How to optimize Prometheus performance and achieve horizontal scaling?

Prometheus performance optimization and scaling solutions:

Scraping Optimization:

1. Adjust Scrape Intervals:

yaml
scrape_configs:
  - job_name: 'critical'
    scrape_interval: 15s
  - job_name: 'normal'
    scrape_interval: 30s
  - job_name: 'low-priority'
    scrape_interval: 60s

1. Filter Unnecessary Metrics:

yaml
metric_relabel_configs:
  - source_labels: [__name__]
    regex: 'go_.*|process_.*'
    action: drop

1. Use Scrape Limits:

yaml
scrape_configs:
  - job_name: 'api'
    sample_limit: 10000
    target_limit: 100

Storage Optimization:

1. Compression Configuration:

yaml
storage:
  tsdb:
    compression: zstd

1. Memory Mapping Optimization:

bash
--storage.tsdb.memory-map-on-write=true
--storage.tsdb.max-block-duration=2h

1. WAL Optimization:

bash
--storage.tsdb.wal-compression=true
--storage.tsdb.wal-segment-size=20MB

Query Optimization:

1. Use Recording Rules:

yaml
groups:
  - name: precompute
    interval: 30s
    rules:
      - record: job:qps:5m
        expr: sum by (job) (rate(http_requests_total[5m]))

1. Query Sharding:

yaml
query:
  max_samples: 50000000
  timeout: 2m
  parallelism: 16

1. Cache Configuration:

yaml
query:
  lookback-delta: 5m

Horizontal Scaling Solutions:

1. Thanos Solution:

• Sidecar mode: Attach Sidecar to each Prometheus
• Store Gateway: Long-term storage
• Query Frontend: Query caching and sharding
• Querier: Distributed querying

1. Cortex Solution:

• Fully distributed architecture
• Multi-tenant support
• Unlimited horizontal scaling
• Object storage backend

1. VictoriaMetrics Solution:

• Single-node or cluster mode
• High-performance compression
• Prometheus API compatible
• Low resource usage

Federation Architecture:

yaml
scrape_configs:
  - job_name: 'federate'
    scrape_interval: 15s
    honor_labels: true
    metrics_path: '/federate'
    params:
      'match[]':
        - '{__name__=~"job:.*"}'
    static_configs:
      - targets: ['prometheus-1:9090', 'prometheus-2:9090']

Resource Limits:

yaml
resources:
  requests:
    memory: "4Gi"
    cpu: "2"
  limits:
    memory: "8Gi"
    cpu: "4"

Performance Monitoring Metrics:

promql
# Scraping performance
rate(prometheus_tsdb_head_samples_appended_total[5m])
rate(prometheus_scrape_samples_post_metric_relabeling_total[5m])

# Query performance
prometheus_query_duration_seconds_sum
prometheus_query_duration_seconds_count

# Storage performance
prometheus_tsdb_compaction_duration
prometheus_tsdb_retention_limit_bytes

Best Practices:

1. Set different scrape intervals based on business importance
2. Use Recording Rules to pre-compute common queries
3. Regularly clean up unnecessary metrics
4. Monitor Prometheus self-performance metrics
5. Set reasonable resource limits
6. Use external storage for long-term retention
7. Consider using Thanos or Cortex for horizontal scaling