M3 review
A self-hosted M3 stack for distributed metrics, PromQL, Graphite, and horizontal scaling.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
M3 is an open-source metrics engine built around M3DB, a distributed time-series database originally developed at Uber Technologies. It is aimed at teams managing high-volume monitoring workloads, especially those using Prometheus or Graphite. The stack includes M3Coordinator for coordinating reads and writes, M3Aggregator for streaming aggregation, and M3 Query for querying stored metrics. Deployment is self-hosted through binaries, Docker, or Kubernetes, with Linux, macOS, and API access supported.
Its main technical strength is distributed storage for operational metrics. M3 supports sharding across physical nodes for horizontal scaling, synchronous replication with configurable consistency levels, a write-ahead commit log, and crash recovery. Configurable namespaces and retention periods help separate workloads and control data lifecycles, while M3TSZ and protobuf compression reduce storage requirements. A tag-based inverted index supports metric lookup, and ingestion can use Prometheus remote write, Carbon plaintext, InfluxDB line protocol, or StatsD. Prometheus, Graphite, Grafana, InfluxDB, StatsD, and Carbon fit naturally into the surrounding monitoring ecosystem.
M3 is a strong match for infrastructure teams that want open-source, distributed metrics storage and are prepared to operate the database themselves. Its PromQL support makes it suitable for Prometheus remote storage, while Graphite and M3 query engines broaden compatibility with established metrics workflows. The trade-off is focus: the listed query engines and ingestion methods are centered on time-series monitoring rather than broad database use, and the self-hosted model places deployment, scaling, retention, and recovery responsibilities on the adopting team. Teams seeking a managed cloud service or a general-purpose SQL database should choose a different product.
M3 pros and cons
- Where it wins
- Shards and synchronously replicates time-series data across nodes
- Supports PromQL, Graphite, and M3 query engines
- Offers configurable retention and compressed storage
- Where it doesn't
- Requires self-hosted deployment and operational ownership
- Query options focus on metrics rather than general SQL workloads
- No managed cloud offering is part of the published deployment model
M3 fact sheet, pricing and score →
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