feast get_historical_features failed error point in time join produced nulls

TL;DR

Rebuild the join as an as-of join on event timestamp with created timestamp as the tie breaker, drop the label and any post outcome columns from the feature list, and compare offline and online values for the same entity and timestamp until they match. Start in the Feast registry 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.

feast get_historical_features failed error point in time join produced nulls

Use this when

• You see this exact message or symptom in Feast: feast gethistoricalfeatures failed error point in time join produced nulls
• 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

• Feast 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 Feast registry 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 feature_store.yaml, the feature view definition, the entity definition, and the TTL on the view, and check the event and created timestamp columns, the excluded label and post outcome columns, and an offline versus online comparison for one entity at one timestamp. 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: rebuild the join as an as-of join on event timestamp with created timestamp as the tie breaker, drop the label and any post outcome columns from the feature list, and compare offline and online values for the same entity and timestamp until they match. Verify: the corrected value or setting is visible in Feast itself, not only in a local config file
4. Re-run the original operation on the single affected item first: run feast apply, then feast materialize for a short window covering only the affected feature view. 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 retrieval for one known entity and a gethistoricalfeatures call 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

feast gethistoricalfeatures failed error point in time join produced nulls

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

Feast 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 feast gethistoricalfeatures failed error point in time join produced nulls 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 0.36 or later, Python feature definitions, Redis or DynamoDB online store, Parquet or warehouse offline store. The steps assume you can read the job or build log and change the named object in Feast.

Why it happens

In Feast, this failure usually means the join or the feature set lets information from after the prediction moment, or the label itself, into the features, or the online path computes a value the offline path computes differently. 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_riuY6VHK61T9LYrZFAouxQ