Open-Source Databases · apache

datafusion-ballista

Apache DataFusion Ballista is a distributed query engine built in Rust that extends Apache DataFusion to execute SQL workloads across multiple nodes. It allows developers to convert single-machine DataFusion applications into distributed systems with minimal code changes, using a scheduler–executor architecture deployable as Docker containers or Kubernetes pods.

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Source: GitHub — github.com/apache/datafusion-ballista

2.1k

GitHub stars

295

Forks

Rust

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	apache/datafusion-ballista
Owner	apache
Primary language	Rust
License	Apache-2.0 — OSI-approved
Stars	2.1k
Forks	295
Open issues	157
Latest release	Unknown
Last updated	2026-07-08
Source	https://github.com/apache/datafusion-ballista

What datafusion-ballista is

Ballista is a Rust-based distributed query execution engine that layers atop Apache DataFusion, using an explicit scheduler–executor model with task distribution, shuffle operations via Arrow IPC, and optional integrations (Substrait, Spark compatibility, Prometheus metrics). It currently exhibits a compatibility gap with DataFusion that the community is actively addressing.

Quickstart

Get the datafusion-ballista source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/apache/datafusion-ballista.git2cd datafusion-ballista3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-node OLAP query execution at scale

Organizations running large TPC-H-like analytical workloads can distribute query execution across a cluster of machines. Benchmarks show ~2.9x speedup versus Spark on certain queries, making it suitable for 100 GB+ data processing with complex joins, CTEs, and aggregations.

Rust-native distributed data processing

Teams already using Rust for data pipelines can add distributed execution without switching languages or adopting JVM dependencies. The ability to convert single-machine DataFusion code with minimal changes reduces migration friction.

Containerized analytics infrastructure on Docker/Kubernetes

Cloud-native deployments can leverage pre-built Docker images and Kubernetes manifests for scheduler and executor processes, enabling straightforward autoscaling and orchestration in containerized environments.

Implementation considerations

Plan for DataFusion–Ballista compatibility testing; not all DataFusion SQL is guaranteed to work in distributed mode. Reference the supported SQL list in DataFusion documentation before committing critical queries.
Decide early on deployment model (standalone in-process, Docker Compose, or Kubernetes); each has different operational and performance characteristics. Standalone mode simplifies testing but limits horizontal scaling.
Configure optional features (Substrait, Prometheus metrics, REST API, Spark compatibility) at build time; plan feature selection based on integration needs and operational visibility requirements.
Account for distributed debugging and task observation; set up logging and monitoring (e.g., via Prometheus) to troubleshoot shuffle failures, executor hangs, and scheduler bottlenecks.
Evaluate mimalloc and Arrow IPC optimization features; these are default but can be tuned for workload-specific performance gains in memory-bound or network-bound scenarios.

When to avoid it — and what to weigh

Production-critical systems requiring feature parity with mature engines — README explicitly states a gap between DataFusion and Ballista 'which may bring incompatibilities.' Use only if your workload falls within documented supported SQL and you can tolerate potential incompatibilities.
Streaming or real-time analytics pipelines — Ballista is designed for batch OLAP queries. No evidence in the data of streaming, CEP, or sub-second latency support. Consider Apache Flink or Kafka Streams for streaming use cases.
Minimal operational overhead or non-technical team management — Ballista requires running separate scheduler and executor processes, managing cluster state, and debugging distributed execution. Teams without DevOps experience should evaluate managed alternatives or single-node DataFusion.
Mature, feature-complete SQL engine (e.g., replacing PostgreSQL, Snowflake) — Ballista is relatively young (created May 2022) with known gaps. Mission-critical transactional or analytic workloads requiring guaranteed stability should use proven enterprise engines.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and redistribution under clear terms. Must include license and copyright notice in distributions.

Apache License 2.0 is permissive for commercial use without seeking vendor permission. However, Ballista is young (first released May 2022) and carries a documented gap with DataFusion; production deployment should be treated as adopting an actively evolving project rather than a stable, versioned product. No commercial support vendor, SLA, or indemnification is evident from the repository.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

No security audit, CVE history, or threat model disclosed in the provided data. Ballista runs distributed processes with scheduler–executor communication; ensure cluster network isolation if handling sensitive data. Consider authentication/TLS for scheduler API if exposed. No claims of secure-by-default or formal hardening are evident. Audit project security posture and advisories independently before handling production data.

Alternatives to consider

Apache Spark

Mature, widely adopted distributed query engine with comprehensive SQL support, extensive integrations, and commercial support options. Trade-off: JVM overhead; Ballista claims 2.9x speedup on certain benchmarks but Spark's ecosystem and stability are proven.

Dask (Python)

Python-native distributed computing framework with SQL support via Dask-SQL. Better for teams using Python; lower operational complexity. Trade-off: less optimized for pure SQL than Ballista; Python GIL overhead.

DuckDB (single-node or DuckDB Cloud)

Extremely fast in-process OLAP engine; no distributed orchestration needed. DuckDB Cloud adds managed distributed queries. Trade-off: DuckDB Cloud is closed-source/commercial; single-node DuckDB does not scale across nodes.

Software development agency

Build on datafusion-ballista with DEV.co software developers

Ballista offers promising performance and Rust-native design, but requires thorough testing for DataFusion compatibility and careful operational planning. Speak with our team to assess fit for your data pipeline and architecture.

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Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

datafusion datafusion-sqlparser-rs spark

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datafusion-ballista FAQ

Can I use Ballista with my existing DataFusion code?

Mostly yes. The README shows minimal code changes are needed (swap SessionContext creation). However, the README also states there is an active gap between DataFusion and Ballista that may cause incompatibilities. Test your SQL workload thoroughly before moving to production.

Does Ballista support streaming or real-time queries?

No evidence in the provided data. Ballista is designed for batch OLAP query execution. For streaming, consider Apache Flink, Kafka Streams, or other stream processing engines.

What is the difference between Ballista standalone and distributed deployment?

Standalone mode (`SessionContext::standalone()`) starts scheduler and executor processes in-memory within your application, simplifying development and testing. Distributed deployment runs separate scheduler and executor binaries (e.g., as Docker containers or Kubernetes pods), enabling horizontal scaling and multi-node workloads.

Is Ballista production-ready?

Ballista is actively maintained but young (created May 2022) and has no formal versioned releases visible. The README explicitly mentions a gap with DataFusion that is being worked on. Use in production only after thorough testing of your specific workload, and expect the project to evolve.

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