5.5 KiB
API Monitoring Dashboard: Visualization Examples
This document provides examples of different visualizations you can use in your API monitoring dashboards, created with the api-monitoring-dashboard plugin. Use these examples as inspiration and adapt them to your specific API and monitoring needs.
1. Line Charts: Time-Series Data
Line charts are excellent for visualizing trends over time. They are particularly useful for showing API response times, request rates, and error rates.
Example: API Response Time over the Past 24 Hours
- Metric: Average API Response Time (milliseconds)
- Time Range: Past 24 hours
- Granularity: 1 hour
- Visualization: Line Chart
- Data Source: [Placeholder: Your API Monitoring Data Source (e.g., Prometheus, Datadog, New Relic)]
Instructions:
- Configure your data source to collect API response time data.
- Specify the time range and granularity for the chart. Shorter granularities (e.g., 5 minutes) are useful for identifying short-term spikes, while longer granularities (e.g., 1 hour) are better for identifying long-term trends.
- Ensure your data source returns data in a format compatible with the charting library used by the
api-monitoring-dashboardplugin.
Placeholder for Chart Image (Optional):
[Insert Image of API Response Time Line Chart Here]
2. Bar Graphs: Categorical Data
Bar graphs are useful for comparing different categories of data, such as API endpoints, HTTP status codes, or geographic regions.
Example: API Request Count by Endpoint
- Metric: Number of API Requests
- Category: API Endpoint (e.g.,
/users,/products,/orders) - Time Range: Past 7 days
- Visualization: Bar Graph
- Data Source: [Placeholder: Your API Monitoring Data Source]
Instructions:
- Configure your data source to track API requests by endpoint.
- Specify the time range for the chart.
- Consider using different colors to represent different API endpoints.
Placeholder for Chart Image (Optional):
[Insert Image of API Request Count Bar Graph Here]
3. Gauge Charts: Single Value Performance
Gauge charts are effective for displaying a single, critical performance metric and its current status relative to a threshold.
Example: CPU Utilization of API Server
- Metric: CPU Utilization (%)
- Threshold: 80% (Warning), 95% (Critical)
- Visualization: Gauge Chart
- Data Source: [Placeholder: Your Server Monitoring Data Source]
Instructions:
- Configure your server monitoring data source to collect CPU utilization data.
- Define appropriate thresholds for warning and critical levels. These thresholds should be based on your API's performance requirements and resource constraints.
- The gauge chart should visually indicate when the metric exceeds the warning or critical thresholds.
Placeholder for Chart Image (Optional):
[Insert Image of CPU Utilization Gauge Chart Here]
4. Heatmaps: Correlation and Density
Heatmaps are useful for visualizing correlations between different metrics or the density of events over time.
Example: Latency Distribution by API Endpoint and Time of Day
- Metric: API Latency (milliseconds)
- X-Axis: API Endpoint
- Y-Axis: Time of Day
- Visualization: Heatmap
- Data Source: [Placeholder: Your API Monitoring Data Source]
Instructions:
- Configure your data source to track API latency by endpoint and time of day.
- Choose a color palette that effectively represents the range of latency values.
- Consider using a logarithmic scale for the latency values to better visualize variations in the data.
Placeholder for Chart Image (Optional):
[Insert Image of Latency Distribution Heatmap Here]
5. Tables: Detailed Data
Tables are useful for displaying detailed data and allowing users to sort and filter the data.
Example: Recent API Errors
- Columns: Timestamp, API Endpoint, HTTP Status Code, Error Message, Client IP Address
- Data Source: [Placeholder: Your API Error Logs]
- Visualization: Table
Instructions:
- Configure your data source to collect detailed API error logs.
- Include relevant columns in the table, such as timestamp, API endpoint, HTTP status code, error message, and client IP address.
- Allow users to sort and filter the data by different columns.
Placeholder for Table Data (Example):
| Timestamp | API Endpoint | HTTP Status Code | Error Message | Client IP Address |
|---|---|---|---|---|
| 2023-10-27 10:00:00 | /users | 500 | Internal Server Error | 192.168.1.100 |
| 2023-10-27 10:01:00 | /products | 404 | Not Found | 192.168.1.101 |
| 2023-10-27 10:02:00 | /orders | 503 | Service Unavailable | 192.168.1.102 |
Important Considerations
- Data Source Integration: Ensure the
api-monitoring-dashboardplugin can seamlessly integrate with your existing monitoring data sources. Provide clear instructions on how to configure these integrations. - Customization: Allow users to customize the appearance and behavior of the visualizations, such as color palettes, axis labels, and threshold values.
- Alerting: Integrate alerts with the visualizations to notify users when critical performance metrics exceed predefined thresholds.
- Accessibility: Ensure the visualizations are accessible to users with disabilities, following WCAG guidelines.
- Performance: Optimize the visualizations for performance, especially when dealing with large datasets.