D
Denim Herring
Export Pipeline Metrics to DataDog
Description:
As a DevOps Engineer, I want to export Pipeline, Stage, and Step related metrics from Harness to DataDog in real-time so that I can monitor and track the performance of the pipelines, build success rates, and gain operational insights.
Acceptance Criteria:
Pipeline Metrics Export:
The system must export the following metrics to DataDog:
Total Build Time (start and end times)
Build Time for Each Stage (start and end times for individual stages)
Status of the Build (overall and per-stage status)
Contextual Information Export:
The exported metrics must include the following contextual data:
Account> Organization> Application>Build Number>Manifest Name
Metrics in DataDog Dashboard:
DataDog must display the following metrics based on the data exported from Harness
  • harness.build.time: Displays the total build time for the pipeline execution.
  • harness.build.status: Shows the overall status of the build (success/failure).
  • harness.build.feedback.time: Tracks the feedback time from code commit to job failure in GitHub (measured in seconds).
For DataDog, The metrics end-point allows you to post time-series data that can be graphed on Datadog’s dashboards. The maximum payload size is 500 kilobytes (512000 bytes). Compressed payloads must have a decompressed size of less than 5 megabytes (5242880 bytes).
If you’re submitting metrics directly to the Datadog API without using DogStatsD, expect:
64 bits for the timestamp
64 bits for the value
20 bytes for the metric names
50 bytes for the timeseries
The full payload is approximately 100 bytes.
https://docs.datadoghq.com/api/latest/metrics/
Real-time Data Export:
-The metrics should be updated and available in real-time on the DataDog dashboard, allowing for immediate monitoring.
Business Justification:
-This feature is essential for enhancing the real-time monitoring and observability of pipeline performance. By exporting key metrics to DataDog, the team will gain:
  • Improved Monitoring: Real-time visibility into build performance and potential pipeline bottlenecks.
  • Faster Feedback Loops: Reduced time from code commit to failure detection, improving developer efficiency and reducing overall pipeline downtime.
  • Actionable Insights: A comprehensive overview of pipeline health that supports data-driven decisions, allowing the team to troubleshoot and optimize builds effectively.
By leveraging DataDog dashboards for these metrics, we enhance operational visibility, enabling the team to quickly identify and resolve issues that impact pipeline success rates and build times.