Principle:Treeverse LakeFS Hook Result Review
| Knowledge Sources | |
|---|---|
| Domains | Data_Quality, Automation |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Reviewing and auditing action hook execution results enables verification that data quality gates passed and supports diagnosis of validation failures.
Description
After action hooks execute -- whether triggered by commits, merges, or other repository events -- their results must be reviewed for several purposes:
- Verification -- Confirming that all quality gates passed for a given commit or merge
- Diagnosis -- Understanding why a pre-commit or pre-merge hook rejected an operation
- Auditing -- Maintaining a compliance trail of all validation checks performed on data
- Monitoring -- Tracking hook execution times and failure rates over time
The lakeFS Actions API provides a hierarchical view of hook execution:
- Runs -- The top-level entity representing a single action execution triggered by a repository event. Each run records:
- The event type (pre-commit, post-merge, etc.)
- The branch and commit associated with the event
- Start and end timestamps
- Overall status (completed or failed)
- Hook runs -- Individual hook executions within a run. Each hook run records:
- The hook ID and action name
- Individual status (completed or failed)
- Start and end timestamps
This two-level hierarchy allows both high-level monitoring ("Did all checks pass for this commit?") and detailed debugging ("Which specific hook failed and when?").
Usage
Use hook result review when you need to:
- Verify commit quality -- After a commit succeeds, confirm all post-commit hooks completed successfully
- Debug rejected operations -- When a commit or merge returns HTTP 412, identify which hook failed
- Build audit reports -- Extract hook execution history for compliance documentation
- Monitor hook performance -- Track execution times to identify slow or unreliable hooks
- Investigate incidents -- Trace back through hook execution history to find when a validation started failing
Theoretical Basis
The hook result review model implements an observability pattern for data quality:
Structured audit trail: Every hook execution generates a structured record that persists independently of the hook's success or failure. This creates an immutable audit log of all validation activity, which is essential for regulatory compliance in data-intensive industries.
Hierarchical decomposition: The run/hook-run hierarchy mirrors the action/hook structure in YAML configuration files. This makes it straightforward to correlate a failure with its source configuration.
Temporal correlation: Each run and hook run includes precise timestamps, enabling correlation with external events (data pipeline runs, source system changes, infrastructure incidents).
Separation of execution and observation: Hook execution happens synchronously (for pre-event hooks) or asynchronously (for post-event hooks), but hook result review is always a separate, on-demand operation. This ensures that reviewing results never impacts the performance of the hooks themselves.