# 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:**

1.  Configure your data source to collect API response time data.
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.
3.  Ensure your data source returns data in a format compatible with the charting library used by the `api-monitoring-dashboard` plugin.

**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:**

1.  Configure your data source to track API requests by endpoint.
2.  Specify the time range for the chart.
3.  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:**

1.  Configure your server monitoring data source to collect CPU utilization data.
2.  Define appropriate thresholds for warning and critical levels.  These thresholds should be based on your API's performance requirements and resource constraints.
3.  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:**

1.  Configure your data source to track API latency by endpoint and time of day.
2.  Choose a color palette that effectively represents the range of latency values.
3.  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:**

1.  Configure your data source to collect detailed API error logs.
2.  Include relevant columns in the table, such as timestamp, API endpoint, HTTP status code, error message, and client IP address.
3.  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-dashboard` plugin 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.