feat: Add configurable data point limit for Traffic Over Time

[NotYuSheng]

Implement automatic interval adjustment to limit the number of data points returned by timeline endpoints, improving performance and rendering for large PCAP files.

Changes:

• Add AnalysisProperties configuration class with timeline limits
• Add max-timeline-data-points (default: 1000) to application.yml
• Add calculateOptimalInterval() method in TimelineService
• Update timeline endpoints to accept optional maxDataPoints parameter
• Add validation for maxDataPoints (10-10,000 range)
• Update frontend timelineService to support maxDataPoints parameter

The system automatically adjusts the time interval when expected bins exceed the limit, preserving full time range coverage and data accuracy through aggregation rather than sampling.

[gemini-code-assist]

Summary of Changes

Hello @NotYuSheng, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a significant enhancement to the traffic over time analysis feature by allowing users to configure a maximum limit for data points returned by timeline endpoints. This is crucial for improving the performance and rendering efficiency, especially when dealing with large PCAP files that could otherwise generate an excessive number of data points. The system now intelligently adjusts the time interval for binning, ensuring that the full time range is covered while keeping the data point count within a manageable limit, thus providing a smoother user experience without sacrificing data accuracy through aggregation.

Highlights

• Configurable Data Point Limit: Introduced a configurable maximum data point limit for timeline endpoints, allowing control over the number of data points returned for traffic over time analysis.
• Automatic Interval Adjustment: Implemented an automatic interval adjustment mechanism in the backend service to dynamically increase the binning interval when the number of expected data points exceeds the configured limit, ensuring optimal performance and rendering.
• New Configuration Properties: Added a new AnalysisProperties configuration class to manage analysis-related settings, including maxTimelineDataPoints, minTimelineInterval, and autoAdjustInterval.
• API Endpoint Enhancements: Updated existing timeline API endpoints (/api/timeline/{fileId} and /api/timeline/{fileId}/range) to accept an optional maxDataPoints parameter, with validation for its acceptable range (10-10,000).
• Frontend Integration: Modified the frontend timelineService to support passing the optional maxDataPoints parameter to the backend timeline APIs.

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[gemini-code-assist]

Code Review

This pull request introduces a valuable feature for performance improvement by limiting the number of data points in timeline queries and automatically adjusting the interval. However, a high-severity Denial of Service (DoS) vulnerability was identified due to missing validation of the interval query parameter, which could lead to application crashes or infinite loops if a zero or negative value is provided. To address this and improve overall robustness, I recommend using Spring's declarative validation framework for input validation in the controller, which will simplify the code, eliminate duplication, and provide more appropriate HTTP error responses. A minor simplification on the frontend is also suggested. These improvements will make the new feature more resilient, secure, and easier to maintain.

[NotYuSheng]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces a valuable feature for performance optimization by adding a configurable data point limit for timeline queries and automatically adjusting the time interval. However, a critical security vulnerability was identified due to a lack of access control on the timeline endpoints, which could lead to Insecure Direct Object Reference (IDOR) attacks. Additionally, potential Denial of Service (DoS) risks are present from inefficient algorithmic complexity and possible integer overflow in the binning logic, and a log injection vulnerability was found in the range-based timeline endpoint. From a code quality perspective, a high-severity bug in TimelineService miscalculates expected bins, potentially exceeding data point limits. There are also opportunities to refactor duplicated validation logic in TimelineController and simplify frontend code by leveraging apiClient capabilities.