Open-Source Databases · heavyai

heavydb

HeavyDB is an open-source GPU-accelerated SQL database designed for rapid analysis of massive datasets (billions of rows) without indexing or pre-aggregation. It runs on hybrid CPU/GPU systems with Nvidia support and CPU-only configurations across multiple architectures.

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Source: GitHub — github.com/heavyai/heavydb

3.1k

GitHub stars

456

Forks

C++

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	heavyai/heavydb
Owner	heavyai
Primary language	C++
License	Apache-2.0 — OSI-approved
Stars	3.1k
Forks	456
Open issues	287
Latest release	v9.0.0 (2025-10-20)
Last updated	2026-06-25
Source	https://github.com/heavyai/heavydb

What heavydb is

C++ columnar OLAP database leveraging CUDA and JIT compilation for parallel query execution across CPUs and GPUs. Features multi-tiered caching (storage, CPU, GPU memory) and hybrid CPU/GPU query optimization; supports standard SQL with X86, Power, and ARM (experimental) backends.

Quickstart

Get the heavydb source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/heavyai/heavydb.git2cd heavydb3# follow the project's README for install & configuration

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

Best use cases

Real-time Analytics on Large Datasets

Interactive queries against multi-billion row datasets in milliseconds without pre-aggregation. Suitable for financial market data, sensor telemetry, or log analysis requiring sub-second response times.

GPU-Accelerated Columnar Data Warehousing

OLAP workloads where Nvidia GPU resources are available and columnar data layout provides performance benefits. Ideal for organizations with existing GPU infrastructure seeking to consolidate query acceleration.

Interactive Visualization and Exploration

Discovery and ad-hoc query patterns over large datasets with minimal latency. Mentioned in docs as supporting visualization workflows, useful for exploratory data analysis and dashboarding.

Implementation considerations

GPU memory capacity will constrain dataset size per node; plan data partitioning and tiered caching strategy upfront to avoid unexpected out-of-memory failures.
CMake build system with optional features (CUDA, AWS S3, ASAN/TSAN) requires careful dependency management; use pre-built binaries where possible to reduce build complexity.
JIT compilation overhead during first-run queries; monitor query planning and warm-up patterns in production to avoid latency spikes.
Contributor License Agreement (CLA) required for any code contributions; review before committing internal development effort to upstream contributions.
Third-party license complexity: repository includes multiple dependencies under separate licenses (see ThirdParty/licenses/index.md); audit required before distribution.

When to avoid it — and what to weigh

No GPU Hardware Available — While CPU-only mode is supported, the system is designed and optimized for GPU acceleration. CPU-only performance benefits diminish significantly and may not justify operational complexity.
Heavy OLTP Workloads — HeavyDB is columnar and optimized for analytics. Row-oriented transactional use cases with frequent small writes and row-level updates are not a good fit.
Non-Linux Production Environments — Pre-built binaries are provided for CentOS and Ubuntu only. Deployment on Windows, macOS, or non-standard Linux distributions requires custom compilation and carries operational risk.
Minimal DevOps Capacity — Manual builds (CMake), custom CUDA configuration, and multi-tiered caching tuning require infrastructure expertise. Organizations without dedicated ops resources may struggle with maintenance and troubleshooting.

License & commercial use

Licensed under Apache License 2.0 (ASL 2.0), a permissive OSI-approved license permitting commercial use, modification, and distribution with attribution and liability disclaimers. Third-party dependencies included under separate licenses requiring audit.

Apache 2.0 permits commercial use without restrictions or royalties. However, verify all third-party dependencies in ThirdParty/licenses/index.md for compatibility with your commercial distribution model. No commercial support terms, SLAs, or enterprise agreements are evident from the repository; community forum and GitHub issues are primary support channels.

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	Possible
Assessment confidence	High

Security considerations

Repository supports AddressSanitizer and ThreadSanitizer for memory and concurrency testing; these are build-time options, not runtime guarantees. No security audit, CVE history, or hardening posture is documented. GPU drivers (Nvidia CUDA) are third-party dependencies; keep updated separately. CLA requirement suggests change oversight but does not imply formal security review process. Evaluate third-party dependency licenses for known vulnerabilities.

Alternatives to consider

Apache Spark with GPU support (RAPIDS)

Distributed batch/streaming analytics with GPU acceleration; offers broader ecosystem integration but lacks single-node interactive sub-millisecond latency of HeavyDB.

Clickhouse

Open-source columnar OLAP database optimized for fast queries on large datasets, CPU-only, simpler operational model, no GPU dependency; less suitable for interactive exploration at extreme scale.

DuckDB

Lightweight embedded SQL database with vectorized execution and excellent performance on analytical queries; CPU-only, no GPU, simpler deployment, better for OLAP at smaller scale.

Software development agency

Build on heavydb with DEV.co software developers

Contact our team to assess GPU-accelerated database fit for your data volumes, query patterns, and infrastructure. We'll help you navigate deployment complexity and integration requirements.

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heavydb FAQ

Does HeavyDB support non-Nvidia GPUs (AMD, Intel)?

Not clearly stated. README mentions Nvidia GPUs are currently supported; other GPU vendors are not mentioned. Requires review of technical documentation or vendor contact.

Can I deploy HeavyDB on Kubernetes?

Not mentioned in provided data. Linux packages (RPM/DEB/tarball) suggest containerization is possible, but no official Docker images, Helm charts, or orchestration guidance is evident from the repository.

What is the maximum dataset size supported?

Not specified. Documentation notes 'multi-billion row datasets' but maximum practical size depends on GPU/CPU memory, data types, and tiered caching configuration; testing required for your hardware.

Is commercial technical support available?

Not evident from repository. Heavy.ai (commercial entity behind the project) may offer support; contact vendor directly. Open-source support via community forum and GitHub issues only.

Software developers & web developers for hire

DEV.co helps companies turn open-source tools like heavydb into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source databases stack.

Evaluate HeavyDB for Your Analytics Workload

Contact our team to assess GPU-accelerated database fit for your data volumes, query patterns, and infrastructure. We'll help you navigate deployment complexity and integration requirements.

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