sglang/sgl-router

SGLang Router

SGLang router is a standalone Rust module that enables data parallelism across SGLang instances, providing high-performance request routing and advanced load balancing. The router supports multiple load balancing algorithms including cache-aware, power of two, random, and round robin, and acts as a specialized load balancer for prefill-decode disaggregated serving architectures.

Documentation

• User Guide: docs.sglang.ai/router/router.html

Quick Start

Prerequisites

Rust and Cargo:

# Install rustup (Rust installer and version manager)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Follow the installation prompts, then reload your shell
source $HOME/.cargo/env

# Verify installation
rustc --version
cargo --version

Python with pip installed

Installation

Option A: Build and Install Wheel (Recommended)

# Install build dependencies
pip install setuptools-rust wheel build

# Build the wheel package
python -m build

# Install the generated wheel
pip install dist/*.whl

# One-liner for development (rebuild + install)
python -m build && pip install --force-reinstall dist/*.whl

Option B: Development Mode

pip install -e .

⚠️ Warning: Editable installs may suffer performance degradation. Use wheel builds for performance testing.

Basic Usage

# Build Rust components
cargo build

# Launch router with worker URLs
python -m sglang_router.launch_router \
    --worker-urls http://worker1:8000 http://worker2:8000

Configuration

Logging

Enable structured logging with optional file output:

from sglang_router import Router

# Console logging (default)
router = Router(worker_urls=["http://worker1:8000", "http://worker2:8000"])

# File logging enabled
router = Router(
    worker_urls=["http://worker1:8000", "http://worker2:8000"],
    log_dir="./logs"  # Daily log files created here
)

Set log level with --log-level flag (documentation).

Metrics

Prometheus metrics endpoint available at 127.0.0.1:29000 by default.

# Custom metrics configuration
python -m sglang_router.launch_router \
    --worker-urls http://localhost:8080 http://localhost:8081 \
    --prometheus-host 0.0.0.0 \
    --prometheus-port 9000

Request ID Tracking

Track requests across distributed systems with configurable headers:

# Use custom request ID headers
python -m sglang_router.launch_router \
    --worker-urls http://localhost:8080 \
    --request-id-headers x-trace-id x-request-id

Default headers: x-request-id, x-correlation-id, x-trace-id, request-id

Advanced Features

Kubernetes Service Discovery

Automatic worker discovery and management in Kubernetes environments.

Basic Service Discovery

python -m sglang_router.launch_router \
    --service-discovery \
    --selector app=sglang-worker role=inference \
    --service-discovery-namespace default

PD (Prefill-Decode) Mode

For disaggregated prefill/decode routing:

python -m sglang_router.launch_router \
    --pd-disaggregation \
    --policy cache_aware \
    --service-discovery \
    --prefill-selector app=sglang component=prefill \
    --decode-selector app=sglang component=decode \
    --service-discovery-namespace sglang-system

# With separate routing policies:
python -m sglang_router.launch_router \
    --pd-disaggregation \
    --prefill-policy cache_aware \
    --decode-policy power_of_two \
    --service-discovery \
    --prefill-selector app=sglang component=prefill \
    --decode-selector app=sglang component=decode \
    --service-discovery-namespace sglang-system

Kubernetes Pod Configuration

Prefill Server Pod:

apiVersion: v1
kind: Pod
metadata:
  name: sglang-prefill-1
  labels:
    app: sglang
    component: prefill
  annotations:
    sglang.ai/bootstrap-port: "9001"  # Optional: Bootstrap port
spec:
  containers:
  - name: sglang
    image: lmsys/sglang:latest
    ports:
    - containerPort: 8000  # Main API port
    - containerPort: 9001  # Optional: Bootstrap port

Decode Server Pod:

apiVersion: v1
kind: Pod
metadata:
  name: sglang-decode-1
  labels:
    app: sglang
    component: decode
spec:
  containers:
  - name: sglang
    image: lmsys/sglang:latest
    ports:
    - containerPort: 8000

RBAC Configuration

Namespace-scoped (recommended):

apiVersion: v1
kind: ServiceAccount
metadata:
  name: sglang-router
  namespace: sglang-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: sglang-system
  name: sglang-router
rules:
- apiGroups: [""]
  resources: ["pods"]
  verbs: ["get", "list", "watch"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: sglang-router
  namespace: sglang-system
subjects:
- kind: ServiceAccount
  name: sglang-router
  namespace: sglang-system
roleRef:
  kind: Role
  name: sglang-router
  apiGroup: rbac.authorization.k8s.io

Complete PD Example

python -m sglang_router.launch_router \
    --pd-disaggregation \
    --policy cache_aware \
    --service-discovery \
    --prefill-selector app=sglang component=prefill environment=production \
    --decode-selector app=sglang component=decode environment=production \
    --service-discovery-namespace production \
    --host 0.0.0.0 \
    --port 8080 \
    --prometheus-host 0.0.0.0 \
    --prometheus-port 9090

Command Line Arguments Reference

Service Discovery

• --service-discovery: Enable Kubernetes service discovery
• --service-discovery-port: Port for worker URLs (default: 8000)
• --service-discovery-namespace: Kubernetes namespace to watch
• --selector: Label selectors for regular mode (format: key1=value1 key2=value2)

PD Mode

• --pd-disaggregation: Enable Prefill-Decode disaggregated mode
• --prefill: Initial prefill server (format: URL BOOTSTRAP_PORT)
• --decode: Initial decode server URL
• --prefill-selector: Label selector for prefill pods
• --decode-selector: Label selector for decode pods
• --policy: Routing policy (cache_aware, random, power_of_two, round_robin)
• --prefill-policy: Separate routing policy for prefill nodes (optional, overrides --policy for prefill)
• --decode-policy: Separate routing policy for decode nodes (optional, overrides --policy for decode)

Development

Build Process

# Build Rust project
cargo build

# Build Python binding (see Installation section above)

Note: When modifying Rust code, you must rebuild the wheel for changes to take effect.

Troubleshooting

VSCode Rust Analyzer Issues: Set rust-analyzer.linkedProjects to the absolute path of Cargo.toml:

{
  "rust-analyzer.linkedProjects": ["/workspaces/sglang/sgl-router/Cargo.toml"]
}

CI/CD Pipeline

The continuous integration pipeline includes comprehensive testing, benchmarking, and publishing:

Build & Test

1. Build Wheels: Uses cibuildwheel for manylinux x86_64 packages
2. Build Source Distribution: Creates source distribution for pip fallback
3. Rust HTTP Server Benchmarking: Performance testing of router overhead
4. Basic Inference Testing: End-to-end validation through the router
5. PD Disaggregation Testing: Benchmark and sanity checks for prefill-decode load balancing

Publishing

• PyPI Publishing: Wheels and source distributions are published only when the version changes in pyproject.toml
• Container Images: Docker images published using /docker/Dockerfile.router

Features

• High Performance: Rust-based routing with connection pooling and optimized request handling
• Advanced Load Balancing: Multiple algorithms including:
  • Cache-Aware: Intelligent routing based on cache locality for optimal performance
  • Power of Two: Chooses the less loaded of two randomly selected workers
  • Random: Distributes requests randomly across available workers
  • Round Robin: Sequential distribution across workers in rotation
• Prefill-Decode Disaggregation: Specialized load balancing for separated prefill and decode servers
• Service Discovery: Automatic Kubernetes worker discovery and health management
• Monitoring: Comprehensive Prometheus metrics and structured logging
• Scalability: Handles thousands of concurrent connections with efficient resource utilization