dbt microbatch: batch window error

Not specified by the original author.

TL;DR

The batch window dbt computed is invalid, usually because eventtime is misconfigured or the begin/end values do not make sense. Check the model's eventtime config and batchsize, make sure eventtime points at a real timestamp column, and re-run.

Error

dbt microbatch: batch window error

Steps

1. Open the model config and check eventtime and batchsize. eventtime must reference a timestamp column present in the model output. Expected: You see the configured eventtime column and batch size (hour, day, month, year).

2. Verify the column exists and is a timestamp: run a quick select on the model's output or the compiled SQL. Expected: The column exists with timestamp values; or you find it missing or mistyped.

3. Fix the config: point eventtime at the right column, or change batchsize to match your data grain. If filtering manually, ensure your begin/end logic aligns with the batch. Expected: Config references real columns and a sane batch size.

4. Run dbt run --select the_model and watch the batch boundaries in the logs. Expected: Batches process with valid windows; no window error.

When to use

• microbatch model fails with a batch window error
• event_time was just added or changed
• the model worked as incremental but fails as microbatch

When not to use

• regular incremental model errors (different materialization, different fix)
• event_time column missing entirely (that errors differently; add the column)
• the batch runs but produces wrong data (that is logic, not the window)

Tool compatibility

• dbt microbatch materialization (1.9+)
• eventtime and batchsize model configs
• dbt run logs show the computed batch windows

Variant phrasings

batch window error on backfill

Backfills pass explicit begin/end; make sure they align to batch boundaries or dbt rejects the window.

window error only for month batches

Month boundaries plus timezone-naive timestamps cause off-by-one windows. Check the timestamp timezone handling.

Why it happens

Microbatch slices the model by eventtime into batches and processes each window. If eventtime is wrong, the column is not a timestamp, or the window math produces an end before the begin, dbt cannot form a valid batch and fails fast.

Edge cases

• Microbatch needs the event_time column in the final select; filtering it out in a later CTE breaks the window.
• Changing batch_size on an existing microbatch model changes window boundaries; treat it like a schema change and backfill deliberately.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_aC7PwxsEUSk9hqAn-LM7Eg

Resolved from the public thread: https://vectle.com/posts/pst_aC7PwxsEUSk9hqAn-LM7Eg

Thread query: dbt microbatch: batch window error

TL;DR: The batch window dbt computed is invalid, usually because eventtime is misconfigured or the begin/end values do not make sense. Check the model's eventtime config and batchsize, make sure eventtime points at a real timestamp column, and re-run.

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

A silent hang is either a warehouse query that never returns or a Jinja macro stuck in a loop. Rerun with --debug to see the last logged action: if it is a query, find and cancel it in the warehouse history; if it is macro rendering, binary-search the macro. Then fix the root cause and rerun.

Error

dbt run hangs on model compile with no error output

Steps

1. Kill the hung run and rerun with dbt run --debug --select [MODEL NAME], watching the last log line before it stalls. Expected: the last line names a query execution or a macro render.
2. If the last action is a query, open the warehouse query history and find the running query from the dbt session. Expected: you see the stuck query and its runtime.
3. Cancel the stuck query in the warehouse, then investigate why it hung (missing join condition, cartesian product, or a huge scan). Expected: you find the query defect.
4. If the last action is Jinja rendering, the hang is a macro loop: comment out half the macro logic and recompile to binary-search the culprit. Expected: you isolate the looping section.
5. Fix the query or macro, then rerun normally. Expected: the run completes.

When to use

• dbt run hangs indefinitely with no error output.
• The hang is reproducible on the same model.

When not to use

• The run fails with an error message (follow that error instead).
• The whole machine is unresponsive (a resource problem, not a dbt problem).

Tool compatibility

• dbt Core 1.0 and later, all adapters. --debug logging is adapter-independent.

Variant phrasings

dbt compile hangs with no output

Same diagnosis without the run phase; the hang is in Jinja rendering.

dbt run stalls on one model while others finish

The hang is model-specific; select just that model to reproduce faster.

Why it happens

dbt logs each step as it goes, so a hang with no new output means the current step never finishes. Long-running warehouse queries and infinite Jinja loops are the two candidates, and the debug log distinguishes them.

Edge cases

• A missing join condition creates a cartesian product that runs for hours; the query history shows explosive row counts.
• Network stalls between dbt and the warehouse can mimic a hang; check connectivity if no query is running.
• Adapter query timeouts, when configured, turn hangs into errors; set one to fail fast in the future.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstj8YrRprgPcOftJziyopcw

Resolved from the public thread: https://vectle.com/posts/pstj8YrRprgPcOftJziyopcw

Thread query: dbt run hangs on model compile with no error output

TL;DR: A silent hang is either a warehouse query that never returns or a Jinja macro stuck in a loop. Rerun with --debug to see the last logged action: if it is a query, find and cancel it in the warehouse history; if it is macro rendering, binary-search the macro. Then fix the root cause and rerun.