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Performance Optimization and Best Practices

Welcome to Lesson 15! You've now mastered the core features of Grafana - from data sources and dashboards to alerts and enterprise features. In this lesson, we'll focus on optimizing your Grafana deployment for performance and implementing best practices that ensure your monitoring solution remains fast, reliable, and maintainable.

Learning Goals:

  • Optimize dashboard performance and query efficiency
  • Configure Grafana for optimal resource usage
  • Implement caching strategies and data source optimizations
  • Apply monitoring best practices for your Grafana instance
  • Troubleshoot common performance issues

Dashboard Performance Optimization​

Efficient Query Design​

The most significant performance improvements come from optimizing your data queries. Let's examine some common patterns:

inefficient_query.sql
-- DON'T: Querying too much data
SELECT * FROM metrics 
WHERE time > now() - 7d
AND host = 'web-server-01'
efficient_query.sql
-- DO: Targeted queries with aggregation
SELECT 
  time_bucket('1m', time) as time,
  avg(cpu_usage) as cpu_avg,
  max(memory_usage) as memory_max
FROM metrics 
WHERE time > now() - 1h
AND host = 'web-server-01'
GROUP BY time_bucket('1m', time)
tip

Always use the smallest time range necessary for your visualization. For real-time dashboards, consider using relative time ranges like now()-15m instead of fixed ranges.

Panel Optimization Techniques​

Reduce the number of panels and use appropriate refresh intervals:

dashboard_optimized.json
{
  "panels": [
    {
      "title": "CPU Usage",
      "type": "stat",
      "targets": [
        {
          "expr": "rate(node_cpu_seconds_total[5m])",
          "legendFormat": "{{instance}}"
        }
      ],
      "refresh": "5s",  // Appropriate for real-time monitoring
      "maxDataPoints": 1000
    },
    {
      "title": "Daily Trends",
      "type": "timeseries",
      "targets": [
        {
          "expr": "node_memory_MemFree_bytes",
          "legendFormat": "Free Memory"
        }
      ],
      "refresh": "1m",  // Less frequent for trend analysis
      "maxDataPoints": 500
    }
  ]
}

Grafana Server Configuration​

Memory and Cache Settings​

Optimize your grafana.ini configuration:

grafana.ini (performance section)
[dataproxy]
logging = true
timeout = 30
keep_alive_seconds = 30

[database]
max_conns = 100
max_idle_conns = 20
conn_max_lifetime = 14400

[session]
provider = database
provider_config = 
cookie_secure = true
session_life_time = 86400

[analytics]
reporting_enabled = false
check_for_updates = false

Data Source Connection Pooling​

Configure data sources for optimal performance:

datasource_config.yaml
apiVersion: 1

datasources:
  - name: Prometheus
    type: prometheus
    url: http://prometheus:9090
    access: proxy
    isDefault: true
    jsonData:
      timeInterval: 30s
      queryTimeout: 30s
      httpMethod: POST
      manageAlerts: true
      customQueryParameters: "max_source_resolution=auto"
    version: 1
    editable: true

Caching Strategies​

Dashboard and Query Caching​

Implement caching at multiple levels:

grafana.ini with Redis
[redis]
enabled = true
addr = redis:6379
password = 
db = 0
pool_size = 100

[session]
provider = redis
provider_config = addr=redis:6379,pool_size=100,db=0

Monitoring Grafana Itself​

Health Check Dashboard​

Create a dashboard to monitor Grafana's performance:

grafana_health_dashboard.json
{
  "title": "Grafana Health Monitoring",
  "panels": [
    {
      "title": "HTTP Requests",
      "type": "timeseries",
      "targets": [
        {
          "expr": "sum(rate(grafana_http_request_duration_seconds_count[5m])) by (handler)",
          "legendFormat": "{{handler}}"
        }
      ]
    },
    {
      "title": "Database Connections",
      "type": "stat",
      "targets": [
        {
          "expr": "grafana_database_conns_open"
        }
      ],
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "thresholds"
          },
          "thresholds": {
            "steps": [
              {"value": null, "color": "green"},
              {"value": 80, "color": "yellow"},
              {"value": 90, "color": "red"}
            ]
          }
        }
      }
    }
  ]
}

Performance Testing​

Load Testing Dashboards​

Use this script to simulate dashboard loads:

load_test.sh
#!/bin/bash

# Test dashboard loading performance
DASHBOARD_UID="your-dashboard-uid"
GRAFANA_URL="http://localhost:3000"
API_KEY="your-api-key"

for i in {1..50}; do
  echo "Request $i"
  curl -s -H "Authorization: Bearer $API_KEY" \
    "$GRAFANA_URL/api/dashboards/uid/$DASHBOARD_UID" \
    -o /dev/null -w "%{time_total}s\n"
  sleep 0.1
done
warning

Always perform load testing in a staging environment first. High concurrency can impact production performance.

Common Pitfalls​

  • Too many panels: Dashboards with 20+ panels can become slow to load and render
  • Over-aggressive refresh rates: Setting refresh intervals too low (e.g., 1s) can overwhelm data sources
  • Large time ranges: Querying months of high-resolution data instead of using downsampling
  • Inefficient queries: Not using aggregations or filtering in the data source
  • Missing caching: Not leveraging browser or server-side caching for static resources
  • Ignoring connection pooling: Creating new database connections for each request
  • Poor dashboard organization: Not using folders and proper naming conventions

Summary​

In this lesson, you've learned essential performance optimization techniques for Grafana:

  • Optimize dashboard queries with proper time ranges and aggregations
  • Configure Grafana server settings for optimal resource usage
  • Implement caching strategies at multiple levels
  • Monitor Grafana's own performance metrics
  • Test dashboard performance under load
  • Avoid common performance pitfalls

Remember that performance optimization is an ongoing process. Regularly review your dashboards, monitor Grafana's resource usage, and adjust configurations as your usage patterns evolve.

Quiz​

Show quiz

  1. What is the most effective way to improve dashboard performance? a) Increasing server memory b) Optimizing data source queries c) Using more colors in visualizations d) Adding more panels

  2. Which refresh rate is most appropriate for a real-time monitoring dashboard? a) 1s b) 5s c) 1m d) 1h

  3. What is a key benefit of implementing Redis caching for Grafana sessions? a) Better visualization colors b) Reduced database load and faster session management c) Automatic dashboard creation d) Free SSL certificates

  4. Why should you avoid querying large time ranges with high-resolution data? a) It makes the dashboard look better b) It reduces query performance and increases load on data sources c) Grafana doesn't support large time ranges d) It automatically enables caching

  5. What is the purpose of the maxDataPoints setting in panel configuration? a) To limit the number of colors used b) To control query resolution and prevent over-fetching data c) To set the maximum number of panels per dashboard d) To configure user permissions

Answers:

  1. b) Optimizing data source queries
  2. b) 5s (balances real-time needs with performance)
  3. b) Reduced database load and faster session management
  4. b) It reduces query performance and increases load on data sources
  5. b) To control query resolution and prevent over-fetching data