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89 lines
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Markdown
89 lines
4.0 KiB
Markdown
# Lesson 3.2: OpenTelemetry Manual Instrumentation of Python Application
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In the previous tutorial, we set up auto-instrumentation for our Flask application with OpenTelemetry without any code changes. In this tutorial, we will manually configure the agent.
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## When do you need to configure the agent manually?
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The `opentelemetry-instrument` tool is a convenient way to instrument your application without any code changes. However, there are cases where you might need to configure the agent manually:
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- **Instrumentation Customization**: You need to customize the instrumentation such as adding request/response hooks, excluding specific endpoints etc.
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- **Granular Control**: You need more granular control over the instrumentation. For example, you want to instrument only specific libraries.
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- **Capability Limitation**: The `opentelemetry-instrument` tool does not support the specific capability you need because there is no way to configure it with environment variables or command-line arguments. Examples include configuring the buckets for metrics, setting up custom exporters, etc.
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There may be other reasons to configure the agent manually. In this tutorial, we will show you how to manually configure the agent and export telemetry data.
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## Implementing manual instrumentation in Python application
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The following code snippet shows how to configure the agent manually in a Python application. It sets up tracing and metrics SDKs, and instruments Flask, PyMongo, and Requests libraries.
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```python
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def initialize_opentelemetry():
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# tracing setup
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from opentelemetry.trace import set_tracer_provider, get_tracer_provider
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.sdk.trace.export import BatchSpanProcessor
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from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
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set_tracer_provider(TracerProvider())
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span_processor = BatchSpanProcessor(OTLPSpanExporter())
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get_tracer_provider().add_span_processor(span_processor)
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# metrics setup
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from opentelemetry.metrics import set_meter_provider, get_meter_provider
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from opentelemetry.sdk.metrics import MeterProvider
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from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
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from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter
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reader = PeriodicExportingMetricReader(
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OTLPMetricExporter(),
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)
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set_meter_provider(MeterProvider(
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metric_readers=[reader],
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))
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# instrumentations
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from opentelemetry.instrumentation.flask import FlaskInstrumentor
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from opentelemetry.instrumentation.pymongo import PymongoInstrumentor
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from opentelemetry.instrumentation.requests import RequestsInstrumentor
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FlaskInstrumentor().instrument()
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PymongoInstrumentor().instrument()
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RequestsInstrumentor().instrument()
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```
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Then, you need to call this function as the first thing in your application code.
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```python
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initialize_opentelemetry()
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```
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In our sample application, we create a file `otel_setup.py` and add the above code to it. We then import the `initialize_opentelemetry` function in our `app.py` file and call it at the beginning of the file.
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Now, let's see how to run the application.
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```bash
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OTEL_RESOURCE_ATTRIBUTES=service.name=my-application \
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OTEL_EXPORTER_OTLP_ENDPOINT="https://ingest.in.signoz.cloud:443" \
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OTEL_EXPORTER_OTLP_HEADERS="signoz-access-token=<SIGNOZ_INGESTION_KEY>" \
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python lesson-3-2/app.py
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```
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## **Step 4: Test the Application and Generate Trace Data**
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Interact with the Flask application to generate tracing data and send it to SigNoz Cloud.
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## Step 5: See Trace Data in SigNoz
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Once you've created some dummy telemetry by interacting with your application, you will be able to find your application under the `Services` tab of SigNoz.
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## Next Steps
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In this tutorial, we have seen the steps to configure the agent with some code changes.
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In the next lesson, we will learn how to instrument our application and create spans manually.
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--- |