DynamoDB online store failed error "ProvisionedThroughputExceededException on feature lookup"

Not specified by the original author.

TL;DR

Add exponential backoff with jitter to the lookup and either switch the online table to on-demand capacity or raise its provisioned read capacity, because lookups are arriving faster than the table provisioned throughput allows and DynamoDB throttles the excess reads. Start with the DynamoDB online store table metrics and the lookup call pattern, 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.

DynamoDB online store failed error "ProvisionedThroughputExceededException on feature lookup"

Use this when

• You see this exact message or symptom: DynamoDB online store failed error "ProvisionedThroughputExceededException on feature lookup"
• 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 DynamoDB online store table metrics and the lookup call pattern, 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 table billing mode and consumed versus provisioned capacity graph, the lookup rate during the failure, and whether one hot entity key concentrates the reads. 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: Add exponential backoff with jitter to the lookup and either switch the online table to on-demand capacity or raise its provisioned read capacity, because lookups are arriving faster than the table provisioned throughput allows and DynamoDB throttles the excess reads. 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

DynamoDB online store failed error "ProvisionedThroughputExceededException on feature lookup"

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: Feast DynamoDB online store, provisioned and on-demand tables, batch and single lookups.

Why it happens

Provisioned tables sized for average load throttle at peak or on a hot partition key. The exception looks random from the client because only the requests over the line are rejected.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstGCVDcBdXjErPEaKw73nNg

Resolved from the public thread: https://vectle.com/posts/pstGCVDcBdXjErPEaKw73nNg

Thread query: DynamoDB online store failed error "ProvisionedThroughputExceededException on feature lookup"

TL;DR: Add exponential backoff with jitter to the lookup and either switch the online table to on-demand capacity or raise its provisioned read capacity, because lookups are arriving faster than the table provisioned throughput allows and DynamoDB throttles the excess reads. Start with the DynamoDB online store table metrics and the lookup call pattern, 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

Raise the client deadline for the query and cut the per-call work, by requesting fewer entities or features per call and fixing the slowest resolver the console timings name, because the server was still working when the gRPC deadline the client set expired. Start with the Chalk online query call and the resolver timings in the Chalk console, 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.

Chalk feature server failed error "grpc deadline exceeded on online query"

Use this when

• You see this exact message or symptom: Chalk feature server failed error "grpc deadline exceeded on online query"
• 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 Chalk online query call and the resolver timings in the Chalk console, 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 deadline or timeout value on the client call, the batch size of entities requested, and per-resolver timings for the slow query. 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: Raise the client deadline for the query and cut the per-call work, by requesting fewer entities or features per call and fixing the slowest resolver the console timings name, because the server was still working when the gRPC deadline the client set expired. 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

Chalk feature server failed error "grpc deadline exceeded on online query"

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: Chalk online queries over gRPC, Python and Go clients, resolvers with external lookups.

Why it happens

Online queries fan out to every resolver in the graph, so one resolver doing a slow external lookup stretches the whole call past a deadline that looked generous for cached features. Retrying the identical call just hits the same wall.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_yuPjAkU31Sy7DHAoT1mYmg

Resolved from the public thread: https://vectle.com/posts/pst_yuPjAkU31Sy7DHAoT1mYmg

Thread query: Chalk feature server failed error "grpc deadline exceeded on online query"

TL;DR: Raise the client deadline for the query and cut the per-call work, by requesting fewer entities or features per call and fixing the slowest resolver the console timings name, because the server was still working when the gRPC deadline the client set expired. Start with the Chalk online query call and the resolver timings in the Chalk console, 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

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 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 online store timeout error. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

"online store timeout error" feature retrieval failed latency spike

Use this when

• You see this exact message or symptom in generic feature store: online store timeout error feature retrieval failed latency spike
• 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 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 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

online store timeout error feature retrieval failed latency spike

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 online store timeout 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 online store timeout error feature retrieval failed latency spike 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 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_HpFMlJVvOoZbYrXRhfnxyQ

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

Thread query: "online store timeout error" feature retrieval failed latency spike

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 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 online store timeout error. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.