Error Groups & Blocked Queues
When a job fails repeatedly, its group becomes blocked: no new jobs in that group are processed until the error is resolved. The Blocked section clusters these failures by normalized error message, so you can see patterns at a glance. Each error cluster shows:- Count: how many groups are affected
- Error message: normalized sample
- Pipeline: which pipeline stage produced the error
- Sample group IDs: for quick identification
Actions on Blocked Groups
All write actions require the
ops:manage permission.Dead Letter Queue (DLQ)
The DLQ holds groups that were explicitly moved there, either automatically after exceeding retry limits or manually via the “Move to DLQ” action. Items in the DLQ are not processed until an operator takes action. Each DLQ entry shows:- Queue name: which queue the group came from
- Group ID and Pipeline: for identification
- Error message: the error that caused the failure
- Job count: how many jobs are in the group
Redriving from the DLQ
Groups Table
The Groups table provides a detailed per-group view of all processing groups across queues. Each row shows:- Group ID: the logical partition key
- Pipeline: which pipeline stage this group belongs to
- Pending: number of jobs waiting to be processed
- Retries: retry count (orange if > 0)
- Oldest job age: with a warning indicator if overdue
- Status:
OK,Active,Blocked, orStale
Filtering
Filter groups by status to focus on problems:- All: every group
- Blocked: groups stuck due to errors
- Stale: blocked groups that have been waiting too long
- Active: groups currently being processed
- OK: healthy groups
Group Detail
Click any row to open the Group Detail dialog, which shows:- Full status and pipeline information
- Error message and stack trace
- Active job ID (if currently processing)
- Paginated list of all jobs in the group with their scores and raw data
Common Workflows
Recovering from a bad deployment
- Check the Blocked section, a spike in blocked groups after a deploy usually means the new code is crashing
- Roll back the deployment
- Use Canary Unblock to test that the rollback fixes the issue
- If the canary succeeds, Unblock All to resume processing
Clearing stale data after a schema change
- Identify affected groups in the Groups table using status filters
- If the data can be reprocessed: Move to DLQ, fix the schema, then Replay All
- If the data is obsolete: Drain the affected error cluster
Testing a fix before full redrive
- Deploy the fix
- Go to the DLQ section
- Use Canary Redrive with a count of 5-10
- Monitor the dashboard for new failures
- If clean, Replay All to redrive the remaining items