How To Guides

M3 Query is a distributed query engine for querying realtime and historical data stored in M3DB nodes, supporting several query languages. It is designed to support both low latency realtime queries and queries that can take longer to execute, aggregating over larger datasets for analytical use cases. For example, if you are using the Prometheus remote write endpoint with M3 Coordinator, you can use M3 Query instead of the Prometheus remote read endpoint.

M3 Query is used to query data that is stored in M3DB. For instance, if you are using the Prometheus remote write endpoint with m3coordinator, you can use m3query instead of the Prometheus remote read endpoint. By doing so, you get all of the benefits of m3query’s engine such as block processing. Furthermore, since m3query provides a Prometheus compatible API, you can use 3rd party graphing and alerting solutions like Grafana.

Introduction m3aggregator is used to cluster stateful downsampling and rollup of metrics before they are store in M3DB. The M3 Coordinator also performs this role but is not cluster aware. This means metrics will not get aggregated properly if you send metrics in round robin fashion to multiple M3 Coordinators for the same metrics ingestion source (e.g. Prometheus server). Similar to M3DB, m3aggregator supports clustering and replication by default. This means that metrics are correctly routed to the instance(s) responsible for aggregating each metric and multiple m3aggregator replicas can be configured such that there are no single points of failure for aggregation.

Overview M3 has native integrations that make it particularly easy to use it as a metrics storage for Prometheus and Graphite. M3DB can also be used as a general purpose distributed time series database by itself. Data Model IDs and Tags M3DB’s data model allows multiple namespaces, each of which can be configured and tuned independently. Each namespace can also be configured with its own schema (see “Schema Modeling” section below).