Gandiva: A LLVM-based Analytical Expression Compiler for Apache Arrow

Published 05 Dec 2018
By Jacques Nadeau ()

Today we’re happy to announce that the Gandiva Initiative for Apache Arrow, an LLVM-based execution kernel, is now part of the Apache Arrow project. Gandiva was kindly donated by Dremio, where it was originally developed and open-sourced. Gandiva extends Arrow’s capabilities to provide high performance analytical execution and is composed of two main components:

Gandiva works as follows: applications submit an expression tree to the compiler, built in a language agnostic protobuf-based expression representation. From there, Gandiva then compiles the expression tree to native code for the current runtime environment and hardware. Once compiled, the Gandiva execution kernel then consumes and produces Arrow columnar batches. The generated code is highly optimized for parallel processing on modern CPUs. For example, on AVX-128 processors Gandiva can process 8 pairs of 2 byte values in a single vectorized operation, and on AVX-512 processors Gandiva can process 4x as many values in a single operation. Gandiva is built from the ground up to understand Arrow’s in-memory representation and optimize processing against it.

While Gandiva is just starting within the Arrow community, it already supports hundreds of expressions, ranging from math functions to case statements. Gandiva was built as a standalone C++ library built on top of the core Apache Arrow codebase and was donated with C++ and Java APIs construction and execution APIs for projection and filtering operations. The Arrow community is already looking to expand Gandiva’s capabilities. This will include incorporating more operations and supporting many new language bindings. As an example, multiple community members are already actively building new language bindings that allow use of Gandiva within Python and Ruby.

While young within the Arrow community, Gandiva is already shipped and used in production by many Dremio customers as part of Dremio’s execution engine. Experiments have demonstrated 70x performance improvement on many SQL queries. We expect to see similar performance gains for many other projects that leverage Arrow.

The Arrow community is working to ship the first formal Apache Arrow release that includes Gandiva, and we hope this will be available within the next couple months. This should make it much easier for the broader analytics and data science development communities to leverage runtime code generation for high-performance data processing in a variety of contexts and projects.

We started the Arrow project a couple of years ago with the objective of creating an industry-standard columnar in-memory data representation for analytics. Within this short period of time, Apache Arrow has been adopted by dozens of both open source and commercial software products. Some key examples include technologies such as Apache Spark, Pandas, Nvidia RAPIDS, Dremio, and InfluxDB. This success has driven Arrow to now be downloaded more than 1 million times per month. Over 200 developers have already contributed to Apache Arrow. If you’re interested in contributing to Gandiva or any other part of the Apache Arrow project, feel free to reach out on the mailing list and join us!

For additional technical details on Gandiva, you can check out some of the following resources: