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Producer Consumer CSV

Build Status Code Coverage

Table of contents

Description

This is a simple producer consumer application that reads a csv file and writes the data to a database. The application is written in Python.

Todos

  • Add CI pipeline
  • Add unit tests
  • Add code coverage report
  • Add end to end test
  • Add documentation
  • Stablize the RabbitMQ connection
  • Different indexing strategy for performance

Architecture

The application is composed of the following components:

  • Producer (Reads the csv file and sends the data to the message queue)
  • Consumer (Reads the data from the message queue and writes the data to the database)
  • Persistent Message Queue (RabbitMQ)
  • Database (Postgres)

All the components are running in docker containers. The producer and consumer are running in multiple containers. The producer and consumer containers are scaled using docker compose. The producer and consumer containers are running in different containers to simulate a real world scenario where the producer and consumer are running in different servers.

For performance, you can scale the number of consumer containers by changing the CONSUMER_REPLICAS variable in the .env file. The default value is 16.

Requirements

  • Python 3.11
  • Docker
  • Make

Database Schema

The initialization script is located in the database/assets folder. The script will create the following tables:

records

column type description
record_time timestamp with timezone The time when the record was generated
sensor_id text The id of the sensor
value double precision The value of the sensor

Database Indexes

index columns
PK sensor_id, record_time
BRIN record_time
HASH sensor_id

BRIN index is used for the record_time column as the data is generated in a sequential manner (time-series data). The HASH index is used for the sensor_id column as the data is usually queried by equality operation, but not range operation. The HASH index is more efficient than the BTREE index for equality operation.

Queue

The queue is implemented using RabbitMQ. The queue is configured to be persistent. The queue is configured to be durable and the messages are configured to be persistent.

However, due to the complexity of the application, in this project the get operation is prefered over the consume operation, which stimulates a short polling queue.

Test Data

The test data is generated using the generate_csv_demo command. The command will generate a csv file with the following columns:

  • record_time: The time when the record was generated
  • sensor_id: The id of the sensor
  • value: The value of the sensor

You can check the section Running the application for more details on how to generate the csv file.

Installation

  1. Clone the repository
  2. Install make and docker
  • For Ubuntu and Debian
$ sudo apt install make
  • For Fedora and CentOS
$ sudo yum install make

For Docker installation, please refer to the official documentation

Usage

Postgres in Docker

If you don't have a postgres database running, you can use the docker image provided in this repository. The docker image is based on the official postgres docker image. The docker image is configured to create a database and a user with the following .env variables:

  • POSTGRES_VERSION_TAG
  • POSTGRES_PORT
  • POSTGRES_USERNAME
  • POSTGRES_PASSWORD
  • POSTGRES_DATABASE

Please make sure you don't change the name of the variables as they are used in the python application as well.

Running the application

  1. First run the generate_csv_demo command to generate a csv file. You can change the following parameters in the .env:
  • GEN_NUM_SENSORS
  • GEN_NUM_RECORDS
  • GEN_START_DATE
  • GEN_RECORD_INTERVAL
  • GEN_TIMEZONE
$ make gen_csv_demo
  1. Run the following command to build the docker image
$ make build
  1. Run the following command to start the docker compose stack. This will start the postgres database, rabbitmq, producer and different consumer containers. You can change the following parameters in the .env:
  • CONSUMER_REPLICAS
$ make up
  1. Run the following command to stop the docker compose stack
$ make down

End to end test

While the unit tests are run as part of the build process, you can run the end to end test by running the following steps:

  1. Change the

  2. Run the make command generate_csv_end_to_end_test. It will generate 10 sensor's data of 5 records each. The data will be generated in the records_test folder.

$ make generate_csv_end_to_end_test
  1. Run the following command to build the docker image
$ make build
  1. Run the following command to start the docker compose stack. This will start the postgres database, rabbitmq, producer and different consumer containers. You can change the following parameters in the .env:
  • CONSUMER_REPLICAS
$ make up
  1. Query the database to check if the data has been written to the database and check the record with the following data from sql:
SELECT *
    FROM records
    ORDER BY sensor_id ASC, record_time ASC
record_time sensor_id value
2021-01-01 00:00:00.000 +0800 17fc695a_4 0.9100387476052705
2021-01-01 00:00:01.000 +0800 17fc695a_4 0.9470819312177097
2021-01-01 00:00:02.000 +0800 17fc695a_4 0.9646317173285254
2021-01-01 00:00:03.000 +0800 17fc695a_4 0.5588283283219546
2021-01-01 00:00:04.000 +0800 17fc695a_4 0.10032294940781161
2021-01-01 00:00:00.000 +0800 23b8c1e9_1 0.17833466762332717
2021-01-01 00:00:01.000 +0800 23b8c1e9_1 0.5828395773770179
2021-01-01 00:00:02.000 +0800 23b8c1e9_1 0.6709222475097419
2021-01-01 00:00:03.000 +0800 23b8c1e9_1 0.08392094150600504
2021-01-01 00:00:04.000 +0800 23b8c1e9_1 0.519270757199653
2021-01-01 00:00:00.000 +0800 47378190_8 0.8730491223149253
2021-01-01 00:00:01.000 +0800 47378190_8 0.9269235181749119
2021-01-01 00:00:02.000 +0800 47378190_8 0.7912797041193453
2021-01-01 00:00:03.000 +0800 47378190_8 0.7901636441724763
2021-01-01 00:00:04.000 +0800 47378190_8 0.7886736978911509
2021-01-01 00:00:00.000 +0800 6b65a6a4_7 0.10293554590959142
2021-01-01 00:00:01.000 +0800 6b65a6a4_7 0.2888706613682428
2021-01-01 00:00:02.000 +0800 6b65a6a4_7 0.4279942939571587
2021-01-01 00:00:03.000 +0800 6b65a6a4_7 0.23512685053378612
2021-01-01 00:00:04.000 +0800 6b65a6a4_7 0.5272935984703412
2021-01-01 00:00:00.000 +0800 972a8469_3 0.7642357069109641
2021-01-01 00:00:01.000 +0800 972a8469_3 0.5701299072914774
2021-01-01 00:00:02.000 +0800 972a8469_3 0.17473379247794074
2021-01-01 00:00:03.000 +0800 972a8469_3 0.12464021515158785
2021-01-01 00:00:04.000 +0800 972a8469_3 0.5390567336729636
2021-01-01 00:00:00.000 +0800 9a1de644_5 0.3758090093767995
2021-01-01 00:00:01.000 +0800 9a1de644_5 0.33553000407688316
2021-01-01 00:00:02.000 +0800 9a1de644_5 0.9667728274172214
2021-01-01 00:00:03.000 +0800 9a1de644_5 0.9549845776369301
2021-01-01 00:00:04.000 +0800 9a1de644_5 0.7740952070735415
2021-01-01 00:00:00.000 +0800 b74d0fb1_6 0.3213794858378719
2021-01-01 00:00:01.000 +0800 b74d0fb1_6 0.5947556423536645
2021-01-01 00:00:02.000 +0800 b74d0fb1_6 0.8872919823927438
2021-01-01 00:00:03.000 +0800 b74d0fb1_6 0.28297514015876457
2021-01-01 00:00:04.000 +0800 b74d0fb1_6 0.6590113969392454
2021-01-01 00:00:00.000 +0800 bd9c66b3_2 0.36466072100083013
2021-01-01 00:00:01.000 +0800 bd9c66b3_2 0.8408935901254108
2021-01-01 00:00:02.000 +0800 bd9c66b3_2 0.8945802964470245
2021-01-01 00:00:03.000 +0800 bd9c66b3_2 0.027150264273096747
2021-01-01 00:00:04.000 +0800 bd9c66b3_2 0.9236042897439161
2021-01-01 00:00:00.000 +0800 bdd640fb_0 0.0746765216767864
2021-01-01 00:00:01.000 +0800 bdd640fb_0 0.8404332126798344
2021-01-01 00:00:02.000 +0800 bdd640fb_0 0.31870553433981874
2021-01-01 00:00:03.000 +0800 bdd640fb_0 0.825033074919654
2021-01-01 00:00:04.000 +0800 bdd640fb_0 0.7161990766355211
2021-01-01 00:00:00.000 +0800 c241330b_9 0.6940489142492581
2021-01-01 00:00:01.000 +0800 c241330b_9 0.7748088833830469
2021-01-01 00:00:02.000 +0800 c241330b_9 0.85280342321841
2021-01-01 00:00:03.000 +0800 c241330b_9 0.32443698906841056
2021-01-01 00:00:04.000 +0800 c241330b_9 0.4457555011219805

Unit tests

The unit tests are run as part of the CI pipeline. You can run the unit tests locally by running the following steps:

  1. Run the make setup_test_env command to setup the test environment
$ make setup_test_env
  1. Install the following pip packages:
$ pip install -r producer/requirements-dev.txt
$ pip install -r consumer/requirements-dev.txt
  1. Run the following command to run the unit tests
$ make test

The unit test will run the both the producer and consumer unit tests. The coverage report will be generated in the .coverage file.

Code coverage report

You can generate the code coverage report by visiting the following link: https://alex-au-922.github.io/producer_consumer_csv/