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mihomo-rust

High-performance Rust implementation of mihomo proxy core.

Why We Built This

We were playing Honkai: Star Rail, ready for a big pull, prayers done, finger on the button...

Mental preparation: READY
Ritual performed: COMPLETE
Finger pressed: YES
GC Stop-The-World: 50ms
Result: "Network error, please retry"
Gacha luck: RUINED

That 50ms latency spike from Go's garbage collector? It cost us a 5-star character (probably).

So we rewrote the entire proxy protocol layer in Rust. Zero GC, zero jitter, zero excuses.

Now our pulls are smooth. Our trades execute on time. Our real-time apps don't stutter.

Was it overkill? Absolutely. Was it worth it? Also absolutely.

Actual Use Cases

Beyond gacha gaming, mihomo-rust is built for scenarios where latency stability matters more than raw speed:

  • Trading systems: Where milliseconds = money
  • Real-time gaming: Competitive multiplayer, gacha pulls
  • Live streaming: OBS, Discord, real-time communication
  • High-frequency applications: 10K+ concurrent connections
  • API gateways: Edge proxies, latency-sensitive routing

Why Rust over Go?

The Problem with Go

Traditional Go proxy implementations suffer from:

  • GC-induced latency spikes: Garbage collector causes unpredictable 10-100ms pauses
  • p99.9 jitter: Tail latency spikes under pressure
  • Memory bloat: GC overhead grows with connection count

The Rust Solution

mihomo-rust eliminates these issues:

  • Zero-cost abstractions: No runtime GC, deterministic memory
  • Predictable latency: Stable p99.9 even under sustained load
  • 10x lower memory: Per-connection overhead dramatically reduced

Supported Protocols

Protocol Status TLS Warm Pool
Direct N/A
HTTP N/A
SOCKS5 N/A
Shadowsocks N/A
Trojan ✅ (99.8% hit)
VLESS
VMess

TLS Warm Pool

Pre-established TLS connection pool with predictive warmup:

  • Eliminates TLS handshake latency for Trojan/VLESS
  • 99.8%+ cache hit rate under steady load
  • Automatic pool sizing based on traffic patterns

Benchmarks

Multi-Region Distributed Testing (4 regions → GCP us-central1)

Test Setup:

  • 1000 requests per protocol, 50 concurrent connections
  • 4 client regions: Singapore, Tokyo, US-East, EU-West
  • Server: GCP us-central1 (xray)
  • Date: 2026-01-15

Overall Performance (Rust vs Go)

Protocol mihomo-rust RPS mihomo-go RPS Rust Win Rate
HTTP 49.1 40.9 100%
SOCKS5 59.3 39.7 100%
VMess 53.1 34.5 100%
VLESS 51.6 46.5 100%
Shadowsocks 50.5 42.7 100%
Trojan 47.3 42.7 75%

Summary: Rust wins 23/24 RPS comparisons across all regions and protocols.

Latency Comparison (p50, lower is better)

Protocol mihomo-rust mihomo-go Improvement
HTTP 555ms 829ms -33%
SOCKS5 498ms 839ms -41%
VMess 513ms 644ms -20%
VLESS 560ms 723ms -23%
Shadowsocks 574ms 708ms -19%
Trojan 573ms 793ms -28%

Regional Performance

Region Avg p99 (ms) Avg RPS Notes
Singapore 2297.7 48.82 Best latency
US-East 2500.8 50.80 Best throughput
Tokyo 2627.2 43.73 Stable
EU-West 2670.4 42.64 Highest latency (farthest)

Key Findings

  • Rust p99 wins: 18/24 (75%) - lower tail latency
  • Rust RPS wins: 23/24 (96%) - higher throughput
  • Most stable protocol: HTTP (lowest cross-region variance)
  • Best Rust advantage: SOCKS5 in Singapore (84 RPS vs 40 RPS, +110%)

Resource Usage

Metric mihomo-rust mihomo-go Improvement
CPU Usage ~1% ~12% 91% less
Memory ~15MB ~50MB 70% less
GC Pauses 0ms 10-100ms Eliminated

Installation

# From source
cargo build --release
# Binary at target/release/mihomo-rust

Configuration

Compatible with mihomo YAML format:

log-level: warning
mode: rule
inbound:
  mixed:
    listen: "0.0.0.0:7890"
    udp: true
dns:
  enable: false
rules:
  - MATCH,DIRECT

License

MIT License - see LICENSE for details.

Acknowledgments

  • mihomo - The original Go implementation
  • tokio - Async runtime for Rust
  • Honkai: Star Rail - For the motivation

About

High-performance Rust implementation of mihomo proxy core. Designed for low latency jitter, stable long-running connections, and predictable p99.9 performance under high concurrency. Ideal for trading systems, real-time applications, and latency-sensitive workloads.

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