# apache/druid

Apache Druid: a high performance real-time analytics database.

Repository: https://github.com/apache/druid
Canonical: https://ross.abutalabs.com/products/apache-druid
Homepage: https://druid.apache.org/
Language: Java
License: Apache-2.0
License Family: permissive
Topics: druid
Last push: 2026-08-26T23:58:48+00:00

## Health v2 (maintenance only)
Score: 89/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 71, longevity 100
- inputs: {"age_days": 5062, "days_push": 7, "days_rel": 117, "gap_med": 50, "n_releases_24m": 12}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 14045, forks 3800 (observed 2026-08-28T04:11:05.388710+00:00)

## What it is
Apache Druid is a high-performance, distributed, real-time analytics database written in Java for fast OLAP-style slice-and-dice queries on large event-oriented datasets. It natively ingests streaming data from Kafka and Kinesis with query-on-arrival, and supports sub-second queries at high concurrency over billions to trillions of rows.

## Use cases
- run sub-second OLAP queries on billions of rows of event data
- power user-facing analytics dashboards with high query concurrency
- ingest and query streaming data from Kafka in real time
- analyze clickstream and user behavior on websites and apps
- store and query server and application performance metrics
- build a backend for digital marketing and advertising analytics
- explore time-series and telemetry data with fast filtering and aggregation

## When to choose
- you need low-latency aggregations on streaming or event-driven data
- your application requires hundreds to thousands of concurrent analytical queries per second
- you want instant visibility into freshly ingested data (query-on-arrival)
- you need time-partitioned storage with fast search and filtering on high-cardinality data
- you want a self-hosted, horizontally scalable analytics backend integrated with Kafka, S3, or HDFS

## When to avoid
- you need full-text log search like Elasticsearch or Splunk
- you need heavy joins, transactions, or updates typical of OLTP databases
- your workload is small batch reporting better served by a traditional data warehouse like Snowflake or BigQuery
- you cannot dedicate significant memory and cluster resources (quickstart alone needs 6 GiB RAM)
- you need Windows support, which is not officially supported

## Facets
- artifact type: application
- maturity: stable
- function: database, search-engine, streaming, analytics, etl
- domain: databases, analytics, big-data
- platform: jvm, self-hosted, cloud
- tags: olap, real-time-analytics, columnar-database, timeseries, apache-software-foundation, distributed-database, kafka-integration, real-time, data-engineering, linux, macos, docker, kubernetes

## Member repositories
- apache/druid (main) score 89

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:05.388710+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 2026-08-29T17:12:51.165765+00:00, confidence not recorded.
  - readme: https://github.com/apache/druid (fetched 2026-08-28T04:11:05.388710+00:00, sha 1270d34576a3)
  - homepage: https://druid.apache.org/ (fetched 2026-08-29T08:06:43.968213+00:00, sha 96264c82b47f)
  - site_page: https://druid.apache.org/docs/latest/design (fetched 2026-08-29T08:06:43.977783+00:00, sha a70e506b9a5a)
  - site_page: https://druid.apache.org/docs/latest/tutorials (fetched 2026-08-29T08:06:43.981694+00:00, sha 6c53a0e14568)
  - site_page: https://druid.apache.org/docs/latest/operations/security-overview.html (fetched 2026-08-29T08:06:43.983382+00:00, sha c82f0c4d8f11)
  - site_page: https://druid.apache.org/faq (fetched 2026-08-29T08:06:43.979852+00:00, sha 26b6552b13b6)
- Data as of 2026-08-30T08:39:29.467469+00:00.