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name: datadog-api-traces description: Implements application performance monitoring (APM) using the Datadog API for tracing, including best practices for initiating and managing traces and spans. license: MIT compatibility: opencode metadata: version: "1.0.0" domain: coding triggers: datadog traces, send traces, trace management, datadog APM, how do I send traces to datadog role: implementation scope: implementation output-format: code related-skills: datadog-api-logs, datadog-api-metrics, datadog-api-monitors archetypes: [monitoring, application performance] anti_triggers: [manual instrumentation, performance degradation] response_profile: verbosity: medium directive_strength: high

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Datadog APM Tracing

Implements APM tracing for applications using Datadog API. This skill covers how to create, manage, and submit traces for effective performance monitoring and alerting based on APM data.

TL;DR Checklist

• Use Datadog APM tracing library to instrument applications.
• Create spans around significant operations to monitor performance.
• Include context (trace_id, span_id) in all logs to correlate traces and logs.
• Ensure rate limits are managed during trace submission.

Core Workflow

1. Initialize the Tracing Client: Create and configure the tracing client using ddtrace library ensuring proper API key handling. Checkpoint: Validate connectivity to the Datadog APM endpoints on startup.

2. Create Spans for Key Operations: Wrap operations (e.g., HTTP calls, database queries) in spans to measure performance metrics clearly. Checkpoint: Ensure spans have meaningful names and include error capture.

3. Submit Traces to Datadog: Send traces, including all spans, to Datadog using the provided API endpoints. Checkpoint: Validate that traces arrive at the endpoint without errors; implement retries for transient failures.

Implementation Patterns

Pattern 1: Initializing the Tracing Client

from ddtrace import tracer, patch
import os

# Patch auto-instrumentation to collect traces from HTTP requests
def setup_datadog_apm():
    patch(sqlalchemy=True, requests=True)
    trace_agent_url = os.environ.get("DD_TRACE_AGENT_URL", "http://localhost:8126")
    tracer.configure(service="my-service", hostname=trace_agent_url)
    # Optionally set other global settings here
def example_function():
    with tracer.trace("example.function") as span:
        # Perform work here
        pass

Pattern 2: Submitting Error Information

def submit_error_trace(span, error):
    span.set_tag("error", True)
    span.set_tag("error.message", str(error))
    span.set_tag("error.stack", error.__traceback__)

Constraints

MUST DO

• Use ddtrace to collect and submit traces.
• Always set trace IDs in logs for correlation with APM data.

MUST NOT DO

• Do not create spans without measuring their execution context (e.g., response times, errors).
• Never assume traces will be processed at a constant rate; handle rate limits gracefully.

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