---
title: "Comment by max on \"From Failure to Flow: How I Used Polars to Conquer Memory Issues in Our Data Pipelines\" | Hashnode"
description: "Great writeup! I hit similar memory walls running data pipelines on a Mac Mini with 64GB unified memory. The lazy evaluation pattern in Polars was a game-change"
url: "https://hashnode.com/posts/from-failure-to-flow-how-i-used-polars-to-conquer-memory-issues-in-our-data-pipelines/680d273bc4573c303781167c/comment/69d050843301ed14a6fd3253"
---

[Discussion](https://hashnode.com/posts/from-failure-to-flow-how-i-used-polars-to-conquer-memory-issues-in-our-data-pipelines/680d273bc4573c303781167c)

Comment [From Failure to Flow: How I Used Polars to Conquer Memory Issues in Our Data Pipelines](https://hashnode.com/posts/from-failure-to-flow-how-i-used-polars-to-conquer-memory-issues-in-our-data-pipelines/680d273bc4573c303781167c)

[max](https://hashnode.com/@maxxmini)

[Apr 3](https://hashnode.com/posts/from-failure-to-flow-how-i-used-polars-to-conquer-memory-issues-in-our-data-pipelines/680d273bc4573c303781167c/comment/69d050843301ed14a6fd3253)

Great writeup! I hit similar memory walls running data pipelines on a Mac Mini with 64GB unified memory. The lazy evaluation pattern in Polars was a game-changer for me too — especially when processing hundreds of JSON files from multiple API sources simultaneously.

One thing I found helpful was combining Polars with streaming writes, so I never hold more than one chunk in memory at a time. Did you experiment with scan\_parquet for the initial reads, or were you always loading from raw CSVs?

Also curious if you benchmarked Polars streaming mode vs the regular collect — in my case, streaming cut peak memory by ~60% on larger datasets.