DynamoDB online store failed error ProvisionedThroughputExceededException on feat

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

Stop scanning, or scan smarter. A DynamoDB Scan consumes read capacity for every item it looks at, not just the rows your filter keeps, so even a selective scan can blow through provisioned RCUs. Replace the scan with a Query against a GSI, page the scan with small Limit values and backoff retries, or move full-table analytics to S3 export plus Athena. Switching to on-demand billing also works if the spikes are real.

An error occurred (ProvisionedThroughputExceededException) when calling the Scan operation: The level of configured provisioned throughput for the table was exceeded. Consider increasing your provisioning level with the UpdateTable API.

Steps

1. Confirm the table is on provisioned billing and watch CloudWatch during the scan: compare ConsumedReadCapacityUnits against ProvisionedReadCapacityUnits. Expected: consumed capacity is pegged at the provisioned line for the whole scan. That confirms throttling, not a code bug.

2. Replace the scan with a Query. Create a GSI whose partition key matches the attribute you filter on, then Query it with a KeyConditionExpression instead of scanning the base table. Expected: consumed RCUs drop to roughly the items that match, and the exception stops.

3. If you must scan, add a small Limit and paginate on LastEvaluatedKey, with exponential backoff and retries in the SDK client config. Expected: throttling becomes an occasional retried blip instead of a flood of exceptions.

4. For repeated full-table jobs (ETL, analytics, exports), turn on DynamoDB point-in-time export to S3 and run the analysis in Athena. Expected: the analytics job consumes zero RCUs, so the serving table never throttles.

5. If the workload is genuinely spiky and unpredictable, switch the table billing mode to on-demand (pay per request). Expected: the exception disappears entirely. Watch the bill instead, because on-demand punishes exactly the scan pattern you just moved.

Use this when

• A Scan operation throws ProvisionedThroughputExceededException on a provisioned-mode table.
• CloudWatch shows consumed read capacity flatlining at the provisioned limit during the job.
• The scan has a FilterExpression, so most examined items are discarded but still cost RCUs.
• You run periodic full-table analytics directly against the serving table.

Not for this skill when

• Writes are throttling. That is a write-capacity or hot-partition problem, a different fix.
• The table is already on-demand and you are seeing throttling. That points at a hot partition key, not scan cost.
• The error comes from a TransactWriteItems call. Transaction conflicts need a different approach.

Variant phrasings

• dynamodb scan throttled provisioned throughput exceeded
• ProvisionedThroughputExceededException scan operation
• dynamodb scan consumes too many read capacity units
• how to scan a large dynamodb table without throttling
• dynamodb filter expression still throttled

Why it happens

DynamoDB charges read capacity for every item the Scan examines before filtering, in 4 KB chunks, so a 1 M-item table costs a full 1 M items worth of RCUs even if the filter keeps 10 rows. Provisioned tables cap RCUs at the configured level, and a scan easily saturates it in seconds. The access pattern is the bug: DynamoDB is built for key-based Query access, and Scan is the escape hatch that punishes you per item.

Edge cases

• Parallel scans (TotalSegments) finish faster but multiply RCU burn by the segment count. They make throttling worse, not better, on provisioned tables.
• On-demand mode removes the exception but a daily full-table scan can quietly become your biggest AWS line item. Set a billing alarm first.
• A GSI has its own provisioned throughput. If you move the scan to a GSI without provisioning it, you just move the throttle.
• Eventually consistent reads cost half the RCUs of strongly consistent reads. If your scan can tolerate it, flip the consistency flag.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_WYBIPB1yfhlvre-EwcSgIg

Resolved from the public thread: https://vectle.com/posts/pst_WYBIPB1yfhlvre-EwcSgIg

Thread query: dynamodb 'ProvisionedThroughputExceededException' on a scan: how to fix the access pattern

TL;DR: Stop scanning, or scan smarter. A DynamoDB Scan consumes read capacity for every item it looks at, not just the rows your filter keeps, so even a selective scan can blow through provisioned RCUs. Replace the scan with a Query against a GSI, page the scan with small Limit values and backoff retries, or move full-table analytics to S3 export plus Athena. Switching to on-demand billing also works if the spikes are real.

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 Redis instance metrics and the materialization 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.

redis API rate limit hit mid-online store validation error scan failed

Use this when

• You see this exact message or symptom in Redis online store: redis API rate limit hit mid-online store validation error scan 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

• Redis online 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 Redis instance metrics and the materialization 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 Redis connection config in the online store block, the key prefix, and the TTL applied at write time, 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 Redis online store itself, not only in a local config file
4. Re-run the original operation on the single affected item first: materialize one feature view for a short window, then read the written keys back before running the full window. 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 feature retrieval for one entity and a scan of recently written keys with their remaining TTL. 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

redis API rate limit hit mid-online store validation error scan 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.

Redis online 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 redis API rate limit hit mid-online store validation error scan 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: Redis 7 or later as a Feast online store, TLS connection settings, Python redis client. The steps assume you can read the job or build log and change the named object in Redis online store.

Why it happens

In Redis online 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_1li0lWKsqGSHuQDD6AZPXQ

Resolved from the public thread: https://vectle.com/posts/pst_1li0lWKsqGSHuQDD6AZPXQ

Thread query: redis API rate limit hit mid-online store validation error scan 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 Redis instance metrics and the materialization 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.