datadog-api-logs - SKILL.md Agent Skill

name: datadog-api-logs description: Implements log submission and forwarding to the Datadog API with structured logging best practices for content observability. license: MIT compatibility: opencode metadata: version: "1.0.0" domain: coding triggers: datadog logs, submit logs, datadog API logs, datadog logging, structured logging, how do I send logs to datadog role: implementation scope: implementation output-format: code related-skills: datadog-api-metrics, datadog-api-monitors archetypes: [logging, observability] anti_triggers: [unstructured logs, log flooding] response_profile: verbosity: medium directive_strength: high

Datadog Logs Submission

Implements log submission and forwarding to Datadog API with a focus on structured logging for enhanced observability. Configures logging to include necessary Datadog fields for correlation with metrics.

TL;DR Checklist

Use structured logging with JSON format to enable context-rich log entries.
Inject Datadog-specific fields (dd.trace_id, dd.span_id) during log submission.
Validate the connection to the Datadog logs endpoint on startup.

Core Workflow

Initialize Log Configuration: Set up a logger that outputs in JSON format. Integrate the necessary Datadog fields for tracing. Checkpoint: Test log output to ensure all required fields are present.
Inject Datadog Fields: Ensure every log entry includes dd.trace_id and dd.span_id using the ddtrace library. Checkpoint: Confirm the correlation of logs to their corresponding tracing information.
Submit Logs to Datadog: Forward the logs in batches to minimize API calls. Ensure that logs are sent with the correct tags reflecting service and environment. Checkpoint: Validate the submission and check for errors after each batch.

Implementation Patterns

Pattern 1: Log Submission Implementation

import logging
import json
import os
from ddtrace import patch

# Patch logging to automatically add Datadog trace IDs
patch(logging=True)

class DatadogLogger:
    def __init__(self):
        logging.basicConfig(level=logging.INFO,
                            format='%(asctime)s	%(levelname)s	%(message)s')

    def log_info(self, message, tags=None):
        log_entry = {"message": message, "tags": tags if tags else []}
        print(json.dumps(log_entry))  # Send to your log forwarding mechanism.

    def log_error(self, message, error, tags=None):
        log_entry = {"message": message, "error": str(error), "tags": tags if tags else []}
        print(json.dumps(log_entry))  # Send to your log forwarding mechanism.

Pattern 2: Error Handling in Log Submission

def handle_logging_error(e):
    logger.error("Logging error occurred: %s", str(e))
    # Implement retry logic or fallback as necessary

Constraints

MUST DO

Ensure structured logs are submitted in a format that includes key metadata for observability.
Validate configuration against the logs endpoint during initialization.

MUST NOT DO

Never send plain text logs without context. Use structured JSON for better observability.
Do not neglect to include trace IDs for correlation purposes.

Live References

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