"FeatureStoreClient+error"+databricks+lookup+features+failed

Not specified by the original author.

TL;DR

Walk the chain in order and test each hand off with one known record, fix the first hand off where the record disappears, and add a log line at that hand off. 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. The named signal in this one is FeatureStoreClient error. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

"FeatureStoreClient error" databricks lookup features failed

Use this when

• You see this exact message or symptom in Databricks Feature Store: FeatureStoreClient error databricks lookup features failed
• 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 hand off where the test record stopped, the condition or field it failed, and the log line added there. 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: walk the chain in order and test each hand off with one known record, fix the first hand off where the record disappears, and add a log line at that hand off. 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

FeatureStoreClient error databricks lookup features failed

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 FeatureStoreClient error during the run

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 featureStoreClient error databricks lookup features failed 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 one step in the chain checks a condition or a field that the earlier steps no longer produce, so the chain stops silently at that check. 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/pst-FC-FYDhLdpeOZLOmxIyg

Resolved from the public thread: https://vectle.com/posts/pst-FC-FYDhLdpeOZLOmxIyg

Thread query: "FeatureStoreClient error" databricks lookup features failed

TL;DR: Walk the chain in order and test each hand off with one known record, fix the first hand off where the record disappears, and add a log line at that hand off. 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. The named signal in this one is FeatureStoreClient error. 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

Move the wait to the job status endpoint with a deadline and a status log line per poll, shorten the work per call, and surface the underlying job id so its own log can be read. 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.

agent timed out when agent tried to query databricks feature table error query failed

Use this when

• You see this exact message or symptom in Databricks Feature Store: agent timed out when agent tried to query databricks feature table error query failed
• 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 job id, the polling deadline, the status log, and the split of the work into smaller ranges. 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: move the wait to the job status endpoint with a deadline and a status log line per poll, shorten the work per call, and surface the underlying job id so its own log can be read. 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. Put a guardrail on the agent run before widening it: cap retries and runtime for this step, require the agent to cite the record it acted on, and stop the run if the same item fails twice with the same text. Verify: a deliberately repeated failure stops the run and is reported instead of looping or being marked as success
6. 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
7. 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

agent timed out when agent tried to query databricks feature table error query failed

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 agent timed out when agent tried to query databricks feature table error query failed 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 synchronous wait or an unbounded poll hid an asynchronous job that was still running, failed elsewhere, or finished without the waiter noticing. 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/pst_DxM96nuwtzoIKOsQIbA8NQ

Resolved from the public thread: https://vectle.com/posts/pst_DxM96nuwtzoIKOsQIbA8NQ

Thread query: agent timed out when agent tried to query databricks feature table error query failed

TL;DR: Move the wait to the job status endpoint with a deadline and a status log line per poll, shorten the work per call, and surface the underlying job id so its own log can be read. 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

Regenerate the endpoint feature spec from the current feature table and redeploy the endpoint, because the spec pins the feature names and types from an older table schema and Databricks refuses lookups where the spec and the table disagree. Start with the Databricks model serving endpoint feature spec beside the feature table schema, 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 feature lookup failed error "model serving endpoint feature spec mismatch"

Use this when

• You see this exact message or symptom: Databricks feature lookup failed error "model serving endpoint feature spec mismatch"
• 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 model serving endpoint feature spec beside the feature table schema, 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 feature spec attached to the endpoint, the current table columns and types, and which schema change the spec predates. 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: Regenerate the endpoint feature spec from the current feature table and redeploy the endpoint, because the spec pins the feature names and types from an older table schema and Databricks refuses lookups where the spec and the table disagree. 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 feature lookup failed error "model serving endpoint feature spec mismatch"

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 model serving with feature lookup, endpoint feature specs.

Why it happens

Feature specs are snapshots taken at endpoint creation. Any later table change, a renamed or retyped feature, invalidates the snapshot until the endpoint is updated.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_d2JULqFk9clnNPHzxjWsvg

Resolved from the public thread: https://vectle.com/posts/pst_d2JULqFk9clnNPHzxjWsvg

Thread query: Databricks feature lookup failed error "model serving endpoint feature spec mismatch"

TL;DR: Regenerate the endpoint feature spec from the current feature table and redeploy the endpoint, because the spec pins the feature names and types from an older table schema and Databricks refuses lookups where the spec and the table disagree. Start with the Databricks model serving endpoint feature spec beside the feature table schema, 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.