# heavyai/heavydb

HeavyDB (formerly MapD/OmniSciDB)

Repository: https://github.com/heavyai/heavydb
Canonical: https://ross.abutalabs.com/products/heavydb
Homepage: https://heavy.ai
Language: C++
License: Apache-2.0
License Family: permissive
Topics: gpu, database, olap, visualization, sql, machine-learning, interactive, real-time, mapd, omnisci, llvm, heavyai, cuda
Last push: 2026-08-24T00:40:03+00:00

## Health v2 (maintenance only)
Score: 72/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 21, longevity 100
- inputs: {"age_days": 3405, "days_push": 10, "days_rel": 317, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3057, forks 457 (observed 2026-08-28T04:07:39.789120+00:00)

## What it is
HeavyDB (formerly MapD/OmniSciDB) is an open-source SQL-based, relational, columnar database engine that uses CPUs and NVIDIA GPUs to query multi-billion row datasets in milliseconds. It features multi-tiered caching across storage, CPU memory, and GPU memory, plus JIT query compilation, without requiring indexes, pre-aggregation, or downsampling.

## Use cases
- query billions of rows in milliseconds with SQL
- run interactive analytics on GPU-accelerated hardware
- visualize large datasets in real time without downsampling
- run OLAP workloads on hybrid CPU/GPU systems
- avoid indexing and pre-aggregation for ad-hoc analytical queries
- accelerate machine learning feature queries on large tables

## When to choose
- you need sub-second interactive queries over multi-billion-row datasets
- you have NVIDIA GPUs and want to exploit them for analytics
- you want a columnar SQL engine without index maintenance overhead
- you need real-time dashboards and visualization backends

## When to avoid
- you need a general-purpose transactional (OLTP) database
- your deployment is Windows or macOS only (prebuilt binaries target Linux)
- you lack GPU hardware and need ARM support beyond experimental
- you need a fully managed cloud database with vendor support

## Facets
- artifact type: service
- maturity: active
- function: database, search-engine, data-visualization, machine-learning
- domain: databases, analytics, big-data, data-visualization, gpu-computing
- platform: cloud
- tags: olap, columnar, gpu-accelerated, jit-compilation, real-time-analytics, sql-database, mapd, omnisci, sql, linux, docker, gpu

## Member repositories
- heavyai/heavydb (main) score 72

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:39.789120+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:28:48.839821+00:00, confidence not recorded.
  - readme: https://github.com/heavyai/heavydb (fetched 2026-08-28T04:07:39.789120+00:00, sha 056c9eb8adf9)
- Data as of 2026-08-30T08:39:29.467469+00:00.