# 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

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## 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
