This document is a getting started guide to integrating M3DB with Prometheus.
M3 Coordinator configuration
To write to a remote M3DB cluster the simplest configuration is to run
m3coordinator as a sidecar alongside Prometheus.
Start by downloading the config template. Update the
namespaces and the
client section for a new cluster to match your cluster's configuration.
You'll need to specify the static IPs or hostnames of your M3DB seed nodes, and the name and retention values of the namespace you set up. You can leave the namespace storage metrics type as
unaggregated since it's required by default to have a cluster that receives all Prometheus metrics unaggregated. In the future you might also want to aggregate and downsample metrics for longer retention, and you can come back and update the config once you've setup those clusters. You can read more about our aggregation functionality here.
It should look something like:
listenAddress: type: "config" value: "0.0.0.0:7201" metrics: scope: prefix: "coordinator" prometheus: handlerPath: /metrics listenAddress: 0.0.0.0:7203 # until https://github.com/m3db/m3/issues/682 is resolved sanitization: prometheus samplingRate: 1.0 extended: none clusters: - namespaces: # We created a namespace called "default" and had set it to retention "48h". - namespace: default retention: 48h type: unaggregated client: config: service: env: default_env zone: embedded service: m3db cacheDir: /var/lib/m3kv etcdClusters: - zone: embedded endpoints: # We have five M3DB nodes but only three are seed nodes, they are listed here. - M3DB_NODE_01_STATIC_IP_ADDRESS:2379 - M3DB_NODE_02_STATIC_IP_ADDRESS:2379 - M3DB_NODE_03_STATIC_IP_ADDRESS:2379 writeConsistencyLevel: majority readConsistencyLevel: unstrict_majority writeTimeout: 10s fetchTimeout: 15s connectTimeout: 20s writeRetry: initialBackoff: 500ms backoffFactor: 3 maxRetries: 2 jitter: true fetchRetry: initialBackoff: 500ms backoffFactor: 2 maxRetries: 3 jitter: true backgroundHealthCheckFailLimit: 4 backgroundHealthCheckFailThrottleFactor: 0.5
Now start the process up:
m3coordinator -f <config-name.yml>
Or, use the docker container:
docker pull quay.io/m3/m3coordinator:latest docker run -p 7201:7201 --name m3coordinator -v <config-name.yml>:/etc/m3coordinator/m3coordinator.yml quay.io/m3/m3coordinator:latest
Add to your Prometheus configuration the
m3coordinator sidecar remote read/write endpoints, something like:
remote_read: - url: "http://localhost:7201/api/v1/prom/remote/read" # To test reading even when local Prometheus has the data read_recent: true remote_write: - url: "http://localhost:7201/api/v1/prom/remote/write"
Also, we recommend adding
M3Query to your list of jobs under
scrape_configs so that you can monitor them using Prometheus. With this scraping setup, you can also use our pre-configured M3DB Grafana dashboard.
- job_name: 'm3db' static_configs: - targets: ['<M3DB_HOST_NAME_1>:7203', '<M3DB_HOST_NAME_2>:7203', '<M3DB_HOST_NAME_3>:7203'] - job_name: 'm3coordinator' static_configs: - targets: ['<M3COORDINATOR_HOST_NAME_1>:7203']
NOTE: If you are running
M3DB with embedded
M3Coordinator, you should only have one job. We recommend just calling this job
m3. For example:
- job_name: 'm3' static_configs: - targets: ['<HOST_NAME>:7203']
Querying With Grafana
When using the Prometheus integration with Grafana, there are two different ways you can query for your metrics. The first option is to configure Grafana to query Prometheus directly by following these instructions.
Alternatively, you can configure Grafana to read metrics directly from
M3Coordinator in which case you will bypass Prometheus entirely and use M3's
PromQL engine instead. To set this up, follow the same instructions from the previous step, but set the