Pinghawk: Automating Root Cause Analysis with Hawk Mode Snapshots

Why I’m Building an API Monitoring Tool That Tells You Why Things Broke — Not Just That They Did

Riyon Sebastian is building Pinghawk to solve the problem of contextless monitoring alerts that lack root cause data. The system utilizes ‘Hawk Mode’ to capture DNS, TLS, and response body evidence at the exact moment a 503 error occurs.

Why This Matters

Traditional monitoring identifies service downtime but often leaves engineers to investigate transient failures after the evidence has vanished due to container restarts or network shifts. By capturing snapshots from multiple regions like ap-south and us-east simultaneously, developers can distinguish between localized network latency and global database connection pool exhaustion.

Key Insights

• Pinghawk (2026) employs multi-region checks via Cloudflare Workers to identify localized network failures vs global outages.
• Hawk Mode captures debugging snapshots including DNS timing and response bodies at the moment of failure to provide immediate context.
• Scheduled monitoring jobs are managed via BullMQ to ensure high-throughput reliability across check intervals.
• The system uses a three-strike failure threshold to eliminate false positives from transient restarts or short network timeouts.
• PostgreSQL is used as the primary storage for persistent debugging evidence and regional snapshots captured during incidents.

Working Examples

Example of a response body captured during a 503 Service Unavailable failure.

{"error": "db pool exhausted"}

Hawk Mode snapshot data identifying a regional DNS bottleneck.

Region: ap-south (Mumbai)\nDNS lookup: 340ms\nTLS handshake: 48ms\nTime to first byte: 28,400ms\nStatus code: 503

Practical Applications

• System: API Monitoring. Use case: Capturing the response body during a 503 error to identify database exhaustion. Pitfall: Alerting without context forcing manual SSH and log hunting.
• System: Global Infrastructure. Use case: Comparing DNS lookup times across regions to isolate provider outages. Pitfall: Manual curl reproduction from a single local machine which may not reflect global user experience.
• System: On-call Management. Use case: Silencing alerts until three consecutive failures occur to avoid transient noise. Pitfall: Waking engineers for self-correcting issues like short network timeouts.

References:

• https://dev.to/riyon_sebastian/why-im-building-an-api-monitoring-tool-that-tells-you-why-things-broke-not-just-that-they-did-2akl