We are proud to announce the release of DataFusion 51.0.0. This post highlights some of the major improvements since DataFusion 50.0.0. The complete list of changes is available in the changelog. Thanks to the 128 contributors for making this release possible.
We continue …
The Apache DataFusion PMC is pleased to announce version 0.11.0 of the Comet subproject.
Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for improved performance and efficiency without requiring any code changes.
This release covers approximately five weeks of development …
We are proud to announce the release of DataFusion 50.0.0. This blog post highlights some of the major improvements since the release of DataFusion 49.0.0. The complete list of changes is available in the changelog. Thanks to numerous contributors for making this release possible!
Apache DataFusion significantly improves support for user defined types and metadata. The user defined function APIs let users access metadata on the input columns to functions and produce metadata in the output.
DataFusion directly uses Apache Arrow's DataTypes as its type system. This has …
The Apache DataFusion PMC is pleased to announce version 0.10.0 of the Comet subproject.
Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for improved performance and efficiency without requiring any code changes.
This release covers approximately ten weeks of development …
This blog post introduces the query engine optimization techniques called TopK and dynamic filters. We describe the motivating use case, how these optimizations work, and how we implemented them with the Apache DataFusion community to improve performance by an order of magnitude for some query patterns.
The …
It is a common misconception that Apache Parquet requires (slow) reparsing of metadata and is limited to indexing structures provided by the format. In fact, caching parsed metadata and using custom external indexes along with Parquet's hierarchical data organization can significantly speed up query processing.
In this blog, I describe …
We are proud to announce the release of DataFusion 49.0.0. This blog post highlights some of the major improvements since the release of DataFusion 48.0.0. The complete list of changes is available in the changelog.
DataFusion continues to focus on enhancing performance, as …
We’re excited to announce the release of Apache DataFusion 48.0.0! As always, this version packs in a wide range of improvements and fixes. You can find the complete details in the full changelog. We’ll highlight the most important changes below and guide you through upgrading.
It’s a common misconception that Apache Parquet files are limited to basic Min/Max/Null Count statistics and Bloom filters, and that adding more advanced indexes requires changing the specification or creating a new file format. In fact, footer metadata and offset-based addressing already provide everything needed to embed …
We’re excited to announce the release of Apache DataFusion 47.0.0! This new version represents a significant milestone for the project, packing in a wide range of improvements and fixes. You can find the complete details in the full changelog. We’ll highlight the most important changes below …
The Apache DataFusion PMC is pleased to announce version 0.9.0 of the Comet subproject.
Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for improved performance and efficiency without requiring any code changes.
This release covers approximately ten weeks of development …
Have you ever tried to cancel a query that just wouldn't stop?
In this post, we'll review how Rust's async programming model works, how …
Note: this blog was originally published on the InfluxData blog
Sometimes Query Optimizers are seen as a sort of black magic, “the most challenging problem in computer science,” according to Father Pavlo, or some behind-the-scenes player. We believe this perception is because:
One must implement the rest of a …
Note, this blog was originally published on the InfluxData blog.
In the first part of this post, we discussed what a Query Optimizer is, what role it plays, and described how industrial optimizers are organized. In this second post, we describe various optimizations that are found in Apache DataFusion and …
The Apache DataFusion PMC is pleased to announce version 0.8.0 of the Comet subproject.
Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for improved performance and efficiency without requiring any code changes.
This release covers approximately six weeks of development …
Window functions are a powerful feature in SQL, allowing for complex analytical computations over a subset of data. However, efficiently implementing them, especially sliding windows, can be quite challenging. With Apache DataFusion's user-defined window functions, developers can easily take advantage of all the effort put into DataFusion's implementation.
In …
TLDR: TPC-H SF=100 in 1min using tpchgen-rs vs 30min+ with dbgen.
3 members of the Apache DataFusion community used Rust and open source development to build tpchgen-rs, a fully open TPC-H data generator over …
We are happy to announce that datafusion-python 46.0.0 has been released. This release brings in all of the new features of the core DataFusion 46.0.0 library. Since the last blog post for datafusion-python 43.1.0, a large number of improvements have been made that can …
We’re excited to announce the release of Apache DataFusion 46.0.0! This new version represents a significant milestone for the project, packing in a wide range of improvements and fixes. You can find the complete details in the full changelog. We’ll highlight the most important changes below …
Editor's Note: This blog was first published on Xiangpeng Hao's blog. Thanks to InfluxData for sponsoring this work as part of his PhD funding.
In the previous post …
The Apache DataFusion PMC is pleased to announce version 0.7.0 of the Comet subproject.
Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for improved performance and efficiency without requiring any code changes.
Comet runs on commodity hardware and aims to …
Editor's Note: This blog was first published on Xiangpeng Hao's blog. Thanks to InfluxData for sponsoring this work as part of his PhD funding.
Apache Parquet has become the industry standard for storing columnar data, and reading Parquet efficiently -- especially from remote storage -- is crucial for query performance.
In this blog post, we explain when an ordering requirement of an operator is satisfied by its input data. This analysis is essential for order-based optimizations and is often more complex than one might initially think.
Ordering Requirement for an operator describes how the input data to that operator …
We are very proud to announce DataFusion 45.0.0. This blog highlights some of the many major improvements since we released DataFusion 40.0.0 and a preview of what the community is thinking about in the next 6 months. It has been an exciting period of development …
The Apache DataFusion PMC is pleased to announce version 0.6.0 of the Comet subproject.
Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for improved performance and efficiency without requiring any code changes.
Comet runs on commodity hardware and aims to …
We are pleased to announce version 43.0.0 of the DataFusion Ballista. Ballista allows existing DataFusion applications to be scaled out on a cluster for use cases that are not practical to run on a single node.
The primary objective of …
The Apache DataFusion PMC is pleased to announce version 0.5.0 of the Comet subproject.
Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for improved performance and efficiency without requiring any code changes.
Comet runs on commodity hardware and aims to …
We are happy to announce that datafusion-python 43.1.0 has been released. This release brings in all of the new features of the core DataFusion 43.0.0 library. Since the last blog post for datafusion-python 40.1.0, a large number of improvements have been made that can …
The Apache DataFusion PMC is pleased to announce version 0.4.0 of the Comet subproject.
Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for improved performance and efficiency without requiring any code changes.
Comet runs on commodity hardware and aims to …
For a few months now I’ve been working with Apache DataFusion, a fast query engine written in Rust. From my experience the language that nearly all data scientists are working in is Python. In general, data scientists often use Pandas for in-memory tasks and PySpark for larger …
I am extremely excited to announce that Apache DataFusion is the fastest engine for querying Apache Parquet files in ClickBench. It is faster than DuckDB, chDB and Clickhouse using the same hardware. It also marks the first time a Rust-based engine holds the top spot, which has previously been …
The Apache DataFusion PMC is pleased to announce version 0.3.0 of the Comet subproject.
Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for improved performance and efficiency without requiring any code changes.
Comet runs on commodity hardware and aims to …
Editor's Note: This is the first of a two part blog series that was first published on the InfluxData blog. Thanks to InfluxData for sponsoring this work as Xiangpeng Hao's summer intern project
This blog describes our experience implementing StringView in the Rust implementation of Apache Arrow, and integrating …
Editor's Note: This blog series was first published on the InfluxData blog. Thanks to InfluxData for sponsoring this work as Xiangpeng Hao's summer intern project
In the first post, we discussed the nuances required to accelerate Parquet loading using StringViewArray by reusing buffers and reducing copies. In this second …
The Apache DataFusion PMC is pleased to announce version 0.2.0 of the Comet subproject.
Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for improved performance and efficiency without requiring any code changes.
Comet runs on commodity hardware and aims to …
We are happy to announce that DataFusion in Python 40.1.0 has been released. In addition to bringing in all of the new features of the core DataFusion 40.0.0 package, this release contains significant updates to the user interface and documentation. We listened to the python …
We are proud to announce DataFusion 40.0.0. This blog highlights some of the many major improvements since we released DataFusion 34.0.0 and a preview of what the community is thinking about in the next 6 months. We are hoping to make more regular blog posts …
The Apache DataFusion PMC is pleased to announce the first official source release of the Comet subproject.
Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for improved performance and efficiency without requiring any code changes.
Comet runs on commodity hardware and aims …
TLDR; Apache Arrow DataFusion --> Apache DataFusion
The Arrow PMC and newly created DataFusion PMC are happy to announce that as of April 16, 2024 the Apache Arrow DataFusion subproject is now a top level Apache Software Foundation project.
Apache DataFusion is a fast, extensible query engine for building …
The Apache Arrow PMC is pleased to announce the donation of the Comet project, a native Spark SQL Accelerator built on Apache Arrow DataFusion.
Comet is an Apache Spark plugin that uses Apache Arrow DataFusion to accelerate Spark workloads. It is designed as a drop-in replacement for Spark's JVM …
We recently released DataFusion 34.0.0. This blog highlights some of the major improvements since we released DataFusion 26.0.0 (spoiler alert there are many) and a preview of where the community plans to focus in the next 6 months.
Apache Arrow DataFusion is an extensible query …
Andrew Lamb, Daniël Heres, Raphael Taylor-Davies,
Note: this article was originally published on the InfluxData Blog
Grouped aggregations are a core part of any analytic tool, creating understandable summaries of huge data volumes. Apache Arrow DataFusion’s parallel aggregation capability …
It has been a whirlwind 6 months of DataFusion development since our last update: the community has grown, many features have been added, performance improved and we are discussing branching out to our own top level Apache Project.
Apache Arrow DataFusion is an extensible query engine and database toolkit …
DataFusion is an extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format. It is targeted primarily at developers creating data intensive analytics, and offers mature SQL support, a DataFrame API, and many extension points.
Systems based on DataFusion perform very well in benchmarks …
Ballista is an Arrow-native distributed SQL query engine implemented in Rust.
Ballista 0.9.0 is now available and is the most significant release since the project was donated to Apache Arrow in 2021.
This release represents 4 weeks of work, with 66 commits from 14 contributors:
22 Andy …Apache Arrow DataFusion 13.0.0 is released, and this blog contains an update on the project for the 5 months since our last update in May 2022.
DataFusion is an extensible and embeddable query engine, written in Rust used to create modern, fast and efficient data pipelines, ETL …
DataFusion is an extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format.
When you want to extend your Rust project with SQL support, a DataFrame API, or the ability to read and process Parquet, JSON, Avro or CSV data, DataFusion is definitely worth …
Apache Arrow DataFusion is an extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format.
When you want to extend your Rust project with SQL support, a DataFrame API, or the ability to read and process Parquet, JSON, Avro or CSV data, DataFusion is …
DataFusion is an extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format.
When you want to extend your Rust project with SQL support, a DataFrame API, or the ability to read and process Parquet, JSON, Avro or CSV data, DataFusion is definitely worth …
DataFusion is an embedded query engine which leverages the unique features of Rust and Apache Arrow to provide a system that is high performance, easy to connect, easy to embed, and high quality.
The Apache Arrow team is pleased to announce the DataFusion 6.0.0 release. This covers …
Ballista extends DataFusion to provide support for distributed queries. This is the first release of Ballista since the project was donated to the Apache Arrow project and includes 80 commits from 11 contributors.
git shortlog -sn 4.0.0..5.0.0 ballista/rust/client ballista/rust/core ballista/rust …The Apache Arrow team is pleased to announce the DataFusion 5.0.0 release. This covers 4 months of development work and includes 211 commits from the following 31 distinct contributors.
$ git shortlog -sn 4.0.0..5.0.0 datafusion datafusion-cli datafusion-examples
61 Jiayu Liu
47 Andrew Lamb
27 …We are excited to announce that Ballista has been donated to the Apache Arrow project.
Ballista is a distributed compute platform primarily implemented in Rust, and powered by Apache Arrow. It is built on an architecture that allows other programming languages (such as Python, C++, and Java) to be supported …
We are excited to announce that DataFusion has been donated to the Apache Arrow project. DataFusion is an in-memory query engine for the Rust implementation of Apache Arrow.
Although DataFusion was started two years ago, it was recently re-implemented to be Arrow-native and currently has limited capabilities but does support …