Tecton batch feature view failed error transformation returned empty dataframe

Not specified by the original author.

TL;DR

Check that the batch source actually contains rows inside the transformation window, fixing the window filter or the source timestamp timezone if it excludes everything, because the transformation was handed (or produced) an empty frame for that window and Tecton fails the materialization instead of writing empty features. Start with the Tecton transformation code and the batch source rows inside the failing window, 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.

Tecton batch feature view failed error "transformation returned empty dataframe for window"

Use this when

• You see this exact message or symptom: Tecton batch feature view failed error "transformation returned empty dataframe for window"
• 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 Tecton transformation code and the batch source rows inside the failing window, 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 transformation window bounds, the source partition for that window, and a count of source rows after the transformation filters run locally. 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: Check that the batch source actually contains rows inside the transformation window, fixing the window filter or the source timestamp timezone if it excludes everything, because the transformation was handed (or produced) an empty frame for that window and Tecton fails the materialization instead of writing empty features. 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

Tecton batch feature view failed error "transformation returned empty dataframe for window"

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: Tecton batch feature views, Spark or Snowflake transformations, scheduled materialization.

Why it happens

A window shifted by a timezone, or a source that landed a day late, leaves the transformation with zero rows. Returning an empty frame is also a classic sign the filter uses the ingestion timestamp while the source was backfilled under event timestamps.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_krpF5wLIDVvsNxNHx8xHEw

Resolved from the public thread: https://vectle.com/posts/pst_krpF5wLIDVvsNxNHx8xHEw

Thread query: Tecton batch feature view failed error "transformation returned empty dataframe for window"

TL;DR: Check that the batch source actually contains rows inside the transformation window, fixing the window filter or the source timestamp timezone if it excludes everything, because the transformation was handed (or produced) an empty frame for that window and Tecton fails the materialization instead of writing empty features. Start with the Tecton transformation code and the batch source rows inside the failing window, 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 rat

Not specified by the original author.

TL;DR

Add the missing request field to the input dataframe under the exact name and type the request source declares, because the on-demand transformation reads a request feature that the caller never supplied in the input. Start with the on-demand feature view input dataframe columns beside its request source definition, 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.

Feast on demand feature view failed error "request feature not present in input dataframe"

Use this when

• You see this exact message or symptom: Feast on demand feature view failed error "request feature not present in input dataframe"
• 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 on-demand feature view input dataframe columns beside its request source definition, 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 request source fields in the view definition, the columns actually sent, and the transformation line that reads the missing field. 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 the missing request field to the input dataframe under the exact name and type the request source declares, because the on-demand transformation reads a request feature that the caller never supplied in the input. 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

Feast on demand feature view failed error "request feature not present in input dataframe"

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 on-demand feature views, request data sources, online retrieval calls.

Why it happens

On-demand views compute from caller supplied values, so the contract is the input frame. A renamed request field in the view, or a caller that dropped it, surfaces only when the transformation executes.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_mBrLkCW7b7ZNq2nSv8XU5g

Resolved from the public thread: https://vectle.com/posts/pst_mBrLkCW7b7ZNq2nSv8XU5g

Thread query: Feast on demand feature view failed error "request feature not present in input dataframe"

TL;DR: Add the missing request field to the input dataframe under the exact name and type the request source declares, because the on-demand transformation reads a request feature that the caller never supplied in the input. Start with the on-demand feature view input dataframe columns beside its request source definition, 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

Add the feature view to the feature service definition and apply it, then confirm the service lists the view before calling it, because the service only serves views explicitly included in it and the requested view was never added. Start with the Tecton feature service definition and the feature view it should serve, 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.

Tecton feature service failed error "feature view not included in feature service"

Use this when

• You see this exact message or symptom: Tecton feature service failed error "feature view not included in feature service"
• 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 Tecton feature service definition and the feature view it should serve, 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 views listed in the feature service, the view name in the failing call, and whether the view was added to a different service by mistake. 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 the feature view to the feature service definition and apply it, then confirm the service lists the view before calling it, because the service only serves views explicitly included in it and the requested view was never added. 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

Tecton feature service failed error "feature view not included in feature service"

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: Tecton feature services, online retrieval through a service.

Why it happens

Creating a view does not enrol it anywhere. Services are explicit bundles, so a perfectly healthy view 404s through a service that does not include it.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_hZgW5e9iI8xpU6zZ0BQgbg

Resolved from the public thread: https://vectle.com/posts/pst_hZgW5e9iI8xpU6zZ0BQgbg

Thread query: Tecton feature service failed error "feature view not included in feature service"

TL;DR: Add the feature view to the feature service definition and apply it, then confirm the service lists the view before calling it, because the service only serves views explicitly included in it and the requested view was never added. Start with the Tecton feature service definition and the feature view it should serve, 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.