Mining/AUDIT-PERFORMANCE.md

Performance Audit Report

This report details the findings of a performance audit conducted on the codebase. It covers several areas, including database performance, memory usage, concurrency, API performance, and build/deploy performance.

Database Performance

The application uses SQLite with WAL (Write-Ahead Logging) enabled, which is a good choice for the application's needs, as it allows for concurrent reads and writes. The database schema is well-defined, and the indexes on the hashrate_history and miner_sessions tables are appropriate for the queries being performed.

• N+1 Queries: No evidence of N+1 queries was found. The database interactions are straightforward and do not involve complex object relational mapping.
• Missing Indexes: The existing indexes are well-suited for the application's queries. No missing indexes were identified.
• Large Result Sets: The history endpoints could potentially return large result sets. Implementing pagination would be a good proactive measure to prevent performance degradation as the data grows.
• Inefficient Joins: The database schema is simple and does not involve complex joins. No inefficient joins were identified.
• Connection Pooling: The connection pool is configured to use a single connection, which is appropriate for SQLite.

Memory Usage

• Memory Leaks: No obvious memory leaks were identified. The application's memory usage appears to be stable.
• Large Object Loading: The log and history endpoints could potentially load large amounts of data into memory. Implementing streaming for these endpoints would be a good way to mitigate this.
• Cache Efficiency: The API uses a simple time-based cache for some endpoints, which is effective but could be improved. A more sophisticated caching mechanism, such as an LRU cache, could be used to improve cache efficiency.
• Garbage Collection: No issues with garbage collection were identified.

Concurrency

• Blocking Operations: The CheckInstallation function in xmrig.go shells out to the command line, which is a blocking operation. This could be optimized by using a different method to check for the miner's presence.
• Lock Contention: The Manager uses a mutex to protect the miners map, which is good for preventing race conditions. However, the stats collection iterates over all miners and collects stats sequentially, which could be a bottleneck. This could be improved by collecting stats in parallel.
• Thread Pool Sizing: The application does not use a thread pool.
• Async Opportunities: The build-all target in the Makefile builds for multiple platforms sequentially. This could be parallelized to reduce build times. Similarly, the before hook in .goreleaser.yaml runs tests and UI builds sequentially, which could also be parallelized.

API Performance

• Response Times: The API response times are generally good.
• Payload Sizes: The log and history endpoints could potentially return large payloads. Implementing response compression would be a good way to reduce payload sizes.
• Caching Headers: The API uses Cache-Control headers, which is good.
• Rate Limiting: The API has rate limiting in place, which is good.

Build/Deploy Performance

• Build Time: The build-all target in the Makefile builds for multiple platforms sequentially. This could be parallelized to reduce build times. The before hook in .goreleaser.yaml runs tests and UI builds sequentially, which could also be parallelized.
• Asset Size: The UI assets are not minified or compressed, which could increase load times.
• Cold Start: The application has a fast cold start time.