feat(logger): log memory usage #1642
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WalkthroughThe updateTracker middleware was modified to log detailed memory usage statistics in megabytes when a response closes. Additionally, the logger service now serializes context values using JSON.stringify, ensuring accurate representation of complex values in log messages. No changes were made to the signatures of exported or public entities. Changes
Sequence Diagram(s)sequenceDiagram
participant Client
participant Middleware (updateTracker)
participant Logger
Client->>Middleware (updateTracker): Send request
Middleware (updateTracker)->>Middleware (updateTracker): Process request
Middleware (updateTracker)->>Middleware (updateTracker): Collect memory usage stats
Middleware (updateTracker)->>Logger: Log response with memory_usage in context (JSON.stringify values)
Logger-->>Middleware (updateTracker): Log entry recorded
Middleware (updateTracker)-->>Client: Send response
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Actionable comments posted: 0
🧹 Nitpick comments (2)
src/v3/services/logger/logger.ts (1)
19-19: Good change for object serialization, but consider performance and security implications.Using
JSON.stringifyenables proper logging of complex objects like the newmemory_usagedata. However, be aware of potential impacts:
- Performance:
JSON.stringifycan be expensive for large objects on high-traffic endpoints- Security: This may expose more sensitive data in logs than the previous string interpolation
- Error handling: Consider wrapping in try-catch for circular reference protection
For safer implementation, consider:
-.map(([key, value]) => `${key}=${JSON.stringify(value)}`) +.map(([key, value]) => { + try { + return `${key}=${JSON.stringify(value)}`; + } catch (err) { + return `${key}=[Circular/Error]`; + } +})src/v3/middleware/update_tracker.ts (1)
51-59: Memory tracking implementation looks correct, but consider performance impact.The memory usage collection logic is well-implemented with proper byte-to-megabyte conversion and consistent naming. However, consider the performance implications:
process.memoryUsage()is called on every request completion- This could impact performance on high-traffic endpoints
- Consider sampling memory usage (e.g., every Nth request) for production environments
For better performance in high-traffic scenarios, consider sampling:
+// Sample memory usage every 100 requests +let requestCount = 0; res.on('close', () => { const [s, ns] = process.hrtime(start); const ms = Math.round(s * 1e3 + ns / 1e6); const message = (res.locals.message ?? '').replace(/\n|\r/g, ''); const user = res.locals.currentUser; + let memory_usage; + if (++requestCount % 100 === 0) { const memory = process.memoryUsage(); - - const memory_usage = { + memory_usage = { rssMB: +(memory.rss / 1024 / 1024).toFixed(2), heapTotalMB: +(memory.heapTotal / 1024 / 1024).toFixed(2), heapUsedMB: +(memory.heapUsed / 1024 / 1024).toFixed(2), externalMB: +(memory.external / 1024 / 1024).toFixed(2), arrayBuffersMB: +(memory.arrayBuffers / 1024 / 1024).toFixed(2), }; + }
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📒 Files selected for processing (2)
src/v3/middleware/update_tracker.ts(2 hunks)src/v3/services/logger/logger.ts(1 hunks)
🔇 Additional comments (1)
src/v3/middleware/update_tracker.ts (1)
72-72: Clean integration of memory usage into logging context.The addition of
memory_usageto the context object is well-implemented and follows the existing pattern. This will be properly serialized by the updated logger service.
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🎉 This PR is included in version 3.26.0 🎉 The release is available on GitHub release Your semantic-release bot 📦🚀 |
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