debug-deployment

name	debug-deployment
description	Debug a failed or misbehaving dltHub Platform deployment. Use when a runtime job fails, produces unexpected results, or the user wants to check job status and logs.

Debug dltHub Platform deployment

Reference: https://dlthub.com/docs/hub/pipeline-operations/monitoring.md (pipeline health, logs, failure diagnosis)

Check job status

Commands accept job names or selectors (fnmatch patterns):

dlthub job list                              # all jobs
dlthub job list batch                        # only batch jobs
dlthub job list "tag:ingest"                 # jobs tagged "ingest"
dlthub job list "schedule:*"                 # jobs with a schedule trigger
dlthub job info <name>                       # details for one job
dlthub job runs list [name_or_selector]      # runs for matching jobs
dlthub job runs list [name_or_selector] --running  # only active runs
dlthub job runs info <name> [run#]           # specific run details

Debug job definitions

dlthub deploy --dry-run                      # preview manifest reconciliation
dlthub deploy --show-manifest                # dump full manifest as YAML

View logs

dlthub job logs <name_or_selector>           # latest run
dlthub job runs logs <name> [run#]           # specific run
dlthub job logs <name> -f                    # stream in real-time

Cancel running jobs

dlthub job cancel <name>                     # cancel active runs for one job
dlthub job cancel "tag:backfill"             # cancel by selector
dlthub job cancel batch --dry-run            # preview what would be cancelled
dlthub job runs cancel <name> [run#]         # cancel a specific run

Access production data (read only)

1. Figure out right profile for data access.

• access profile if it is configured (list profiles). if not: prod profile (if configured)
• if none is present ask user which profile to use

2. ALWAYS ask human before accessing production data. Confirm the profile
3. pin the profile
4. use mcp tools, run cli, python scripts
5. pin dev profile after work is done

to run a single command on given profile use:

dlthub local run my_pipeline.py --profile prod          # run under prod profile
WORKSPACE__PROFILE=prod uv run dlthub local pipeline info my_pipeline   # env var for dlthub commands

Note: you must pin the production profile for mcp server to see the change

Other useful commands

dlthub job trigger <selector>                        # trigger jobs by selector (e.g. tag:backfill)
dlthub job trigger <selector> --refresh              # trigger with refresh signal
dlthub job trigger <selector> --profile <name>       # trigger under a specific profile (e.g. prod)
dlthub job trigger <selector> --dry-run              # preview which jobs would fire
dlthub pipeline run <pipeline_name>         # trigger job by pipeline name
dlthub workspace connect <name_or_id>       # switch workspace without re-login
dlthub info                                 # workspace deployment overview
dlthub workspace deployment list                        # deployment version history
dlthub workspace deployment info [version]              # details for a deployment version
dlthub workspace deployment sync [version] [--dry-run]  # sync local files to remote (creates new deployment)
dlthub workspace configuration list                     # configuration version history
dlthub workspace configuration info [version]           # details for a configuration version
dlthub workspace configuration sync [version] [--dry-run]  # sync local config files to remote
dlthub local clean                          # clean local workspace state
dlthub local clean --skip-data-dir          # clean but keep data directory

Inspect local pipeline state

dlthub local pipeline list                        # all local pipelines
dlthub local pipeline info [pipeline_name]        # local pipeline details
dlthub local pipeline failed-jobs [pipeline_name] # list failed load packages
dlthub local pipeline trace [pipeline_name]       # show last trace

Open the web dashboard (for humans)

dlthub show

Prints the dltHub web UI URL. It should open automatically, but if the user says it does not, ask them to open it themselves.

Quick diagnosis

If a job failed:

1. dlthub job runs info <name> [run#] -- check exit status and timing
2. dlthub job runs logs <name> [run#] -- read the error output
3. Common causes:
   • Missing dependencies in pyproject.toml -- all packages must be declared, not just locally installed
   • Secrets not configured for prod profile -- runtime uses prod profile, ask the user to check .dlt/prod.secrets.toml — NEVER access it directly, only the user may modify it
   • Script missing if __name__ == "__main__": -- the job does nothing without it
   • dev_mode=True left in -- drops and recreates dataset on every run
   • Wrong destination credentials -- prod profile may point to a different destination than dev
   • Job timeout -- default is 120 minutes; override with execute={"timeout": "6h"} in the decorator
4. After fixing, relaunch with dlthub run <name_or_file>