databricks API rate limit hit mid-feature backfill error job 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 Databricks workspace, the feature table page, and the job run history, 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.

databricks API rate limit hit mid-feature backfill error job failed

Use this when

• You see this exact message or symptom in Databricks Feature Store: databricks API rate limit hit mid-feature backfill error job 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

• Databricks 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 Databricks workspace, the feature table page, and the job run history 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 table primary key and timestamp column, the Delta table schema, and the FeatureStoreClient lookup spec, 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 Databricks 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 notebook or job that creates or writes the feature table for one partition, then republish to the online store. 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 FeatureStoreClient lookup for one known entity and a read of the Delta table rows for the same keys. 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

databricks API rate limit hit mid-feature backfill error job 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.

Databricks 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 databricks API rate limit hit mid-feature backfill error job 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: Databricks Runtime ML 13 or later, Unity Catalog feature tables, Delta Lake, FeatureStoreClient in Python. The steps assume you can read the job or build log and change the named object in Databricks Feature Store.

Why it happens

In Databricks 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-rHzQZeWjBMBAR0scWhDw