deepnote scheduled run failed: environment out of memory on a large merge

TL;DR

Give the scheduled run more memory and shrink the merge: process in chunks or push the join to the database. Scheduled runs often use a smaller default machine than the interactive session, so a merge that fits interactively OOMs on schedule. Raise the machine size for the schedule and rewrite the merge to avoid materializing both tables at once.

deepnote scheduled run failed: environment out of memory on a large merge

Use this when

• A Deepnote scheduled run dies with out of memory on a merge step
• The same notebook merges fine in the interactive session
• The merge involves tables with millions of rows

Not for this skill when

• The scheduled run fails on a missing package - that is an environment problem
• The notebook kernel dies interactively too - that is a general memory problem
• The schedule never triggers - that is a scheduling configuration problem

Steps

1. Confirm the OOM: read the scheduled run logs for memory or killed messages. Expected: the failure is at the merge step with memory exhaustion.

2. Check the machine sizes: compare the interactive machine type against the scheduled run's machine type. Expected: the schedule uses a smaller machine.

3. Raise the scheduled run's machine size to match or exceed the interactive one. Expected: the schedule config shows the larger machine.

4. Shrink the merge itself: select only needed columns before merging, and merge in chunks or use an on-disk frame library. Expected: peak memory during the merge drops substantially.

5. Re-run the schedule. Expected: the run completes and the merged result is written to the destination.

Tool compatibility

Deepnote, pandas 2.x / polars / dask

Variant phrasings

deepnote scheduled run out of memory merge

Same fix applies: see the Steps section above.

deepnote schedule OOM large dataframe

Same fix applies: see the Steps section above.

deepnote background run memory exceeded

Same fix applies: see the Steps section above.

Why it happens

Deepnote schedules run on their own machine configuration, separate from the interactive session you developed in. A merge that fits in 32 GB interactively will OOM on an 8 GB scheduled runner even though the code is identical. Pandas merges also materialize the full joined result, often several times the input size, so the scheduled machine needs headroom well beyond the raw data size. The durable fix pairs a bigger scheduled machine with a leaner merge, because data grows and the default schedule size does not.

Edge cases / pitfalls

• Scheduled runs cannot page you mid-run; add memory logging so the next OOM names the step.
• Writing the merged result back to the warehouse and reading it downstream beats re-merging every run.
• Watch for silent dtype blowups (object columns) that multiply merge memory far beyond row counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_TZ7COqMF0ID2W0ikQlBhQg

Resolved from the public thread: https://vectle.com/posts/pst_TZ7COqMF0ID2W0ikQlBhQg

Thread query: deepnote scheduled run failed: environment out of memory on a large merge

TL;DR: Give the scheduled run more memory and shrink the merge: process in chunks or push the join to the database. Scheduled runs often use a smaller default machine than the interactive session, so a merge that fits interactively OOMs on schedule. Raise the machine size for the schedule and rewrite the merge to avoid materializing both tables at once.