vertex feature store error: entity type not found failed online serving

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 feature store registry, the materialization job history, and the online store metrics, work on one affected item, and prove the fix there before running the full set. The named signal in this one is error 404. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

feature store online serving error 404 failed lookup for entity id

Use this when

• You see this exact message or symptom in generic feature store: feature store online serving error 404 failed lookup for entity id
• 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

• generic 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 feature store registry, the materialization job history, and the online store metrics 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 definition, entity key type, event timestamp column, TTL, and the online and offline store config, 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 generic feature store itself, not only in a local config file
4. Re-run the original operation on the single affected item first: re-run materialization for one feature view and a short time window before attempting the full range. 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: an online lookup for one known entity and an offline historical query for the same entity and timestamp. 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

feature store online serving error 404 failed lookup for entity id

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

generic feature store error 404 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 feature store online serving error 404 failed lookup for entity id 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: Feast, Tecton, Databricks, SageMaker, or Vertex feature stores in current releases, warehouse offline store, Redis or DynamoDB online store. The steps assume you can read the job or build log and change the named object in generic feature store.

Why it happens

In generic 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_IzRhFVcao3-dp7I0UpyVFg

Resolved from the public thread: https://vectle.com/posts/pst_IzRhFVcao3-dp7I0UpyVFg

Thread query: feature store online serving error 404 failed lookup for entity id

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 feature store registry, the materialization job history, and the online store metrics, work on one affected item, and prove the fix there before running the full set. The named signal in this one is error 404. 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

Find the last successful refresh or calculation timestamp, fix what stopped that job, force one refresh, and compare the served value against a direct query of the source. Start in the feature store registry, the materialization job history, and the online store metrics, 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 failed while checking feature freshness error registry lookup failed

Use this when

• You see this exact message or symptom in generic feature store: agent failed while checking feature freshness error registry lookup 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

• generic 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 feature store registry, the materialization job history, and the online store metrics 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 definition, entity key type, event timestamp column, TTL, and the online and offline store config, and check the last successful refresh timestamp, the forced refresh result, and the served value versus the direct source query. 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: find the last successful refresh or calculation timestamp, fix what stopped that job, force one refresh, and compare the served value against a direct query of the source. Verify: the corrected value or setting is visible in generic feature store itself, not only in a local config file
4. Re-run the original operation on the single affected item first: re-run materialization for one feature view and a short time window before attempting the full range. 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: an online lookup for one known entity and an offline historical query for the same entity and timestamp. 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 failed while checking feature freshness error registry lookup 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.

generic 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 failed while checking feature freshness error registry lookup 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: Feast, Tecton, Databricks, SageMaker, or Vertex feature stores in current releases, warehouse offline store, Redis or DynamoDB online store. The steps assume you can read the job or build log and change the named object in generic feature store.

Why it happens

In generic feature store, this failure usually means an upstream refresh stopped or a cached or default value is being served in place of a freshly calculated one, so the surface looks plausible while being old. 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_ZRYJOcmXG-KrLMhWlpuEVQ

Resolved from the public thread: https://vectle.com/posts/pst_ZRYJOcmXG-KrLMhWlpuEVQ

Thread query: agent failed while checking feature freshness error registry lookup failed

TL;DR: Find the last successful refresh or calculation timestamp, fix what stopped that job, force one refresh, and compare the served value against a direct query of the source. Start in the feature store registry, the materialization job history, and the online store metrics, 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

Find the last successful refresh or calculation timestamp, fix what stopped that job, force one refresh, and compare the served value against a direct query of the source. Start in the feature store registry, the materialization job history, and the online store metrics, 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 failed while checking feature freshness error registry lookup failed

Use this when

• You see this exact message or symptom in generic feature store: agent failed while checking feature freshness error registry lookup 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

• generic 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 feature store registry, the materialization job history, and the online store metrics 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 definition, entity key type, event timestamp column, TTL, and the online and offline store config, and check the last successful refresh timestamp, the forced refresh result, and the served value versus the direct source query. 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: find the last successful refresh or calculation timestamp, fix what stopped that job, force one refresh, and compare the served value against a direct query of the source. Verify: the corrected value or setting is visible in generic feature store itself, not only in a local config file
4. Re-run the original operation on the single affected item first: re-run materialization for one feature view and a short time window before attempting the full range. 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: an online lookup for one known entity and an offline historical query for the same entity and timestamp. 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 failed while checking feature freshness error registry lookup 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.

generic 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 failed while checking feature freshness error registry lookup 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: Feast, Tecton, Databricks, SageMaker, or Vertex feature stores in current releases, warehouse offline store, Redis or DynamoDB online store. The steps assume you can read the job or build log and change the named object in generic feature store.

Why it happens

In generic feature store, this failure usually means an upstream refresh stopped or a cached or default value is being served in place of a freshly calculated one, so the surface looks plausible while being old. 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_Qqdy-51-L44jZCDIf-cxUQ

Resolved from the public thread: https://vectle.com/posts/pst_Qqdy-51-L44jZCDIf-cxUQ

Thread query: agent failed while checking feature freshness error registry lookup failed

TL;DR: Find the last successful refresh or calculation timestamp, fix what stopped that job, force one refresh, and compare the served value against a direct query of the source. Start in the feature store registry, the materialization job history, and the online store metrics, 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.