Prometheus
Prometheus is an open-source monitoring and alerting system that collects, stores, and queries time-series metrics.
Learn Docker in a Month of Lunches, Second Edition
Prometheus is an open-source monitoring system and time-series database that collects and queries numerical measurements.
- How it is used
- Collects container and Docker Engine metrics for ongoing monitoring of dynamic applications.
- How deep it goes
- Walks through metrics endpoints, scheduled scraping, target discovery and basic queries; leaves application-specific metric choices and client-library mechanics to the runtime.
- Examples
- Uses an image gallery with runtime and request metrics, a scaled access-log service, and a to-do application monitoring lab.
- What you can do after
- Operate/configure it with Docker Compose to scrape application metrics, check queries and supply Grafana dashboards.
Read how Learn Docker in a Month of Lunches, Second Edition explains Prometheus →
ScyllaDB in Action
Prometheus is an open-source monitoring and alerting toolkit that collects and stores time-series metrics, typically by scraping instrumented endpoints.
- How it is used
- Supports ScyllaDB production monitoring by ingesting cluster metrics for dashboards, performance investigation, and alert evaluation.
- How deep it goes
- Explains target configuration, periodic scraping, time-series storage, web exploration, PromQL aggregation, and alert-rule evaluation without detailing Prometheus internals.
- Examples
- Uses a scylla-reviews target, cache-insertion metrics, read-latency PromQL queries, and an InstanceDown alert rule.
- What you can do after
- Operate/configure it by declaring scrape targets, checking target health, querying metrics, and supplying dashboards and alert rules.
LLMs in Production
Prometheus is an open-source monitoring system that collects time-series metrics and supports querying and alerting.
- How it is used
- Aggregates GPU metrics for monitoring and Kubernetes autoscaling of LLM services.
- How deep it goes
- Explains collection, time-series storage, querying, and the adapter requirement, but does not provide a Prometheus configuration walkthrough.
- Examples
- Illustrates GPU-utilization scaling with a diagram linking DCGM, Prometheus Operator, and a custom metrics API.
- What you can do after
- Evaluate/understand it as a metrics store for GPU-aware scaling, while recognizing that HPA needs an adapter to access those metrics.
Spring AI in Action
Prometheus is an open-source monitoring system that collects and stores time-series metrics and supports querying and alerting.
- How it is used
- Collects Spring and Spring AI metrics for querying, monitoring, alerting, and time-series visualization.
- How deep it goes
- Explains dependency, endpoint, Docker Compose, and scrape configuration, then stops short of tracing internals.
- Examples
- Uses Spring AI ChatClient and advisor metrics, HTTP metric queries, Docker Compose services, and Grafana dashboards.
- What you can do after
- Operate/configure Prometheus to scrape Actuator metrics and inspect Spring AI activity through queries and Grafana dashboards.
Data Pipelines with Apache Airflow, Second Edition
Prometheus is an open-source monitoring and alerting system that collects time-series metrics from instrumented or exported endpoints.
- How it is used
- Collects Airflow monitoring metrics through a StatsD exporter for querying and Grafana dashboards.
- How deep it goes
- Walks through exporter and scrape configuration, target verification, labels, and dashboard setup; leaves alert configuration outside the walkthrough.
- Examples
- Uses a Docker-based exporter, an airflow scrape target, an UP target check, and a DAG parse-time dashboard.
- What you can do after
- Operate/configure it to scrape translated Airflow metrics, verify collection, and supply metrics to Grafana.
Read how Data Pipelines with Apache Airflow, Second Edition explains Prometheus →
Machine Learning Platform Engineering
Prometheus is an open-source monitoring system that collects, stores, and queries time-series metrics.
- How it is used
- Monitors deployed applications and ML services for performance, availability, request behavior, and operational failures.
- How deep it goes
- Explains components, endpoint instrumentation, scraping, queries, dashboards, alert rules, and routing, with Kubernetes installation examples.
- Examples
- Uses FastAPI middleware, /metrics, request counters, PromQL response-code queries, Helm installation, and BentoML uptime alerts.
- What you can do after
- Operate/configure metric scraping, visualization, PromQL checks, uptime rules, and Alertmanager email routing.
Read how Machine Learning Platform Engineering explains Prometheus →
Cloud Observability in Action
Prometheus is an open-source monitoring system and time-series database for collecting and querying metrics.
- How it is used
- Collects cloud-native metrics and evaluates operational alert conditions.
- How deep it goes
- Explains scraping, exposition, PromQL rules, Alertmanager routing, and signal correlation, with Docker-based configuration examples.
- Examples
- Uses /metrics endpoints, Node exporter targets, PromQL alert rules, Docker Compose services, and a microservices incident investigation.
- What you can do after
- Operate and configure metric scraping, PromQL alerts, Alertmanager routing, and cross-signal incident investigation.
Read how Cloud Observability in Action explains Prometheus →
Effective Platform Engineering
Prometheus is an open-source monitoring system that collects and stores time-series metrics and supports querying and alerting.
- How it is used
- Supports observability by storing metrics that teams use for monitoring, alerting, diagnosis, and reliability decisions.
- How deep it goes
- Explains metric routing, persistent storage, SLO usage, and dashboard diagnosis without detailing Prometheus query or rule syntax.
- Examples
- Uses Persistent Volume Claims, SLO dashboards, error-budget charts, and Epetech payment-processing monitoring as concrete applications.
- What you can do after
- Apply it via Prometheus data to define SLIs, formulate SLOs, visualize service performance, and support proactive issue detection.
Read how Effective Platform Engineering explains Prometheus →
Related concepts
- pull modeluses · A pull model is a pattern in which a consumer or recipient initiates requests for data, messages, configuration, or work according to its needs or readiness.
- service discoveryuses · Service discovery is the process of identifying available services and obtaining the names, locations, or connection information needed to access them.
- scrapinguses · Scraping is the automated or systematic retrieval and extraction of information from digital sources.
- Grafanaused by · Grafana is an open-source observability and visualization platform for querying, exploring, and displaying data from multiple sources and configuring alerts.
- metricuses · A metric is a quantitative measure used to characterize or assess a property, condition, behavior, performance, or outcome.
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