Error tracking that turns exceptions into resolved issues

What is error tracking?

Error tracking is the practice of capturing exceptions and failures from running software, grouping identical problems together and managing them as issues until they are fixed. A good error tracking tool answers three questions quickly: what broke, how many users are affected, and what changed. It sits between raw logs, which contain every error but no structure, and incident management, which deals with full-blown outages.

Without grouping, a single bug in a busy service can produce hundreds of thousands of log lines per hour. Error tracking collapses those occurrences into one issue with a count, a trend, a first-seen and last-seen timestamp and a representative stack trace. Engineers work on issues, not on individual log lines.

Why error tracking matters

Not every error triggers an alert, and not every alert is caused by an error. Many exceptions are silent: a failed retry, a validation error in a rarely used form, a crash on one specific mobile OS version. These silent failures erode user trust and conversion long before they show up in uptime metrics.

• Faster detection: new error types are visible within seconds of their first occurrence, often right after a deployment.
• Better prioritization: impact data such as affected users and sessions separates critical bugs from harmless noise.
• Accountability: clear owners and states prevent issues from being seen by everyone and fixed by no one.
• Lower MTTR: full context, including the trace and the release, removes the need to reproduce failures locally.

How the Errors Inbox works: group, assign, resolve

The Errors Inbox is the central place where your teams triage errors from every source, including backend services, browser applications and mobile apps.

1. Group. Each occurrence is fingerprinted from its exception type, a normalized message with variable values removed, and the in-app stack frames. Occurrences with the same fingerprint form one issue. You can merge issues or adjust grouping rules where needed.
2. Prioritize. Issues are ranked by occurrences, affected users, trend and the criticality of the service. New issues and regressions are highlighted at the top.
3. Assign. Assign issues to a team or person manually or by rule, for example based on service ownership from the service catalog. Comments keep the discussion attached to the issue.
4. Resolve. Mark an issue as resolved, optionally in a specific release. If the same fingerprint appears again in a later version, Obseria reopens it as a regression and notifies the owner.

Error tracking with Obseria

Obseria captures exceptions through OpenTelemetry span events and logs, through the Obseria Agent and through the RUM and mobile SDKs. Because errors are part of the same data in Obseria Lake as traces, logs and metrics, every occurrence carries its full context: the distributed trace that shows which upstream call triggered the failure, the logs emitted just before it, the host or pod it ran on and the deployment that introduced it.

Obseria Pipeline masks personal data in error messages and attributes before storage. Obseria Intelligence analyzes new issues together with topology and deployment events and can explain why an error spike started, for example because a downstream dependency began returning timeouts. You can query errors with OQL, such as fetch logs | filter level == "error" | summarize count(), by:{service, exception.type}, and build alerts and SLOs on error rates.

Error tracking best practices

• Tag every event with a release version so you can tie new issues to deployments and detect regressions.
• Upload source maps and debug symbols in your CI pipeline to get readable stack traces.
• Define ownership through service metadata so that issues are routed to the right team automatically.
• Triage daily and keep the inbox small. Ignore known, harmless errors explicitly instead of letting them pile up.
• Alert on change, not on volume. Notify on new issues, regressions and sudden spikes rather than on absolute counts.
• Never log secrets or personal data in exception messages, and rely on ingest masking as a safety net.