End-to-End Tracing Best Practices

Introduction

End-to-end tracing is a crucial aspect of observability that allows teams to follow a request as it flows through various services in a distributed system. This lesson covers best practices to implement effective tracing.

Key Concepts

• Trace: A collection of related operations across different services.
• Span: Represents a single operation within a trace, containing information such as start time, duration, and metadata.
• Context Propagation: The mechanism by which trace context (like trace ID) is passed from one service to another.

Best Practices

1. Implement context propagation to ensure trace IDs are passed along with requests.
2. Ensure all services, including third-party APIs, are instrumented to capture traces.
3. Use structured logging to correlate logs with trace IDs.
4. Set a maximum span duration to avoid excessive resource usage.
5. Aggregate and analyze traces regularly to identify performance bottlenecks.

Code Examples

Example: OpenTelemetry in Python

from opentelemetry import trace
from opentelemetry.trace import TracerProvider
from opentelemetry.exporter.otlp.proto.grpc.exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import BatchSpanProcessor

# Set up tracing
resource = Resource(attributes={"service.name": "my-service"})
trace.set_tracer_provider(TracerProvider(resource=resource))
tracer = trace.get_tracer(__name__)

# Export spans
otlp_exporter = OTLPSpanExporter()
span_processor = BatchSpanProcessor(otlp_exporter)
trace.get_tracer_provider().add_span_processor(span_processor)

# Create a span
with tracer.start_as_current_span("my_span"):
    # Your code here
    pass
            

FAQ