PromQL

PromQL (Prometheus Query Language) is a query language for querying and analyzing time series data in Prometheus.

Why Learn PromQL?#

Use Case	Description
Dashboards	Visualize data in Grafana panels
Alerting	Write condition-based automatic alert rules
Analysis	Analyze root causes with ad-hoc queries
Recording Rules	Optimize performance by pre-computing complex queries

Learning Path#

Fundamentals (1 hour)#

Basic Syntax - Selectors, label matching, time ranges
Aggregation Operators - sum, avg, count, topk, by/without

Practical Application (2 hours)#

rate and increase - Core concepts for Counter metrics
histogram_quantile - Calculate P50/P95/P99 percentiles

Advanced (1 hour)#

Recording Rules - Pre-compute complex queries
Alerting Rules - Write alert rules

Quick Reference#

Commonly Used Functions#

Function	Purpose	Example
`rate()`	Per-second rate of Counter	`rate(http_requests_total[5m])`
`increase()`	Total increase of Counter	`increase(http_requests_total[1h])`
`sum()`	Sum	`sum(rate(http_requests_total[5m]))`
`avg()`	Average	`avg(node_cpu_seconds_total)`
`histogram_quantile()`	Percentile	`histogram_quantile(0.99, rate(...[5m]))`

Common Patterns#

# Error rate
sum(rate(http_requests_total{status=~"5.."}[5m]))
/ sum(rate(http_requests_total[5m]))

# P99 response time
histogram_quantile(0.99,
  sum(rate(http_request_duration_seconds_bucket[5m])) by (le)
)

# CPU usage
100 - (avg(rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)

Learning Path#

graph LR
    A["Basic Syntax"] --> B["Aggregation Operators"]
    B --> C["rate/increase"]
    C --> D["histogram_quantile"]
    D --> E["Recording Rules"]
    E --> F["Alerting Rules"]

    style A fill:#e3f2fd
    style F fill:#c8e6c9