Introduction¶

We welcome and encourage contributions of all kinds, from all levels, such as:

Tickets with issue reports or feature requests
Discussions
Documentation improvements
Code, both PR and (especially) PR Review.

In addition to submitting new PRs, we have a healthy tradition of community members reviewing each other’s PRs. Doing so is a great way to help the community as well as get more familiar with Rust and the relevant codebases.

Finding and Creating Issues to Work On¶

You can find a curated good-first-issue list to help you get started.

DataFusion is an open contribution project, and thus there is no particular project imposed deadline for completing any issue or any restriction on who can work on an issue, nor how many people can work on an issue at the same time.

Contributors drive the project forward based on their own priorities and interests and thus you are free to work on any issue that interests you.

If someone is already working on an issue that you want or need but hasn’t been able to finish it yet, you should feel free to work on it as well. In general it is both polite and will help avoid unnecessary duplication of work if you leave a note on an issue when you start working on it.

If you want to work on an issue which is not already assigned to someone else and there are no comment indicating that someone is already working on that issue then you can assign the issue to yourself by submitting a single word comment take. This will assign the issue to yourself. However, if you are unable to make progress you should unassign the issue by using the unassign me link at the top of the issue page (and ask for help if are stuck) so that someone else can get involved in the work.

If you plan to work on a new feature that doesn’t have an existing ticket, it is a good idea to open a ticket to discuss the feature. Advanced discussion often helps avoid wasted effort by determining early if the feature is a good fit for DataFusion before too much time is invested. It also often helps to discuss your ideas with the community to get feedback on implementation.

Developer’s guide¶

Pull Request Overview¶

We welcome pull requests (PRs) from anyone from the community.

DataFusion is a very active fast-moving project and we try to review and merge PRs quickly to keep the review backlog down and the pace up. After review and approval, one of the many people with commit access will merge your PR.

Review bandwidth is currently our most limited resource, and we highly encourage reviews by the broader community. If you are waiting for your PR to be reviewed, consider helping review other PRs that are waiting. Such review both helps the reviewer to learn the codebase and become more expert, as well as helps identify issues in the PR (such as lack of test coverage), that can be addressed and make future reviews faster and more efficient.

Creating Pull Requests¶

We recommend splitting your contributions into smaller PRs rather than large PRs (500+ lines) because:

The PR is more likely to be reviewed quickly – our reviewers struggle to find the contiguous time needed to review large PRs.
The PR discussions tend to be more focused and less likely to get lost among several different threads.
It is often easier to accept and act on feedback when it comes early on in a small change, before a particular approach has been polished too much.

If you are concerned that a larger design will be lost in a string of small PRs, creating a large draft PR that shows how they all work together can help.

Reviewing Pull Requests¶

When reviewing PRs, please remember our primary goal is to improve DataFusion and its community together. PR feedback should be constructive with the aim to help improve the code as well as the understanding of the contributor.

Please ensure any issues you raise contains a rationale and suggested alternative – it is frustrating to be told “don’t do it this way” without any clear reason or alternate provided.

Some things to specifically check:

Is the feature or fix covered sufficiently with tests (see Test Organization below)?
Is the code clear, and fits the style of the existing codebase?

“Major” and “Minor” PRs¶

Since we are a worldwide community, we have contributors in many timezones who review and comment. To ensure anyone who wishes has an opportunity to review a PR, our committers try to ensure that at least 24 hours passes between when a “major” PR is approved and when it is merged.

A “major” PR means there is a substantial change in design or a change in the API. Committers apply their best judgment to determine what constitutes a substantial change. A “minor” PR might be merged without a 24 hour delay, again subject to the judgment of the committer. Examples of potential “minor” PRs are:

Documentation improvements/additions
Small bug fixes
Non-controversial build-related changes (clippy, version upgrades etc.)
Smaller non-controversial feature additions

The good thing about open code and open development is that any issues in one change can almost always be fixed with a follow on PR.

Stale PRs¶

Pull requests will be marked with a stale label after 60 days of inactivity and then closed 7 days after that. Commenting on the PR will remove the stale label.

Getting Started¶

This section describes how you can get started at developing DataFusion.

Windows setup¶

wget https://az792536.vo.msecnd.net/vms/VMBuild_20190311/VirtualBox/MSEdge/MSEdge.Win10.VirtualBox.zip
choco install -y git rustup.install visualcpp-build-tools
git-bash.exe
cargo build

Protoc Installation¶

Compiling DataFusion from sources requires an installed version of the protobuf compiler, protoc.

On most platforms this can be installed from your system’s package manager

# Ubuntu
$ sudo apt install -y protobuf-compiler

# Fedora
$ dnf install -y protobuf-devel

# Arch Linux
$ pacman -S protobuf

# macOS
$ brew install protobuf

You will want to verify the version installed is 3.12 or greater, which introduced support for explicit field presence. Older versions may fail to compile.

$ protoc --version
libprotoc 3.12.4

Alternatively a binary release can be downloaded from the Release Page or built from source.

Bootstrap environment¶

DataFusion is written in Rust and it uses a standard rust toolkit:

cargo build
cargo fmt to format the code
cargo test to test
etc.

Note that running cargo test requires significant memory resources, due to cargo running many tests in parallel by default. If you run into issues with slow tests or system lock ups, you can significantly reduce the memory required by instead running cargo test -- --test-threads=1. For more information see this issue.

Testing setup:

rustup update stable DataFusion uses the latest stable release of rust
git submodule init
git submodule update

Formatting instructions:

or run them all at once:

dev/rust_lint.sh

Testing¶

Tests are critical to ensure that DataFusion is working properly and is not accidentally broken during refactorings. All new features should have test coverage.

DataFusion has several levels of tests in its Test Pyramid and tries to follow the Rust standard Testing Organization in the The Book.

Unit tests¶

Tests for code in an individual module are defined in the same source file with a test module, following Rust convention.

sqllogictests Tests¶

DataFusion’s SQL implementation is tested using sqllogictest which are run like any other Rust test using cargo test --test sqllogictests.

sqllogictests tests may be less convenient for new contributors who are familiar with writing .rs tests as they require learning another tool. However, sqllogictest based tests are much easier to develop and maintain as they 1) do not require a slow recompile/link cycle and 2) can be automatically updated via cargo test --test sqllogictests -- --complete.

Like similar systems such as DuckDB, DataFusion has chosen to trade off a slightly higher barrier to contribution for longer term maintainability.

Rust Integration Tests¶

There are several tests of the public interface of the DataFusion library in the tests directory.

You can run these tests individually using cargo as normal command such as

cargo test -p datafusion --test parquet_exec

Benchmarks¶

Criterion Benchmarks¶

Criterion is a statistics-driven micro-benchmarking framework used by DataFusion for evaluating the performance of specific code-paths. In particular, the criterion benchmarks help to both guide optimisation efforts, and prevent performance regressions within DataFusion.

Criterion integrates with Cargo’s built-in benchmark support and a given benchmark can be run with

cargo bench --bench BENCHMARK_NAME

A full list of benchmarks can be found here.

cargo-criterion may also be used for more advanced reporting.

Parquet SQL Benchmarks¶

The parquet SQL benchmarks can be run with

 cargo bench --bench parquet_query_sql

These randomly generate a parquet file, and then benchmark queries sourced from parquet_query_sql.sql against it. This can therefore be a quick way to add coverage of particular query and/or data paths.

If the environment variable PARQUET_FILE is set, the benchmark will run queries against this file instead of a randomly generated one. This can be useful for performing multiple runs, potentially with different code, against the same source data, or for testing against a custom dataset.

The benchmark will automatically remove any generated parquet file on exit, however, if interrupted (e.g. by CTRL+C) it will not. This can be useful for analysing the particular file after the fact, or preserving it to use with PARQUET_FILE in subsequent runs.

Comparing Baselines¶

By default, Criterion.rs will compare the measurements against the previous run (if any). Sometimes it’s useful to keep a set of measurements around for several runs. For example, you might want to make multiple changes to the code while comparing against the master branch. For this situation, Criterion.rs supports custom baselines.

 git checkout main
 cargo bench --bench sql_planner -- --save-baseline main
 git checkout YOUR_BRANCH
 cargo bench --bench sql_planner --  --baseline main

Note: For MacOS it may be required to run cargo bench with sudo

sudo cargo bench ...

More information on Baselines

Upstream Benchmark Suites¶

Instructions and tooling for running upstream benchmark suites against DataFusion can be found in benchmarks.

These are valuable for comparative evaluation against alternative Arrow implementations and query engines.

HOWTOs¶

How to add a new scalar function¶

Below is a checklist of what you need to do to add a new scalar function to DataFusion:

Add the actual implementation of the function to a new module file within:
- here for array functions
- here for crypto functions
- here for datetime functions
- here for encoding functions
- here for math functions
- here for regex functions
- here for string functions
- here for unicode functions
- create a new module here for other functions.
New function modules - for example a vector module, should use a rust feature (for example vector_expressions) to allow DataFusion users to enable or disable the new module as desired.
The implementation of the function is done via implementing ScalarUDFImpl trait for the function struct.
- See the advanced_udf.rs example for an example implementation
- Add tests for the new function
To connect the implementation of the function add to the mod.rs file:
- a mod xyz; where xyz is the new module file
- a call to make_udf_function!(..);
- an item in export_functions!(..);
In sqllogictest/test_files, add new sqllogictest integration tests where the function is called through SQL against well known data and returns the expected result.
- Documentation for sqllogictest here
Add SQL reference documentation here

How to add a new aggregate function¶

Below is a checklist of what you need to do to add a new aggregate function to DataFusion:

Add the actual implementation of an Accumulator and AggregateExpr:
- here for string functions
- here for math functions
- here for datetime functions
- create a new module here for other functions
In datafusion/expr/src, add:
- a new variant to AggregateFunction
- a new entry to FromStr with the name of the function as called by SQL
- a new line in return_type with the expected return type of the function, given an incoming type
- a new line in signature with the signature of the function (number and types of its arguments)
- a new line in create_aggregate_expr mapping the built-in to the implementation
- tests to the function.
In sqllogictest/test_files, add new sqllogictest integration tests where the function is called through SQL against well known data and returns the expected result.
- Documentation for sqllogictest here
Add SQL reference documentation here

How to display plans graphically¶

The query plans represented by LogicalPlan nodes can be graphically rendered using Graphviz.

To do so, save the output of the display_graphviz function to a file.:

// Create plan somehow...
let mut output = File::create("/tmp/plan.dot")?;
write!(output, "{}", plan.display_graphviz());

Then, use the dot command line tool to render it into a file that can be displayed. For example, the following command creates a /tmp/plan.pdf file:

dot -Tpdf < /tmp/plan.dot > /tmp/plan.pdf

Specifications¶

We formalize some DataFusion semantics and behaviors through specification documents. These specifications are useful to be used as references to help resolve ambiguities during development or code reviews.

You are also welcome to propose changes to existing specifications or create new specifications as you see fit.

Here is the list current active specifications:

All specifications are stored in the docs/source/specification folder.

How to format `.md` document¶

We are using prettier to format .md files.

You can either use npm i -g prettier to install it globally or use npx to run it as a standalone binary. Using npx required a working node environment. Upgrading to the latest prettier is recommended (by adding --upgrade to the npm command).

$ prettier --version
2.3.0

After you’ve confirmed your prettier version, you can format all the .md files:

prettier -w {datafusion,datafusion-cli,datafusion-examples,dev,docs}/**/*.md

How to format `.toml` files¶

We use taplo to format .toml files.

For Rust developers, you can install it via:

cargo install taplo-cli --locked

Refer to the Installation section on other ways to install it.

$ taplo --version
taplo 0.9.0

After you’ve confirmed your taplo version, you can format all the .toml files:

taplo fmt

Profiling Cookbook

Communication