and evaluate for project activity, stability and quality of APIs / abstractions -2. Investigate if a (cloud agnostic) PaaS vendor offers it - and can support multi-cloud deployments -3. As a next resort, does your preferred public cloud vendor provide a managed service? -4. As a final resort, does your second preference public cloud vendor provide a managed service? -5. Can you run it yourself? (but combined with tooling - particularly K8S operators?read further) - -In doing so, you will end up choosing best-in-breed (or prospectively best-in-breed) platforms such that even in the worst case scenario, you will have portability and bargaining power with no significant replatforming or migration cost. This is a winning strategy. - ------------------------- - -# The 3 Key Pillars - -It is important to think deeply through what it means to run platform(s) at central IT scale; There are two main points here: - -Almost no platforms are turnkey. It is imminent that you need some extendability; The platform should provide the hooks for it. -Ideally, the platform should offer a plugin ecosystem that is community supported. -Digital platform IT runs for the sake of product teams: Platforms should offer configurable multi tenancy primitives out of the box, so as for platform teams to provide tooling (ex: Infrastructure as Code, CI/CD pipelines for DevOps automation, starter kits), security, governance and other cross cutting concerns, while relinquishing control to enable product teams to craft their own solutions with full flexibility within the bounds of their tenancy. -Based on the DevOps/SRE maturity, enable flexible operations with some shared responsibility model (such as delineating operations of the platform and operations in the platform) that gels well with the culture of the organization - -Even if we hate central IT structures as being inefficient in the longer run, centralization invariably creates short term efficiency centers for shared services atleast at a BU level. - ------------------------- - -From our experience, as diverse as the platform landscape can get, there are 3 important pillars that stand out. This must be at the very heart of your Digital Platform Strategy - -1. Hybrid Cloud Infrastructure -2. Real time core for data & service integration -3. APIs and API Management - - -## Hybrid Cloud Infrastructure: - -As detailed previously, in the ever evolving cloud landscape, hardware comes cheap and commoditized. While cloud vendors will still seek to differentiate, it is now amply clear that containers are pretty much the standard level of atomicity for any workload. Therefore, the underlying IaaS, at least in theory, is very fungible. - -This also signals the rise of Kubernetes as a great leveler and making multi-cloud – hybrid-cloud strategies actually viable without rocket surgery. - -As Kelsey Hightower puts it "Kubernetes is the interface to the cloud". If there's one thing K8S has cracked, it is the declarative control plane API model, and by that it ensures that even if a competitor were to emerge, it would begin by baselining itself against the same API standard. - -We will go out on a limb and make another prediction: in the same spirit of Atwood's law: Any sufficiently complicated distributed system that can be natively run (with the operator pattern or full blown controllers) on Kubernetes will eventually be run as ... exactly that. - -That means, for those who may need to steer away from managed services for whatever reason, running an operator is very literally the next best thing. We expect K8S will be the chassis for building literally just about anything cloud native in the next 10 years; PaaS vendors will come up with even better and specialized Developer and operator experiences to run K8S-at-the-core apps with lesser hassle. - -There are naysayers who think K8S is just too complex (there maybe a hint of truth in this even in 2022), but the sheer rate of increasing production workloads going into k8s is obviously data based testimony staring at you in the face that this is hardly as complex as they say it is. This is definitely led by the cloud providers themselves, but there is also a long tail of hybrid cloud compatible K8S distributions (such as VMWare Tanzu, RedHat OpenShift, Rancher, Nirmata, amongst others) that are enabling this. There are discretionary choices available for which flavor of Kubernetes suits you best and the vendor posture is towards K8S not as infrastructure modernization but as the foundational need for modernizing apps in itself; - -Even capabilities that were seemingly a point of proprietary leverage for public cloud, such as serverless functions have now gained a first class foothold in Kubernetes, allowing product teams to build towards a deployment target that may run on any IaaS, with Kubernetes. - -Our advice - if you are willing to make some investments towards Kubernetes, it is about time. Do it. - -## Real time core for data & service integration - -As DB sizes grow, it is inevitable that megaliths and monoliths must die; Shared state and data just has practical limits; Services must own data and therefore, enable themselves towards decomposition into smaller parts and distribution. When that happens, the needs for integrations arise. - -The platform opportunities in integration are no recent discovery. Messaging middleware in the order of ESB -> iPaaS -> API Management have been around for decades. In the cloud native world, Enterprise integration patterns may still hold (give or take), but the aforementioned integration technologies no longer make sense for microservices communication. The services get smaller, chattier and desire eventual consistency patterns which these systems suck at, not to mention that they barely lend themselves to distributed, hybrid cloud deployments without being bottlenecks in themselves. - -There's a second, larger problem: Data integration. Analytical problem space increasingly converges into the operational (this is a great thing) and therefore real time data integration is a much bigger problem than ever. Traditional big data approaches fail to fulfill the needs of business expectations where time-to-useful-data (whether be it insight, reporting or other analytics) has to be optimized to its bounds. - -The best way to solve for both,is through embracing Event Driven, Reactive architecture as the de-facto way to architect applications. In this model: - -Choreography must be preferred over orchestration. -Applications and services exchange data through streams: Event stream processing becomes the mechanism to synchronize and process data continuously and consistently. -Most integrations can be handled through the same eventing backbone: An event store holds the logstream of events that can be applied (and replayed if necessary) to derive the state of an application - -Note that this problem has large-scale distributed system underpinnings to it. As it emerges, it is clear that Apache Kafka is the standout winner for event streaming. Its use-cases are myriad: Everything from (S)EDA, event-sourcing, stream processing to data movement. Kafka solves core problems, such as that of exactly once Processing, a brokered, pub-sub API model that offers the highest number of tuning knobs and customizability to fit purpose, first class data persistence for reliable processing and lends itself to global scale, high throughput, ow latency, fault tolerant deployments. - -Apart from these, Kafka also offers an ecosystem of connectors, stream processing bolt ons and schema management. - -Most of all, similar to K8S, Apache Kafka's data plane protocol has emerged as the defacto API standard for brokered eventing. This is evident in that even with Kafka's emergent competition, such as Azure event hubs, Apache pulsar or the new kid on the block, RedPanda (formerly vectorized.io) – that are innovating in the path of Kafka (in arguably superior ways), there is still conformance and compatibility to the same API protocol. - -The Kafka based pub-sub streaming model is clearly emerging as the integration & data backbone of the enterprise. Data mesh like architectures will evolve on top of Kafka as the data plane standard and enable Data Productization that will be owned and stewarded by domains. App and services integrations will on the other hand, leverage changelog streams and increasingly lead to event-sourced architectures with newer Kafka centric API standards (such as AsyncAPI, CloudEvents) being the guardrails. - -## APIs (everything as a service) - -Many may have heard of Jeff Bezos’s API mandate, towards making service interfaces the only mechanism for integration. The dictum seems obvious in 2022 but at that time goes as testament to the visionary nature of leaders and the importance of APIs as socio-technical contracts that enables teams to have bounded responsibilities and ownership – an important aspect of organizational design that is required in order to scale. - -The explosion of services in the SOA era has resulted in the emergence of API Management as a formal discipline, over and above classic integration middleware platforms (such as ESBs); API Platforms focus on API based mediation, composition patterns and policy based controls on security, traffic management and quotas. - -While API Management has traditionally applied in SOA context, the microservices landscape brings in a different set of challenges than the Northbound-Southbound communication that API gateways traditionally addressed. This is the challenge of inter-service communication or East-West traffic. This is the emerging solution space of service meshes, where the opportunities include the same as that of API gateways and API management (albeit with more intelligent proxy or agent sidecars), but also extending to routing, load balancing, circuit breaking, traffic shifting, mirroring and most of all, observability through tracing across a graph of services. - -Till date, there have been attempts to converge gateways and service meshes, but in our opinion, the most complete vision towards this is by Kong (and to a lesser extent, Solo.io). Kong offers a service mesh (Kuma - albeit not SMI compatible, uses Envoy as the data plane), an API gateway (nginx in the data plane) and a programmable plugin layer that supports Lua, Golang, Python and JS. - -While it is unfair to say Kong is in the de-facto standard of it’s category class as Kafka and K8S, we find it the most plausible candidate worthy of that status eventually; Kong’s core concepts reduce to services and upstreams, routes and consumers, which maybe declaratively targeted by plugins. Kong boasts a large ecosystem of plugins and forks (such as mentionable Apisix), support for HTTP2, gRPC on both VMs, as well as K8S (with K8s ingress); Most of all, Kong provides typically an order of magnitude lift in performance vis-a-vis others and this constitutes a very compelling proposition. - -The coalescence of API gateways and Service meshes has only begun. We predict Envoy based data-planes will become better and more efficient; We will increasingly look at architectures that not only externalize all network level cross cutting concerns (such as security, traffic management, routing) to the ubiquitous Envoy-at-the-core mesh layer, but also see much “smarter” wire level interception capabilities for different technologies, thus enabling sophisticated capabilities around resilience, load balancing and much more. This is only the beginning, but there are several interesting possibilities just in the networking world with eBPF at the core. On the other hand, HTTP/2 is emerging and there is a rebound of RPC protocols (gRPC, Thrift, Dubbo) that provide consistent typing and deep, idiomatic programming language support; Finally, we also recognize heterogeneous API standards emerging (GraphQL, AsyncAPI, CloudEvents to name some) for a variety of use-cases beyond the core request-response oriented REST/SOAP standards that have been prevalent; To address these and other possibilities, if we had to make a choice today - it would almost certainly be Kong. - -Finally, we must stress that APIs are just endpoints unless bundled together or built to suit use-cases of their audiences (ie, developers); For API Management to mature in an organization (regardless of API gateway or service mesh infrastructure), the curation of API products (or in an even more abstract sense, integration products) is the most important goal. This enables a transformation from APIs as integration endpoints towards APIs being literally, your business as a service. - ------------------- - -# Conclusion - -In this lengthy exploration, we've attempted to provide you a view of the emergent "k3" platform stack, consisting of Kubernetes, Kafka, Kong. These address respectively, the three most important pillars of an enterprise digital platform strategy - namely, infrastructure, data and APIs. For the discerning CT(I)O or platform engineering leader, the opportunities are vast and the OSS community has a lot to offer. It is important to take note and strategize accordingly. diff --git a/_posts/2021-05-30-platform-engineering-manifesto.md b/_posts/2021-05-30-platform-engineering-manifesto.md new file mode 100644 index 0000000000..0cb4e00318 --- /dev/null +++ b/_posts/2021-05-30-platform-engineering-manifesto.md @@ -0,0 +1,63 @@ +--- +layout: post +title: "A Platform engineering manifesto" +author: p6 +categories: [ Platform Engineering, Data, Infrastructure, Kafka, Kong, Kubernetes ] +image: assets/blog-images/platform-strategy.svg +featured: true +hidden: true +teaser: Platform Engineering is dead. Long live platform engineering! +toc: true +--- + + +![50-shades](/blog/assets/blog-images/50-shades.jpg "50 Shades of DevOps") + +There are many hot takes on Platform Engineering. There are success stories and there are horror stories. When done well, there is a team topology that enables a "do it like a digital native" operating model. When done not so well, you end up with a mess of 99 problems cherrypicked straight from the CNCF landscape graveyard of tools. + +As a company that has chosen to ride the trend, our belief is that platform engineering should be considered essential for any tech company operating in a complex domain, basically with the objective of providing a Dev X that helps you scale. Platforms don't exist in a vaccum; They are grounded ultimately in the reality of business domains and MUST accelerate product engineering teams in their endeavours. + + +To be clear, not everyone needs a platform engineering team in the first place. One is largely justified when the size of the engineering organization exceeds the Dunbar number or atleast a non-trivial problem of scale. + +Nonetheless, if you do choose to build a platform engineering team, there are 3 key performance indicators to consider in order to measure success. + + - NPS, CSAT from product engineering teams, as a measure of developer experience + - Usage metrics of the platform products in themselves + - Coverage of platform concerns and time to first production deployment (for Product teams) + +# Own a platform architecture & platform product vision + +Everything begins in robust product foundations and an (evolving) architecture vision. Platform teams must own both a product & an architecture charter. The unique requirements of platform products may themselves be driven by unique challenges in the domain (or specifically that of domain centred product engineering teams) and the incumbent IT landscape + +> ProTip: A pareto principle compliant north star vision of a robust platform architecture must think through the following concenrs: Smart Infrastructure, Data Integration & APIs. + +# Building tooling and plugins: For the sake of developers, operators and everything in between + +The platform is a product that is mainly built with engineers in mind. You build it, you run it is ultimately an elusively aspirational operating model. In reality, re-use dictates efficiency and continuous reinvention of the wheel is harmful to productivity, which is almost a side-effect of autonomous but silo'd teams. + +> A platform team topology on the other hand vests a lean, craft-focused engineering team to build specialization across a variety of cross cutting concerns while enabling a well governed tech estate. + +Such "Platform engineering" teams are suited ideally to building re-usable artefacts and easily adapted tooling for most concerns. Notably, this will include for example: + +- Infrastructure as code modules +- Golden images (as also other build-time, run-time tooling) +- Common Build / Deploy (CI-CD) Pipelines +- Observability & monitoring toolkits + + +# Enable shared-services Operations (securely) in a multi-tenant, hybrid cloud enterprise + +At the heart of enterprise grade operations is really a tenancy problem; Tenancy is really "who shares what in the house"; A platform-based operating model needs to provide mechanisms for housing tenants in dedicated or shared models, based on resource competition (and thus need for isolation), security, availability, compliance and cost requirements. + +Operating model flexibility should also provide for having resources managed directly via embedded DevOps teams that "build and run" things or shared SRE (or IT/Cloud-ops teams) that takeover this function for several applications or teams. + +> Protip: There is a need for governance as well as potentially cost attribution or chargeback to teams that foot the bill. + +# Specialize in running complex, distributed systems + +Fully managed services attempt to dumb down details of large scale distributed system, yet many are actually very leaky abstractions. A trade-off of having cloud vendor neutrality is that one must accept the reality of having fully or partially managed services co-existing with fully self-managed ones, particularly when costs become prohibitive either due to scale (or the lack thereof). This poses a dilemma for technology leaders around the operating model fit. + + There is a defining line of separation between quality platform engineering teams and vanilla (cloud\|dev)-ops teams. Platform engineers will need to gain specific expertise in running complex, distributed systems that have become de-facto platform primitives, providing lego-blocks that allow you to build higher order platforms. + +> Platform adoption is ultimately constrained by expertise and therefore it is crucial that platform engineering teams build highly artisanal guilds that can respond to architectural problems in distributed systems and provide a POV on performance, security an d a variety of cross cutting concerns. diff --git a/_posts/2021-05-31-three-pillars-of-platform-strategy.md b/_posts/2021-05-31-three-pillars-of-platform-strategy.md new file mode 100644 index 0000000000..623ae7da88 --- /dev/null +++ b/_posts/2021-05-31-three-pillars-of-platform-strategy.md @@ -0,0 +1,84 @@ +--- +layout: post +title: "The 3 pillars of a platform strategy" +author: p6 +categories: [ Platform Engineering, Data, Infrastructure, Kafka, Kong, Kubernetes ] +image: assets/blog-images/platform-arch-model.svg +featured: true +hidden: true +teaser: Data, APIs and Cloud native infrastructure +toc: true +--- + +Platform Engineering is a hot topic. There is an optimist's view: That it can potentially simplify the tech operating model of large, complex organizations. Yet there is also a cynical take: This is no panacea for conway problems. + +The crucial question is, what even should be extracted into a "platform" boundary? Greedy platforms tend to stifle team autonomy. Loose platforms tend to add little value and more harm anything else. + +Allow us to provide a take on what we think should be the three most important pillars for a sound platform strategy. Our recommendation is based on real-life experience of enabling platform-led transformations and areas that have proved most conducive for such an operating model, while providing clear, discernbile business value. We will also talk through some universal platform building blocks that you must consider. + + +## Hybrid Cloud infrastructure platform + +In the ever evolving cloud landscape, hardware comes cheap and commoditized. While cloud vendors will still seek to differentiate, it is now amply clear that containers are pretty much the standard level of atomicity for any workload. Therefore, the underlying IaaS, at least in theory, is very fungible. + +This also signals the rise of Kubernetes as a great leveler and making multi-cloud – hybrid-cloud strategies actually viable without rocket surgery. + +As Kelsey Hightower puts it "Kubernetes is the interface to the cloud". If there's one thing K8S has cracked, it is the declarative control plane API model, and by that it ensures that even if a competitor were to emerge, it would begin by baselining itself against the same API standard. + +> We will go out on a limb and make another prediction: in the same spirit of Atwood's law: Any sufficiently complicated distributed system that can be natively runon Kubernetes will eventually be run as an operator. + +That means, for those who may need to steer away from managed services for whatever reason, running an operator is very literally the next best thing. We expect K8S will be the chassis for building literally just about anything cloud native in the next 10 years; PaaS vendors will come up with even better and specialized Developer and operator experiences to run K8S-at-the-core apps with lesser hassle. + +There are naysayers who think K8S is just too complex (there maybe a hint of truth in this even in 2022), but the sheer rate of increasing production workloads going into k8s is obviously data based testimony staring at you in the face that this is hardly as complex as they say it is. This is definitely led by the cloud providers themselves, but there is also a long tail of hybrid cloud compatible K8S distributions (such as VMWare Tanzu, RedHat OpenShift, Rancher, Nirmata, amongst others) that are enabling this. There are discretionary choices available for which flavor of Kubernetes suits you best and the vendor posture is towards K8S not as infrastructure modernization but as the foundational need for modernizing apps in itself; + +Even capabilities that were seemingly a point of proprietary leverage for public cloud, such as serverless functions have now gained a first class foothold in Kubernetes, allowing product teams to build towards a deployment target that may run on any IaaS, with Kubernetes. + +Our advice - if you are willing to make some investments towards Kubernetes, it is about time. Do it. It will certainly prove to be a very robust foundation for building higher order platforms. + + +## The Data exchange platform + +As DB sizes grow, it is inevitable that megaliths and monoliths must die; Shared state and data just has practical limits; Services must own data and therefore, enable themselves towards decomposition into smaller parts and distribution. When that happens, the needs for integrations arise. + +The platform opportunities in integration are no recent discovery. Messaging platforms have been around for decades. In the cloud native world, Enterprise integration patterns may still hold (give or take), but the aforementioned integration technologies no longer make sense for microservices communication. The services get smaller, chattier and desire eventual consistency patterns which these systems suck at, not to mention that they barely lend themselves to distributed, hybrid cloud deployments without being bottlenecks in themselves. + +There's a second, larger problem: Data integration. Analytical problem space increasingly converges into the operational (this is a great thing) and therefore real time data integration is a much bigger problem than ever. Traditional big data approaches fail to fulfill the needs of business expectations where time-to-useful-data (whether be it insight, reporting or other analytics) has to be optimized to its bounds. + +The best way to solve for both,is through embracing Event Driven, Reactive architecture as the de-facto way to architect applications. In this model: + +1. Choreography must be preferred over orchestration. +2. Applications and services exchange data through streams: Event stream processing becomes the mechanism to synchronize and process data continuously and consistently. +3. Most integrations can be handled through the same eventing backbone: An event store holds the logstream of events that can be applied (and replayed if necessary) to derive the state of an application + +Note that this problem has large-scale distributed system underpinnings to it. As it emerges, it is clear that Apache Kafka is the standout winner for event streaming. Its use-cases are myriad: Everything from (S)EDA, event-sourcing, stream processing to data movement. Kafka solves core problems, such as that of exactly once Processing, a brokered, pub-sub API model that offers the highest number of tuning knobs and customizability to fit purpose, first class data persistence for reliable processing and lends itself to global scale, high throughput, ow latency, fault tolerant deployments. + +Apart from these, Kafka also offers an ecosystem of connectors, stream processing bolt ons and schema management. + +> Most of all, similar to K8S, Apache Kafka's data plane protocol has emerged as the defacto API standard for brokered eventing. This is evident in that even with Kafka's emergent competition, such as Azure event hubs, Apache pulsar or the new kid on the block, RedPanda (formerly vectorized.io) – that are innovating in the path of Kafka (in arguably superior ways), there is still conformance and compatibility to the same API protocol. + +The Kafka based pub-sub streaming model is clearly emerging as the integration & data backbone of the enterprise. Data and eventing mesh like architectures will evolve on top of Kafka as the data plane standard and enable Data Productization that will be owned and stewarded by domains. App and services integrations will on the other hand, leverage changelog streams and increasingly lead to event-sourced architectures with newer Kafka centric API standards (such as AsyncAPI, CloudEvents) being the guardrails. + +Our advise - if there's one thing you'd want to do to tame data across the enterprise, consider a platform architecture with a "universal" stream bus like Kafka. This will certainly provide definitive ROI as an operational platform, while unlocking possibilities in real-time analytics, AI/ML and traditional big data processing. + +## The Connectivity platform + +APIs are the very connective tissue of the modern enterprise. + +The explosion of services in the SOA era has resulted in the emergence of API Management as a formal discipline, over and above classic integration middleware platforms (such as ESBs); API Platforms focus on API based mediation, composition patterns and policy based controls on security, traffic management and quotas. + +While API Management has traditionally applied in SOA context, the microservices landscape brings in a different set of challenges than the Northbound-Southbound communication that API gateways traditionally addressed. This is the challenge of inter-service communication or East-West traffic. This is the emerging solution space of service meshes, where the opportunities include the same as that of API gateways and API management (albeit with more intelligent proxy or agent sidecars), but also extending to routing, load balancing, circuit breaking, traffic shifting, mirroring and most of all, observability through tracing across a graph of services. + +Till date, there have been attempts to converge gateways and service meshes, but in our opinion, the most complete vision towards this is by Kong (and to a lesser extent, Solo.io). Kong offers a service mesh (Kuma - albeit not SMI compatible, uses Envoy as the data plane), an API gateway (nginx in the data plane) and a programmable plugin layer that supports Lua, Golang, Python and JS. + +While it is unfair to say Kong is in the de-facto standard of it’s category class as Kafka and K8S, we find it the most plausible candidate worthy of that status eventually; Kong’s core concepts reduce to services and upstreams, routes and consumers, which maybe declaratively targeted by plugins. Kong boasts a large ecosystem of plugins and forks (such as mentionable Apisix), support for HTTP2, gRPC on both VMs, as well as K8S (with K8s ingress); Most of all, Kong provides typically an order of magnitude lift in performance vis-a-vis others and this constitutes a very compelling proposition. + +> The coalescence of API gateways and Service meshes has only begun. We predict Envoy based data-planes will become better and more efficient; We will increasingly look at architectures that not only externalize all network level cross cutting concerns (such as security, traffic management, routing) to the ubiquitous Envoy-at-the-core mesh layer, but also see much “smarter” wire level interception capabilities for different technologies, thus enabling sophisticated capabilities around resilience, load balancing and most of all, the API itself as an abstraction. + +This is only the beginning, but there are several interesting possibilities just in the networking world with eBPF at the core. On the other hand, HTTP/2 is emerging and there is a rebound of RPC protocols (gRPC, Thrift, Dubbo) that provide consistent typing and deep, idiomatic programming language support; Finally, we also recognize heterogeneous API standards emerging (GraphQL, AsyncAPI, CloudEvents to name some) for a variety of use-cases beyond the core request-response oriented REST/SOAP standards that have been prevalent; To address these and other possibilities, if we had to make a choice today - it would almost certainly be Kong. + +Finally, we must stress that APIs are just endpoints unless bundled together or built to suit use-cases of their audiences (ie, developers); For API Management to mature in an organization (regardless of API gateway or service mesh infrastructure), the curation of API products (or in an even more abstract sense, integration products) is the most important goal. This enables a transformation from APIs as integration endpoints towards APIs being literally, your business as a service. + + +# Conclusion + +There is clearly the emergence of a "k3" platform stack, consisting of Kubernetes, Kafka, Kong. These address respectively, the three most important pillars of an enterprise digital platform strategy - namely, infrastructure, data and APIs. For the discerning CT(I)O or platform engineering leader, the opportunities are vast and the OSS community has a lot to offer. It is important to take note and strategize accordingly. diff --git a/_posts/2023-03-03-SaaS-architecture-primer.md b/_posts/2023-03-03-SaaS-architecture-primer.md new file mode 100644 index 0000000000..dbe79bf7e7 --- /dev/null +++ b/_posts/2023-03-03-SaaS-architecture-primer.md @@ -0,0 +1,73 @@ +--- +layout: post +title: "Architecture primer for upstart SaaS founders" +author: p6 +categories: [ Platform Engineering, Data, SaaS, API Metering ] +image: assets/blog-images/mainfesto.jpg +featured: true +hidden: true +teaser: 5 areas to plan upfront & build right +toc: true +--- + + +# Architecture isn't dead + +Founders have a lot on their hand. Even seasoned engineering leaders struggle with the dilemma of choices when the primary pressure is the market opportunity and everything is hinged on time to ship. Technical debt is pretty much the state of affairs. Architecture thinking might be frowned upon or looked at as unrealistic. It is true that, "architecture upfront" is a red herring, particularly at early stage, in a volatile setting. + +Nonetheless, we can't stress enough about the importance of a north star vision and guard rails even in evolving architectures. Truly emergent architectures don't just emerge organically. Rather the most successful ones are almost always the result of good principles chosen with careful deliberation. + +# Everything is a tenancy problem. + +The SaaS economy is fueled by multi-tenancy. The more, the merrier. However, this spawns its own set of issues. Even the most mature SaaS companies struggle with these problems: + +- Noisy Neighbours: Likely to degrade performance for others +- Freeloaders: doing more than what they should be, at their pricing tier +- Under-utilizers: not getting enough bang for buck. Churn risk + +The reality is that "pay for only what you use" is notoriously difficult to implement in practice, as it puts a huge amount of pressure on what to meter, thus spawning notoriously difficult to understand (and unpredictable) billing models. No wonder many SaaS companies end up pricing their solutions with a tenant quota around a facade of some business value, while many still struggle ending up passing infrastructure tiered offerings. + +In our own working experience, we have seen even mature, hyperscale SaaS providers struggle to understand usage patterns and fairness of their billing models. The other side of the same problem is a fin-ops one, of cost attribution of compute and infrastructure resources, relative to pricing subscribed. + +The least you must do is to start with the following fin-ops principles: + +- Price the solution to the customer around the most self-evident business metric (aka consumption units). This must make sense! Occam's Razor applies. Meter and measure these with the most granular labels possible +- Consider tenancy and clustering assumptions carefully: Be in a position to hold up customer usage (measured in consumption units) against an aggregate metric of your underlying cost (measured in internal cost units) +- The above constitute an exchange rate that must provide a clearer picture of your economics + +# Don't reinvent the wheel - build on great platform primitives + +Tech-choices are hard, our biases notwithstanding. Unless you're building specifically for a totally different class of problems, you're likely to be building on the public cloud. While cloud providers seek to provide differentiated offerings, there is a risk of lock-in. Don't get us worng -- there are very many benefits in these offerings and the vertically integrated experience provided by the hyperscaler is very compelling; However, it does decrease your leverage in potentially migrating elsewhere when push comes to shove. This is also materially significant for those building with a multi-cloud, hybrid-cloud offering in the first place. + +It makes eminent sense to balance such risks pragmatically. + +- Standardize on mature open-source +- Adopt ubiquitous tools, that have become platform interfaces by themselves +- Adopt something conforms to both the above, with a managed service provided by the cloud (as opposed to a completely propreitary offering) + +# BYOC is critical in a multi-cloud hybrid-cloud landscape + +Based on the nature of your product, markets you want to cater to and your tenancy model, you must consider + +- Whether your solution may ever need to be deployed outside your choice of cloud provider (including on-premise, ex: for regulated domains) +- Data sovereignity and legal considerations for markets you cater to +- If a cloud provider marketplace could itself be a GTM channel + +In all these models, it makes sense to think about tenanting the customer around clusters of availability, along with freedom to choose the cloud provider. For sufficiently complex products, a control-plane data-plane separation of concerns clearly emerges; And although a cloud provider choice for a control plane need not be within customer discretion, it would most times make sense for the control plane to be colocated in the same provider. + +Finally, hybrid cloud is a reality. If you need to support on-premise, the most unreasonably effective way is to standardize on Kubernetes as the underlying chassis, an architecture choice that would work resonate with all the other points we've made. + +# Think ecosystems + +The maker's mind is fundamentally creative. There is always an urge to capture the greatest part of the value chain. However, one must weigh the costs, efforts and the RoI of building (and overreaching in the process); + +The SaaS offering is fundamentally centred around owning the critical path of some user experience around a business problem. A PaaS offering is centred around the developer experience of a specific technology. No matter what the tierage of your offering is, there will alwyas likely be areas which could be catered to by someone else with a (even if marginal) competitive advantage. + +SaaS companies must therefore be brutally honest about areas that are core, adjacennt or peripheral to their business. A partner ecosystem can be a very effective leverage to creating an ecosystem and thus compounding the scale of your own business while improving integration and customer stickiness. It is crucial to think about: + +- Your ecosystem proposition: +- What is in it for your partners (particularly makers or developers) - typically converging to a revenue sharing goal +- API Strategy as part of your product, focused around key integrations +- Extension interfaces through plugins, that run in sandboxed runtimes provided by you (consider Web Assembly) +- Ecosystem fairness: Ensure you don't compete for the market share that the partner network competes for + diff --git a/_posts/platform-strategy-back.md b/_posts/platform-strategy-back.md new file mode 100644 index 0000000000..a9d6fda3dd --- /dev/null +++ b/_posts/platform-strategy-back.md @@ -0,0 +1,91 @@ +--- +layout: post +title: "A guide to platform strategy" +author: p6 +categories: [ Platform Engineering, Data, Infrastructure, Kafka, Kong, Kubernetes ] +image: assets/blog-images/platform-strategy.svg +featured: true +hidden: true +teaser: A technology leader's guide to operating like a digital native +toc: true +--- + +# Prelude + +Since we started Platormatory in 2021, much of work has been focused on niche themes surrounding "platform play" & engineering in enterprise Digital / IT organizations. We've come across several recurring themes around organizational, cultural and technological challenges that inhibit customers from optimizing their digital operating model, This thesis is a distillation of our consulting experience and specifically, the mental models that will enable technology leaders to adopt & execute to a sound digital platform strategy. + + +# Why is a Platform Strategy so important? + +IT has changed over the years: Well up to the mid 2000s, most IT orgs operated as a services centric organization that administered servers, infrastructure and big ticket enterprise ~~bloatware~~ software; In contrast, modern digital organizations operate a heterogenous, multi-cloud stack comprising a plethora of complex applications -- the space of which has exploded. DevOps is almost ubiquitous. + +Or is it really? + +Let's inspect the problem with a healthy amount of skepticism. Don't get us wrong: Most organizations claim to be Agile enabled (albeit embrace it with a liberal sprinkling of "two-speed IT" amongst other tropes); There have been multi year transformation programs; Org structures have changed and much makeover has happened. However, gaps remain at large: Old wine in new bottle. + +Let's begin by calling a few things out: + +1. Most teams are barely product oriented: Organizations still tend to look at "software systems" and hyperspecialized teams rather than true product orientation. +2. Business IT barrier: IT still owns governance functions - so DevOps teams are barely empoerwd in practice and anything that hits IT is a case study in the theory of constraints. +3. Dev(Sec)Ops is actually mostly ops -- much less, Dev or Sec. +4. Data is typically a large scale analytics silo + + +We could go on and on. If you think these are strawmen, look deeply into your organizations and introspect. Even with somewhat mature Agile enterprises, we see these dysfunctions at some level or the other. + +It isn't that hyperspecialized organzations and silos are universally bad in themselves. Infact, every silo is actually a context bound efficiency center. The problem is more that truly DevOps oriented - "you build it, you run it" teams are too risky an operating model in the enterprise at scale. This realization leads to the slipper slope of half measures, resistance, badly designed team topologies and hampered decision making at all levels leading to the dysfunction we described previously. + +# How do you build a Platform-Product Strategy fit to purpose? + +We feel that the most important trend IT / Digital organizations need to subscribe to is to adopt platforms. + +Let's begin by defining Platforms: + +> A platform is a foundation for enterprise applications, that enables diverse product teams to do more while inventing fewer bespoke solutions and enabling at-scale operations and governance. + +> Platform Engineering is the collective set of practices & patterns associated with building and running platforms and opinionated by the lens of software engineering practices applied in the context of operations. + +Why platforms? primarily because in the business that you run, your efficiency is best directed at running the business, rather than being in the business of platform software itself. This can’t be stressed enough; We see digital platform teams go down the rabbit hole of attempting to be pure tech, sometimes out of aspiration and other times out of misguided hubris. Operating platforms is a way to increase efficiencies at getting stuff done, and decrease the operational toil. Platforms should be the foundation of the Digital IT operating model. + +Platform engineering may itself sound like a recombination of DevOps and SRE (this is true to some extent), but differs in it's coverage that practitioners of Platform engineering must command a deeper grasp of the software stack, including but not restricted to security, integration and data. + +So, by those definitions -- you can look at all cloud services (even infrastructure primitives) that are operated by engineering teams as constituting a platform, as long as it serves the needs of one or more product teams. Chances are however that primitive platforms leave a lot to be developed and for an operating model to be efficient, you need to run higher order platforms. + + +This is all easier said than done. Embracing platforms is not without problems. + +1. Enterprise software comes with nuances, feature based differentiation, hiring/skilling challenges and lock-in risks +2. Even cloud platforms come with lock-in risks + +We feel it makes a lot of sense for companies to walk not only the cloud native path – which is that everything in the longer run must be built in the cloud, for the cloud, but also adopt a multi-cloud posture so as to hedge your bets and preserve flexibility towards aligning contracts that meet the bottomline. + +We also have one noteworthy addition, and that is to adopt an open-source first policy. + +This is rooted in solid fundamentals: + +1. OSS rules the roost today. Server/OS market has been won long ago, and particularly since the advent of the cloud. +2. Cloud providers majorly embrace OSS - providing managed services for popular OSS projects +3. A whole class of PaaS software deploys to any cloud (run it yourself model) - supporting use-cases where conventional SaaS cannot be supported +4. Finally, OSS foundations (CNCF, Apache Foundation, amongst others) are providing ecosystems where software development thrives, and are well funded typically by big tech that are invested in these technologies. + +As a digital/tech/IT leader, if you've sought active risk management against vendor lock in and navigating the potential consequences of choosing the wrong tech (particularly proprietary), you will find long term risk management success in the principled approach that we propose. + +We will summarize this as follows: + +1. Choose the best OSS technology catering to your problems and evaluate for project activity, stability and quality of APIs / abstractions +2. Investigate if a (cloud agnostic) PaaS vendor offers it - and can support multi-cloud deployments +3. As a next resort, does your preferred public cloud vendor provide a managed service? +4. As a final resort, does your second preference public cloud vendor provide a managed service? +5. Can you run it yourself? (but combined with tooling - particularly K8S operators?read further) + + In doing so, you will end up choosing best-in-breed (or prospectively best-in-breed) platforms such that even in the worst case scenario, you will have portability and bargaining power with no significant replatforming or migration cost. This is a winning strategy. + +It is important to think deeply through what it means to run platform(s) at central IT scale; There are two main points here: + +1. Almost no platforms are turnkey. It is imminent that you need some extendability; The platform should provide the hooks for it. +2. Ideally, the platform should offer a plugin ecosystem that is community supported. +3. Digital platform IT runs for the sake of product teams: Platforms should offer configurable multi tenancy primitives out of the box, so as for platform teams to provide tooling (ex: Infrastructure as Code, CI/CD pipelines for DevOps automation, starter kits), security, governance and other cross cutting concerns, while relinquishing control to enable product teams to craft their own solutions with full flexibility within the bounds of their tenancy. +4. 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71c33329a617b0576d178dd05b388fd554fe2891 Mon Sep 17 00:00:00 2001 From: Ashwin Venkatesan Date: Sun, 16 Jul 2023 17:28:22 +0530 Subject: [PATCH 07/76] Added concurrency comparision for FaustVsKstreams --- _posts/2023-03-17-Faust_101.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/_posts/2023-03-17-Faust_101.md b/_posts/2023-03-17-Faust_101.md index f0c4a171f6..83f628889c 100644 --- a/_posts/2023-03-17-Faust_101.md +++ b/_posts/2023-03-17-Faust_101.md @@ -18,7 +18,10 @@ Kafka Streams is one of the best streaming applications to do distributed real t ## What is Faust? -Faust is the stream processing library written in Python which uses the capabilities of Kafka Streams underneath. Popular libraries like Pandas, NumPy, SQL Alchemy etc. can be used in conjunction with Faust. It uses a superfast embedded database (written in C++) called RocksDB to store states during stream processing. +Faust is a stream processing library written in Python which uses the capabilities of Kafka Streams underneath. It is used at [Robinhood](http://robinhood.com/) to build high performance distributed systems and real-time data pipelines that process billions of events every day. The original [repo](https://github.com/robinhood/faust) which has around 6.5k stars is currently deprecated but it has been [forked](https://github.com/faust-streaming/faust) and actively maintained by community. + + +For reliability Faust uses a Kafka topic as "write-ahead-log”. Popular libraries like Pandas, NumPy, SQL Alchemy etc. can be used in conjunction with Faust. It uses a superfast embedded database (written in C++) called RocksDB to store states during stream processing. Following are the concepts used by Faust, @@ -38,6 +41,8 @@ Some of the key differences between Faust and Kafka Streams are as follows: - Python has better support for data engineering and machine learning libraries like Pandas, Scikit-learn etc. which can be effectively utilized by Faust. - Kafka Streams uses a Domain Specific Language(DSL) whereas Faust is just Python. - Faust provides a simpler, higher-level API that is easier to use than Kafka Streams' lower-level API. If you are new to stream processing or prefer a simpler API, Faust may be a better choice. +- The concept of “agents” comes from the actor model, and means the stream processor can execute concurrently on many CPU cores, and on hundreds of machines at the same time. +- Thanks to Faust and asyncio you can now embed your stream processing topology into your existing asyncio/ eventlet/Twisted/Tornado applications. No need for complex topologies as it can be simplified with just python. Both Kafka Streams and Faust are tightly integrated with Kafka and can leverage Kafka's features such as availability, concurrency and fault tolerance. @@ -383,6 +388,11 @@ In this tutorial, we were able to accomplish the following, Faust is great way to leverage the capabilities of Kafka Streams in conjunction with popular python libraries like Pandas, NumPy, Scikit-learn etc. to write distributed, fault-tolerant streaming applications. Faust is simple and can be used to build real-time applications involving machine learning, deep learning etc. +Some of the other python coded stream processing library include, +- https://github.com/wintoncode/winton-kafka-streams +- https://github.com/bytewax/bytewax +- https://github.com/rh-marketingops/fluvii + ## References 1. [https://faust.readthedocs.io/en/latest/userguide/tables.html](https://faust.readthedocs.io/en/latest/userguide/tables.html) From bbbe3ccdbc8d1b3dc562c364b6bd1f4305adeae2 Mon Sep 17 00:00:00 2001 From: Ashwin Venkatesan Date: Sun, 16 Jul 2023 18:02:23 +0530 Subject: [PATCH 08/76] Added End-To-End Pipeline using KSQL and Kafka Connect blog --- ...Pipeline-using-KSQLDB-and-Kafka-Connect.md | 426 ++++++++++++++++++ .../e2e_ksql_connect_blog/CreateKSQLinCC.png | Bin 0 -> 132205 bytes .../CreateKafkaApiKey.png | Bin 0 -> 124650 bytes .../InputTopicMessagesCrop.png | Bin 0 -> 195750 bytes .../e2e_ksql_connect_blog/KSQLdbCLI.png | Bin 0 -> 37065 bytes .../OutputTopicMessagesCrop.png | Bin 0 -> 197621 bytes .../PostgresTableOutputCrop.png | Bin 0 -> 142062 bytes .../StreamDesignerPipeline.png | Bin 0 -> 53752 bytes .../end-to-end-pipeline.jpg | Bin 0 -> 1224147 bytes .../e2e_ksql_connect_blog/faust_blog_1.png | Bin 0 -> 120546 bytes .../streamsgovernanceimage.png | Bin 0 -> 172704 bytes 11 files changed, 426 insertions(+) create mode 100644 _posts/2023-06-28-End-To-End-Streaming-Pipeline-using-KSQLDB-and-Kafka-Connect.md create mode 100644 assets/blog-images/e2e_ksql_connect_blog/CreateKSQLinCC.png create mode 100644 assets/blog-images/e2e_ksql_connect_blog/CreateKafkaApiKey.png create mode 100644 assets/blog-images/e2e_ksql_connect_blog/InputTopicMessagesCrop.png create mode 100644 assets/blog-images/e2e_ksql_connect_blog/KSQLdbCLI.png create mode 100644 assets/blog-images/e2e_ksql_connect_blog/OutputTopicMessagesCrop.png create mode 100644 assets/blog-images/e2e_ksql_connect_blog/PostgresTableOutputCrop.png create mode 100644 assets/blog-images/e2e_ksql_connect_blog/StreamDesignerPipeline.png create mode 100644 assets/blog-images/e2e_ksql_connect_blog/end-to-end-pipeline.jpg create mode 100644 assets/blog-images/e2e_ksql_connect_blog/faust_blog_1.png create mode 100644 assets/blog-images/e2e_ksql_connect_blog/streamsgovernanceimage.png diff --git a/_posts/2023-06-28-End-To-End-Streaming-Pipeline-using-KSQLDB-and-Kafka-Connect.md b/_posts/2023-06-28-End-To-End-Streaming-Pipeline-using-KSQLDB-and-Kafka-Connect.md new file mode 100644 index 0000000000..e98e743121 --- /dev/null +++ b/_posts/2023-06-28-End-To-End-Streaming-Pipeline-using-KSQLDB-and-Kafka-Connect.md @@ -0,0 +1,426 @@ +--- +layout: post +title: "End-To-End Streaming Pipeline using KSQLDB and Kafka Connect" +author: ashwin +categories: [ KSQL, Kafka Connect, Confluent Cloud, Kafka, Stream Design, Docker] +image: assets/blog-images/e2e_ksql_connect_blog/end-to-end-pipeline.jpg +featured: true +hidden: true +teaser: Complete End-to-End stream processing pipeline using KSQLDB and Kafka Connect with both Confluent Cloud and Standalone Confluent Platform +toc: true +--- + +## Introduction + +KSQLDB and Kafka Connect are two technologies that are commonly used together in the Apache Kafka ecosystem for stream processing and data integration. + +Kafka Connect is a framework for building and running connectors between Kafka and other data systems, such as databases, file systems, and message queues. Connectors are plugins that are responsible for moving data between Kafka and external systems, either in a one-way or a two-way fashion. Kafka Connect provides a scalable and fault-tolerant way of integrating data between different systems. + +KSQLDB, on the other hand, is a streaming SQL engine for Apache Kafka that allows developers to write SQL queries on top of Kafka streams. KSQLDB makes it easy to perform stream processing tasks, such as filtering, aggregating, and joining streams of data in real-time. KSQLDB also provides support for stream-table joins, which allows developers to join a Kafka stream with a Kafka table. + +## KSQLDB and Kafka Connect in Confluent Architecture + +Confluent Cloud is a fully managed cloud-native event streaming platform that provides a scalable and secure way to build real-time streaming applications in the cloud. It offers connectors that are pre-configured, fully managed, and can be deployed with just a few clicks in the Confluent Cloud Console or via the Confluent Cloud CLI. It also provides a user-friendly web interface for managing and executing queries in KSQLDB, as well as an API for programmatic access. + +Confluent Platform, on the other hand, is a self-managed version of the Confluent stack that can be deployed on-premises or in the cloud. Confluent Platform provides additional features such as multi-datacenter replication, advanced security options, and enhanced monitoring and management capabilities. The Kafka Connect and KSQLDB servers are self managed and available through HTTP API for use. The required connectors need to be manually loaded into Kafka Connect server and configured. + +In summary, both Confluent Cloud and Confluent Platform provide Kafka clusters, Kafka Connect, and KSQLDB, but Confluent Platform offers additional features and customization options for those who require more control over their Kafka infrastructure. + +## Managed vs Self-hosted + +### Operation Cost + +Confluent Cloud requires a Kafka cluster for using Connectors and KSQLDB. It primarily offers three types of clusters, Basic, Standard and Dedicated. The pricing are as follows, + +- Basic - $0 per hour +- Standard - $1.50 per hour +- Dedicated - Cost based on number of CKUs (Confluent Unit for Kafka) + +The connector deployments are priced based on the connector task instance and the amount of data being transferred. Please look at this [link](https://www.confluent.io/confluent-cloud/connect-pricing/) for more details. + +KSQLDB servers are priced as **$0.23 per CSU hour**. A CSU (Confluent Streaming Unit) is the compute unit for fully managed ksqlDB. Clusters can be configured with 1, 2, 4, 8, or 12 CSUs based upon the workload. + +In Confluent Platform, both Kafka Connect cluster and KSQLDB server are available under Confluent Community license and does not have any licensing cost. The components have its own requirement on Memory and CPU based on the expected usage, so the cost of setting up the infrastructure locally or in cloud exists. The commercial connectors still needs to be licensed before use in Kafka Connect. + +### Deployment Options + +Kafka Connect and KSQL clusters can be deployed in Confluent Cloud using any of the following methods, + +- Confluent Cloud console +- [Confluent CLI](https://docs.confluent.io/confluent-cli/current/overview.html#confluent-cli-overview) +- [Confluent Terraform provider](https://registry.terraform.io/providers/confluentinc/confluent/latest) +- [Confluent Pulumi provider](https://www.pulumi.com/registry/packages/confluentcloud/) + +Both Terraform and Pulumi providers automate deployments and offer version control when used in conjunction with Github, Gitlab etc. + +Kafka Connect and KSQL clusters can be deployed in Confluent Platform using any of the following methods, + +- [Manual Confluent Package Installation](https://docs.confluent.io/platform/current/installation/overview.html#install-cp-on-premises) +- [Docker images](https://docs.confluent.io/platform/current/installation/docker/installation.html) +- [Ansible playbook for Confluent Platform](https://docs.confluent.io/ansible/current/overview.html) +- [Confluent for Kubernetes](https://docs.confluent.io/operator/current/overview.html) + +Ansible playbooks offer a simple automated way to deploy Confluent Platform components in multiple machines. Confluent for Kubernetes (CFK) is a cloud-native control plane for deploying and managing Confluent in your private cloud environment. + +Based on this execution model and depending on how we want to run our queries, currently, ksqlDB provides two deployment options. + +- ****Headless ksqlDB deployments -**** In the headless mode, you write all of your queries in a SQL file, and then start ksqlDB server instances with this file as an argument. Confluent Platform supports this type of KSQL deployment. +- ****Interactive ksqlDB deployments -**** In interactive mode, you interact with the ksqlDB servers through a REST API either directly via your favorite REST clients, through ksqlDB CLI or through Confluent Control Center. Both Confluent Cloud and Confluent Platform support this type of KSQL deployment. + +### Multi-Tenancy + +Kafka Connect and KSQLDB does not natively support multi-tenancy in both Confluent Cloud and Confluent Platform, but it is possible to implement a multi-tenancy model. + +Deploy multiple Kafka Connect and KSQLDB clusters, each dedicated to a specific tenant or application. Each Kafka Connect cluster can be configured with its own set of connectors and configurations, and can run on a separate set of hardware resources. This will lead to resource isolation in terms of connector tasks and scaling resources based on application requirements. + +Similarly, each KSQLDB cluster can be used to host one application or use case with dedicated resources. Deploying multiple applications in a single KSQL cluster can lead to resource issues because of an application being greedy for resources. It also makes it easier to reason about scaling, failovers, replication and resource utilization. + +### Future + +KSQLDB provides the ability to define and use user-defined functions (UDFs) in SQL queries. UDFs allow developers to extend the functionality of KSQLDB by defining their own custom functions that can be used in SQL expressions. As of now, the support for KSQL UDF is only present in Confluent Platform (self-hosted) deployments. Support for KSQL UDF might be introduced for Confluent Cloud as well. + +Recently, Confluent acquired Immerok which is a contributor to the open source stream processing application technology Apache Flink. So, there might be a support released for FlinkSQL in KSQLDB in the future. + +## End-To-End Streaming Pipeline + +We will now build a sample end-to-end streaming pipeline using Kafka Connect and KSQLDB in both Confluent Cloud and Confluent Platform. Both the scenarios will be using Kafka Cluster deployed in Confluent Cloud for Kafka Topic storage. So, please create a Basic Kafka cluster in Confluent Cloud by registering in this [link](https://confluent.cloud/signup). + +![faust_blog_1.png](/assets/blog-images/e2e_ksql_connect_blog/faust_blog_1.png) + +This demo will also require Schema registry cluster for storing Avro schema of the record values. You can create a Schema registry cluster in the Confluent Cloud by enabling the Streams Governance Essentials Package. You can follow this [link](https://docs.confluent.io/cloud/current/stream-governance/packages.html#stream-governance-packages-features-and-limits) for more information. + +![streamsgovernanceimage.png](/assets/blog-images/e2e_ksql_connect_blog/streamsgovernanceimage.png) + +Create a Kafka cluster API Key in your Confluent cloud account from under the Cluster Overview section, so that the connectors and KSQLDB can access data from Kafka topics during stream processing. + +![CreateKafkaApiKey.png](/assets/blog-images/e2e_ksql_connect_blog/CreateKafkaApiKey.png) + +Following are the details of the streaming pipeline, + +1. Deploy a Datagen source connector to inject “Shoe Clickstream” data into the input topic +2. Create a KSQL Stream for the input topic +3. Create a aggregated KSQL table to calculate the total view time per product per user for every 1 minute time window and write it to a output topic. +4. Sink the data from output topic to a Postgres database using the JDBC Sink Connector. + +The connectors used in this demo will be deployed using SQL queries in KSQL in both Confluent Cloud and Confluent Platform deployments. + +![StreamDesignerPipeline.png](/assets/blog-images/e2e_ksql_connect_blog/StreamDesignerPipeline.png) + +### Confluent Cloud + +We will use the Confluent Cloud console to create KSQL cluster but this can be achieved through any of the above mentioned deployment options for Confluent Cloud. + +Create a KSQL cluster in your Confluent Cloud account with Global access (only for testing). + +![CreateKSQLinCC.png](/assets/blog-images/e2e_ksql_connect_blog/CreateKSQLinCC.png) + +Once, the KSQL cluster is provisioned. we will create a Datagen source connector to push data into the input topic `clickstream_cloud_input`. We will use the `SHOE_CLICKSTREAM` quick start data generator and set the data type as `AVRO` for the record values. The Kafka API key pair created above will be used to create the input topic and write data into it. + +```sql +CREATE SOURCE CONNECTOR "ClickstreamDataGenSourceConnector" WITH ( + "connector.class"='DatagenSource', + "kafka.api.key"='${KAFKA_API_KEY}', + "kafka.api.secret"='${KAFKA_API_SECRET}', + "kafka.topic"='clickstream_cloud_input', + "output.data.format"='AVRO', + "quickstart"='SHOE_CLICKSTREAM', + "tasks.max"='1' +); +``` + +Once the Datagen source connector is successfully deployed, we should be able to see the generated records for the input topic in the Confluent Cloud UI. + +![InputTopicMessagesCrop.png](/assets/blog-images/e2e_ksql_connect_blog/InputTopicMessagesCrop.png) + +Let’s create a Kafka stream called `CLICKSTREAM_CLOUD_STREAM` from this input topic `clickstream_cloud_input` for further processing. We will mention the timestamp field in the message value to be used for windowing by using the `timestamp` variable under the `WITH` clause. + +```sql +CREATE OR REPLACE STREAM CLICKSTREAM_CLOUD_STREAM +(PRODUCT_ID VARCHAR, USER_ID VARCHAR, VIEW_TIME INT, +PAGE_URL VARCHAR, IP VARCHAR, TS BIGINT) +WITH (kafka_topic='clickstream_cloud_input', partitions=1, +value_format='AVRO', timestamp='TS'); +``` + +Create a Kafka Table called `USER_PRODUCT_VIEWTIME` backed by a Kafka topic `clickstream_cloud_output` which aggregates the total view time per product per user for every 1 minute time window. The aggregated sum of the view time is saved as `TOTAL_VIEW_TIME` field in the table. + +```sql +CREATE OR REPLACE TABLE USER_PRODUCT_VIEWTIME +WITH (kafka_topic='clickstream_cloud_output', partitions=1, +key_format='AVRO', value_format='AVRO') +AS SELECT USER_ID, PRODUCT_ID, AS_VALUE(USER_ID) AS USERID, +AS_VALUE(PRODUCT_ID) AS PRODUCTID, SUM(VIEW_TIME) AS TOTAL_VIEW_TIME, +WINDOWEND AS END_TIME, WINDOWSTART AS START_TIME +FROM CLICKSTREAM_CLOUD_STREAM +WINDOW TUMBLING (SIZE 1 MINUTES, RETENTION 7 DAYS) +GROUP BY USER_ID, PRODUCT_ID; +``` + +Following, is a sample view of the records received by the ouput topic `clickstream_cloud_output` + +![OutputTopicMessagesCrop.png](/assets/blog-images/e2e_ksql_connect_blog/OutputTopicMessagesCrop.png) + +Let’s create a Postgres JDBC Sink connector which writes the aggregated values from the output topic to a remote Postgres database. + +```sql +CREATE SINK CONNECTOR "PostgresClickstreamSinkConnector" WITH ( + 'connector.class' = 'PostgresSink', + 'kafka.api.key' = '${KAFKA_API_KEY}', + 'kafka.api.secret' = '${KAFKA_API_SECRET}', + 'connection.host' = 'postgresql-119942-0.cloudclusters.net', + 'connection.port' = '10091', + 'connection.user' = '${POSTGRES_USER}', + 'connection.password' = '${POSTGRES_PASSWORD}', + 'db.name' = 'default', + 'topics' = 'clickstream_cloud_output', + 'input.data.format' = 'AVRO', + 'input.key.format' = 'AVRO', + 'delete.enabled' = 'false', + 'ssl.mode' = 'prefer', + 'insert.mode' = 'UPSERT', + 'table.name.format' = 'clickstream_user_product_viewtime', + 'db.timezone' = 'UTC', + 'pk.mode' = 'record_value', + 'pk.fields' = 'USERID,PRODUCTID', + 'auto.create' = 'true', + 'auto.evolve' = 'true', + 'quote.sql.identifiers' = 'ALWAYS', + 'batch.sizes' = '3000', + 'tasks.max' = '1' +); +``` + +Here is the sample view of the `clickstream_user_product_viewtime` table in the Postgres database post the sink connector deployment. + +![PostgresTableOutputCrop.png](/assets/blog-images/e2e_ksql_connect_blog/PostgresTableOutputCrop.png) + +### Confluent Platform + +For Confluent Platform, we will use the Kafka Connect and KSQLDB docker images to deploy the respective clusters in a hybrid mode i.e. use the Kafka cluster in Confluent Cloud for Kafka topics. + +We will be deploying the streaming pipeline using the KSQL interactive mode by using KSQLDB CLI image to connect to the KSQLDB cluster. + +We will need to supply the credentials of the Schema Registry, which can be obtained from Confluent Cloud, in case of using Avro, JsonSchema or Protobuf formats for record key or value. + +The Kafka Connect image needs to be baked with the required connector jars before deployment. We will build a custom Kafka connect docker image by using the below `Dockerfile`, + +```docker +FROM confluentinc/cp-kafka-connect:7.3.1 + +RUN wget http://client.hub.confluent.io/confluent-hub-client-latest.tar.gz && tar -xzf confluent-hub-client-latest.tar.gz + +RUN chmod +x ./bin/confluent-hub + +ENV CONNECT_PLUGIN_PATH: "/usr/share/java,/usr/share/confluent-hub-components" + +RUN ./bin/confluent-hub install --no-prompt confluentinc/kafka-connect-datagen:0.6.0 +RUN ./bin/confluent-hub install --no-prompt confluentinc/kafka-connect-jdbc:10.7.0 +``` + +Following is the docker compose file for deploying Kafka Connect, KSQLDB server, KSQLDB CLI and Postgres DB + +`docker-compose.yml` + +```yaml +version: '3.7' +services: + connect: + build: . + hostname: connect + container_name: sample-connect + ports: + - "8083:8083" + environment: + CONNECT_BOOTSTRAP_SERVERS: "${KAFKA_BOOTSTRAP_SERVER}" + CONNECT_SECURITY_PROTOCOL: SASL_SSL + CONNECT_SASL_MECHANISM: PLAIN + CONNECT_SASL_JAAS_CONFIG: | + org.apache.kafka.common.security.plain.PlainLoginModule required \ + username="${KAFKA_API_KEY}" \ + password="${KAFKA_API_SECRET}"; + CONNECT_REST_ADVERTISED_HOST_NAME: connect + CONNECT_REST_PORT: 8083 + CONNECT_GROUP_ID: test-connect-group + CONNECT_CONFIG_STORAGE_TOPIC: docker-connect-configs + CONNECT_CONFIG_STORAGE_REPLICATION_FACTOR: 3 + CONNECT_OFFSET_FLUSH_INTERVAL_MS: 10000 + CONNECT_OFFSET_STORAGE_TOPIC: docker-connect-offsets + CONNECT_OFFSET_STORAGE_REPLICATION_FACTOR: 3 + CONNECT_STATUS_STORAGE_TOPIC: docker-connect-status + CONNECT_STATUS_STORAGE_REPLICATION_FACTOR: 3 + CONNECT_PLUGIN_PATH: "/usr/share/java,/usr/share/confluent-hub-components" + CONNECT_TOPIC_CREATION_ENABLE: "true" + CONNECT_LOG4J_ROOT_LOGLEVEL: "INFO" + CONNECT_INTERNAL_KEY_CONVERTER: "org.apache.kafka.connect.json.JsonConverter" + CONNECT_INTERNAL_VALUE_CONVERTER: "org.apache.kafka.connect.json.JsonConverter" + CONNECT_KEY_CONVERTER: "io.confluent.connect.avro.AvroConverter" + CONNECT_KEY_CONVERTER_SCHEMAS_ENABLE: "true" + CONNECT_KEY_CONVERTER_SCHEMA_REGISTRY_URL: "${SCHEMA_REGISTRY_URL}" + CONNECT_KEY_CONVERTER_BASIC_AUTH_CREDENTIALS_SOURCE: USER_INFO + CONNECT_KEY_CONVERTER_SCHEMA_REGISTRY_BASIC_AUTH_USER_INFO: "${SCHEMA_REGISTRY_API_KEY}:${SCHEMA_REGISTRY_API_SECRET}" + CONNECT_VALUE_CONVERTER: "io.confluent.connect.avro.AvroConverter" + CONNECT_VALUE_CONVERTER_SCHEMAS_ENABLE: "true" + CONNECT_VALUE_CONVERTER_SCHEMA_REGISTRY_URL: "${SCHEMA_REGISTRY_URL}" + CONNECT_VALUE_CONVERTER_BASIC_AUTH_CREDENTIALS_SOURCE: USER_INFO + CONNECT_VALUE_CONVERTER_SCHEMA_REGISTRY_BASIC_AUTH_USER_INFO: "${SCHEMA_REGISTRY_API_KEY}:${SCHEMA_REGISTRY_API_SECRET}" + CONNECT_CONNECTOR_CLIENT_CONFIG_OVERRIDE_POLICY: "All" + CONNECT_CONUSMER_SECURITY_PROTOCOL: SASL_SSL + CONNECT_CONUSMER_SASL_MECHANISM: PLAIN + CONNECT_CONUSMER_SASL_JAAS_CONFIG: | + org.apache.kafka.common.security.plain.PlainLoginModule required \ + username="${KAFKA_API_KEY}" \ + password="${KAFKA_API_SECRET}"; + CONNECT_PRODUCER_SECURITY_PROTOCOL: SASL_SSL + CONNECT_PRODUCER_SASL_MECHANISM: PLAIN + CONNECT_PRODUCER_SASL_JAAS_CONFIG: | + org.apache.kafka.common.security.plain.PlainLoginModule required \ + username="${KAFKA_API_KEY}" \ + password="${KAFKA_API_SECRET}"; + + ksqldb-server: + image: confluentinc/cp-ksqldb-server:7.3.1 + hostname: ksqldb-server + container_name: ksqldb-server-container + depends_on: + - connect + ports: + - "8088:8088" + # volumes: + # - ./ksql.sql:/opt/ksql/ksql.sql + environment: + KSQL_CONFIG_DIR: "/etc/ksql" + KSQL_AUTO_OFFSET_RESET: "earliest" + KSQL_BOOTSTRAP_SERVERS: "${KAFKA_BOOTSTRAP_SERVER}" + KSQL_HOST_NAME: ksqldb-server + KSQL_LISTENERS: "http://0.0.0.0:8088" + KSQL_CACHE_MAX_BYTES_BUFFERING: 0 + KSQL_KSQL_SCHEMA_REGISTRY_URL: "${SCHEMA_REGISTRY_URL}" + KSQL_KSQL_SCHEMA_REGISTRY_BASIC_AUTH_CREDENTIALS_SOURCE: USER_INFO + KSQL_KSQL_SCHEMA_REGISTRY_BASIC_AUTH_USER_INFO: "${SCHEMA_REGISTRY_API_KEY}:${SCHEMA_REGISTRY_API_SECRET}" + KSQL_KSQL_CONNECT_URL: "http://connect:8083" + KSQL_KSQL_LOGGING_PROCESSING_TOPIC_REPLICATION_FACTOR: 3 + KSQL_KSQL_LOGGING_PROCESSING_TOPIC_AUTO_CREATE: 'true' + KSQL_KSQL_LOGGING_PROCESSING_STREAM_AUTO_CREATE: 'true' + KSQL_LOG4J_ROOT_LOGLEVEL: INFO + KSQL_KSQL_SERVICE_ID: "test-ksql-cluster" + KSQL_KSQL_STREAMS_REPLICATION_FACTOR: 3 + KSQL_KSQL_INTERNAL_TOPIC_REPLICAS: 3 + KSQL_SECURITY_PROTOCOL: SASL_SSL + KSQL_SASL_MECHANISM: PLAIN + KSQL_SASL_JAAS_CONFIG: | + org.apache.kafka.common.security.plain.PlainLoginModule required \ + username="${KAFKA_API_KEY}" \ + password="${KAFKA_API_SECRET}"; + + ksqldb-cli: + image: confluentinc/cp-ksqldb-cli:7.3.1 + container_name: ksqldb-cli + entrypoint: /bin/sh + tty: true + + pg-database: + image: "postgres:11" + ports: + - "5432:5432" + environment: + POSTGRES_USER: postgres + POSTGRES_PASSWORD: password + POSTGRES_DB: postgres +``` + +The Kafka Connect and KSQLDB services will connect to Kafka cluster in Confluent Cloud using the bootstrap server and the SASL credentials provided as environment variables for the respective services. The Schema registry URL and its credentials are also provided for both the services. + +The config `CONNECT_TOPIC_CREATION_ENABLE: "true"` needs to be set up in Kafka Connect cluster to enable the input topic creation through Source connectors. + +Start the docker services in `build` mode to build the custom Kafka Connect image using the `Dockerfile` we created, + +```bash +docker-compose up -d --build +``` + +Once all the services are successfully up, attach to the KSQLDB CLI container’s bash, + +```bash +docker exec -it ksqldb-cli bash +``` + +Connect to the KSQLDB server inside the KSQLDB CLI container, + +```bash +ksql http://ksqldb-server:8088 +``` + +![KSQLdbCLI.png](/assets/blog-images/e2e_ksql_connect_blog/KSQLdbCLI.png) + +Deploy the Datagen Source connector, + +```sql +CREATE SOURCE CONNECTOR "ClickstreamDataGenSourceConnector" WITH ( + "connector.class"='io.confluent.kafka.connect.datagen.DatagenConnector', + "kafka.api.key"='${KAFKA_API_KEY}', + "kafka.api.secret"='${KAFKA_API_SECRET}', + "kafka.topic"='clickstream_cloud_input', + "output.data.format"='AVRO', + "quickstart"='SHOE_CLICKSTREAM', + "topic.creation.default.partitions"='1', + "topic.creation.default.replication.factor"='3', + "tasks.max"='1' +); +``` + +Create the Kafka stream from the input topic `clickstream_cloud_input`, + +```sql +CREATE OR REPLACE STREAM CLICKSTREAM_CLOUD_STREAM +(PRODUCT_ID VARCHAR, USER_ID VARCHAR, VIEW_TIME INT, +PAGE_URL VARCHAR, IP VARCHAR, TS BIGINT) +WITH (kafka_topic='clickstream_cloud_input', partitions=1, +value_format='AVRO', timestamp='TS'); +``` + +Create the aggregated table backed by a output topic `clickstream_cloud_output`, + +```sql +CREATE OR REPLACE TABLE USER_PRODUCT_VIEWTIME +WITH (kafka_topic='clickstream_cloud_output', partitions=1, +key_format='AVRO', value_format='AVRO') +AS SELECT USER_ID, PRODUCT_ID, AS_VALUE(USER_ID) AS USERID, +AS_VALUE(PRODUCT_ID) AS PRODUCTID, SUM(VIEW_TIME) AS TOTAL_VIEW_TIME, +WINDOWEND AS END_TIME, WINDOWSTART AS START_TIME +FROM CLICKSTREAM_CLOUD_STREAM WINDOW TUMBLING (SIZE 1 MINUTES, RETENTION 7 DAYS) +GROUP BY USER_ID, PRODUCT_ID; +``` + +Create the JDBC Postgres Sink Connector to sink data to the local Postgres database, + +```sql +CREATE SINK CONNECTOR "PostgresClickstreamSinkConnector" WITH ( + 'connector.class' = 'io.confluent.connect.jdbc.JdbcSinkConnector', + 'connection.url' = 'jdbc:postgresql://pg-database:5432/postgres?user=postgres&password=password&ssl=false', + 'topics' = 'clickstream_cloud_output', + 'input.data.format' = 'AVRO', + 'input.key.format' = 'AVRO', + 'delete.enabled' = 'false', + 'ssl.mode' = 'prefer', + 'insert.mode' = 'UPSERT', + 'table.name.format' = 'clickstream_user_product_viewtime', + 'db.timezone' = 'UTC', + 'pk.mode' = 'record_value', + 'pk.fields' = 'USERID,PRODUCTID', + 'auto.create' = 'true', + 'auto.evolve' = 'true', + 'quote.sql.identifiers' = 'ALWAYS', + 'batch.sizes' = '3000', + 'tasks.max' = '1', + 'consumer.override.sasl.mechanism' = 'PLAIN', + 'consumer.override.security.protocol'= 'SASL_SSL', + 'consumer.override.sasl.jaas.config' = 'org.apache.kafka.common.security.plain.PlainLoginModule required username=\"${KAFKA_API_KEY}\" password=\"${KAFKA_API_SECRET}\";' +``` + +## Conclusion + +In conclusion, building an end-to-end streaming pipeline using Kafka Connect and KSQLDB can be a powerful way to enable real-time data processing and analytics. By leveraging Kafka Connect for data ingestion and KSQLDB for data processing and analysis, developers can build complex streaming applications that can scale horizontally as needed. + +Deploying this architecture in a managed environment such as Confluent Cloud provides several benefits, including reduced infrastructure management overhead, automatic scaling, and built-in support for high availability and disaster recovery. However, this comes at the cost of higher pricing and potentially reduced flexibility in terms of configuration and customization. + +On the other hand, self-hosted deployment mode provides greater control and flexibility over the deployment and configuration of Kafka Connect and KSQLDB. 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#disqus: 'demowebsite' mailchimp-list: 'https://platformatory.us17.list-manage.com/subscribe/post?u=8cc2e44e80300ae193f198bcc&id=6246b12a61' From 88d1a75b8e48f2de6dfb902ee874d803f328cc3d Mon Sep 17 00:00:00 2001 From: Ashwin Venkatesan Date: Mon, 17 Jul 2023 12:58:39 +0530 Subject: [PATCH 10/76] Corrected the image location for the blogs --- ..._Workflows_with_Apache_Kafka_and_Airflow.md | 4 ++-- ...-Pipeline-using-KSQLDB-and-Kafka-Connect.md | 18 +++++++++--------- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/_posts/2023-03-30-Command_Processing_and_Event_Driven_Workflows_with_Apache_Kafka_and_Airflow.md b/_posts/2023-03-30-Command_Processing_and_Event_Driven_Workflows_with_Apache_Kafka_and_Airflow.md index 5010bba266..ad203f0537 100644 --- a/_posts/2023-03-30-Command_Processing_and_Event_Driven_Workflows_with_Apache_Kafka_and_Airflow.md +++ b/_posts/2023-03-30-Command_Processing_and_Event_Driven_Workflows_with_Apache_Kafka_and_Airflow.md @@ -61,7 +61,7 @@ A general architecture is as follows, 2. A command dispatcher service (like a Kafka consumer) handles reading & committing offsets including hand off to Airflow by invoking the concerned DAG (via it's REST interface). By doing this, we therefore separate consuming from processing. 3. Airflow DAG itself is parametrized to process one command / event at a time. This therefore provides granular observability and auditable controls of what happened, thus enabling retrying/re-processing or dead-letter queue. -![Dispatcher_Architecture.png](/assets/blog-images/command_processing_blog/Dispatcher_Architecture.png) +![Dispatcher_Architecture.png](../assets/blog-images/command_processing_blog/Dispatcher_Architecture.png) This pattern could thus be used for: @@ -311,7 +311,7 @@ send_email_task In this example, we've defined a DAG called `send_email_alert` that runs once (**`schedule_interval='@once'`**) and has a single task called **`send_email_task`**. The **`EmailOperator`** is used to send the email, and we've defined the recipient, subject, and body of the email as arguments to the operator. The recipient parameter is passed from dag run conf parameter. We've also specified some default arguments for the DAG like the number of times to retry the task if it fails etc. -![EmailDAG.png](/assets/blog-images/command_processing_blog/EmailDAG.png) +![EmailDAG.png](../assets/blog-images/command_processing_blog/EmailDAG.png) To run this DAG, you'll need to have a working SMTP server set up to send the email. You can configure the SMTP server settings in your Airflow configuration file. diff --git a/_posts/2023-06-28-End-To-End-Streaming-Pipeline-using-KSQLDB-and-Kafka-Connect.md b/_posts/2023-06-28-End-To-End-Streaming-Pipeline-using-KSQLDB-and-Kafka-Connect.md index e98e743121..7f1e53670a 100644 --- a/_posts/2023-06-28-End-To-End-Streaming-Pipeline-using-KSQLDB-and-Kafka-Connect.md +++ b/_posts/2023-06-28-End-To-End-Streaming-Pipeline-using-KSQLDB-and-Kafka-Connect.md @@ -85,15 +85,15 @@ Recently, Confluent acquired Immerok which is a contributor to the open source s We will now build a sample end-to-end streaming pipeline using Kafka Connect and KSQLDB in both Confluent Cloud and Confluent Platform. Both the scenarios will be using Kafka Cluster deployed in Confluent Cloud for Kafka Topic storage. So, please create a Basic Kafka cluster in Confluent Cloud by registering in this [link](https://confluent.cloud/signup). -![faust_blog_1.png](/assets/blog-images/e2e_ksql_connect_blog/faust_blog_1.png) +![faust_blog_1.png](../assets/blog-images/e2e_ksql_connect_blog/faust_blog_1.png) This demo will also require Schema registry cluster for storing Avro schema of the record values. You can create a Schema registry cluster in the Confluent Cloud by enabling the Streams Governance Essentials Package. You can follow this [link](https://docs.confluent.io/cloud/current/stream-governance/packages.html#stream-governance-packages-features-and-limits) for more information. -![streamsgovernanceimage.png](/assets/blog-images/e2e_ksql_connect_blog/streamsgovernanceimage.png) +![streamsgovernanceimage.png](../assets/blog-images/e2e_ksql_connect_blog/streamsgovernanceimage.png) Create a Kafka cluster API Key in your Confluent cloud account from under the Cluster Overview section, so that the connectors and KSQLDB can access data from Kafka topics during stream processing. -![CreateKafkaApiKey.png](/assets/blog-images/e2e_ksql_connect_blog/CreateKafkaApiKey.png) +![CreateKafkaApiKey.png](../assets/blog-images/e2e_ksql_connect_blog/CreateKafkaApiKey.png) Following are the details of the streaming pipeline, @@ -104,7 +104,7 @@ Following are the details of the streaming pipeline, The connectors used in this demo will be deployed using SQL queries in KSQL in both Confluent Cloud and Confluent Platform deployments. -![StreamDesignerPipeline.png](/assets/blog-images/e2e_ksql_connect_blog/StreamDesignerPipeline.png) +![StreamDesignerPipeline.png](../assets/blog-images/e2e_ksql_connect_blog/StreamDesignerPipeline.png) ### Confluent Cloud @@ -112,7 +112,7 @@ We will use the Confluent Cloud console to create KSQL cluster but this can be a Create a KSQL cluster in your Confluent Cloud account with Global access (only for testing). -![CreateKSQLinCC.png](/assets/blog-images/e2e_ksql_connect_blog/CreateKSQLinCC.png) +![CreateKSQLinCC.png](../assets/blog-images/e2e_ksql_connect_blog/CreateKSQLinCC.png) Once, the KSQL cluster is provisioned. we will create a Datagen source connector to push data into the input topic `clickstream_cloud_input`. We will use the `SHOE_CLICKSTREAM` quick start data generator and set the data type as `AVRO` for the record values. The Kafka API key pair created above will be used to create the input topic and write data into it. @@ -130,7 +130,7 @@ CREATE SOURCE CONNECTOR "ClickstreamDataGenSourceConnector" WITH ( Once the Datagen source connector is successfully deployed, we should be able to see the generated records for the input topic in the Confluent Cloud UI. -![InputTopicMessagesCrop.png](/assets/blog-images/e2e_ksql_connect_blog/InputTopicMessagesCrop.png) +![InputTopicMessagesCrop.png](../assets/blog-images/e2e_ksql_connect_blog/InputTopicMessagesCrop.png) Let’s create a Kafka stream called `CLICKSTREAM_CLOUD_STREAM` from this input topic `clickstream_cloud_input` for further processing. We will mention the timestamp field in the message value to be used for windowing by using the `timestamp` variable under the `WITH` clause. @@ -158,7 +158,7 @@ GROUP BY USER_ID, PRODUCT_ID; Following, is a sample view of the records received by the ouput topic `clickstream_cloud_output` -![OutputTopicMessagesCrop.png](/assets/blog-images/e2e_ksql_connect_blog/OutputTopicMessagesCrop.png) +![OutputTopicMessagesCrop.png](../assets/blog-images/e2e_ksql_connect_blog/OutputTopicMessagesCrop.png) Let’s create a Postgres JDBC Sink connector which writes the aggregated values from the output topic to a remote Postgres database. @@ -192,7 +192,7 @@ CREATE SINK CONNECTOR "PostgresClickstreamSinkConnector" WITH ( Here is the sample view of the `clickstream_user_product_viewtime` table in the Postgres database post the sink connector deployment. -![PostgresTableOutputCrop.png](/assets/blog-images/e2e_ksql_connect_blog/PostgresTableOutputCrop.png) +![PostgresTableOutputCrop.png](../assets/blog-images/e2e_ksql_connect_blog/PostgresTableOutputCrop.png) ### Confluent Platform @@ -350,7 +350,7 @@ Connect to the KSQLDB server inside the KSQLDB CLI container, ksql http://ksqldb-server:8088 ``` -![KSQLdbCLI.png](/assets/blog-images/e2e_ksql_connect_blog/KSQLdbCLI.png) +![KSQLdbCLI.png](../assets/blog-images/e2e_ksql_connect_blog/KSQLdbCLI.png) Deploy the Datagen Source connector, From 8729540eff16b832e411dfac1f19bc1cb01b478c Mon Sep 17 00:00:00 2001 From: Lakshmi Narasimhan Date: Tue, 18 Jul 2023 10:22:50 +0530 Subject: [PATCH 11/76] Updated kafka perf metrics blog post. --- ...-03-30-kafka-client-performance-metrics.md | 517 ++++++++++++++++++ .../consumer-dashboard-01.png | Bin 0 -> 619124 bytes .../consumer-dashboard-02.png | Bin 0 -> 316124 bytes .../consumer-dashboard-03.png | Bin 0 -> 339399 bytes .../producer-dashboard-01.png | Bin 0 -> 752097 bytes .../producer-dashboard-02.png | Bin 0 -> 377867 bytes .../producer-dashboard-03.png | Bin 0 -> 303051 bytes 7 files changed, 517 insertions(+) create mode 100644 _posts/2023-03-30-kafka-client-performance-metrics.md create mode 100644 assets/blog-images/kafka-perf-suite/consumer-dashboard-01.png create mode 100644 assets/blog-images/kafka-perf-suite/consumer-dashboard-02.png create mode 100644 assets/blog-images/kafka-perf-suite/consumer-dashboard-03.png create mode 100644 assets/blog-images/kafka-perf-suite/producer-dashboard-01.png create mode 100644 assets/blog-images/kafka-perf-suite/producer-dashboard-02.png create mode 100644 assets/blog-images/kafka-perf-suite/producer-dashboard-03.png diff --git a/_posts/2023-03-30-kafka-client-performance-metrics.md b/_posts/2023-03-30-kafka-client-performance-metrics.md new file mode 100644 index 0000000000..43cc9a644d --- /dev/null +++ b/_posts/2023-03-30-kafka-client-performance-metrics.md @@ -0,0 +1,517 @@ +--- +layout: post +title: "The Kafka benchmarking suite" +categories: [ Kafka, Kubernetes, Performance, Helm ] +--- + +There are numerous parameters we have to consider tweaking when benchmarking a Kafka cluster. Irrespective of these parameters, we optimize along the following dimensions: + +1. Throughput: The volume of data moved across the cluster in a given unit of time. +2. Durability: Are we minimizing the chance of messages getting lost +3. Availability: How quickly can my Kafka cluster recover from a failure scenario and be operational again? +4. Latency: How soon can a message be consumed after it is produced? + +## Defining our problem statement + +The purpose of a Kafka benchmarking exercise is to initially establish baseline performance characteristics, mainly for throughput and end to end latency. We then iteratively optimize these quantities by modifying various producer and consumer parameters, running the benchmark again, measuring the quantities. These steps are done in a “rinse and repeat” fashion till we hit our desired performance metrics. + +There are quite a few performance testing frameworks in the open which aid in doing this. We prepare a wishlist of items required for a good benchmarking system, like: + +1. It should be close to a realistic production scenario. No toy benchmarking. +2. Ability to snapshot and visualize the data from various test runs. +3. Ease of setup. The benchmarking system itself shouldn’t be complicated to setup with a lot of configuration landmines. Nothing is more dangerous than a misconfigured benchmarking system which gives us false promises. + +Let’s see how existing setups measure up against this wishlist. + +## Openmessaging benchmarks + +The [Openmessaging benchmark framework](https://openmessaging.cloud/docs/benchmarks/) is a vendor-neutral suite of tools which eases benchmarking of distributed benchmarking systems. In addition to Kafka, this framework provides benchmarking tools for other messaging systems like RabbitMQ and Apache Pulsar. +In essence, Openmessaging benchmarking involves the following steps broadly: + +1. Spin up the infrastructure needed to run Kafka in the cloud using Terraform +2. Provision and install Kafka clusters and clients using Ansible playbook +3. Run benchmarks from client hosts. NOTE that this can be run in a distributed manner as well across multiple hosts. + +One of the biggest selling points of Openmessaging benchmarks is that they are as close to realistic scenarios as possible. In fact, this is one of the stated goals of the project. There is also the provision to download the metrics data for each test run/scenario. + +We observed a few shortcomings with Openmessaging benchmarks. + +1. The inability to measure end to end latency. This is a key metric in many scenarios and there is no documented way to measure this in Openmessaging suite. +2. Ease of setup. Openmessaging provides all the necessary scaffold scripts like Terraform and Ansible to run the test. This could be made easier. There is an option to deploy it as a [Helm chart](https://github.com/openmessaging/benchmark/blob/master/deployment/kubernetes/helm/README.md), which closely resembles our approach as well. But this isn’t documented. +3. As a sub-idea of 2, we don’t have an easy way to visualize these numbers. We all love to see the big picture instead of staring at a spreadsheet full of numbers. It would be great if we can pipe the output straight to tools like Grafana. + +## Apache Trogdor + +[Apache Trogdor](https://github.com/apache/kafka/tree/trunk/trogdor) is a test framework for Kafka. Although it does excellent performance testing, Trogdor emphasizes a lot of [fault testing](https://cwiki.apache.org/confluence/display/KAFKA/Fault+Injection). The idea is to run a Trogdor agent in every cluster node and a Trogdor coordinator daemon which manages the agents and tasks in the cluster. The agent can simulate a fault by crashing one of the brokers. We measure the message durability, adjust the parameters and re-run the process again. Confluent cloud [uses Trogdor](https://www.confluent.io/blog/cloud-kafka-as-a-service/) to performance test its infrastructure. + +Again, we found the following things lacking in Trogdor. + +1. No way to measure end to end latency. The [RoundTripWorkload](https://github.com/apache/kafka/tree/trunk/trogdor#roundtripworkload) is a close match for what we were looking for, but doesn’t quite cut it. +2. It doesn’t emit metrics which can be visualized. There is a provision to show the status of a submitted task. This gives a snapshot of the task(a task is a specific, defined benchmark scenario), like time taken, number of messages sent, 99th percentile latency etc. +3. We have to run the agent and coordinator daemon processes in the cluster. Sometimes, due to various limitations, we might not be able to do so. + +## Jmeter + +Jmeter is an excellent tool to benchmark Kafka clusters and scores pretty high on the “ease of setup” factor. [PepperBox](https://pepperbox.gslab.com/), a specialized Kafka load generator for Jmeter, is a prerequisite for running load tests using Jmeter. However, Jmeter suffers from the following limitations: + +1. We have to write the consumer client code for running the end to end tests. +2. There is no way to measure the latency. We had to tweak the consumer to write the timestamp to a file. Ideally, there should be a complementing tool for PepperBox at the consumer end for this. + +## Kafka perf tools + +Kafka offers a set of performance testing tools for the producer, consumer and also to measure end to end latency. This is the least common denominator of all the tools out there which fits our bill, but we’re not quite out there yet. We don’t have the option to load test a production replica scenario(Ex. Parallel producers/consumers). There’s also the “ease of setup” factor missing. We have to clean up the resources and re-run the process for a new scenario. Finally, there is no way to record and view the metrics. We will build a Prometheus based scaffold to send these metrics. In the rest of the post, we shall focus on how we’ve fixed these issues and created a robust and reliable performance testing framework which fulfills our wishlist. + +## Prerequisites + +You should have a Kafka cluster which needs to be benchmarked(Duh!). Arguably the fastest way to get your hands on a fairly production ready Kafka cluster is to sign up for a Confluent cloud account and spin up a new cluster. + +## Benchmarking using kafka provided tools +For those of you who are new to benchmarking using Kafka perf-tools, here’s a brief recap. +First, you create a new topic where you want to send your records. + +```bash +$ kafka-topics \ # <-------- (1) + --if-not-exists \ + --topic mytopic \ # <-------- (2) + --create \ + --bootstrap-server xxx-yyyy.us-west4.gcp.confluent.cloud:9092 \ # <-------- (3) + --replication-factor 3 \ # <-------- (4) + --partitions 1 \ # <-------- (5) + --command-config kafka.properties # <-------- (6) +``` + +1. `kafka-topics` is the CLI tool which ships with [Apache Kafka](https://kafka.apache.org/downloads)(as `kafka-topics.sh`) and [confluent platform package](https://docs.confluent.io/platform/current/installation/installing_cp/zip-tar.html#prod-kafka-cli-install). +2. Name of the topic you want to create +3. The bootstrap server URL. You get this from your confluent cloud account. +4. Replication factor for your topic. Please update it with your desired replication factor. +5. The number of partitions for the topic. Again, update it to suit your needs +6. The `kafka.properties` file, needed for all the CLI tools we will run. + +Let’s quickly take a look at the contents of kafka.properties. + +``` +bootstrap.servers=xxx-yyyy.us-west4.gcp.confluent.cloud:9092 # <-- (1) +security.protocol=SASL_SSL # <-- (2) +sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username='BADRI123SZK5QZJI' password='Supersecretlongpasswordgivenbyconfluentcloud'; # <-- (3) +sasl.mechanism=PLAIN # <-- (4) +``` + +1. The bootstrap server URL, which you can obtain from confluent cloud. +2. Confluent cloud uses SASL_SSL for authentication. +3. The user name and password can be obtained from confluent cloud. + +**NOTE** that this properties file will be used across the board for the producer and consumer benchmark tools as well. + +Time to pump in some messages to this topic. + +```bash +$ kafka-producer-perf-test \ # <-- (1) + --topic mytopic \ # <-- (2) + --num-records 10000 \ # <-- (3) + --record-size 1024 \ # <-- (4) + --throughput -1 \ # <-- (5) + --producer-props acks=all client.id=producer-01 \ # <-- (6) + --producer.config kafka.properties + --print-metrics # < -- (7) +``` + +1. We run the `kafka-producer-perf-test` CLI tool for this. +2. We send messages to the topic we created just now. +3. We’re sending 10k records. +4. The size of each of these records is 1024 bytes. +5. We don’t want to throttle the number of messages per second. Hence, we set it to -1. +6. Number of acknowledgements the leader broker must have received before acknowledging to the producer. We also specify the client ID here. +7. Print the producer metrics. + +We should get an output which looks similar to this. + +``` +1 records sent, 0.2 records/sec (0.00 MB/sec), 5289.0 ms avg latency, 5289.0 ms max latency. +1215 records sent, 240.6 records/sec (0.24 MB/sec), 4727.5 ms avg latency, 7196.0 ms max latency. +1275 records sent, 246.3 records/sec (0.24 MB/sec), 9746.1 ms avg latency, 12365.0 ms max latency. +1500 records sent, 269.5 records/sec (0.26 MB/sec), 14727.0 ms avg latency, 17925.0 ms max latency. +1425 records sent, 283.6 records/sec (0.28 MB/sec), 20428.6 ms avg latency, 22947.0 ms max latency. +1650 records sent, 320.0 records/sec (0.31 MB/sec), 25312.8 ms avg latency, 28097.0 ms max latency. +1200 records sent, 238.0 records/sec (0.23 MB/sec), 30665.2 ms avg latency, 33137.0 ms max latency. +1200 records sent, 212.7 records/sec (0.21 MB/sec), 35153.8 ms avg latency, 38780.0 ms max latency. +10000 records sent, 223.613596 records/sec (0.22 MB/sec), 21165.35 ms avg latency, 41544.00 ms max latency, 21647 ms 50th, 38785 ms 95th, 40823 ms 99th, 41544 ms 99.9th. + +Metric Name Value +app-info:commit-id:{client-id=producer-1} : 06652ad189d0cbf0 +app-info:start-time-ms:{client-id=producer-1} : 1653549654259 +app-info:version:{client-id=producer-1} : 6.2.1-ce +kafka-metrics-count:count:{client-id=producer-1} : 113.000 +producer-metrics:batch-size-avg:{client-id=producer-1} : 15548.256 +producer-metrics:batch-size-max:{client-id=producer-1} : 15556.000 +producer-metrics:batch-split-rate:{client-id=producer-1} : 0.000 +producer-metrics:batch-split-total:{client-id=producer-1} : 0.000 +producer-metrics:buffer-available-bytes:{client-id=producer-1} : 33554432.000 + +... +producer-topic-metrics:record-error-total:{client-id=producer-1, topic=mytopic} : 0.000 +producer-topic-metrics:record-retry-rate:{client-id=producer-1, topic=mytopic} : 0.000 +producer-topic-metrics:record-retry-total:{client-id=producer-1, topic=mytopic} : 0.000 +producer-topic-metrics:record-send-rate:{client-id=producer-1, topic=mytopic} : 250.752 +producer-topic-metrics:record-send-total:{client-id=producer-1, topic=mytopic} : 10000.000 +``` + +Let’s consume the messages we created. + +```bash +$ kafka-consumer-perf-test \ + --topic mytopic \ # <-- (1) + --bootstrap-server xxx-yyyy.us-west4.gcp.confluent.cloud:9092 \ # <-- (2) + --messages 1000 \ # <-- (3) + --consumer.config kafka.properties # <-- (4) + --print-metrics \ + --timeout=100000 # <-- (5) + ``` + +1. We use the same topic. +2. We have to specify the bootstrap server. +3. The number of messages we want to consume. +4. We refer to the same kafka.properties file. +5. The amount of time the consumer process waits before the broker returns records. + + +Here’s a sample output from a consumer perf test run. + +``` +start.time, end.time, data.consumed.in.MB, MB.sec, data.consumed.in.nMsg, nMsg.sec, rebalance.time.ms, fetch.time.ms, fetch.MB.sec, fetch.nMsg.sec +2023-03-30 13:18:56:066, 2023-03-30 13:19:11:826, 0.9766, 0.0620, 1000, 63.4518, 6219, 9541, 0.1024, 104.8108 + +Metric Name Value +consumer-coordinator-metrics:assigned-partitions:{client-id=consumer-perf-consumer-24667-1} : 0.000 +consumer-coordinator-metrics:commit-latency-avg:{client-id=consumer-perf-consumer-24667-1} : 218.500 +consumer-coordinator-metrics:commit-latency-max:{client-id=consumer-perf-consumer-24667-1} : 222.000 +consumer-coordinator-metrics:commit-rate:{client-id=consumer-perf-consumer-24667-1} : 0.057 +consumer-coordinator-metrics:commit-total:{client-id=consumer-perf-consumer-24667-1} : 2.000 +consumer-coordinator-metrics:failed-rebalance-rate-per-hour:{client-id=consumer-perf-consumer-24667-1} : 88.703 +consumer-coordinator-metrics:failed-rebalance-total:{client-id=consumer-perf-consumer-24667-1} : 1.000 +consumer-coordinator-metrics:heartbeat-rate:{client-id=consumer-perf-consumer-24667-1} : 0.081 + +... +consumer-fetch-manager-metrics:records-lag-max:{client-id=consumer-perf-consumer-24667-1} : 41500.000 +consumer-fetch-manager-metrics:records-lag:{client-id=consumer-perf-consumer-24667-1, topic=mytopic, partition=0} : 41000.000 +consumer-fetch-manager-metrics:records-lead-avg:{client-id=consumer-perf-consumer-24667-1, topic=mytopic, partition=0} : 750.000 +consumer-fetch-manager-metrics:records-lead-min:{client-id=consumer-perf-consumer-24667-1, topic=mytopic, partition=0} : 500.000 +consumer-fetch-manager-metrics:records-lead-min:{client-id=consumer-perf-consumer-24667-1} : 500.000 +consumer-fetch-manager-metrics:records-lead:{client-id=consumer-perf-consumer-24667-1, topic=mytopic, partition=0} : 1000.000 +consumer-fetch-manager-metrics:records-per-request-avg:{client-id=consumer-perf-consumer-24667-1, topic=mytopic} : 1000.000 +consumer-fetch-manager-metrics:records-per-request-avg:{client-id=consumer-perf-consumer-24667-1} : 1000.000 +kafka-metrics-count:count:{client-id=consumer-perf-consumer-24667-1} : 61.000 +``` +The typical way to run Kafka benchmarks is to take a set of parameters for the producer and consumer, do a set of sample runs with those parameters, and record the metrics we get. We repeat this loop until we get the desired numbers. This can be likened to an OODA (Observe Orient Decide Act) loop, where the mapping looks like this: + +- Observe - Look at the printed metrics for each run. +- Orient - Find out what configuration tweak led to this set of outcomes. +- Decide - Figure what configuration parameters to change for next run. +- Act - Run the benchmarks CLI program. + +Applying the OODA loop to the plain vanilla kafka benchmark CLI tools can get messy quickly. For instance, what should I be doing to run 20 parallel producers? Also, how can I move all these metrics to a central location for later analysis/charting? +What numbers did we get the last time we ran for a particular set of config values? + +We fix these open questions in the rest of the post. + +## Take 2 - Kubernetes + +One major change in our benchmarking procedure will be the fact that we will move all our benchmark CLI tools to Kubernetes services. So, the prerequisite here would be for you to have a working Kubernetes cluster with the kubectl client. + +Let’s try running this as a Kubernetes Job. + +```yaml +apiVersion: batch/v1 +kind: Job +metadata: + name: producer # <-------- (1) + labels: + type: producer +spec: + completions: 3 # <-------- (2) + parallelism: 3 + template: + spec: + initContainers: # <-------- (3) + - name: topics + image: confluentinc/cp-kafka:7.3.2 # <-------- (4) + command: + - /bin/sh + - -c + - | + kafka-topics \ + --if-not-exists \ + --topic mytopic \ + --create \ + --bootstrap-server xxx-yyyy.us-west4.gcp.confluent.cloud:9092 \ + --replication-factor 3 \ + --partitions 1 \ + --command-config /mnt/kafka.properties + volumeMounts: + - name: kafka-properties # <-------- (5) + mountPath: /mnt + containers: + - name: producer + image: confluentinc/cp-kafka:7.3.2 + command: + - /bin/sh + - -c + - | + kafka-producer-perf-test \ # <-------- (6) + --topic perf-test \ + --num-records 10000 \ + --record-size 1024 \ + --throughput -1 \ + --producer-props acks=1 client.id=foo batch.size=1000 linger.ms=100 compression.type=lz4 \ + --producer.config /mnt/kafka.properties + volumeMounts: + - name: kafka-properties + mountPath: /mnt + volumes: + - name: kafka-properties + secret: + secretName: "kafka-client-config" + restartPolicy: Never + backoffLimit: 4 +``` + +1. We run a job called "producer". +2. Indicates the number of parallel producer jobs we want to run. +3. We run an initcontainer which creates the topic if it doesn’t exist. +4. The latest confluent kafka container image at the time of writing this. Please substitute with a more recent image if applicable. +5. We model the `kafka.properties` as a kubernetes secret and mount it here as a volume. +6. We run the actual producer perf test process with all the parameters. + +Now, we can run this in a Kubernetes cluster and get the same OODA loop running. But what changed, other than moving everything inside containers? For starters, we can parallelize the process simply by increasing the `parallelism` spec of the job. + +Also, we can ship these metrics to Prometheus if we bake the kafka container image with a JMX agent. + +```Dockerfile +FROM confluentinc/cp-kafka:7.3.2 # <---------- (1) + +COPY jmx /usr/app # <---------- (2) +# Install Prometheus +RUN curl -s https://api.github.com/repos/prometheus/prometheus/releases/latest \ + | grep browser_download_url \ + | grep linux-amd64 \ + | cut -d '"' -f 4 \ + | wget -qi - && \ + tar xf prometheus*.tar.gz && \ + cd prometheus*/ && \ + mv prometheus .. # <---------- (3) +``` + +1. We start with the official Confluent Kafka base image. +2. We copy over the JMX agent jar and corresponding YAML configuration file tailored for Kafka. +3. We install Prometheus binary. + +Prometheus is typically configured for pull based metrics. In our case, we run the prometheus binary in a push-based fashion. We have a JMX server running in port `7071` for the duration of the Job. We configure Prometheus to run in agent mode, where it will fetch metrics from the JMX server and push to a pre-configured write endpoint in the Prometheus server. + +```bash +./prometheus --enable-feature=agent --config.file="/prom/prometheus.yml" --log.level=error +``` + +Let's take a quick look at the prometheus agent configuration. + +```yaml +# my global config +global: + scrape_interval: 15s # Set the scrape interval to every 15 seconds. Default is every 1 minute. + evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute. + # scrape_timeout is set to the global default (10s). + + +# A scrape configuration containing exactly one endpoint to scrape: +# Here it's Prometheus itself. +scrape_configs: + # The job name is added as a label `job=` to any timeseries scraped from this config. + - job_name: "jmx" # <---- (1) + + # metrics_path defaults to '/metrics' + # scheme defaults to 'http'. + + static_configs: + - targets: ["localhost:7071"] # <---- (2) + labels: + env: 'dev' # <---- (3) + relabel_configs: + - source_labels: [__address__] + target_label: hostname + regex: '([^:]+)(:[0-9]+)?' + replacement: '${1}' +remote_write: + - url: 'http://your-prometheus-url/api/v1/write' # <---- (4) +``` + +1. Name of the prometheus Job. +2. The target from which we scrape the metrics. +3. A unique identifier for every run. +4. The Prometheus server url to write the metrics to. + +**NOTE** that the Prometheus server should have "remote write receiver" [enabled](https://prometheus.io/docs/prometheus/latest/feature_flags/#remote-write-receiver) for the prometheus agent to send JMX metrics. + +I’d argue that we didn’t gain much from this transition, except for the Prometheus integration. I still have to change the config parameters for the next run, kubectl apply it, and track the metrics. We can do better. We could run a Helm chart with a parameterized set of input values and ship off these metrics to Prometheus. Before we go into the details of the Helm chart, let's define how a typical testing loop looks like. + +1. Come up with a baseline test on standard hardware / test conditions +2. Identify specific scenarios (these translate into producer and consumer configs) +3. Configure accordingly and run the test +4. Look at the client "blackbox" profile for throughput, latency +5. Look at the client "whitebox" profile - extended JMX metrics +6. Look at the server-side "blackbox" profile - throughput, latency +7. Look at the server-side "whitebox" profile - extended JMX metrics +8. Look at the log levels both client and server side - if needed, increase it to look at specific problems, if any + +Repeat-rinse steps 3 - 8 and infer performance based on deviation from the baseline. + + +## Take 3 - Helm chart +We take the following artifacts, + +1. The kafka.config secret +2. Producer perf test job +3. Consumer perf test job +4. Prometheus config + +And templatize them, package them as a Helm chart. We model every iteration in our OODA loop as a new helm release with its own set of helm values. + +The Helm chart does the following: +1. Run a distributed set of producer jobs(defined by the `producer.count` parameter) +2. run a distributed consumer - simplest is to have an array of consumers (and topics) defined so we don't get into managing concurrency; instead, we just create `consumer.count` number of consumer Jobs to paralelly consume from the topic. + +**Note** that we will need the consumer to run with a countdown latch or timeout. + +Here’s a performance test release with values optimized for throughput. + +```yaml +topic: + name: "t1_benchmark" + replicationFactor: 3 + partitions: 1 + +broker: + url: "xxx-yyyy.us-west4.gcp.confluent.cloud:9092" + username: "XXXXX" + password: "YXYXYXYXY2341234wesfdf345234" + +producer: + enabled: true + image: "confluentinc/cp-kafka:7.3.2" + recordsCount: 1000 + recordSizeBytes: 1024 + throughput: "-1" + acks: 1 + count: 3 + params: "batch.size=100000 linger.ms=100 compression.type=lz4" + +consumer: + enabled: true + image: "confluentinc/cp-kafka:7.3.2" + messagesCount: 1000 + count: 1 + timeout: 100000 + ``` + +And here’s another set of values optimized for high durability. + +```yaml +topic: + name: "t1_benchmark" + replicationFactor: 3 + partitions: 1 + +broker: + url: "xxx-yyyy.us-west4.gcp.confluent.cloud:9092" + username: "XXXXX" + password: "YXYXYXYXY2341234wesfdf345234" + +producer: + enabled: true + image: "confluentinc/cp-kafka:7.3.2" + recordsCount: 1000 + recordSizeBytes: 1024 + throughput: "-1" + acks: "all" + count: 3 + params: "enable.idempotence=true max.in.flight.requests.per.connection=1" + +consumer: + enabled: true + image: "confluentinc/cp-kafka:7.3.2" + messagesCount: 1000 + count: 1 + timeout: 100000 +``` + +### A quick aside on Prometheus and Grafana + +The code accompanying this post ships with a custom Helm chart which piggy backs on the [Kube Prometheus stack](https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack) to install Prometheus, Grafana and a couple of associated producer and consumer dashboards. This chart will also wire Prometheus as a datasource for the Grafana installation. + +You can install this setup using the following command. + +```bash +helm upgrade --install prom-stack ./kube-prometheus-kafka-chart +``` + +The Prometheus console can be viewed in the browser by issuing a port-forward command. + +```bash + kubectl port-forward svc/prometheus-operated 9090:9090 + ``` + +We can do something similar for the Grafana web console as well. + +```bash +kubectl port-forward svc/prom-stack-grafana 8080:80 +``` + +Grafana requires credentials, which are `admin` and `platformatory`(the default password in the Helm chart). + +### Running the performance tests + +Once we have the scaffold ready, we run a helm release using the following command, + +```bash +helm upgrade --install march-25-2023-durability-producer ./confluent-performance-suite --values durability-producer-values.yml +``` + +This is how a typical producer dashboard would look like. + +![Producer Dashboard 1](../assets/blog-images/kafka-perf-suite/producer-dashboard-01.png) + +![Producer Dashboard 2](../assets/blog-images/kafka-perf-suite/producer-dashboard-02.png) + +![Producer Dashboard 3](../assets/blog-images/kafka-perf-suite/producer-dashboard-03.png) + +Next, we run the corresponding consumer job using the Helm chart. + +![Consumer Dashboard 1](../assets/blog-images/kafka-perf-suite/consumer-dashboard-01.png) + +![Consumer Dashboard 2](../assets/blog-images/kafka-perf-suite/consumer-dashboard-02.png) + +![Consumer Dashboard 3](../assets/blog-images/kafka-perf-suite/consumer-dashboard-03.png) + +```bash +helm upgrade --install march-25-2023-durability-consumer ./confluent-performance-suite --values durability-consumer-values.yml +``` + +...and process the consumer dashboard. + +### Shipping metrics to other systems + +The performance metrics Helm chart has provision to write openmetrics to any system which supports the Prometheus remote write feature. This configuration can be changed in the `values.yaml` as illustrated below: + +```yaml +prometheus: + remote_write: + - url: "http://prom-stack-kube-prometheus-prometheus:9090/api/v1/write" +``` + +As a quick example, if you want to use New Relic to process the metrics, your configuration would look like this: + +```yaml +prometheus: + remote_write: + - url: https://metric-api.newrelic.com/prometheus/v1/write?prometheus_server=kafka-perf-test + bearer_token: xxxxxxxxxxxxxxxxxxxxxxxxxxx +``` + +The Helm charts and the associated code mentioned in this post can be found [here](https://github.com/Platformatory/confluent-performance-suite). diff --git a/assets/blog-images/kafka-perf-suite/consumer-dashboard-01.png b/assets/blog-images/kafka-perf-suite/consumer-dashboard-01.png new file mode 100644 index 0000000000000000000000000000000000000000..4c6c081b5ec39bb66506514fa1989d705e021f25 GIT binary patch literal 619124 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    + + + diff --git a/_includes/postbox.html b/_includes/postbox.html index ffd59c6c9c..e8c2e15d99 100644 --- a/_includes/postbox.html +++ b/_includes/postbox.html @@ -1,36 +1,41 @@ {% assign author = site.authors[post.author] %} -
    -
    - -
    -

    - {{ post.title }} - {% if post.rating %} -
    - {% include star_rating_postbox.html %} -
    - {% endif %} -

    -

    {{ post.teaser | strip_html | truncatewords:30 }}

    +
    +
    +
    + + {% if post.image %} {% if site.lazyimages == "enabled" %} {{ post.title }} {% + else %} {{ post.title }} {% endif %} {% endif %} + +
    +
    +

    + + {{ post.title }} + + {% if post.rating %} +
    + {% include star_rating_postbox.html %}
    -

    +

    + {{ post.teaser | strip_html | truncatewords:30 }} +

    +
    + +
    diff --git a/_includes/postcta.html b/_includes/postcta.html new file mode 100644 index 0000000000..2d81916f0d --- /dev/null +++ b/_includes/postcta.html @@ -0,0 +1,15 @@ + diff --git a/_layouts/default.html b/_layouts/default.html index cbd481e70a..40c635ab93 100644 --- a/_layouts/default.html +++ b/_layouts/default.html @@ -54,6 +54,13 @@ +
    +

    {{ site.name }}

    +

    + {{ site.description }} +

    +
    + @@ -95,12 +102,12 @@ -
    + diff --git a/_layouts/post.html b/_layouts/post.html index ee4501e660..e31e868f55 100644 --- a/_layouts/post.html +++ b/_layouts/post.html @@ -8,64 +8,55 @@
    -
    +
    -
    +
    {% include share.html %}
    {% assign author = site.authors[page.author] %} -
    +
    - {% if page.author %} + -
    Authored by
    + - - -
    + + - {% endif %} + - {% if page.authors %} + - - - - - {% endif %} - {% endfor %} -
    - {% endif %} + +
    Authored by
    +
    + {% for author_name in authors %} + {% assign author = site.authors[author_name] %} + {% if author %} + + {% endif %} + {% endfor %} +
    + {% endif %} -->

    {{ page.title }}

    @@ -74,7 +65,7 @@

    {{ page.title }}

    {% if site.adsense == "enabled" %} - {% include adsense-under-header.html %} + {% include adsense-under-header.html %} {% endif %} @@ -82,9 +73,13 @@

    {{ page.title }}

    {% if page.image %} {% if site.lazyimages == "enabled" %} - {{ page.title }} + {{ page.title }} {% else %} - {{ page.title }} + {{ page.title }} {% endif %} {% endif %} @@ -94,13 +89,13 @@

    {{ page.title }}

    {% if page.toc %} - {% if page.beforetoc %} -

    {{page.beforetoc}}

    - {% endif %} -
    -

    Summary

    - {% include toc.html html=content %} -
    + {% if page.beforetoc %} +

    {{page.beforetoc}}

    + {% endif %} +
    +

    Summary

    + {% include toc.html html=content %} +
    {% endif %} {{content}} @@ -113,62 +108,70 @@

    Summary

    {% endif %} - - {% if page.author %} -

    About The Author

    -

    About The Author

    -
    -
    - {% if author.avatar %} - {{ author.display_name }} - {% else %} - {{ author.display_name }} - {% endif %} -
    -
    - {{ author.display_name }} - {{ author.description }} -
    -
    - {% endif %} + + {% if page.author %} +

    About The Author

    +

    About The Author

    +
    +
    + {% if author.avatar %} + {{ author.display_name }} + {% else %} + {{ author.display_name }} + {% endif %} +
    +
    + {{ author.display_name }} + + {{ author.description }} +
    +
    + {% endif %} {% if page.authors %} {% assign authors = page.authors | split: "," %} -

    About The Authors

    -

    About The Authors

    -
    - {% for author_name in authors %} - {% assign author = site.authors[author_name] %} - {% if author %} - -
    -
    - {% if author.avatar %} - {{ author.display_name }} - {% else %} - {{ author.display_name }} - {% endif %} -
    -
    - {{ author.display_name }} - - {{ author.description }} -
    -
    +

    About The Authors

    +

    About The Authors

    +
    + {% for author_name in authors %} + {% assign author = site.authors[author_name] %} + {% if author %} + +
    +
    + {% if author.avatar %} + {{ author.display_name }} + {% else %} + {{ author.display_name }} {% endif %} - {% endfor %} +
    +
    + {{ author.display_name }} + + {{ author.description }} +
    + {% endif %} + {% endfor %} +
    {% endif %}

    - - - {% if page.last_modified_at %} - (Updated: ) - {% endif %} + + + {% if page.last_modified_at %} + (Updated: ) + {% endif %}

    @@ -178,7 +181,8 @@

    About The Authors

    {% assign sortedCategories = page.categories | sort %} {% for category in sortedCategories %}
  • - {{ category }} + {{ + category }}
  • {% endfor %} @@ -191,30 +195,39 @@

    About The Authors

    {% assign sortedTags = page.tags | sort %} {% for tag in sortedTags %}
  • - #{{ tag }} + #{{ tag + }}
  • {% endfor %}
    - +
    - - + {% if page.ctas%} +
    + {% include postcta.html %} +
    + {% endif %} +
    + {% include share.html %} +
    @@ -224,13 +237,13 @@

    About The Authors

    {% if page.comments != false %} -
    -
    -
    - {% include disqus.html %} -
    +
    +
    +
    + {% include disqus.html %}
    +
    {% endif %} diff --git a/_posts/2021-05-30-platform-engineering-manifesto.md b/_posts/2021-05-30-platform-engineering-manifesto.md index 0cb4e00318..c1d591e722 100644 --- a/_posts/2021-05-30-platform-engineering-manifesto.md +++ b/_posts/2021-05-30-platform-engineering-manifesto.md @@ -1,32 +1,36 @@ --- layout: post -title: "A Platform engineering manifesto" +title: "A Platform engineering manifesto" author: p6 -categories: [ Platform Engineering, Data, Infrastructure, Kafka, Kong, Kubernetes ] +categories: + [Platform Engineering, Data, Infrastructure, Kafka, Kong, Kubernetes] image: assets/blog-images/platform-strategy.svg featured: true hidden: true -teaser: Platform Engineering is dead. Long live platform engineering! +ctas: + - title: Get In Touch + description: "Have questions or need assistance? Our team is here to help" + url: "/contact/" + +teaser: Platform Eng ineering is dead. Long live platform engineering! toc: true --- - ![50-shades](/blog/assets/blog-images/50-shades.jpg "50 Shades of DevOps") -There are many hot takes on Platform Engineering. There are success stories and there are horror stories. When done well, there is a team topology that enables a "do it like a digital native" operating model. When done not so well, you end up with a mess of 99 problems cherrypicked straight from the CNCF landscape graveyard of tools. +There are many hot takes on Platform Engineering. There are success stories and there are horror stories. When done well, there is a team topology that enables a "do it like a digital native" operating model. When done not so well, you end up with a mess of 99 problems cherrypicked straight from the CNCF landscape graveyard of tools. -As a company that has chosen to ride the trend, our belief is that platform engineering should be considered essential for any tech company operating in a complex domain, basically with the objective of providing a Dev X that helps you scale. Platforms don't exist in a vaccum; They are grounded ultimately in the reality of business domains and MUST accelerate product engineering teams in their endeavours. +As a company that has chosen to ride the trend, our belief is that platform engineering should be considered essential for any tech company operating in a complex domain, basically with the objective of providing a Dev X that helps you scale. Platforms don't exist in a vaccum; They are grounded ultimately in the reality of business domains and MUST accelerate product engineering teams in their endeavours. - -To be clear, not everyone needs a platform engineering team in the first place. One is largely justified when the size of the engineering organization exceeds the Dunbar number or atleast a non-trivial problem of scale. +To be clear, not everyone needs a platform engineering team in the first place. One is largely justified when the size of the engineering organization exceeds the Dunbar number or atleast a non-trivial problem of scale. Nonetheless, if you do choose to build a platform engineering team, there are 3 key performance indicators to consider in order to measure success. - - NPS, CSAT from product engineering teams, as a measure of developer experience - - Usage metrics of the platform products in themselves - - Coverage of platform concerns and time to first production deployment (for Product teams) +- NPS, CSAT from product engineering teams, as a measure of developer experience +- Usage metrics of the platform products in themselves +- Coverage of platform concerns and time to first production deployment (for Product teams) -# Own a platform architecture & platform product vision +# Own a platform architecture & platform product vision Everything begins in robust product foundations and an (evolving) architecture vision. Platform teams must own both a product & an architecture charter. The unique requirements of platform products may themselves be driven by unique challenges in the domain (or specifically that of domain centred product engineering teams) and the incumbent IT landscape @@ -34,9 +38,9 @@ Everything begins in robust product foundations and an (evolving) architecture v # Building tooling and plugins: For the sake of developers, operators and everything in between -The platform is a product that is mainly built with engineers in mind. You build it, you run it is ultimately an elusively aspirational operating model. In reality, re-use dictates efficiency and continuous reinvention of the wheel is harmful to productivity, which is almost a side-effect of autonomous but silo'd teams. +The platform is a product that is mainly built with engineers in mind. You build it, you run it is ultimately an elusively aspirational operating model. In reality, re-use dictates efficiency and continuous reinvention of the wheel is harmful to productivity, which is almost a side-effect of autonomous but silo'd teams. -> A platform team topology on the other hand vests a lean, craft-focused engineering team to build specialization across a variety of cross cutting concerns while enabling a well governed tech estate. +> A platform team topology on the other hand vests a lean, craft-focused engineering team to build specialization across a variety of cross cutting concerns while enabling a well governed tech estate. Such "Platform engineering" teams are suited ideally to building re-usable artefacts and easily adapted tooling for most concerns. Notably, this will include for example: @@ -45,7 +49,6 @@ Such "Platform engineering" teams are suited ideally to building re-usable artef - Common Build / Deploy (CI-CD) Pipelines - Observability & monitoring toolkits - # Enable shared-services Operations (securely) in a multi-tenant, hybrid cloud enterprise At the heart of enterprise grade operations is really a tenancy problem; Tenancy is really "who shares what in the house"; A platform-based operating model needs to provide mechanisms for housing tenants in dedicated or shared models, based on resource competition (and thus need for isolation), security, availability, compliance and cost requirements. @@ -56,8 +59,8 @@ Operating model flexibility should also provide for having resources managed dir # Specialize in running complex, distributed systems -Fully managed services attempt to dumb down details of large scale distributed system, yet many are actually very leaky abstractions. A trade-off of having cloud vendor neutrality is that one must accept the reality of having fully or partially managed services co-existing with fully self-managed ones, particularly when costs become prohibitive either due to scale (or the lack thereof). This poses a dilemma for technology leaders around the operating model fit. +Fully managed services attempt to dumb down details of large scale distributed system, yet many are actually very leaky abstractions. A trade-off of having cloud vendor neutrality is that one must accept the reality of having fully or partially managed services co-existing with fully self-managed ones, particularly when costs become prohibitive either due to scale (or the lack thereof). This poses a dilemma for technology leaders around the operating model fit. - There is a defining line of separation between quality platform engineering teams and vanilla (cloud\|dev)-ops teams. Platform engineers will need to gain specific expertise in running complex, distributed systems that have become de-facto platform primitives, providing lego-blocks that allow you to build higher order platforms. +There is a defining line of separation between quality platform engineering teams and vanilla (cloud\|dev)-ops teams. Platform engineers will need to gain specific expertise in running complex, distributed systems that have become de-facto platform primitives, providing lego-blocks that allow you to build higher order platforms. > Platform adoption is ultimately constrained by expertise and therefore it is crucial that platform engineering teams build highly artisanal guilds that can respond to architectural problems in distributed systems and provide a POV on performance, security an d a variety of cross cutting concerns. diff --git a/assets/css/screen.css b/assets/css/screen.css index b65fd4c398..3101fe960f 100644 --- a/assets/css/screen.css +++ b/assets/css/screen.css @@ -7,9 +7,9 @@ License: https://www.wowthemes.net/freebies-license/ html { font-size:18px; } /* Increase the font size on higher resolutions */ .container {max-width:80%;} } -.mainheading { +/* .mainheading { padding: 1rem 0rem; -} +} */ a { color: #00ab6b; @@ -20,6 +20,26 @@ a:hover { color: #038252; text-decoration: none; } +.wrapfooter{ + display: flex; + +} +.wrapfooter .read-btn{ + min-width: 132px; + margin-left: auto; + color: white; + font-size: 1.8em; + font-weight: 500; + background-color: #00ab6b; + padding: 0.4em 0.8em; + /* border-radius: 4px; */ +} +.wrapfooter .read-btn:hover{ + background-color: #039d62; +} +.wrapfooter .read-btn:hover span{ + margin-left: 5px; +} pre { -moz-box-sizing: border-box; @@ -50,11 +70,17 @@ pre { .site-content { min-height: 60vh; - padding-top: 1.5rem; + padding-top: 3.5rem; margin-top: 57px; transition: all 0.4s; } +@media (max-width:768px) { + .site-content{ + padding-top: 7rem !important; + } +} + section { margin-bottom: 20px; } @@ -105,16 +131,32 @@ section.recent-posts { } .listfeaturedtag { + border: 1px solid rgba(0, 0, 0, .125); border-radius: .25rem; transition: all 0.3s cubic-bezier(.25, .8, .25, 1); } +.listfeaturedtag:hover .card-text, .card:hover .card-text{ + color: white !important; +} +.listfeaturedtag:hover, .post-box .card:hover { + color: white; + background-color: rgb(0, 0, 0); + border: 2px solid rgba(0, 0, 0, 0.181) !important ; + box-shadow: 0 10px 15px -3px rgb(0 0 0 / 0.1), 0 4px 6px -4px rgb(0 0 0 / 0.1); +} + .listfeaturedtag .wrapthumbnail { height: 290px; flex: 0 0 auto; height: 100%; } +.card-body{ + display: flex; + flex-direction: column; + justify-content: center; +} .maxthumb { max-height: 300px; @@ -122,12 +164,13 @@ section.recent-posts { } .listfeaturedtag .card, -.card-footer { + .card-footer, .post-box .card-footer .wrapfooter { border: 0; -} + background-color: inherit; +} .listfeaturedtag .thumbnail { - background-size: cover; + background-size: cover; height: 100%; display: block; background-position: 38% 22% !important; @@ -216,8 +259,8 @@ section.recent-posts { } .toc ul { - list-style: decimal; - font-weight: 400; + list-style: lower-roman; + font-weight: 500; } .author-meta { @@ -320,7 +363,13 @@ span.post-read-more a:hover { } .mainheading h1.sitetitle { - font-family: Righteous; + font-weight: 600; + /* font-family: Merriweather; */ + font-size: 2em; +} +.mainheading .lead { + font-size: 0.8em; + text-align: center; } .mainheading h1.posttitle { @@ -345,7 +394,7 @@ span.post-read-more a:hover { } .article-post { - font-family: Merriweather; + /* font-family: Merriweather; */ font-size: 1.1rem; line-height: 1.84; color: rgba(0, 0, 0, .8); @@ -367,6 +416,30 @@ blockquote { display: block; margin-bottom: 1.5rem; } +.post-ctas ul { + padding: 10px !important; + background-color: #f1f1f1; +} +.post-ctas ul> li { + display: flex !important; + flex-direction: column; + justify-content: space-between; + min-height: 120px; + padding: 6px 0; +} +.post-ctas a { + display: block; + margin-top: 1em; + padding: 10px; + border-radius: 4px; + color: white !important; + background-color: #00ab6b; + font-weight: 600; +} +.post-ctas a:hover{ + text-decoration: underline; + color: #0000007c; +} .share { text-align: center; @@ -914,3 +987,7 @@ iframe { opacity: 0; transition: opacity .25s; } + +.navbar-collapse{ + flex-grow: 0 !important; +} \ No newline at end of file From 3a7ada9925b0ab00e4800c2c928daf3d5d2d2c9e Mon Sep 17 00:00:00 2001 From: SathishKumar Date: Sun, 21 Jul 2024 17:34:27 +0530 Subject: [PATCH 50/76] theme updated --- _layouts/default.html | 2 +- _layouts/post.html | 39 ++++++++++++++++++++++----------------- assets/css/screen.css | 21 ++++++++++++++------- 3 files changed, 37 insertions(+), 25 deletions(-) diff --git a/_layouts/default.html b/_layouts/default.html index 40c635ab93..e1caf8886b 100644 --- a/_layouts/default.html +++ b/_layouts/default.html @@ -56,7 +56,7 @@

    {{ site.name }}

    -

    +

    {{ site.description }}

    diff --git a/_layouts/post.html b/_layouts/post.html index e31e868f55..73ad19dc60 100644 --- a/_layouts/post.html +++ b/_layouts/post.html @@ -7,8 +7,8 @@ -
    -
    +
    +
    @@ -18,7 +18,7 @@ {% assign author = site.authors[page.author] %} -
    +
    @@ -87,17 +87,7 @@

    {{ page.title }}

    - - {% if page.toc %} - {% if page.beforetoc %} -

    {{page.beforetoc}}

    - {% endif %} -
    -

    Summary

    - {% include toc.html html=content %} -
    - {% endif %} - + {{content}}
    @@ -220,12 +210,27 @@

    About The Authors

    +
    +
    + + {% if page.toc %} + {% if page.beforetoc %} +

    {{page.beforetoc}}

    + {% endif %} +
    +

    Summary

    + {% include toc.html html=content %} +
    + {% endif %} + + +
    {% if page.ctas%} -
    {% include postcta.html %} -
    {% endif %} -
    +
    + +
    {% include share.html %}
    diff --git a/assets/css/screen.css b/assets/css/screen.css index 3101fe960f..eaf6520f85 100644 --- a/assets/css/screen.css +++ b/assets/css/screen.css @@ -11,14 +11,20 @@ License: https://www.wowthemes.net/freebies-license/ padding: 1rem 0rem; } */ -a { - color: #00ab6b; +li a { + color: #111111; transition: all 0.2s; + text-decoration: underline; } - -a:hover { +.lead h3{ + font-family: 'Gill Sans', 'Gill Sans MT', Calibri, 'Trebuchet MS', sans-serif; +} +li a{ + font-weight: bold ; +} +li a:hover { color: #038252; - text-decoration: none; + text-decoration: underline; } .wrapfooter{ display: flex; @@ -259,7 +265,7 @@ section.recent-posts { } .toc ul { - list-style: lower-roman; + list-style: decimal; font-weight: 500; } @@ -364,11 +370,12 @@ span.post-read-more a:hover { .mainheading h1.sitetitle { font-weight: 600; + font-family: fangsong; /* font-family: Merriweather; */ font-size: 2em; } .mainheading .lead { - font-size: 0.8em; + font-size: 0.9em; text-align: center; } From 8f5b47c3c91823b19a1e399bc873211ae6b96665 Mon Sep 17 00:00:00 2001 From: SathishKumar Date: Sun, 21 Jul 2024 17:51:00 +0530 Subject: [PATCH 51/76] removed hover border --- assets/css/screen.css | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/assets/css/screen.css b/assets/css/screen.css index eaf6520f85..a2543563b9 100644 --- a/assets/css/screen.css +++ b/assets/css/screen.css @@ -147,8 +147,8 @@ section.recent-posts { } .listfeaturedtag:hover, .post-box .card:hover { color: white; - background-color: rgb(0, 0, 0); - border: 2px solid rgba(0, 0, 0, 0.181) !important ; + background-color: rgb(0, 0, 0); + /* border: 2px solid rgba(0, 0, 0, 0.181) !important ; */ box-shadow: 0 10px 15px -3px rgb(0 0 0 / 0.1), 0 4px 6px -4px rgb(0 0 0 / 0.1); } From 17cff8d86798701fd6ebd054e7872f5230e85d73 Mon Sep 17 00:00:00 2001 From: SathishKumar Date: Mon, 22 Jul 2024 11:53:38 +0530 Subject: [PATCH 52/76] fixed mobile view --- assets/css/screen.css | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/assets/css/screen.css b/assets/css/screen.css index a2543563b9..d02240c4ed 100644 --- a/assets/css/screen.css +++ b/assets/css/screen.css @@ -377,13 +377,28 @@ span.post-read-more a:hover { .mainheading .lead { font-size: 0.9em; text-align: center; -} +} .mainheading h1.posttitle { font-weight: 700; margin-bottom: 1rem; } +@media (max-width:780px) { + .toc { + padding: 0 4px; + } + .toc h3 { + padding: 0 8px; + } + .mainheading h1.sitetitle{ + font-size: 1.5em !important; + } + .mainheading .lead { + font-size: 0.8em !important; + } +} + .footer { border-top: 1px solid rgba(0, 0, 0, .05) !important; padding-top: 15px; From 1aaba3a1d36238f9c2b5a766bea42a40db6ad3c3 Mon Sep 17 00:00:00 2001 From: SathishKumar Date: Mon, 22 Jul 2024 12:15:55 +0530 Subject: [PATCH 53/76] mobile view --- assets/css/screen.css | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/assets/css/screen.css b/assets/css/screen.css index d02240c4ed..952b5d49ec 100644 --- a/assets/css/screen.css +++ b/assets/css/screen.css @@ -385,9 +385,12 @@ span.post-read-more a:hover { } @media (max-width:780px) { - .toc { + .navbar-expand-lg.navbar-light.bg-white.fixed-top> div{ + justify-content: space-evenly; + } + .toc { padding: 0 4px; - } + } .toc h3 { padding: 0 8px; } From 4040d4271c51ab27e1f9ffa6be44690792fd7943 Mon Sep 17 00:00:00 2001 From: Akkishiva1 <142904126+Akkishiva1@users.noreply.github.com> Date: Mon, 22 Jul 2024 20:11:40 +0530 Subject: [PATCH 54/76] Shiva (#37) * Added the file converted to markdown format * Update and rename Migrating Zookeeper to kraft.md to Migrating-Zookeeper-to-kraft.md * Update Migrating-Zookeeper-to-kraft.md * Update Migrating-Zookeeper-to-kraft.md * insert images * Delete _posts/migration/images * insert images * Add files via upload * Delete _posts/migrations directory * insert images * Add files via upload * Update Migrating-Zookeeper-to-kraft.md * Rename Screenshot from 2024-07-21 23-10-03.png to image1.png * Rename Screenshot from 2024-07-21 23-09-32.png to Image2.png * Update Migrating-Zookeeper-to-kraft.md * Update Migrating-Zookeeper-to-kraft.md * Update Migrating-Zookeeper-to-kraft.md * Update Migrating-Zookeeper-to-kraft.md * Update Migrating-Zookeeper-to-kraft.md * Update Migrating-Zookeeper-to-kraft.md * Blog post updated --------- Co-authored-by: shiva --- _config.yml | 9 + ...2024-07-22-Migrating-Zookeeper-to-kraft.md | 225 ++++++++++++++++++ .../migrations/0_TB4m7ZfPMgHF_ljN.png | Bin 0 -> 758029 bytes assets/blog-images/migrations/Image2.png | Bin 0 -> 51052 bytes assets/blog-images/migrations/image1.png | Bin 0 -> 22874 bytes 5 files changed, 234 insertions(+) create mode 100644 _posts/2024-07-22-Migrating-Zookeeper-to-kraft.md create mode 100644 assets/blog-images/migrations/0_TB4m7ZfPMgHF_ljN.png create mode 100644 assets/blog-images/migrations/Image2.png create mode 100644 assets/blog-images/migrations/image1.png diff --git a/_config.yml b/_config.yml index a364614635..0457c647e8 100644 --- a/_config.yml +++ b/_config.yml @@ -75,6 +75,15 @@ authors: web: https://www.platformatory.io twitter: https://twitter.com/platformatory description: "Director & Practitioner, North America;" + Shivaprakash: + name: Shivaprakash Akki + display_name: Shivaprakash Akki + avatar: 'assets/images/avatar.png' + gravatar: b1cc14991db7a456fcd761680bbc8f81 + email: akkishiva@platformatory.com + web: https://www.platformatory.io + twitter: https://twitter.com/platformatory + description: "DevOps engineer at Platformatory specializing in Site Reliability Engineering (SRE) and Infrastructure." # Plugins plugins: diff --git a/_posts/2024-07-22-Migrating-Zookeeper-to-kraft.md b/_posts/2024-07-22-Migrating-Zookeeper-to-kraft.md new file mode 100644 index 0000000000..68ad92f96b --- /dev/null +++ b/_posts/2024-07-22-Migrating-Zookeeper-to-kraft.md @@ -0,0 +1,225 @@ +--- +layout: post +title: "Migrating Zookeeper to Kraft" +categories: [Kafka Migration, Kafka Architecture, KRaft, Distributed Systems] +teaser: Ready to simplify your Kafka setup? Discover why moving from ZooKeeper to KRaft could be a game-changer for your infrastructure. This guide walks you through each step of the migration process, from retrieving your cluster ID to completing the transition. Unlock the benefits of KRaft and streamline your Kafka management today! +author: Shivaprakash +image: assets/blog-images/migrations/0_TB4m7ZfPMgHF_ljN.png +toc: true +--- + +# **Introduction** + +In its evolution, Kafka has recently shifted away from relying on ZooKeeper to manage its essential metadata and now embraces a quorum-based controller using Kafka Raft, or KRaft (pronounced 'craft'), which has been generally available since Confluent Platform version 7.4. + +Apache Kafka has traditionally entrusted ZooKeeper with crucial tasks such as storing metadata, managing brokers, controlling topics, and overseeing cluster configurations. Initially, this centralized approach with ZooKeeper was ideal for ensuring robust distributed coordination and reliable leader election mechanisms. However, the complexity of maintaining ZooKeeper alongside a Kafka cluster has become evident over time. Moreover, ZooKeeper's limitations have emerged as a bottleneck, restricting the scalability of Kafka clusters by limiting the number of partitions a single broker can effectively manage. + +Recognizing these challenges and seeking to streamline Kafka's architecture, the Kafka community introduced [KIP-500](https://cwiki.apache.org/confluence/display/KAFKA/KIP-500%3A+Replace+ZooKeeper+with+a+Self-Managed+Metadata+Quorum). This initiative aims to phase out ZooKeeper and introduce a more efficient metadata quorum solution. This blog post is dedicated to guiding you through the transition from ZooKeeper to KRaft. We will provide a step-by-step roadmap for migrating your Kafka deployment to KRaft. + +# **Why Bye to ZooKeeper ?** + +ZooKeeper data is replicated across a number of nodes forming an ensemble, using the ZooKeeper Atomic Broadcast (ZAB) protocol to ensure data consistency across all nodes. However, integrating ZooKeeper with Kafka involves managing a separate distributed system alongside Kafka itself, which introduces additional complexities in deployment, management, and troubleshooting. + +Moreover, ZooKeeper introduces scalability bottlenecks that limit the number of topics and partitions supported within a Kafka cluster. For example, during controller failover events, the newly elected controller must retrieve metadata from ZooKeeper, including details for all topics, which can impact performance. Additionally, any metadata updates require propagation to other brokers via RPCs, and as the number of partitions increases, the propagation time grows, potentially slowing down the system. + +In contrast, other distributed systems like MongoDB, Cassandra, and Elasticsearch handle metadata management internally, eliminating the need for external tools like ZooKeeper. This streamlined approach significantly simplifies deployment and operational management—imagine handling just one distributed system instead of two! Furthermore, internal metadata management enhances scalability and efficiency, optimizing operations and providing stronger guarantees for Kafka's functionality. + +# **Hello from KRaft!** + +To address challenges with ZooKeeper, the Kafka community introduced KRaft, a new way to handle metadata directly within Kafka itself. Unlike ZooKeeper, which required managing a separate system alongside Kafka, KRaft integrates metadata management into Kafka using an event-driven approach. + +KRaft uses a quorum-based controller with an event-driven implementation of the Raft protocol. This controller manages an event log stored in a special topic named "__cluster_metadata." Unlike regular topics, data in "__cluster_metadata" is written synchronously to disk, ensuring reliability required by Raft. + +## **Advantages of KRaft** + + + +* Simplicity: KRaft streamlines Kafka’s architecture by eliminating the need for a separate coordination service like ZooKeeper. Users and operators only need to manage one system, and KRaft uses the same configuration, failure handling, and security mechanisms as Kafka’s data plane, making it easier to learn and operate. +* Scalability: KRaft enhances Kafka’s scalability by reducing the load on the metadata store. In KRaft mode, only a subset of brokers, known as the controller quorum, handles metadata. This setup minimizes connections and requests to the metadata store, enabling Kafka to support more brokers and topics without impacting performance. +* Availability: KRaft improves Kafka’s availability by allowing the system to handle partial failures. Only a quorum of controllers is needed to process requests, so if some controllers are down or disconnected, the remaining ones can still maintain cluster operations. This makes Kafka more resilient to network issues and data center failures. +* Simplified Deployment and Management: With KRaft, you no longer need to manage a separate ZooKeeper cluster, reducing operational complexity and costs. Kafka users can continue using existing tools and APIs, such as the Admin API and kafka-reassign-partitions tool, for managing their clusters. +* Increased Security: KRaft supports encryption and authentication for client-server communication using SSL/TLS or SASL, ensuring that Kafka metadata is protected from unauthorized access or tampering. + +By adopting KRaft, Kafka enhances its scalability, simplifies operations, and integrates metadata management directly into its architecture, offering a more efficient alternative to ZooKeeper. + +# **How to migrate from ZooKeeper to KRaft** + +While KRaft has been production-ready for several releases now, with continuous feature enhancements, the majority of existing Kafka clusters still rely on ZooKeeper. However, there's growing interest among users to migrate to KRaft to overcome the limitations associated with ZooKeeper, as discussed earlier. + +Moreover, ZooKeeper will be deprecated within the Kafka project, with support expected to be fully removed in an upcoming release. This development underscores the urgency for Kafka users to plan and execute migrations from ZooKeeper to KRaft. + +Before beginning the migration process, our Kafka brokers are currently operating in ZooKeeper-mode and are connected to the ZooKeeper ensemble where metadata is stored. Let's follow the steps below to migrate to KRaft. + +## **Step 1: Retrieve the cluster ID** + + + +* You must format storage for your Kafka cluster with the ID of the existing cluster. You can get this ID with the zookeeper-shell tool. + + +![Image-2](../assets/blog-images/migrations/Image2.png) + + +## **Step 2: Configure a KRaft controller quorum** + + + +* Set up the KRaft controller quorum responsible for managing metadata in KRaft mode. +* Number of KRaft controller nodes should match the current ZooKeeper ensemble size. +* Migration does not support transitioning to "combined" mode nodes. +* Configure each KRaft controller node with connections to ZooKeeper. +* Add the flag zookeeper.metadata.migration.enable=true to signal the migration intent. +* Upon startup, KRaft controllers establish a quorum and elect a leader. +* Controllers enter a waiting state for Kafka brokers to register. + +``` +process.roles=controller +node.id=3000 +controller.quorum.voters=3000@localhost:9093 +controller.listener.names=CONTROLLER +listeners=CONTROLLER://:9093 +# Enable the migration + zookeeper.metadata.migration.enable=true +# ZooKeeper client configuration + zookeeper.connect=localhost:2181 +# Enable migrations for cluster linking + confluent.cluster.link.metadata.topic.enable=true +# Other configuration entries +``` + + + +## **Step 3: Format storage with the ID you saved previously** + + + +* Format storage on each node with the ID and the controller configuration file. + + +![Image-1](../assets/blog-images/migrations/image1.png) + + +## **Step 4: Start each controller** + + + +* Start each controller, specifying the configuration file with migration enabled. +``` +./bin/kafka-server-start.sh ./etc/kafka/kraft/controller.properties +``` + +## **Step 5: Enable migration on the brokers** + + + +* Modify the Kafka broker configurations to include connection details for all nodes in the KRaft controller quorum. +* Enable the migration process using `zookeeper.metadata.migration.enable=true` in the broker configurations. +* Roll restart each Kafka broker one by one after updating its configuration. +* Upon restart, brokers will register with the KRaft controller quorum instead of ZooKeeper. +* This transition phase is referred to as the "hybrid phase," where some brokers are still using ZooKeeper while the KRaft controller quorum starts managing metadata. +* The KRaft controller leader initiates the migration process by copying all metadata from ZooKeeper to the __cluster_metadata topic in Kafka. +* This ensures that all metadata (broker registrations, topic configurations, etc.) are replicated and synchronized across the KRaft quorum. + + Following is an example configuration file for a broker that is ready for the KRaft migration. + + +``` +broker.id=0 +listeners=PLAINTEXT://:9092 +advertised.listeners=PLAINTEXT://localhost:9092 +listener.security.protocol.map=PLAINTEXT:PLAINTEXT,CONTROLLER:PLAINTEXT + +# Set the IBP + inter.broker.protocol.version=3.6 + +# Enable the migration + zookeeper.metadata.migration.enable=true + +# Cluster linking metadata topic enabled + confluent.cluster.link.metadata.topic.enable=true + +# ZooKeeper client configuration + zookeeper.connect=localhost:2181 + +# KRaft controller quorum configuration + controller.quorum.voters=3000@localhost:9093 + controller.listener.names=CONTROLLER +``` + +## **Step 6: Migrate the brokers** + + + +* The KRaft controller operates in migration mode, sending RPCs like UpdateMetadata and LeaderAndIsr to ZooKeeper-mode brokers. +* Update broker configuration to switch from ZooKeeper-mode to KRaft-mode. +* Replace `broker.id` with `node.id` while maintaining the same identifier. +* Add `process.roles=broker` to indicate KRaft mode. +* Remove ZooKeeper configuration and any cluster linking entries from broker configuration. +* Update ACLs if necessary, especially changing the authorizer class. +* Restart the broker to apply the new configuration and complete the migration process. + + Following is an example of how a server.properties file for a migrated broker might look. Note that ZooKeeper-specific properties are commented out. + + +``` +process.roles=broker +node.id=0 +listeners=PLAINTEXT://:9092 +advertised.listeners=PLAINTEXT://localhost:9092 +listener.security.protocol.map=PLAINTEXT:PLAINTEXT,CONTROLLER:PLAINTEXT + +# Remove the IBP, KRaft uses "metadata.version" feature flag +# inter.broker.protocol.version=3.6 + +# Remove the migration enabled flag +# zookeeper.metadata.migration.enable=true + +# Remove the cluster linking metadata topic setting +# confluent.cluster.link.metadata.topic.enable=true + +# Remove ZooKeeper client configuration +# zookeeper.connect=localhost:2181 + +# Keep the KRaft controller quorum configuration + controller.quorum.voters=3000@localhost:9093 + controller.listener.names=CONTROLLER + +# If using ACLs, change the authorizer from AclAuthorizer used for ZooKeeper to the StandardAuthorizer used for KRaft. + authorizer.class.name=kafka.security.authorizer +``` + + +## **Step 7: Take KRaft controllers out of migration mode** + + + +* Disable Migration: Comment out or remove the zookeeper.metadata.migration.enable=true property. This signals that the controllers are no longer in migration mode and are fully operational in KRaft mode. +* Remove ZooKeeper Configuration: Comment out or remove any ZooKeeper-specific configuration entries (zookeeper.connect=localhost:2181 in this example). Since KRaft mode no longer relies on ZooKeeper for metadata management, these entries are no longer needed. + + Example `controller.properties` File for KRaft Mode + + +``` +process.roles=controller +node.id=3000 +controller.quorum.voters=3000@localhost:9093 +controller.listener.names=CONTROLLER +listeners=CONTROLLER://:9093 + +# Disable migration +# zookeeper.metadata.migration.enable=true + +# Remove ZooKeeper client configuration. +# zookeeper.connect=localhost:2181 +``` + + + +# **Conclusion** + +In this article, we've explored the role of ZooKeeper in Kafka and why Kafka has opted to transition to a quorum-based controller, KRaft. The Apache Kafka community has deprecated ZooKeeper for storing cluster metadata, with plans for complete removal in the upcoming version release. This signifies that users will need to transition towards KRaft-based clusters in the near future. 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