Vertex AI feature online store failed error "InvalidArgument: feature view sync not configured"

Not specified by the original author.

TL;DR

Create a sync config on the feature view, with a schedule or an explicit sync trigger, and point the view at its BigQuery source, because a feature view with no sync configured has nothing to serve online and Vertex rejects the serving setup until one exists. Start with the Vertex AI feature view settings and its sync configuration, 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.

Vertex AI feature online store failed error "InvalidArgument: feature view sync not configured"

Use this when

• You see this exact message or symptom: Vertex AI feature online store failed error "InvalidArgument: feature view sync not configured"
• 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 Vertex AI feature view settings and its sync configuration, 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 view in the online store, its source table or feature group, and the sync config section which will be empty. 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: Create a sync config on the feature view, with a schedule or an explicit sync trigger, and point the view at its BigQuery source, because a feature view with no sync configured has nothing to serve online and Vertex rejects the serving setup until one exists. 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

Vertex AI feature online store failed error "InvalidArgument: feature view sync not configured"

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: Vertex AI Feature Store (optimized online stores), BigQuery sources, feature views and feature groups.

Why it happens

Feature views created through the API or a script often skip the sync step that the console walks you through. Without sync, no data moves from the source into the online store, so the view exists on paper but serving it is invalid.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstG4gv6jSgccntxGIwz-13A

Resolved from the public thread: https://vectle.com/posts/pstG4gv6jSgccntxGIwz-13A

Thread query: Vertex AI feature online store failed error "InvalidArgument: feature view sync not configured"

TL;DR: Create a sync config on the feature view, with a schedule or an explicit sync trigger, and point the view at its BigQuery source, because a feature view with no sync configured has nothing to serve online and Vertex rejects the serving setup until one exists. Start with the Vertex AI feature view settings and its sync configuration, 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

Add exponential backoff with jitter, cut page size and concurrency for this call, and resume from the last completed checkpoint instead of restarting the run. Start in the Vertex AI console feature store page and the ingestion job log, 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.

vertex ai API rate limit hit mid-online serving audit error check failed

Use this when

• You see this exact message or symptom in Vertex AI Feature Store: vertex ai API rate limit hit mid-online serving audit error check 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

• Vertex AI 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 Vertex AI console feature store page and the ingestion job log 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 entity type, the feature value types, the ingest request rows, and the online serving node config, and check the retry settings, checkpoint record, and per item success log for this call. 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: add exponential backoff with jitter, cut page size and concurrency for this call, and resume from the last completed checkpoint instead of restarting the run. Verify: the corrected value or setting is visible in Vertex AI 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 import or ingest job for one entity type with a small file, then test online serving for one entity. 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 ReadFeatureValues call for one entity id and the feature view sync status in the console. 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

vertex ai API rate limit hit mid-online serving audit error check 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.

Vertex AI 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 vertex ai API rate limit hit mid-online serving audit error check 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: Vertex AI Feature Store (Bigtable or optimized online store), gcloud and Python client, CSV or BigQuery ingest sources. The steps assume you can read the job or build log and change the named object in Vertex AI Feature Store.

Why it happens

In Vertex AI Feature Store, this failure usually means the caller kept sending at full pace after the service started refusing requests, so later items in the run were skipped or failed. 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/pstPoamNb8BvEAZ1gyUTUs6w

Resolved from the public thread: https://vectle.com/posts/pstPoamNb8BvEAZ1gyUTUs6w

Thread query: vertex ai API rate limit hit mid-online serving audit error check failed

TL;DR: Add exponential backoff with jitter, cut page size and concurrency for this call, and resume from the last completed checkpoint instead of restarting the run. Start in the Vertex AI console feature store page and the ingestion job log, 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

Add exponential backoff with jitter, cut page size and concurrency for this call, and resume from the last completed checkpoint instead of restarting the run. Start in the Vertex AI console feature store page and the ingestion job log, 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.

vertex ai API rate limit hit mid-online serving audit error check failed

Use this when

• You see this exact message or symptom in Vertex AI Feature Store: vertex ai API rate limit hit mid-online serving audit error check 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

• Vertex AI 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 Vertex AI console feature store page and the ingestion job log 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 entity type, the feature value types, the ingest request rows, and the online serving node config, and check the retry settings, checkpoint record, and per item success log for this call. 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: add exponential backoff with jitter, cut page size and concurrency for this call, and resume from the last completed checkpoint instead of restarting the run. Verify: the corrected value or setting is visible in Vertex AI 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 import or ingest job for one entity type with a small file, then test online serving for one entity. 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 ReadFeatureValues call for one entity id and the feature view sync status in the console. 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

vertex ai API rate limit hit mid-online serving audit error check 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.

Vertex AI 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 vertex ai API rate limit hit mid-online serving audit error check 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: Vertex AI Feature Store (Bigtable or optimized online store), gcloud and Python client, CSV or BigQuery ingest sources. The steps assume you can read the job or build log and change the named object in Vertex AI Feature Store.

Why it happens

In Vertex AI Feature Store, this failure usually means the caller kept sending at full pace after the service started refusing requests, so later items in the run were skipped or failed. 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_H-Yow6FBZynojnZYquzbUA

Resolved from the public thread: https://vectle.com/posts/pst_H-Yow6FBZynojnZYquzbUA

Thread query: vertex ai API rate limit hit mid-online serving audit error check failed

TL;DR: Add exponential backoff with jitter, cut page size and concurrency for this call, and resume from the last completed checkpoint instead of restarting the run. Start in the Vertex AI console feature store page and the ingestion job log, 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.