pandas

Ramp · Jul 22, 2026

Apache Arrow Cut Snowflake Fetch Memory Growth by Up to 79%

An ML training run was exhausting worker memory before training could start because a cold Snowflake fetch peaked at 88.8 GiB RSS, versus 17.6 GiB when loading local Parquet. The shared Snowflake-to-pandas path created Python objects and SQLAlchemy Row proxies before pandas built the DataFrame, with temporary representations requiring roughly three times the final DataFrame size at peak. An Apache Arrow path for supported reads reduced median peak process RSS growth by 60–79% across three query shapes, while peak Python allocations fell by 69–87%. The narrow and wide tests also ran faster, but aggregate timing was mixed, so elapsed time was not the headline claim. Existing jobs adopted the default without query rewrites, with normalization preserving the pandas contract and SQLAlchemy retained for unsupported reads; in one workflow, lower fetch peaks doubled the training-data window on the same cluster size.