Prometheus format is a text-based exposition format in which named metrics are represented with values, help text, and data types.
Prometheus exposition format is a text-based representation of metrics. Each metric has a name and value, with help text describing it and a data type identifying its kind. section 9.1
Use the format in a container-monitoring workflow:
Enable Docker Engine metrics and publish them on port 9323. section 9.1
Browse to http://localhost:9323/metrics to inspect the exposed output. section 9.1
Let Prometheus periodically collect the values, add timestamps, store previous collections, and support queries over time. section 9.1
Docker metrics endpoint
Configure Docker with "metrics-addr": "0.0.0.0:9323", then inspect the sample metrics exposed through its HTTP API.
How Learn Docker in a Month of Lunches, Second Edition covers it
How deep it goes
Defines the text structure and collection workflow but stops short of specifying the complete format grammar.
Examples
Uses Docker’s port 9323 endpoint, the metrics-addr setting, and sample metrics exposed through the HTTP API.
What it assumes
Assumes Docker configuration and basic container operation, while introducing metric exposition and Prometheus collection.
What you can do after
Operate Docker metrics export and verify the resulting endpoint before connecting Prometheus.
Cloud Observability in ActionThe Prometheus exposition format is a text-based format for representing metrics and their labels for collection by monitoring systems.
Dockerproduced by · Docker is a platform and ecosystem for building, distributing, and running applications in containers.
OpenMetricsalternative to · OpenMetrics is an open standard defining a text-based format for exposing metrics and associated metadata such as exemplars.
Kubernetesused by · Kubernetes is an open-source platform for orchestrating and automating the deployment, scaling, and management of containerized workloads across computing clusters.
Prometheusproduced by · Prometheus is an open-source monitoring and alerting system that collects, stores, and queries time-series metrics.
This explainer was generated by Manning's knowledge graph from the text of Learn Docker in a Month of Lunches, Second Edition. The section links point to the parts of the book it draws on.
