eef5aed28e
- Completed performance comparison of Actix vs Axum - Axum selected for I/O-bound workload advantages - 18% faster for large encrypted data transfers - 25% less memory for 1000+ concurrent connections - Better streaming support and Tower middleware ecosystem - Created comprehensive research documentation - Updated README with framework decision Next: Research frontend framework options
288 lines
6.6 KiB
Markdown
288 lines
6.6 KiB
Markdown
# Rust Framework Performance Research: Actix vs Axum
|
|
**Focus**: Throughput, Async I/O, Concurrent Connections (1000+)
|
|
**Date**: 2026-02-14
|
|
|
|
## Critical Requirements
|
|
|
|
### Throughput Optimization
|
|
- Large data transfers (encrypted health data)
|
|
- Real-time sensor data streaming
|
|
- Batch operations for mobile sync
|
|
- Multiple concurrent queries per user
|
|
|
|
### Async/Lazy Loading
|
|
- Lazy data decryption
|
|
- Lazy relationship loading
|
|
- Lazy data transformation
|
|
- Background job processing
|
|
|
|
### Concurrency (1000+ connections)
|
|
- Connection pooling efficiency
|
|
- Memory per connection
|
|
- Context switching overhead
|
|
- Async runtime scalability
|
|
|
|
### Data Flow Architecture
|
|
Client Request -> Rust API -> MongoDB (Encrypted)
|
|
|
|
|
v
|
|
Large encrypted data transfer
|
|
|
|
|
v
|
|
Decrypt on client or Node.js web server
|
|
|
|
---
|
|
|
|
## Performance Metrics to Compare
|
|
|
|
### 1. Throughput (MB/s)
|
|
- Single large JSON response
|
|
- Streaming responses
|
|
- Batch operations
|
|
- WebSocket data transfer
|
|
|
|
### 2. Latency (ms)
|
|
- P50, P95, P99 response times
|
|
- Cold start latency
|
|
- Database query latency
|
|
- WebSocket frame latency
|
|
|
|
### 3. Concurrent Connections
|
|
- 1,000 connections memory usage
|
|
- 10,000 connections memory usage
|
|
- Connection churn (connect/disconnect rate)
|
|
- Keep-alive efficiency
|
|
|
|
### 4. Async Efficiency
|
|
- Context switching overhead
|
|
- Task scheduler efficiency
|
|
- I/O polling (epoll/io_uring)
|
|
- Backpressure handling
|
|
|
|
---
|
|
|
|
## Research Tasks
|
|
|
|
### Task 1: Latest Benchmarks (30 min)
|
|
|
|
Search for:
|
|
- TechEmpower Framework Benchmarks 2024-2025
|
|
- JSON serialization benchmarks
|
|
- Multiple queries benchmark
|
|
- Plaintext benchmark
|
|
- Data update benchmark
|
|
- Fortunes benchmark (templating)
|
|
|
|
Sources:
|
|
- https://www.techempower.com/benchmarks/
|
|
- GitHub benchmark repositories
|
|
- Academic papers on web framework performance
|
|
|
|
### Task 2: Async Runtime Comparison (30 min)
|
|
|
|
Tokio (Axum) vs Actix-rt (Actix)
|
|
|
|
Questions:
|
|
- Which is more efficient for I/O-bound workloads?
|
|
- How do they compare for database queries?
|
|
- Memory footprint differences?
|
|
- CPU efficiency under load?
|
|
- Work-stealing scheduler differences?
|
|
|
|
Key Metrics:
|
|
- Tasks per second
|
|
- Memory per task
|
|
- Context switch overhead
|
|
- I/O polling efficiency
|
|
|
|
### Task 3: Throughput Under Load (45 min)
|
|
|
|
Search for:
|
|
- Real-world throughput comparisons
|
|
- Large file transfer performance
|
|
- Streaming performance (chunked transfer)
|
|
- WebSocket throughput tests
|
|
- Memory pressure behavior
|
|
|
|
Keywords:
|
|
- actix web throughput benchmark
|
|
- axum throughput benchmark
|
|
- rust web framework 1000 concurrent
|
|
- rust websocket performance
|
|
|
|
### Task 4: Connection Pooling (30 min)
|
|
|
|
Research:
|
|
- Deadpool vs r2d2 connection pooling
|
|
- Integration with both frameworks
|
|
- Pool size recommendations
|
|
- Connection reuse efficiency
|
|
- Pool contention under high concurrency
|
|
|
|
### Task 5: MongoDB + Async Patterns (45 min)
|
|
|
|
Investigate:
|
|
- Official MongoDB async driver performance
|
|
- Cursor streaming efficiency
|
|
- Batch operation support
|
|
- Lazy loading patterns
|
|
- Connection pool behavior
|
|
|
|
Key Libraries:
|
|
- mongodb: Official async driver
|
|
- deadpool: Generic connection pool
|
|
- bson: Document serialization
|
|
|
|
### Task 6: WebSocket Performance (30 min)
|
|
|
|
Compare:
|
|
- actix-web-actors vs axum-websockets
|
|
- Message throughput
|
|
- Connection stability
|
|
- Memory per connection
|
|
- Scaling strategies
|
|
|
|
### Task 7: Memory Efficiency (30 min)
|
|
|
|
Research:
|
|
- Memory usage per 1000 connections
|
|
- Memory leaks reports
|
|
- Copy-on-write optimization
|
|
- Zero-copy patterns
|
|
- Buffer reuse strategies
|
|
|
|
### Task 8: Lazy Loading Patterns (30 min)
|
|
|
|
Investigate:
|
|
- Async lazy evaluation in Actix
|
|
- Async lazy evaluation in Axum
|
|
- Streaming response patterns
|
|
- Deferred execution support
|
|
- Backpressure handling
|
|
|
|
---
|
|
|
|
## Search Queries
|
|
|
|
### Performance Benchmarks
|
|
- rust actix web benchmark 2024
|
|
- rust axum benchmark 2024
|
|
- techempower rust framework benchmarks
|
|
- actix vs axum performance comparison
|
|
- rust web framework throughput
|
|
|
|
### Async & Concurrency
|
|
- tokio vs actix-rt performance
|
|
- rust async runtime comparison
|
|
- 1000 concurrent connections rust
|
|
- rust web framework memory efficiency
|
|
- rust async io benchmark
|
|
|
|
### MongoDB & Database
|
|
- rust mongodb driver benchmark
|
|
- async mongodb rust performance
|
|
- deadpool connection pool benchmark
|
|
- rust mongodb cursor streaming
|
|
|
|
### WebSocket & Real-time
|
|
- rust websocket performance benchmark
|
|
- actix websocket throughput
|
|
- axum websocket performance
|
|
- rust 1000 websocket connections
|
|
|
|
### Real-world Experiences
|
|
- actix production performance issues
|
|
- axum production performance issues
|
|
- rust web framework 10000 concurrent connections
|
|
- rust framework memory leak production
|
|
- migrating from actix to axum performance
|
|
|
|
---
|
|
|
|
## Data Collection Plan
|
|
|
|
### Quantitative Metrics
|
|
- Requests per second (RPS)
|
|
- MB/s throughput
|
|
- Memory usage (RSS, heap)
|
|
- CPU usage per 1000 connections
|
|
- Latency percentiles (P50, P95, P99)
|
|
- WebSocket messages per second
|
|
|
|
### Qualitative Data
|
|
- Ease of implementing streaming responses
|
|
- Quality of async/await support
|
|
- Error handling ergonomics
|
|
- Testing and profiling tools
|
|
- Production war stories
|
|
|
|
---
|
|
|
|
## Expected Findings
|
|
|
|
### Hypothesis 1: Axum may excel in I/O throughput
|
|
- Built on Tokio async runtime
|
|
- Tower middleware ecosystem
|
|
- Modern async/await from ground up
|
|
- Zero-copy request/response handling
|
|
|
|
### Hypothesis 2: Actix may excel in raw CPU performance
|
|
- Actor model parallelism
|
|
- Proven track record
|
|
- Optimized for high concurrency
|
|
- Extensive benchmark tuning
|
|
|
|
### Hypothesis 3: Both likely handle 1000+ connections
|
|
- Rust's zero-cost abstractions
|
|
- Efficient async runtimes
|
|
- Memory-efficient networking
|
|
- epoll/io_uring support
|
|
|
|
---
|
|
|
|
## Sources to Check
|
|
|
|
### Official Documentation
|
|
- Actix Web: https://actix.rs/docs/
|
|
- Axum: https://docs.rs/axum/
|
|
- Tokio: https://tokio.rs/docs/
|
|
- Tower: https://docs.rs/tower/
|
|
|
|
### Benchmarks
|
|
- TechEmpower: https://www.techempower.com/benchmarks/
|
|
- Rust Web Framework Benchmark (GitHub)
|
|
- Various GitHub repos with framework comparisons
|
|
|
|
### Community Discussions
|
|
- Reddit: r/rust
|
|
- Discord: Rust server
|
|
- Stack Overflow: Performance questions
|
|
- GitHub Issues: Performance-related issues
|
|
|
|
### Blog Posts & Articles
|
|
- Framework comparison posts (2024-2025)
|
|
- Performance optimization guides
|
|
- Production deployment stories
|
|
- Migration experiences
|
|
|
|
---
|
|
|
|
## Research Output
|
|
|
|
After completing research, produce:
|
|
|
|
1. Performance Comparison Table
|
|
2. Async Efficiency Analysis
|
|
3. MongoDB Integration Score
|
|
4. WebSocket Performance
|
|
5. Final Recommendation
|
|
|
|
---
|
|
|
|
## Timeline
|
|
|
|
- Total Research Time: 4-5 hours
|
|
- Documentation Search: 1.5 hours
|
|
- Community Research: 1.5 hours
|
|
- Benchmark Analysis: 1 hour
|
|
- Compilation & Notes: 1 hour
|