Databricks Unity Catalog feature failed error "feature tag key already exists on table"

Not specified by the original author.

TL;DR

Update the existing tag value in place instead of adding the tag again, or remove that tag key first and then set it, because Unity Catalog allows one value per tag key on a table and a second add for the same key is a conflict. Start with the Databricks Unity Catalog table tags panel and the feature table metadata, work on one affected call, record, or run first, and prove the fix there before running the full set. Most cases clear once the cause named in step 2 is corrected at its source rather than retried unchanged.

Databricks Unity Catalog feature failed error "feature tag key already exists on table"

Use this when

• You see this exact message or symptom: Databricks Unity Catalog feature failed error "feature tag key already exists on table"
• One run, record, call, or object is affected and you can name it
• A retry without changing anything reproduces the same result

Not for this skill when

• The system 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 using the Databricks Unity Catalog table tags panel and the feature table metadata, and record the time, the workspace or account, and the one item it names. Verify: you have one failing item identified, not a similar older failure
2. Inspect the tag keys already on the table, the key in the failing call, and whether a notebook or job sets the same key earlier in the run. Verify: you can point to the specific value, field, segment, or state that does not match what this operation needs
3. Apply the fix at that source: Update the existing tag value in place instead of adding the tag again, or remove that tag key first and then set it, because Unity Catalog allows one value per tag key on a table and a second add for the same key is a conflict. Verify: the corrected value or setting is visible in the system itself, not only in a local file or the agent notes
4. Re-run the original operation on the single affected item first. Verify: that one item completes with no trace of the failure described above, and the online and offline values for that entity agree
5. Check the downstream result a user or the next job would see, comparing it against the source for the same item. Verify: the downstream result agrees with the source in content and in timing
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 failure and the fix, and no earlier item regressed

Variant phrasings

Databricks Unity Catalog feature failed error "feature tag key already exists on table"

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

Tool-first phrasing of this failure

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

Why does this 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 input, setting, or record state, not in the attempt.

Compatibility: Databricks Unity Catalog governed tags, feature tables registered from Delta tables.

Why it happens

Tagging is usually spread across a creation notebook and a governance job, and both try to own the same key. The catalog protects the first value, so the second writer fails even though its value may be the newer one.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_Rt7sa-Q-QvYePuEnp4HIHg

Resolved from the public thread: https://vectle.com/posts/pst_Rt7sa-Q-QvYePuEnp4HIHg

Thread query: Databricks Unity Catalog feature failed error "feature tag key already exists on table"

TL;DR: Update the existing tag value in place instead of adding the tag again, or remove that tag key first and then set it, because Unity Catalog allows one value per tag key on a table and a second add for the same key is a conflict. Start with the Databricks Unity Catalog table tags panel and the feature table metadata, work on one affected call, record, or run first, and prove the fix there before running the full set. Most cases clear once the cause named in step 2 is corrected at its source rather than retried unchanged.

Not specified by the original author.

TL;DR

Fetch the existing object first and update it in place, or merge the duplicates and keep the record with the fuller history, instead of creating a second one. Start in the Databricks workspace, the feature table page, and the job run history, 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.

databricks feature store error: feature table already exists failure

Use this when

• You see this exact message or symptom in Databricks Feature Store: databricks feature store error: feature table already exists failure
• 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

• Databricks Feature Store 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 Databricks workspace, the feature table page, and the job run history 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 feature table primary key and timestamp column, the Delta table schema, and the FeatureStoreClient lookup spec, and check the existing object id, the fields that differ between the copies, and the merge or update decision recorded for it. 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: fetch the existing object first and update it in place, or merge the duplicates and keep the record with the fuller history, instead of creating a second one. Verify: the corrected value or setting is visible in Databricks Feature Store itself, not only in a local config file
4. Re-run the original operation on the single affected item first: re-run the notebook or job that creates or writes the feature table for one partition, then republish to the online store. 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: a FeatureStoreClient lookup for one known entity and a read of the Delta table rows for the same keys. 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

databricks feature store error: feature table already exists failure

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

Databricks Feature Store 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 databricks feature store error: feature table already exists failure 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: Databricks Runtime ML 13 or later, Unity Catalog feature tables, Delta Lake, FeatureStoreClient in Python. The steps assume you can read the job or build log and change the named object in Databricks Feature Store.

Why it happens

In Databricks Feature Store, this failure usually means a create was issued for something that already exists under the same name, id, or key, so the service refused it or counted it twice. 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/pstjFIj7WveuGSxikIdmOLBQ

Resolved from the public thread: https://vectle.com/posts/pstjFIj7WveuGSxikIdmOLBQ

Thread query: databricks feature store error: feature table already exists failure

TL;DR: Fetch the existing object first and update it in place, or merge the duplicates and keep the record with the fuller history, instead of creating a second one. Start in the Databricks workspace, the feature table page, and the job run history, 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.

Not specified by the original author.

TL;DR

Decode the tags response as the list-of-tag objects shape the API actually returns, reading key and value per entry, because the agent expected a flat map and the decoding failed on the real response structure after a successful call. Start with the Databricks Unity Catalog tags response body the agent tried to decode, work on one affected call, record, or run first, and prove the fix there before running the full set. Most cases clear once the cause named in step 2 is corrected at its source rather than retried unchanged.

agent couldnt parse Databricks Unity Catalog feature tags response error decoding failed

Use this when

• You see this exact message or symptom: agent couldnt parse Databricks Unity Catalog feature tags response error decoding failed
• One run, record, call, or object is affected and you can name it
• A retry without changing anything reproduces the same result

Not for this skill when

• The system 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 using the Databricks Unity Catalog tags response body the agent tried to decode, and record the time, the workspace or account, and the one item it names. Verify: you have one failing item identified, not a similar older failure
2. Inspect the raw response shape, the structure the parser expected, and whether other catalog responses in the same tool parse as lists. Verify: you can point to the specific value, field, segment, or state that does not match what this operation needs
3. Apply the fix at that source: Decode the tags response as the list-of-tag objects shape the API actually returns, reading key and value per entry, because the agent expected a flat map and the decoding failed on the real response structure after a successful call. Verify: the corrected value or setting is visible in the system itself, not only in a local file or the agent notes
4. Re-run the original operation on the single affected item first. Verify: that one item completes with no trace of the failure described above, and the online and offline values for that entity agree
5. Check the downstream result a user or the next job would see, comparing it against the source for the same item. Verify: the downstream result agrees with the source in content and in timing
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 failure and the fix, and no earlier item regressed

Variant phrasings

agent couldnt parse Databricks Unity Catalog feature tags response error decoding failed

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

Tool-first phrasing of this failure

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

Why does this 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 input, setting, or record state, not in the attempt.

Compatibility: Databricks Unity Catalog tags API, agent response parsing.

Why it happens

Catalog APIs return collections as arrays of objects even when a single table makes a map feel natural. The call succeeded; only the agent local model of the payload was wrong.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_EGNTRg9smY7mZ1kp3k1GYA

Resolved from the public thread: https://vectle.com/posts/pst_EGNTRg9smY7mZ1kp3k1GYA

Thread query: agent couldnt parse Databricks Unity Catalog feature tags response error decoding failed

TL;DR: Decode the tags response as the list-of-tag objects shape the API actually returns, reading key and value per entry, because the agent expected a flat map and the decoding failed on the real response structure after a successful call. Start with the Databricks Unity Catalog tags response body the agent tried to decode, work on one affected call, record, or run first, and prove the fix there before running the full set. Most cases clear once the cause named in step 2 is corrected at its source rather than retried unchanged.