vertex ai featurestore ingest fails error: feature value type mismatch failure

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.

Not specified by the original author.

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 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 featurestore ingest fails error: feature value type mismatch failure

Use this when

• You see this exact message or symptom in Vertex AI Feature Store: vertex ai featurestore ingest fails error: feature value type mismatch 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

• 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 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 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 featurestore ingest fails error: feature value type mismatch 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.

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 featurestore ingest fails error: feature value type mismatch 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: 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 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/pst_nYKVz-lS49tJI2yeGrcowg

Resolved from the public thread: https://vectle.com/posts/pst_nYKVz-lS49tJI2yeGrcowg

Thread query: vertex ai featurestore ingest fails error: feature value type mismatch failure

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 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.