Prometheus Analysis

Overview

Prometheus is a time-series metrics collection and alerting system widely used for monitoring production systems. This skill provides guidance for querying Prometheus metrics, interpreting alert data, and using metrics for root cause analysis.

When to Use This Skill

Apply this skill when:

• Analyzing Prometheus alerts that have fired
• Querying metrics to understand system behavior
• Investigating metric anomalies or spikes
• Correlating metrics with incidents
• Writing PromQL queries for specific metrics
• Interpreting time-series data patterns

Prometheus Fundamentals

Metric Types

Counter: Cumulative value that only increases (e.g., total requests, error count)

• Use rate() or increase() to get per-second rate or total increase
• Example: http_requests_total

Gauge: Value that can go up or down (e.g., CPU usage, memory usage, queue depth)

• Query directly or use functions like avg_over_time()
• Example: node_memory_usage_bytes

Histogram: Distribution of values in buckets (e.g., request durations)

• Provides _sum, _count, and _bucket metrics
• Use for percentile calculations
• Example: http_request_duration_seconds

Summary: Similar to histogram but with pre-calculated quantiles

• Example: http_request_duration_seconds{quantile="0.95"}

Time Series Format

Metrics have format: metric_name{label1="value1", label2="value2"}

Example: http_requests_total{method="POST", status="500", service="api"}

Querying Prometheus

Basic Queries

Instant query (current value):

http_requests_total

Range query (over time):

http_requests_total[5m]

Filter by labels:

http_requests_total{status="500", service="api"}

Rate of increase (per-second rate):

rate(http_requests_total[5m])

Common Aggregation Functions

Sum across dimensions:

sum(rate(http_requests_total[5m])) by (status)

Average:

avg(node_memory_usage_bytes) by (instance)

Max/Min:

max(http_request_duration_seconds) by (endpoint)

Count:

count(up == 0)  # Count instances that are down

Useful RCA Queries

Error rate percentage:

sum(rate(http_requests_total{status=~"5.."}[5m])) /
sum(rate(http_requests_total[5m])) * 100

95th percentile latency:

histogram_quantile(0.95,
  rate(http_request_duration_seconds_bucket[5m])
)

Request rate by endpoint:

sum(rate(http_requests_total[5m])) by (endpoint)

Memory usage percentage:

(node_memory_usage_bytes / node_memory_total_bytes) * 100

Database connection pool usage:

sum(db_connection_pool_active) / sum(db_connection_pool_max) * 100

Working with Alerts

Alert Structure

Prometheus alerts contain:

• Alert name: Identifier for the alert rule
• Labels: Dimensions and metadata (service, severity, etc.)
• Annotations: Human-readable descriptions
• State: pending, firing, resolved
• Active since: When alert started firing
• Value: Current metric value that triggered alert

Fetching Alert Details

Use Prometheus API to fetch alerts:

List active alerts:

GET /api/v1/alerts

Query alert rule:

GET /api/v1/rules

Analyzing Alert Context

When investigating an alert:

1. Check alert expression: What PromQL query triggered the alert?
2. Review threshold: What value caused the alert to fire?
3. Check alert duration: How long has condition been true?
4. Review labels: What services/instances are affected?
5. Query related metrics: Get broader context around the alert

Example Alert Investigation

Alert: HighErrorRate Expression: rate(http_requests_total{status=~"5.."}[5m]) > 0.05

Investigation queries:

Query error rate breakdown:

rate(http_requests_total{status=~"5.."}[5m]) by (endpoint)

Query total request rate:

rate(http_requests_total[5m])

Query error rate percentage:

(sum(rate(http_requests_total{status=~"5.."}[5m])) /
 sum(rate(http_requests_total[5m]))) * 100

Check for correlated latency increase:

histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))

Metrics Patterns for RCA

Pattern 1: Sudden Spike

Signature: Metric jumps sharply at specific time

Possible causes:

• Code deployment
• Configuration change
• Traffic surge
• Dependency failure

Investigation:

• Correlate spike time with deployments
• Check for sudden traffic increase
• Query dependency health metrics
• Review recent configuration changes

Pattern 2: Gradual Increase

Signature: Metric grows steadily over hours/days

Possible causes:

• Memory leak
• Resource exhaustion
• Unbounded data growth
• Missing cleanup job

Investigation:

• Check memory/disk usage trends
• Review data volume growth
• Query for resource leaks
• Check scheduled job execution

Pattern 3: Periodic Pattern

Signature: Metric spikes at regular intervals

Possible causes:

• Scheduled job or cron
• Batch processing
• Cache expiration
• Garbage collection

Investigation:

• Identify period (hourly, daily, etc.)
• Check for scheduled tasks at that interval
• Review batch job schedules
• Query job execution metrics

Pattern 4: Drop to Zero

Signature: Metric suddenly drops to zero or very low value

Possible causes:

• Service crash
• Instance termination
• Network partition
• Monitoring failure

Investigation:

• Check service health (up metric)
• Review instance count
• Query service availability
• Check for infrastructure changes

Pattern 5: High Variability

Signature: Metric fluctuates wildly

Possible causes:

• Intermittent errors
• Race condition
• Resource contention
• Unhealthy load balancing

Investigation:

• Check error logs for patterns
• Review load distribution across instances
• Query resource utilization
• Check for concurrency issues

Time Range Selection

Choose appropriate time ranges for investigation:

Incident detection (5-15 minutes):

rate(metric[5m])

Trend analysis (1-6 hours):

rate(metric[1h])

Long-term patterns (1-7 days):

avg_over_time(metric[1d])

Comparison with past:

# Current value
metric

# Value 1 week ago
metric offset 1w

Correlating Metrics

Multiple Metric Analysis

Query related metrics together to understand full context:

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

# Latency
histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))

# Request rate
rate(http_requests_total[5m])

# CPU usage
rate(process_cpu_seconds_total[5m])

# Memory usage
process_resident_memory_bytes

Identifying Correlations

Look for metrics that change together:

• Error rate ↑ + Latency ↑ → Performance degradation
• Error rate ↑ + CPU ↑ → Resource exhaustion
• Error rate ↑ + Request rate ↑ → Traffic surge
• Error rate ↑ + Dependency metric ↓ → Dependency failure

Best Practices

Query Writing

• Use appropriate time ranges (5m for recent, 1h for trends)
• Filter by relevant labels to reduce cardinality
• Use rate() for counters, not raw values
• Aggregate when dealing with multiple instances
• Use recording rules for expensive queries

Alert Investigation

• Start with alert expression to understand trigger
• Query wider time range to see pattern before/after
• Break down aggregated metrics to find specific instances/endpoints
• Check for correlated metric changes
• Compare current values with historical baseline

Metric Interpretation

• Consider metric type (counter, gauge, histogram)
• Look for patterns over time, not just current values
• Compare across instances to find outliers
• Correlate with other metrics for complete picture
• Validate hypotheses with multiple metrics

Integration with Thufir

This skill works with:

• Prometheus MCP server: Fetches alerts and queries metrics via API
• root-cause-analysis skill: Metrics provide evidence for RCA
• RCA agent: Agent queries Prometheus to gather metric data

Using Prometheus MCP

The Thufir plugin includes Prometheus MCP server for querying:

Query instant value:

Use MCP tool: prometheus_query
Query: rate(http_requests_total[5m])

Query time range:

Use MCP tool: prometheus_query_range
Query: rate(http_requests_total[5m])
Start: 2025-12-19T14:00:00Z
End: 2025-12-19T15:00:00Z
Step: 15s

Fetch active alerts:

Use MCP tool: prometheus_alerts

Additional Resources

Reference Files

For detailed PromQL patterns and advanced queries:

• references/promql-cookbook.md - Common PromQL queries for RCA scenarios

Quick Reference

Error rate: rate(http_requests_total{status=~"5.."}[5m]) Latency p95: histogram_quantile(0.95, rate(duration_bucket[5m])) CPU usage: rate(process_cpu_seconds_total[5m]) Memory: process_resident_memory_bytes Request rate: rate(http_requests_total[5m])

Time ranges: 5m (instant), 1h (trend), 1d (baseline) Aggregations: sum, avg, max, min, count Filters: {label="value"}, {label=~"regex"}

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Use Prometheus metrics to provide objective, time-series evidence for root cause analysis. Correlate metrics with code changes and system events to identify precise incident causes.