Netflix/atlas
In-memory dimensional time series database. observed · 2026-08-28
Health v2 · maintenance only
90/100
- Activity 99
- Release rhythm 72
- 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: 7
- age_days: 4411
- days_rel: 187
- days_push: 7
- n_releases_24m: 4
Adoption not part of the score
3564 stars · 359 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Atlas is Netflix's in-memory dimensional time series database, serving as a backend for storing and querying operational metrics at scale. It provides a query language and HTTP API for analyzing tagged time series data in real time.
Use cases
- store and query application metrics in memory
- run dimensional queries on tagged time series data
- build a monitoring backend for cloud infrastructure
- analyze operational metrics with a query API
- visualize time series data from dashboards
When to choose
- you need fast in-memory queries over high-cardinality tagged metrics
- you operate large-scale cloud infrastructure and need a metrics backend
- you want Netflix-proven time series storage with an expressive query language
When to avoid
- you need durable long-term disk-based storage of time series
- you want a lightweight single-node metrics store
- your stack is not JVM-friendly
Facets
service · maturity active
database monitoring analytics http-server monitoring analytics big-data time-series jvm self-hosted time-series-database metrics observability scala netflix dimensional-data devops web-server
1 source
- readme: https://github.com/Netflix/atlas · fetched 2026-08-28 · fba40296e870
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Netflix/atlas | main | 90 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem