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