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???? Getting Started With Opentelemetry For Python Microservices On Kubernetes
Here’s a clean, step-by-step guide to help you get up and running with OpenTelemetry in a real-world, containerized setup ideal for Python-based services running in Kubernetes.
???? Auto-Instrumentation vs Manual-Instrumentation in OpenTelemetry
| Feature | Auto-Instrumentation | Manual Instrumentation |
|---|---|---|
| Definition | Automatically instruments supported libraries and frameworks using OpenTelemetry wrappers. | Requires you to explicitly define spans in code using the OpenTelemetry API. |
| Setup | Minimal changes — just install dependencies and run via opentelemetry-instrument CLI. |
Requires importing OTEL SDK and wrapping logic with spans manually. |
| Use Cases | Quick observability setup, ideal for supported web frameworks like Flask, Django, FastAPI, SQLAlchemy, etc. | Fine-grained control — custom logic, background jobs, async flows, or business-critical code. |
| Example | opentelemetry-instrument python app.py |
python with tracer.start_as_current_span("process-order"): do_work() |
| Pros | Easy, fast, low effort. Captures most useful traces out of the box. | Complete control over what is traced, how it’s labeled, and where context is passed. |
| Cons | Limited to what the SDK supports; may miss custom logic or async paths. | Requires effort, code changes, and understanding of OTEL trace context. |
???? Architecture Overview
We’ll use:
OpenTelemetry SDK in a Python app
OTEL Collector Agent (sidecar or DaemonSet on app clusters)
OTEL Collector Gateway (centralized, on a GitOps or management cluster)
SigNoz as the visualization backend (optional – you can swap with Jaeger, Datadog, etc.)
???? Step 1: Instrument Your Python Application
Install OpenTelemetry dependencies:
pip install opentelemetry-sdk \
opentelemetry-exporter-otlp \
opentelemetry-instrumentation \
opentelemetry-instrumentation-flask # or your framework
Initialize OTEL in your code:
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
otlp_exporter = OTLPSpanExporter(endpoint="http://otel-collector:4318/v1/traces")
span_processor = BatchSpanProcessor(otlp_exporter)
trace.get_tracer_provider().add_span_processor(span_processor)
Instrument libraries:
opentelemetry-instrument python app.py
????️ Step 2: Deploy OpenTelemetry Collector Agent
Use the official Helm chart or a custom otel-agent.yaml.
Basic example (DaemonSet mode):
receivers:
otlp:
protocols:
http:
grpc:
exporters:
otlp:
endpoint: <gateway-collector>:4317
tls:
insecure: true
service:
pipelines:
traces:
receivers: [otlp]
exporters: [otlp]
???? Step 3: Deploy OTEL Collector Gateway
This collector aggregates all telemetry. Example config:
receivers:
otlp:
protocols:
grpc:
http:
exporters:
signoz:
endpoint: http://signoz-otel-collector:4317
service:
pipelines:
traces:
receivers: [otlp]
exporters: [signoz]
???? Step 4: Install SigNoz or Jaeger as Your Observability Backend
helm repo add signoz https://charts.signoz.io
helm install signoz signoz/signoz
???? Tips
- Use resource attributes to tag services (service.name, env, etc.).
- Add custom spans around critical code blocks.
- Use batch processors to reduce export overhead.
