apache/druid
Apache Druid: a high performance real-time analytics database. observed · 2026-08-28
Health v2 · maintenance only
89/100
- Activity 99
- Release rhythm 71
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 50
- age_days: 5062
- days_rel: 117
- days_push: 7
- n_releases_24m: 12
Adoption not part of the score
14045 stars · 3800 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
application · maturity stable
database search-engine streaming analytics etl databases analytics big-data jvm self-hosted cloud olap real-time-analytics columnar-database timeseries apache-software-foundation distributed-database kafka-integration real-time data-engineering linux macos docker kubernetes
6 sources
- readme: https://github.com/apache/druid · fetched 2026-08-28 · 1270d34576a3
- homepage: https://druid.apache.org/ · fetched 2026-08-29 · 96264c82b47f
- site_page: https://druid.apache.org/docs/latest/design · fetched 2026-08-29 · a70e506b9a5a
- site_page: https://druid.apache.org/docs/latest/tutorials · fetched 2026-08-29 · 6c53a0e14568
- site_page: https://druid.apache.org/docs/latest/operations/security-overview.html · fetched 2026-08-29 · c82f0c4d8f11
- site_page: https://druid.apache.org/faq · fetched 2026-08-29 · 26b6552b13b6
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| apache/druid | main | 89 |
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Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem