datafusion-ballista

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

…

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.

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.

Web Terminal User Interface (Web TUI)

Ballista provides a browser-based Web TUI for monitoring a running cluster. It exposes the TUI views for jobs, executors, metrics, and scheduler information directly in a web browser.

When the scheduler HTTP endpoint is available, opening the scheduler address in a browser, for example http://localhost:50050, redirects to a hosted Web TUI.

For more information, including how to run the Web TUI locally, see the Ballista CLI documentation.

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

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.