How to Use Recording Rules in Prometheus to Reduce Load and Speed Up Queries
How to Use Recording Rules in Prometheus to Reduce Load and Speed Up Queries
I remember the first time my Prometheus server groaned under the weight of a complex Grafana dashboard—it felt like asking a toddler to solve calculus. Dashboards loaded slower than my morning coffee brewed, and my CPU metrics looked like a stress test. That’s when I discovered recording rules, Prometheus’s secret weapon for taming expensive queries. Here’s how to use them effectively.
Why Recording Rules Matter
Recording rules let you precompute frequently used or resource-intensive queries and save the results as new time series. This means:
- Faster dashboards: No more waiting for heavy aggregations.
- Reduced server load: Fewer real-time calculations.
- Reusable metrics: Simplify alerts and dashboards with precomputed data.
Think of it like meal-prepping for your monitoring system—do the heavy lifting once, then enjoy quick “servings” of metrics later.
Prerequisites
Before diving in, ensure you have:
- A running Prometheus server (v2.0+).
- Basic familiarity with PromQL (Prometheus Query Language).
- Access to your Prometheus configuration file (
prometheus.yml
).
Step 1: Plan Your Recording Rules
Start by identifying slow or repetitive queries. For example:
- A dashboard that repeatedly calculates
rate(http_requests_total[5m])
across 20 endpoints. - An alert rule using a complex
histogram_quantile()
expression.
Pro Tip: Check Prometheus’s /targets
and /metrics
endpoints for high-cardinality or expensive metrics.
Step 2: Define Rules in a Separate File
Prometheus loads rules from files specified in prometheus.yml
. Create a new file (e.g., recording_rules.yml
) and add your rules:
groups:
- name: http_requests_rules
interval: 1m # How often to evaluate rules (optional)
rules:
- record: instance_path:http_requests:rate5m
expr: rate(http_requests_total{job="myapp"}[5m])
Key Fields:
record
: The new metric name (use a clear naming convention likelevel:metric:operation
).expr
: The PromQL query to precompute.
Step 3: Link the Rules to Prometheus
Edit prometheus.yml
to include your rules file:
rule_files:
- 'recording_rules.yml' # Path relative to prometheus.yml
Reload Prometheus gracefully to apply changes:
curl -X POST http://localhost:9090/-/reload
Warning: Avoid hot-reloading in production during peak traffic!
Step 4: Verify and Use Your New Metrics
- Check if the rule is active:
curl http://localhost:9090/api/v1/rules | jq '.data.groups[]'
- Query the new metric in Grafana or Prometheus’s UI (e.g.,
instance_path:http_requests:rate5m
).
Troubleshooting:
- If metrics don’t appear, check Prometheus logs for syntax errors.
- Use
recorded_metric:offset 1m
to debug freshness.
Advanced Tips
- Optimize Intervals: Balance precision and load:
interval: 5m # For slower-changing metrics like daily totals.
- Avoid Cardinality Explosions: Never record high-cardinality labels (e.g.,
user_id
). - Chain Rules: Precompute base metrics first, then build atop them:
- record: job:http_errors:percent expr: instance_path:http_errors:rate5m / instance_path:http_requests:rate5m * 100
FAQ
Q: How do recording rules differ from alerting rules?
A: Alerting rules evaluate conditions to trigger alerts; recording rules create new time series. Both use the same YAML structure.
Q: Can recording rules backfill historical data?
A: No—they only compute data from the point of creation onward. For backfilling, use tools like Thanos or Cortex.
Q: What’s the performance impact of too many rules?
A: Each rule adds CPU/memory overhead. Monitor prometheus_rule_evaluations_total
to avoid overloading your server.
Further Reading
- Prometheus Official Documentation on Recording Rules
- My Grafana Alerting Guide for tying rules to alerts.
Now go forth and precompute! Your Prometheus server will thank you with snappier dashboards and cooler temps (both literally and metaphorically).