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Dashboard Best Practices

Introduction​

Creating effective dashboards is both an art and a science. While Grafana provides powerful tools to build visualizations, following established best practices ensures your dashboards remain useful, maintainable, and accessible to all users. This guide covers essential principles for designing dashboards that communicate data clearly, load efficiently, and provide actionable insights.

Core Dashboard Design Principles​

1. Purpose-Driven Design​

Every dashboard should have a clear purpose that answers the question: "What problem does this dashboard solve?"

Good practices:

  • Define a specific audience and their needs
  • Focus on answering specific questions
  • Include only relevant metrics for the dashboard's purpose

Example purpose statements:

  • "To monitor application performance and quickly identify bottlenecks"
  • "To track user engagement metrics across different product features"
  • "To provide executive-level overview of business KPIs"

2. Visual Hierarchy​

Structure information in order of importance to guide viewers' attention.

Good practices:

  • Place critical metrics and alerts at the top left (where eyes naturally start)
  • Group related panels together
  • Use consistent sizing for panels of similar importance
  • Use larger panels for more important information

3. Consistent Naming Conventions​

Establish and follow naming conventions for dashboards, panels, and variables.

Good practices:

  • Use descriptive, specific names
  • Follow a consistent pattern (e.g., "[Service]-[Metrics Type]")
  • Include ownership information

Example:

[Environment]-[Application]-[Component]-[Metrics Type]

Such as:

  • "Prod-UserAPI-Performance"
  • "Dev-PaymentService-Errors"

Technical Best Practices​

1. Query Optimization​

Efficient queries improve dashboard performance and reduce load on your data sources.

Good practices:

  • Limit time ranges when possible
  • Use template variables to filter data
  • Avoid overly complex queries that process unnecessary data

Before optimization:

sql
SELECT * FROM "http_requests" WHERE time > now() - 24h

After optimization:

sql
SELECT mean("response_time") FROM "http_requests" 
WHERE "status_code" != 200 AND time > now() - 24h 
GROUP BY time(5m), "endpoint"

2. Template Variables​

Use template variables to make dashboards flexible and reusable.

Good practices:

  • Create variables for common filters (environment, service, time range)
  • Set appropriate default values
  • Use dependent variables when applicable

Example variable setup:

Variable name: environment
Label: Environment
Type: Query
Data source: Prometheus
Query: label_values(node_exporter_up, env)
Sort: Alphabetical (asc)

Then use in queries:

sum(rate(http_requests_total{env="$environment"}[5m])) by (service)

3. Careful Use of Auto-Refresh​

Balance between real-time data and performance.

Good practices:

  • Set reasonable refresh rates based on data volatility
  • Use longer refresh intervals for historical dashboards
  • Consider disabling auto-refresh for complex dashboards

Recommended settings:

  • Operational dashboards: 30s - 1m
  • Business dashboards: 5m - 15m
  • Historical analysis: Manual refresh or very infrequent

Visual Design Best Practices​

1. Consistent Color Usage​

Colors should convey meaning and support interpretation, not distract.

Good practices:

  • Use consistent colors for the same metrics across panels
  • Follow color-blindness accessibility guidelines
  • Use red for critical issues, yellow for warnings, green for "good" status

Example color scheme:

  • Success/Normal: #7EB26D (green)
  • Warning/Degraded: #EAB839 (yellow)
  • Error/Critical: #E24D42 (red)
  • Informational: #6ED0E0 (blue)

2. Appropriate Visualizations​

Choose visualization types that best represent your data.

Good practices:

  • Time series for trends over time
  • Gauges for values against thresholds
  • Tables for detailed comparisons or exact values
  • Stat panels for current values of important metrics

Visualization selection guide:

Data TypeRecommended Visualization
Trends over timeTime series graph
Single value vs thresholdGauge or Stat panel
Categorical comparisonBar chart
Part-to-whole relationshipPie chart
Geospatial dataGeomap
Detailed metricsTable

3. Annotations and Documentation​

Provide context to help users understand the dashboard.

Good practices:

  • Add a dashboard description explaining purpose and audience
  • Include panel descriptions for complex metrics
  • Use annotations to mark important events
  • Add documentation links for further reference

Example dashboard description:

This dashboard monitors the performance of our payment processing service.
- **Purpose**: Identify performance bottlenecks and errors
- **Primary audience**: Payment team on-call engineers
- **Data source**: Prometheus + Payment service logs
- **Refresh rate**: 30s

Organization and Maintenance​

1. Folder Structure​

Organize dashboards in a logical folder structure.

Good practices:

  • Group by team, service, or function
  • Use consistent naming across folders
  • Implement access controls based on folder structure

Example structure:

Grafana
├── Executive Dashboards
├── Service Dashboards
│   ├── Authentication
│   ├── Payment
│   └── User API
├── Team Dashboards
│   ├── Frontend
│   ├── Backend
│   └── DevOps
└── Infrastructure
    ├── Kubernetes
    ├── Databases
    └── Networking

2. Tagging Strategy​

Use tags to enable better search and filtering.

Good practices:

  • Create a standardized tag taxonomy
  • Include tags for environment, service, team, purpose
  • Don't over-tag—keep it manageable

Example tags:

  • production, staging, development
  • service:payments, service:authentication
  • team:backend, team:frontend
  • type:operations, type:business

3. Version Control​

Treat dashboards as code to maintain history and facilitate collaboration.

Good practices:

  • Export JSON definitions to version control
  • Use Grafana provisioning to load dashboards from files
  • Document changes in commit messages

Example workflow:

  1. Export dashboard JSON
  2. Commit to Git repository
  3. Review changes through pull requests
  4. After approval, provision to Grafana

Simple provisioning example (dashboard.yaml):

yaml
apiVersion: 1

providers:
- name: 'default'
  orgId: 1
  folder: 'Production'
  type: file
  disableDeletion: false
  updateIntervalSeconds: 30
  options:
    path: /var/lib/grafana/dashboards

Performance Optimization​

1. Panel and Row Limits​

Keep dashboards focused and performant.

Good practices:

  • Limit to 20-25 panels per dashboard
  • Use collapsible rows for less critical information
  • Create linked dashboards for drill-down rather than including everything

2. Caching​

Leverage Grafana's caching capabilities.

Good practices:

  • Enable caching for stable data sources
  • Set appropriate TTL (Time To Live) values
  • Use query caching for expensive queries

Example configuration (in grafana.ini):

ini
[unified_alerting.screenshots]
capture_timeout = 10s

[remote_cache]
type = redis
connstr = addr=127.0.0.1:6379,pool_size=100,db=0,ssl=false

3. Resource-Intensive Features​

Be cautious with features that can impact performance.

Good practices:

  • Minimize panels with high data point counts
  • Use data resampling when appropriate
  • Be careful with resource-intensive panel types (like heatmaps with many cells)

Accessibility and Usability​

1. Dashboard Layout​

Design for various screen sizes and user needs.

Good practices:

  • Test on different screen sizes and devices
  • Use responsive layouts
  • Ensure sufficient contrast for text elements

2. Text and Labels​

Make text content clear and readable.

Good practices:

  • Use clear, concise titles
  • Include units in labels
  • Size text appropriately for readability
  • Avoid abbreviations unless standard for your audience

3. Dashboard Variables​

Make dashboards intuitive to navigate and filter.

Good practices:

  • Group related variables
  • Provide sensible defaults
  • Add descriptions to complex variables
  • Order variables logically (broader filters first)

Practical Examples​

Example 1: Application Performance Dashboard​

An effective application performance dashboard might include:

  1. Top Section (Critical):

    • Service availability status (up/down)
    • Error rate graph
    • Response time graph

  2. Middle Section (Important):

    • Request volume by endpoint
    • Resource utilization (CPU, memory)
    • Database query performance

  3. Bottom Section (Details):

    • Individual endpoint performance
    • Slow queries table
    • Dependency health metrics

Variables:

  • Environment (production, staging, development)
  • Service instance
  • Time range

Example 2: Business KPI Dashboard​

A business metrics dashboard typically includes:

  1. Top Section (Critical):

    • Revenue/conversions today vs. target
    • User activity trend
    • Funnel completion rate

  2. Middle Section (Important):

    • User segmentation
    • Geographic distribution
    • Feature usage metrics

  3. Bottom Section (Details):

    • Retention metrics
    • Detailed conversion data
    • Historical comparisons

Variables:

  • Date range
  • User segment
  • Product category

Summary​

Creating effective Grafana dashboards requires thoughtful planning and adherence to best practices. By focusing on clear purpose, optimized queries, consistent design, and proper organization, you can build dashboards that provide actionable insights while remaining maintainable and performant.

Remember these key principles:

  • Design with a specific purpose and audience in mind
  • Optimize for performance and readability
  • Maintain consistency in naming, colors, and organization
  • Document your dashboards and provide context
  • Treat dashboards as code with version control
  • Test on different devices and with different users

Additional Resources​

Exercises​

  1. Audit an Existing Dashboard: Select an existing dashboard and evaluate it against the best practices in this guide. Identify three improvements you could make.

  2. Query Optimization: Take a slow dashboard query and optimize it. Measure the performance improvement.

  3. Design Challenge: Create a dashboard for a specific use case (e.g., website monitoring) that follows all the best practices outlined above.

  4. Documentation Practice: Write comprehensive descriptions for a dashboard and all its panels, following the guidelines in this document.

  5. Version Control Setup: Create a workflow to export, version control, and provision dashboards as code.


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