--- title: DuckDB vs. Polars: Performance & Memory on Parquet Data description: DuckDB and Polars were benchmarked on datasets up to 2 TB to compare performance and memory usage under extreme conditions. DuckDB maintained peak memory below... canonical: https://daily.dev/posts/zfsa4pkk6 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: DuckDB vs. Polars: Performance & Memory on Parquet Data | daily.dev og:description: DuckDB and Polars were benchmarked on datasets up to 2 TB to compare performance and memory usage under extreme conditions. DuckDB maintained peak memory below... og:url: https://daily.dev/posts/zfsa4pkk6 og:image: https://api.daily.dev/og/posts/ZFsa4PKK6.png og:image:alt: Post cover image og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # DuckDB vs. Polars: Performance & Memory on Parquet Data **[DuckDB dev](https://daily.dev/sources/duckdbdev)** · [@mehdio](https://daily.dev/mehdio) · 3 upvotes · 0 comments ## Summary DuckDB and Polars were benchmarked on datasets up to 2 TB to compare performance and memory usage under extreme conditions. DuckDB maintained peak memory below 2.5 GB across all tests while Polars consumed up to 17 GB on large single files due to mmap-based I/O, though this dropped to 4.3 GB with partitioned data. Polars with forced async mode achieved DuckDB-like memory efficiency (750 MB) but sacrificed execution speed. File layout proved critical: partitioning the same 140 GB dataset into smaller files reduced DuckDB's memory by 8× and Polars' by 4×. Row group size optimization improved performance by 2.8× for Polars and 1.6× for DuckDB. The choice between engines depends on memory constraints and data organization, with DuckDB favoring memory-constrained environments and Polars excelling with well-partitioned data on memory-abundant systems. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Polars vs DuckDB vs Pandas: Benchmark on 33 Million Rows](https://daily.dev/posts/polars-vs-duckdb-vs-pandas-benchmark-on-33-million-rows-p8chasjqs) · Medium · 0 upvotes · 0 comments - [650GB of Data \(Delta Lake on S3\). 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Put that laptop hardware to use](https://daily.dev/posts/crunching-data-put-that-laptop-hardware-to-use-1fcnht8lv) · Simple Thread · 0 upvotes · 0 comments --- Tags: [#big-data](https://daily.dev/tags/big-data), [#data-engineering](https://daily.dev/tags/data-engineering), [#duckdb](https://daily.dev/tags/duckdb), [#performance](https://daily.dev/tags/performance), [#polars](https://daily.dev/tags/polars) [View this post on daily.dev](https://daily.dev/posts/zfsa4pkk6) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"DiscussionForumPosting","mainEntityOfPage":"https://daily.dev/posts/zfsa4pkk6","headline":"DuckDB vs. Polars: Performance \u0026 Memory on Parquet Data","text":"Shared: DuckDB vs. Polars: Performance \u0026 Memory on Parquet Data","url":"https://daily.dev/posts/zfsa4pkk6","datePublished":"2026-02-02T10:43:33.314Z","dateModified":"2026-10-14T02:26:51.744Z","author":{"@type":"Person","name":"mehdio","url":"https://daily.dev/mehdio","image":"https://avatars.githubusercontent.com/u/19834862?v=4","interactionStatistic":{"@type":"InteractionCounter","interactionType":{"@type":"EndorseAction"},"userInteractionCount":560}},"image":"https://media.daily.dev/image/upload/s--OHB84bZF--/f_auto/v1722860399/public/Placeholder%2010","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":3},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"sharedContent":{"@type":"WebPage","url":"https://api.daily.dev/r/njUNKR8lf"},"isPartOf":{"@type":"WebPage","url":"https://daily.dev/squads/duckdbdev","name":"DuckDB dev"}} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"DuckDB dev","item":"https://daily.dev/squads/duckdbdev"},{"@type":"ListItem","position":3,"name":"DuckDB vs. Polars: Performance \u0026 Memory on Parquet Data"}]} ```