Monitor and review pipeline and script execution history, logs, and status.
## Overview The Executions page provides a comprehensive view of all pipeline and script runs, both historical and in-progress. Data engineers, platform operators, and pipeline owners use this page to monitor execution health, diagnose failures, and track performance trends over time. Every run triggered by a schedule, manual action, or API call appears in the execution history with its current status, start time, duration, and outcome. Drill-down into individual runs reveals step-level logs and timing breakdowns, making it straightforward to identify which step in a multi-step pipeline caused a failure or introduced a bottleneck. ## Key Features - **Execution history listing.** Browse a chronological list of all pipeline and script executions. Filter by pipeline name, status, date range, or trigger type (scheduled, manual, API) to locate specific runs. - **Status tracking (running, succeeded, failed).** Each execution displays a real-time status indicator. Running executions update their status as steps complete. Failed executions surface the error message and the failing step for immediate triage. - **Step-level execution logs.** Expand an individual execution to view the ordered list of steps, each with its own status, duration, SQL statement, row counts, and log output. Error messages and stack traces appear inline for failed steps. - **Duration and timing metrics.** View total execution duration, per-step timing, and queue wait time. These metrics help identify slow steps, detect regressions across successive runs, and inform capacity planning decisions. ## Workflow 1. Navigate to the Executions page from the Workflow sidebar. 2. Review the execution list to identify any failed or long-running executions. 3. Apply filters (status, pipeline, date range) to narrow the list to the runs of interest. 4. Click an execution entry to expand its step-level detail. 5. For a failed step, read the error message and log output to diagnose the root cause. 6. Navigate to the associated pipeline or script to apply a fix, then re-trigger execution. 7. Monitor the new run on this page to confirm the fix resolves the issue.