Ballista: Making DataFusion Applications Distributed

Ballista is a distributed query execution engine that enhances Apache DataFusion by enabling the parallelized execution of workloads across multiple nodes in a distributed environment.

Existing DataFusion application:

use datafusion::prelude::*;

#[tokio::main]
async fn main() -> datafusion::error::Result<()> {
    let ctx = SessionContext::new();

    // register the table
    ctx.register_csv("example", "tests/data/example.csv", CsvReadOptions::new())
        .await?;

    // create a plan to run a SQL query
    let df = ctx
        .sql("SELECT a, MIN(b) FROM example WHERE a <= b GROUP BY a LIMIT 100")
        .await?;

    // execute and print results
    df.show().await?;
    Ok(())
}

can be distributed with few lines of code changed:

[!IMPORTANT]
There is a gap between DataFusion and Ballista, which may bring incompatibilities. The community is actively working to close the gap

use ballista::prelude::*;
use datafusion::prelude::*;

#[tokio::main]
async fn main() -> datafusion::error::Result<()> {
    // create SessionContext with ballista support
    // standalone context will start all required
    // ballista infrastructure in the background as well
    let ctx = SessionContext::standalone().await?;

    // everything else remains the same

    // register the table
    ctx.register_csv("example", "tests/data/example.csv", CsvReadOptions::new())
        .await?;

    // create a plan to run a SQL query
    let df = ctx
        .sql("SELECT a, MIN(b) FROM example WHERE a <= b GROUP BY a LIMIT 100")
        .await?;

    // execute and print results
    df.show().await?;
    Ok(())
}

For documentation or more examples, please refer to the Ballista User Guide.

Who is Ballista for

Ballista serves several distinct audiences:

DataFusion users going multi-node — you already use Apache DataFusion on a single machine and have outgrown it. Ballista runs the same SQL and DataFrame workloads across a cluster with minimal code changes and the same results.
Spark users wanting the same execution model — you run Spark SQL or batch jobs and want a lighter, Rust-native alternative without relearning a new paradigm. Ballista keeps the familiar model: plans split into stages at shuffle boundaries, one task per partition, executors with vcores, and adaptive query execution (AQE).
Library users building a specialized engine — you are building a bespoke distributed query engine and want reusable scheduler, executor, and plan-serialization building blocks with extension points, instead of writing distributed execution from scratch.

These audiences are documented in more detail, along with the guarantees each relies on, in the User Personas guide.

Architecture

A Ballista cluster consists of one or more scheduler processes and one or more executor processes. These processes can be run as native binaries and are also available as Docker Images, which can be easily deployed with Docker Compose or Kubernetes.

The following diagram shows the interaction between clients and the scheduler for submitting jobs, and the interaction between the executor(s) and the scheduler for fetching tasks and reporting task status.

Ballista Cluster Diagram

See the architecture guide for more details.

Getting Started

The easiest way to get started is to run one of the standalone or distributed examples. After that, refer to the Getting Started Guide.

Cargo Features

Ballista uses Cargo features to enable optional functionality. Below are the available features for each crate.

ballista (client)

Feature	Default	Description
`standalone`	Yes	Enables standalone mode with in-process scheduler and executor

ballista-core

Feature	Default	Description
`arrow-ipc-optimizations`	Yes	Enables Arrow IPC optimizations for better shuffle performance
`spark-compat`	No	Enables Spark compatibility mode via datafusion-spark
`build-binary`	No	Required for building binary executables (AWS S3 support, CLI parsing)
`force_hash_collisions`	No	Testing-only: forces all values to hash to same value

ballista-scheduler

Feature	Default	Description
`build-binary`	Yes	Builds the scheduler binary with CLI and logging
`substrait`	No	Enables Substrait plan support
`prometheus-metrics`	No	Enables Prometheus metrics collection
`graphviz-support`	No	Enables execution graph visualization
`spark-compat`	No	Enables Spark compatibility mode
`keda-scaler`	No	Kubernetes Event Driven Autoscaling integration
`rest-api`	No	Enables REST API endpoints
`disable-stage-plan-cache`	No	Disables caching of stage execution plans

ballista-executor

Feature	Default	Description
`arrow-ipc-optimizations`	Yes	Enables Arrow IPC optimizations
`build-binary`	Yes	Builds the executor binary with CLI and logging
`mimalloc`	Yes	Uses mimalloc memory allocator for better performance
`spark-compat`	No	Enables Spark compatibility mode

ballista-cli

Feature	Default	Description
`tui`	Yes	Enables a REST client with Terminal User Interface

TUI Jobs table

Usage Examples

# Build with standalone support (default)
cargo build -p ballista

# Build with Substrait support
cargo build -p ballista-scheduler --features substrait

# Build with Spark compatibility
cargo build -p ballista-executor --features spark-compat

Project Status

Ballista supports a wide range of SQL, including CTEs, Joins, and subqueries and can execute complex queries at scale, but still there is a gap between DataFusion and Ballista which we want to bridge in near future.

Refer to the DataFusion SQL Reference for more information on supported SQL.

Who uses Ballista

The following organizations use Ballista. To add yours, open a pull request.

Organization
	Spice AI
	Coralogix

Contribution Guide

Please see the Contribution Guide for information about contributing to Ballista.