PromQL
PromQL is Prometheus’s query language for selecting, filtering, aggregating, transforming, and analyzing multidimensional time-series metrics.
Cloud Observability in Action
PromQL is the query language used to select, aggregate, and calculate time-series data in Prometheus.
- How it is used
- Selects, aggregates, and calculates Prometheus metrics for exploration, dashboards, and alerting.
- How deep it goes
- Explains label filtering, rates, aggregation, parameterization, and periodic alert evaluation through concrete queries and configurations.
- Examples
- Uses service error rates, HTTP status filters, handler variables, missing targets, and excessive API-call alerts.
- What you can do after
- Apply it via Prometheus and Grafana to calculate service rates, parameterize dashboards, and define alert conditions.
Learn Docker in a Month of Lunches, Second Edition
PromQL is Prometheus's query language for selecting, filtering, aggregating, and calculating time-series data.
- How it is used
- Turns scraped application metrics into queries for checking activity and powering dashboard visualizations.
- How deep it goes
- Works through label filtering and aggregation, while deferring rates, changes over time, and subqueries.
- Examples
- Uses summed request counts across containers and a query filtering HTTP 500 responses.
- What you can do after
- Apply it via the Prometheus UI and Grafana to graph combined metrics and display HTTP errors.
Read how Learn Docker in a Month of Lunches, Second Edition explains PromQL →
Machine Learning Platform Engineering
PromQL is Prometheus's query language for selecting, aggregating, and transforming time-series data.
- How it is used
- Uses PromQL to visualize scraped application metrics in Grafana and express operational alert conditions in Prometheus.
- How deep it goes
- Defines metric selection, label filtering, aggregation, range increases, absence checks, and alert expressions without descending into language internals.
- Examples
- Demonstrates request-count aggregation, FastAPI metrics exposure, service-down detection, missing-metric detection, and email alert routing.
- What you can do after
- Apply it via Prometheus and Grafana to inspect request metrics, validate scraping, and configure service-availability alerts.
Read how Machine Learning Platform Engineering explains PromQL →
ScyllaDB in Action
PromQL is Prometheus's query language for selecting, aggregating, transforming, and analyzing time-series metrics.
- How it is used
- Uses PromQL to query ScyllaDB metrics and assess changing performance within a Prometheus monitoring stack.
- How deep it goes
- Demonstrates metric selection, labels, aggregation, and average latency, while only briefly noting smoothing and prediction.
- Examples
- Uses ScyllaDB read-latency metrics, metric labels, aggregation expressions, recording rules, and the Prometheus query window.
- What you can do after
- Apply PromQL via Prometheus to inspect, aggregate, and transform ingested metrics for performance observation.
Related concepts
- Prometheusused by · Prometheus is an open-source monitoring and alerting system that collects, stores, and queries time-series metrics.
- alertingused by · Alerting is the generation and delivery of notifications when monitored conditions or events meet defined criteria or require attention or action.
- dashboardused by · A dashboard is a visual interface that consolidates selected data, metrics, or status information for monitoring, analysis, or decision-making.
- error rateproduces · Error rate is the proportion of evaluated operations, cases, or outcomes that produce an error or fail a specified criterion.
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