For metrics whose Metric Name is name and Tags are K1=V1, ..., Kn=Vn, the mapping is as follows, where value is a specific value
Metric Type | Mapping |
---|---|
Counter | name_total{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”} value |
AutoGaugećGauge | name{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”} value |
Histogram | name_max{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”} value name_sum{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”} value name_count{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”} value name{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”, quantile=“0.5”} value name{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”, quantile=“0.99”} value |
Rate | name_total{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”} value name_total{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”, rate=“m1”} value name_total{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”, rate=“m5”} value name_total{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”, rate=“m15”} value name_total{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”, rate=“mean”} value |
Timer | name_seconds_max{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”} value name_seconds_sum{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”} value name_seconds_count{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”} value name_seconds{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”, quantile=“0.5”} value name_seconds{cluster=“clusterName”, nodeType=“nodeType”, nodeId=“nodeId”, k1=“V1”, ..., Kn=“Vn”, quantile=“0.99”} value |
dn_metric_reporter_list=PROMETHEUS dn_metric_level=CORE dn_metric_prometheus_reporter_port=9091
Then you can get metrics data as follows
curl
to visit http://servier_ip:9091/metrics
, you can get the following metric data:... # HELP file_count # TYPE file_count gauge file_count{name="wal",} 0.0 file_count{name="unseq",} 0.0 file_count{name="seq",} 2.0 ...
As shown above, IoTDB exposes monitoring metrics data in the standard Prometheus format to the outside world. Prometheus can be used to collect and store monitoring indicators, and Grafana can be used to visualize monitoring indicators.
The following picture describes the relationships among IoTDB, Prometheus and Grafana
So, we need to do some additional works to configure and deploy Prometheus and Grafana.
For instance, you can config your Prometheus as follows to get metrics data from IoTDB:
job_name: pull-metrics honor_labels: true honor_timestamps: true scrape_interval: 15s scrape_timeout: 10s metrics_path: /metrics scheme: http follow_redirects: true static_configs: - targets: - localhost:9091
The following documents may help you have a good journey with Prometheus and Grafana.
Grafana query metrics from Prometheus
Apache IoTDB Dashboard
is available as a supplement to IoTDB Enterprise Edition, designed for unified centralized operations and management. With it, multiple clusters can be monitored through a single panel. You can access the Dashboard's Json file by contacting Commerce.
Including but not limited to:
Including but not limited to:
Including but not limited to:
Including but not limited to:
Including but not limited to: