--- title: Announcing Polars Cloud 0.10.0 | daily.dev description: Polars Cloud 0.10.0 adds sink_batches() to stream distributed query results directly into Python for custom processing, an experimental new distributed query... canonical: https://daily.dev/posts/announcing-polars-cloud-0-10-0-tyl1tmjgz twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Announcing Polars Cloud 0.10.0 | daily.dev og:description: Polars Cloud 0.10.0 adds sink_batches() to stream distributed query results directly into Python for custom processing, an experimental new distributed query... og:url: https://daily.dev/posts/announcing-polars-cloud-0-10-0-tyl1tmjgz og:image: https://api.daily.dev/og/posts/Tyl1tmjgZ.png og:image:alt: Announcing Polars Cloud 0.10.0 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. # Announcing Polars Cloud 0.10.0 **[Polars Blog](https://daily.dev/sources/polars)** · 4 min read · 0 upvotes · 0 comments ## Summary Polars Cloud 0.10.0 adds sink_batches() to stream distributed query results directly into Python for custom processing, an experimental new distributed query planner called Miso (not yet default but slated to become so), distributed pl.collect_all() for submitting multiple LazyFrames as one query with shared subplan elimination, experimental HDFS support for On-Prem scans and Iceberg metadata access, and hive-partition-aware scans that skip shuffles for partitioned joins and group_bys. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How do I stream distributed Polars Cloud query results into Python without writing to storage first? Use the new sink_batches() method, which takes a Python function and calls it with each batch of results as it becomes ready, for example lf.remote(ctx).sink_batches(lambda df: my_handler(df)). The chunk_size parameter controls how many rows are buffered before each call, and maintain_order defaults to False (unlike open source Polars sink_batches, which maintains order by default). Callbacks should be idempotent since batches can be delivered more than once on retries, and this path is slower than native sinks like sink_parquet. _Developers building custom result-handling logic can track Polars Cloud release notes like this on daily.dev._ ### What is the Miso query planner in Polars Cloud 0.10.0 and is it the default? Miso is a new experimental distributed query planner introduced in Polars Cloud 0.10.0, offered alongside the existing planner called naive. It is opt-in per query via planner="miso" and is not yet the default (planner="auto" still resolves to naive), though it is expected to become the default planner in the near future once more real-world feedback is gathered. _Teams evaluating new distributed query planners can follow updates like this one on daily.dev._ ### Can Polars Cloud On-Prem clusters read data from HDFS? Yes, as of Polars Cloud 0.10.0, On-Prem clusters can use HDFS as a storage backend for scans, with workers connecting to hdfs://host:port/ paths through a pure-Rust client that requires no JVM. Iceberg metadata can also be read over HDFS via a pyiceberg fsspec integration. Both features are off by default and require worker.extras configuration, Hadoop config directory access, and the hdfs-native and pyiceberg packages installed on workers. _Engineers running on-prem lakehouse setups can track infrastructure changes like this via daily.dev._ ## Similar posts on daily.dev - [Announcing Polars 1.44](https://daily.dev/posts/announcing-polars-1-44-nyx9iebfn) · Polars Blog · 1 upvotes · 0 comments - [Release of Polars 2.0](https://daily.dev/posts/release-of-polars-2-0-84cwf427y) · Polars Blog · 2 upvotes · 0 comments - [Apache Polaris with PyIceberg: A Guide](https://daily.dev/posts/apache-polaris-with-pyiceberg-a-guide-ykkijp45z) · Dremio Blog · 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), [#distributed-systems](https://daily.dev/tags/distributed-systems), [#polars](https://daily.dev/tags/polars) [View this post on daily.dev](https://daily.dev/posts/announcing-polars-cloud-0-10-0-tyl1tmjgz) ```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":"Announcing Polars Cloud 0.10.0","url":"https://daily.dev/posts/announcing-polars-cloud-0-10-0-tyl1tmjgz","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/announcing-polars-cloud-0-10-0-tyl1tmjgz"},"datePublished":"2026-08-16T08:53:21.969Z","dateModified":"2026-08-16T08:55:03.337Z","description":"Polars Cloud 0.10.0 adds sink_batches() to stream distributed query results directly into Python for custom processing, an experimental new distributed query...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/ee6c52ddf9746da7e0dcf9689477cd35?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/ee6c52ddf9746da7e0dcf9689477cd35?_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/announcing-polars-cloud-0-10-0-tyl1tmjgz","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"python,big-data,data-engineering,distributed-systems,polars","timeRequired":"PT4M"} {"@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":"Announcing Polars Cloud 0.10.0"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/announcing-polars-cloud-0-10-0-tyl1tmjgz#faq","mainEntity":[{"@type":"Question","name":"How do I stream distributed Polars Cloud query results into Python without writing to storage first?","acceptedAnswer":{"@type":"Answer","text":"Use the new sink_batches() method, which takes a Python function and calls it with each batch of results as it becomes ready, for example lf.remote(ctx).sink_batches(lambda df: my_handler(df)). 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