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How to Monitor and Troubleshoot Applications in Production using OpenTelemetry

The #1 most comprehensive OpenTelemetry tutorial from A to Z.

Presented @

PyCon US 2023  
1:30pm-5pm. Thursday, April 20 2023.

Authored by Ron Nathaniel

Observability:

A measure of how well internal states of a system

can be inferred from knowledge of its external outputs.


A person aging:


A star burning:



Three Pillars of Observability:

= MeLT. (a Telemetry Data stack).



OpenTelemetry:

A collection of tools, APIs, and SDKs

used to instrument, generate, collect, and export telemetry data.


At a high level:



In depth:

Table of Contents

Preparing our Workstation:

Base Case:

Manual Instrumentation Case:

Contrib Instrumentation Case:

No-Code Instrumentation Case:

Conclusion

Pre-Requirements

First clone this repository:

git clone https://github.com/ronnathaniel/pycon23-opentelemetry
cd pycon23-opentelemetry

And then make sure the following dependencies are installed:

All can be installed via HomeBrew / Yum / Rpm / Pacman / Apt-Get / Any OS Package Manager


While we wait...

Are you experienced in:

  • Python?
  • Flask or Web servers?
  • OpenTelemetry or Observabilitity?
  • 1 year?
  • 5 years?
  • 10 years?

Step 1

Getting the Collector

Move into the OpenTelemetry Collector demo. In a Terminal, run:

cd collector-demo

Step 2

Inspecting the Source Code

At collector-demo/, read in order:

  1. docker-compose.yaml

Brings up containers with the OpenTelemetry Collector and 3 observability tools: Jaeger, Zipkin, and Prometheus.

Side Note
(Main difference between Jaeger and Zipkin:
Zipkin runs as 1 single process, Including Collector, Storage, Querying Service, and UI.
Jaeger introduced the concept of splitting into different processes, one being the Collector, another being the UI, etc.

Prompt: Can you guess why?)

  1. otel-collector-config.yaml

Defines how we want to receive Telemetry (as OTLP GRPC requests)
How we want to process Telemetry (in Batches)
And how we want to export Telemetry (to Prometheus, Zipkin, and Jaeger)

  1. prometheus.yaml

Defines where we want to gather Metrics Telemetry from (the Otel Collector)

Step 3

Cleanup

Skip this section..... For now .....

Step 4

Running the Collector

Lets start the collector locally. In a Terminal, run:

docker compose up

And voila, our collector and observability tools are up and running.

Step 5

Monitoring with our Observability Tools

View the tools at:

Step 6

The Base Case (not yet instrumented)

Create a Python file (flask_base.py) with the following:

from flask import Flask

app = Flask(__name__)


@app.route('/')
def hello():
    return 'Hello, World! from our plain Flask App\n'


@app.route('/error')
def error():
    1 / 0


if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

Let's install our dependencies with

pip3 install flask

And open 2 empty terminals side by side (CMD + D, move between with CMD + [ and CMD + ]).

Step 7

Testing the Base Case

In a main terminal, run with file with

python3 flask_base.py

And move to the other terminal.

-> For a successful response, call the server with:

curl localhost:5000

-> For an unsuccessful response, call the server with:

curl localhost:5000/error

And I want you to tell me (as the client)

  • what happened?
  • why did the request fail?
  • did the server crash?
  • is the server still up and running?
  • is my application still working or do I need to restart it?

OpenTelemetry to the rescue...

Step 8

The Manual Instrumentation Case

Manual Instrumentation is the hardest but safest of all instrumentations. The developer is in complete control over what gets instrumented, and how.

Create a Python file by duplicating flask_base.py with:

cp flask_base.py flask_instrumentation_manual.py

and let's install our dependencies:

pip3 install \
   opentelemetry-api \
   opentelemetry-sdk  \
   opentelemetry-exporter-otlp

And in our flask_instrumentation_manual.py file add some boilerplate at the top:

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import (
    BatchSpanProcessor, ConsoleSpanExporter
)
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import SERVICE_NAME, Resource


COLLECTOR_ENDPOINT = 'http://0.0.0.0:4317'
MY_SERVICE = 'MyFlaskAppInProductionManual'

resource = Resource(attributes={
    SERVICE_NAME: MY_SERVICE
})
provider = TracerProvider(resource=resource)

# exports to STDOUT
processor_console = BatchSpanProcessor(ConsoleSpanExporter())
provider.add_span_processor(processor_console)
# exports to Otel Collector
processor_grpc = BatchSpanProcessor(OTLPSpanExporter(endpoint=COLLECTOR_ENDPOINT))
provider.add_span_processor(processor_grpc)

trace.set_tracer_provider(provider)
tracer = trace.get_tracer(__name__)

and replace our hello() function at / route with

@app.route('/')
def hello():
    with tracer.start_as_current_span('root') as root:
        root.add_event('root_event')
        return 'Hello, World! from our Manually Instrumented Flask App\n'

and our error() function at /error with

@app.route('/error')
def error():
    with tracer.start_as_current_span('root') as root:
        root.add_event('root_event')
        1 / 0

Step 9

Testing The Manual Instrumentation Case

In the first terminal, run with file with

python3 flask_instrumentation_manual.py

And move to your second terminal.

-> For a successful response, call the server with:

curl localhost:5000

-> For an unsuccessful response, call the server with:

curl localhost:5000/error

Step 10

Monitoring The Manual Instrumentation Case

View the tools at:

Step 11

The Contrib Instrumentation Case

Contributed packages include support for third party tools.

Restart our instrumentation with

cp flask_base.py flask_instrumentation_contrib.py

and Install the Flask contrib package with:

pip3 install opentelemetry-instrumentation-flask

Only 2 sections are needed to instrument. The top-level import

import os
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import (
    BatchSpanProcessor, ConsoleSpanExporter
)
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.instrumentation.flask import FlaskInstrumentor

os.environ['OTEL_INSTRUMENTATION_HTTP_CAPTURE_HEADERS_SERVER_REQUEST'] = '.*'
os.environ['OTEL_INSTRUMENTATION_HTTP_CAPTURE_HEADERS_SERVER_RESPONSE'] = '.*'

COLLECTOR_ENDPOINT = 'http://0.0.0.0:4317'
MY_SERVICE = 'MyFlaskAppInProductionContrib'

resource = Resource(attributes={
    SERVICE_NAME: MY_SERVICE
})
provider = TracerProvider(resource=resource)

# exports to STDOUT
processor_console = BatchSpanProcessor(ConsoleSpanExporter())
provider.add_span_processor(processor_console)
# exports to Otel Collector
processor_grpc = BatchSpanProcessor(OTLPSpanExporter(endpoint=COLLECTOR_ENDPOINT))
provider.add_span_processor(processor_grpc)

and

FlaskInstrumentor().instrument_app(app, tracer_provider=provider)

given app is your flask app object.

Step 12

Testing The Contrib Instrumentation Case

In the first terminal, run with file with

python3 flask_instrumentation_contrib.py

And move to your second terminal.

-> For a successful response, call the server with:

curl localhost:5000

-> For an unsuccessful response, call the server with:

curl localhost:5000/error

Step 13

Monitoring The Contrib Instrumentation Case

View the tools at:

Step 14

The No Code Instrumentation Case

Our only dependency to install is:

pip3 install opentelemetry-distro

and we must bootstrap it with:

opentelemetry-bootstrap -a install

Step 15

Testing The No Code Instrumentation Case

Our method of running our application must change to

opentelemetry-instrument --traces_exporter otlp,console --service_name MyFlaskAppInProductionAuto --exporter_otlp_endpoint http://localhost:4317 python3 flask_base.py

Now lets break that command ^ down.

  • opentelemetry-instrument = a command line tool which auto-instruments Python files.

  • --traces_exporter otlp,console = flag to declare we will export our spans/traces via OTLP and our local Console (stdout).

  • --service_name MyFlaskAppInProductionAuto = flag to declare our OpenTelemetry's Service name.

  • --exporter_otlp_endpoint http://localhost:4317 = a flag to declare exactly where we will send our OTLP spans.

  • python3 flask_base.py = we tell opentelemetry-instrument how we usually run our application. This allows the CLI tool to run it for us.

Paste the command above into a file named flask_instrumentation_nocode.sh.

Be sure to chmod +x flask_instrumentation_nocode.sh to make it executable!

In the first terminal, run our new file with

./flask_instrumentation_nocode.sh

And move to your second terminal.

-> For a successful response, call the server with:

curl localhost:5000

-> For an unsuccessful response, call the server with:

curl localhost:5000/error

Step 16

Monitoring The No Code Instrumentation Case

View the tools at:

Step 17

What did we learn?

Can you answer these questions:

  • What is Observability?

  • What is OpenTelemetry?

  • What App Observability tools exist? 

  • With what methods can I instrument my app with OpenTelemetry?

  • When should I use each?

  • What is the OpenTelemetry Collector?

And you're free to go. Have a good day!

Thanks for participating.

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OpenTelemetry Tutorial presented in Pycon US 2023

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