rate(http_requests_total[5m])      # Per-second average rate
irate(http_requests_total[5m])     # Instant rate (last 2 points, spiky)
increase(http_requests_total[1h])  # Total increase over range

rate(metric[5m])    # Correct
rate(metric)        # Error: expected range vector

# This fails if label sets differ:
metric_a / metric_b

# Ignore extra labels:
metric_a / ignoring(extra_label) metric_b

# Match on specific labels only:
metric_a / on(common_label) metric_b

histogram_quantile(0.95,
  sum(rate(http_request_duration_seconds_bucket[5m])) by (le)
)

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

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

# Memory usage
1 - (node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)

# Container memory (Kubernetes)
sum by (pod) (container_memory_working_set_bytes{container!=""})

groups:
  - name: example
    rules:
      - alert: HighErrorRate
        expr: |
          sum(rate(http_requests_total{status=~"5.."}[5m])) by (job)
            / sum(rate(http_requests_total[5m])) by (job)
            > 0.05
        for: 5m          # Must be firing for this duration
        labels:
          severity: warning
        annotations:
          summary: "Error rate {{ $value | humanizePercentage }} on {{ $labels.job }}"

rules:
  - record: job:http_requests:rate5m
    expr: sum by (job) (rate(http_requests_total[5m]))