## TL;DR
Declare the partitioning columns in the feature set config, normally the event date column the source is written with, and confirm that column exists in the loaded dataframe, because Butterfree writes partitioned output and refuses to run a load with no partition columns defined. Start with the Butterfree feature set config and the source dataframe schema, 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.

```text
Butterfree feature set load failed error "Spark dataframe has no partitioning columns"
```

## Use this when
- You see this exact message or symptom: Butterfree feature set load failed error "Spark dataframe has no partitioning columns"
- 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 Butterfree feature set config and the source dataframe schema, 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 feature set config partitioning setting, the source dataframe columns, and the sink path layout the load writes to. 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: Declare the partitioning columns in the feature set config, normally the event date column the source is written with, and confirm that column exists in the loaded dataframe, because Butterfree writes partitioned output and refuses to run a load with no partition columns defined. 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
### Butterfree feature set load failed error "Spark dataframe has no partitioning columns"
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: Butterfree feature set pipelines on Spark, partitioned Parquet or Delta sinks.

## Why it happens
The config was usually copied from an unpartitioned example. The dataframe often does have the date column, it is simply never named in the config, so the load fails before any data moves.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_8Cf1hoUDKoueLz3eI-DMnA