Superset SQL Lab query failed: database error column reference is ambiguous in dashboard chart dataset

TL;DR

Diff the definition against the live source schema, fix the named field type or reference at its definition rather than at each consumer, and rebuild or revalidate in dependency order. Start in the Superset chart page, SQL Lab history, and the dataset list, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

Superset SQL Lab query failed: database error column reference is ambiguous in dashboard chart dataset

Use this when

• You see this exact message or symptom in Apache Superset: Superset SQL Lab query failed: database error column reference is ambiguous in dashboard chart dataset
• One run, record, build, or object is affected and you can name it
• A retry without changing anything reproduces the same text

Not for this skill when

• Apache Superset fails with a different message and none of the signals here appear
• Nothing has failed yet and you are doing first time setup or general planning
• Every service from this vendor is down for all users, which is a vendor outage to wait out first

Steps

1. Reproduce the failure once in the Superset chart page, SQL Lab history, and the dataset list and search the log or history for the exact text in the block above, recording the time, the workspace or project, and the one object it names. Verify: you have one failing run or object identified, not a similar older failure
2. Inspect the dataset columns and metrics, the chart access roles, the SQL Lab query, and the dashboard import file ids, and check the schema diff, the corrected field definition, and the validation run that now passes for the whole object. Verify: you can point to the specific value, field, or identity that does not match what this operation needs
3. Apply the fix at that source: diff the definition against the live source schema, fix the named field type or reference at its definition rather than at each consumer, and rebuild or revalidate in dependency order. Verify: the corrected value or setting is visible in Apache Superset itself, not only in a local config file
4. Re-run the original operation on the single affected item first: refresh the dataset metadata, open the chart alone, fix the query or access, then reload the dashboard or re-import a copy. Verify: that one item completes with no trace of the error text from the block above
5. Check the downstream result a user or the next job would see: the chart rendering for an authorized user and SQL Lab returning rows for the corrected query. Verify: the downstream result agrees with the source for the test item, in value and in timestamp
6. Repeat for the full set, watch the first few items in the same log you started from, and write the root cause and the changed setting into the run notes. Verify: the full run finishes, the notes name the error and the changed setting, and no earlier item regressed

Variant phrasings

Superset SQL Lab query failed: database error column reference is ambiguous in dashboard chart dataset

The subject-first phrasing of the same failure. The fix is the same, start at step 1 and confirm the object the log names.

Apache Superset version of this failure

The tool-first phrasing searchers use once they know which system raised it. It is the same underlying problem, not a separate bug.

Why does superset SQL Lab query failed: database error column reference is ambiguous in dashboard chart dataset keep happening after a retry

The repeat-occurrence phrasing. A retry without the step 2 and step 3 checks almost always reproduces it, because the cause sits in the object state, not in the attempt.

Compatibility: Apache Superset 3 or 4, datasets and charts, SQL Lab, dashboard import and export. The steps assume you can read the job or build log and change the named object in Apache Superset.

Why it happens

In Apache Superset, this failure usually means the definition and the real data disagree on a field name, type, or dependency, and the engine refuses to guess which one is right. The visible message is raised at the point where the bad state is consumed, which is rarely where it was created, so fixing the message text or retrying the last step changes nothing. Correcting the state at its source, then re-running one item and comparing the downstream result against the source, is what separates a real fix from a run that only looks successful.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst1cJSQ46A7bTRWVrU8pvQQ