--- title: The power of predicate pushdown | daily.dev description: Predicate pushdown is explained as a core query optimization technique that applies filters as close to the data source as possible, reducing memory usage and... canonical: https://daily.dev/posts/the-power-of-predicate-pushdown-tm3wxscal twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The power of predicate pushdown | daily.dev og:description: Predicate pushdown is explained as a core query optimization technique that applies filters as close to the data source as possible, reducing memory usage and... og:url: https://daily.dev/posts/the-power-of-predicate-pushdown-tm3wxscal og:image: https://api.daily.dev/og/posts/Tm3wxSCal.png og:image:alt: The power of predicate pushdown 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. # The power of predicate pushdown **[Polars Blog](https://daily.dev/sources/polars)** · 6 min read · 0 upvotes · 0 comments ## Summary Predicate pushdown is explained as a core query optimization technique that applies filters as close to the data source as possible, reducing memory usage and I/O. Using Polars examples, the piece walks through how pushdown works for CSV files, then shows more advanced skipping techniques for Parquet (using row-group metadata min/max statistics), Hive partitioning (skipping entire files based on folder structure), and S3 Select (filtering directly on the storage server before data transfer). Each technique progressively reduces the amount of data read or transferred. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How does predicate pushdown work with parquet files to avoid reading unnecessary data? Predicate pushdown in parquet reads the file's metadata section first, which stores per-column statistics like minimum and maximum values for each row group. The query engine compares the filter condition against these min/max values; if a row group cannot satisfy the predicate, it is skipped entirely, avoiding the need to read that memory region from disk or network. _daily.dev surfaces techniques like this for engineers optimizing large-scale parquet-based data pipelines._ ### How does hive partitioning speed up filtering in Polars when scanning parquet datasets? Hive partitioning stores data in folders named after key column values, such as country=USA.parquet, so a query engine can skip entire files without reading their metadata if the folder name doesn't match the filter. In one example, scanning a dataset partitioned by country and filtering for GB skipped 192 files outright. The limitation is that pushdown only works for the pre-chosen partition key columns. _Teams choosing a partitioning strategy for their data lake can track these patterns on daily.dev._ ### What is S3 Select and why is it useful for filtering data stored in S3? S3 Select lets a query engine send a predicate directly to AWS S3, which applies the filter on the storage server before transferring data over the network, avoiding the cost of moving unneeded rows to the compute server. It was still in an early stage with notable restrictions, including no parquet output support. _daily.dev helps engineers evaluating cloud storage filtering options like S3 Select stay informed._ ## Similar posts on daily.dev - [Parquet Files Explained: Why Every Data Engineer should Use](https://daily.dev/posts/parquet-files-explained-why-every-data-engineer-should-use-mz7jytdxj) · Medium · 1 upvotes · 0 comments - [Postgres FDW: Pushdown is a negotiation](https://daily.dev/posts/postgres-fdw-pushdown-is-a-negotiation-1xsc48ccf) · ClickHouse · 7 upvotes · 0 comments - [Database Animations: What’s a Residual Predicate and Why Is It Bad?](https://daily.dev/posts/database-animations-what-s-a-residual-predicate-and-why-is-it-bad--4qsdi9yy5) · Brent Ozar · 0 upvotes · 0 comments - [The Problem with pandas Isn’t Performance. It’s Cognitive Overhead.](https://daily.dev/posts/the-problem-with-pandas-isn-t-performance-it-s-cognitive-overhead--0mg0wtkox) · Towards Data Science · 0 upvotes · 0 comments - [How ClickHouse makes Top-N queries faster with granule-level data skipping](https://daily.dev/posts/how-clickhouse-makes-top-n-queries-faster-with-granule-level-data-skipping-ebmuqp8e1) · ClickHouse · 0 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#big-data](https://daily.dev/tags/big-data), [#data-engineering](https://daily.dev/tags/data-engineering), [#polars](https://daily.dev/tags/polars) [View this post on daily.dev](https://daily.dev/posts/the-power-of-predicate-pushdown-tm3wxscal) ```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":"TechArticle","headline":"The power of predicate pushdown","url":"https://daily.dev/posts/the-power-of-predicate-pushdown-tm3wxscal","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-power-of-predicate-pushdown-tm3wxscal"},"datePublished":"2026-08-16T08:53:18.098Z","dateModified":"2026-08-16T08:54:18.474Z","description":"Predicate pushdown is explained as a core query optimization technique that applies filters as close to the data source as possible, reducing memory usage and...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/2f5d5778de7ff7efaacd41687809c7c0?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/2f5d5778de7ff7efaacd41687809c7c0?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Polars Blog","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Polars Blog","logo":"https://media.daily.dev/image/upload/s--xo60W3UW--/c_limit,w_256/f_auto,q_auto/v1786870390/logos/polars?_a=BAMAMicg0","url":"https://daily.dev/sources/polars"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/the-power-of-predicate-pushdown-tm3wxscal","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"python,big-data,data-engineering,polars","timeRequired":"PT6M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Polars Blog","item":"https://daily.dev/sources/polars"},{"@type":"ListItem","position":3,"name":"The power of predicate pushdown"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/the-power-of-predicate-pushdown-tm3wxscal#faq","mainEntity":[{"@type":"Question","name":"How does predicate pushdown work with parquet files to avoid reading unnecessary data?","acceptedAnswer":{"@type":"Answer","text":"Predicate pushdown in parquet reads the file's metadata section first, which stores per-column statistics like minimum and maximum values for each row group. 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