ExecuteMany performance is insanely bad

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
20/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python, sql
Domain
data-engineering, databases

Research direction

Start by locating the connector's executemany implementation and reading the documented behavior that describes it as a naive loop. Investigate how a single SQL statement using VALUES(...) could support the requested batching for writes to a Delta table, and define completion around improved performance for non-trivial dataframe loads.

Written by the indexing model from the issue text.

Description

I know that the documentation makes it clear that executemany is a naive for loop:

No optimizations of the query (like batching) will be performed.

But it's 2025 and please have a more optimized executemany that just issues a single SQL statement using VALUES(...) or something so this is actually usable in a data pipeline. Otherwise, trying to use the databricks SQL connector to write any non-trivial dataframe to a delta table is pointless.

Thanks!

Dominant language
Python
Stars
233
Forks
152
Avg merge
21h 5m
Merged PRs (30d)
10

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