Skip to content

performance-tuning.md

Latest commit

 

History

History
407 lines (287 loc) · 10 KB

performance-tuning.md

File metadata and controls

407 lines (287 loc) · 10 KB

Performance Tuning Guide

This guide provides recommendations and best practices for optimizing the performance of RFS components in various deployment scenarios.

Introduction

RFS performance can be affected by various factors, including hardware resources, network conditions, storage backend configuration, and usage patterns. This guide will help you identify and address performance bottlenecks in your RFS deployment.

System Requirements

Minimum Requirements

  • CPU: 2 cores
  • RAM: 2 GB
  • Disk: 10 GB (plus storage for content)
  • Network: 10 Mbps

Recommended Requirements

  • CPU: 4+ cores
  • RAM: 8+ GB
  • Disk: SSD storage for cache and metadata
  • Network: 100+ Mbps

RFS Core Performance Tuning

Parallel Downloads

RFS uses parallel downloads to improve performance when retrieving content from storage backends. You can control the number of parallel downloads using the RFS_PARALLEL_DOWNLOAD environment variable:

# Set to a specific number (e.g., 8)
export RFS_PARALLEL_DOWNLOAD=8

# Run RFS command
rfs unpack -m flist.fl ~/extracted

The optimal value depends on your system resources and network conditions:

  • CPU-limited systems: Lower values (4-8)
  • Network-limited systems: Higher values (16-32)
  • Balanced systems: Moderate values (8-16)

If not specified, RFS automatically determines an appropriate value based on system resources.

Cache Configuration

The cache is critical for performance. Consider these optimizations:

  1. Cache Location: Place the cache on fast storage (SSD)

    rfs mount -m flist.fl -c /mnt/ssd/cache /mount/point

  2. Cache Size: Ensure sufficient space for your working set

    # Check available space
df -h /path/to/cache

  3. Cache Persistence: Use a persistent location for long-running mounts

    rfs mount -m flist.fl -c /var/cache/rfs /mount/point

  4. Pre-warming: Access frequently used files to cache them

    find /mount/point/important/path -type f -exec cat {} > /dev/null \;

Storage Backend Selection

Choose appropriate storage backends for your use case:

  1. Local Development: Use directory stores

    rfs pack -m output.fl -s dir:///tmp/store /path/to/directory

  2. Production: Use ZDB or S3 stores

    rfs pack -m output.fl -s zdb://zdb.example.com:9900/namespace /path/to/directory

  3. High Performance: Use local SSD-backed stores

    rfs pack -m output.fl -s dir:///mnt/ssd/store /path/to/directory

Sharding and Replication

Use sharding and replication to optimize performance:

  1. Sharding for Parallel Access: Distribute content across multiple backends

    rfs pack -m output.fl \
  -s 00-3f=dir:///tmp/store1 \
  -s 40-7f=dir:///tmp/store2 \
  -s 80-bf=dir:///tmp/store3 \
  -s c0-ff=dir:///tmp/store4 \
  /path/to/directory

  2. Replication for Availability: Replicate content for redundancy

    rfs pack -m output.fl \
  -s dir:///tmp/store1 \
  -s dir:///tmp/store2 \
  /path/to/directory

  3. Geographic Distribution: Place content close to users

    rfs pack -m output.fl \
  -s 00-7f=s3://us-east-1.amazonaws.com/us-bucket \
  -s 80-ff=s3://eu-west-1.amazonaws.com/eu-bucket \
  /path/to/directory

FL Server Performance Tuning

Server Hardware

The FL server benefits from:

  • Multiple CPU cores: For handling concurrent requests
  • Sufficient RAM: For caching and processing
  • Fast storage: For flist storage and temporary files
  • Network bandwidth: For transferring flists and content

Concurrent Requests

The FL server can handle multiple concurrent requests. To optimize for high concurrency:

  1. Increase system limits: Adjust file descriptor limits

    # Check current limits
ulimit -n

# Set higher limits in /etc/security/limits.conf
# username soft nofile 65536
# username hard nofile 65536

  2. Configure reverse proxy: If using Nginx or similar, optimize for concurrency

    # Nginx example
worker_processes auto;
worker_connections 4096;

Storage Configuration

Optimize the storage configuration for the FL server:

  1. Fast local storage: Use SSD for the flist_dir

    flist_dir = "/mnt/ssd/flists"

  2. Efficient store URLs: Use performant storage backends

    store_url = ["dir:///mnt/ssd/store"]

  3. Multiple stores: Use sharding for better performance

    store_url = [
    "00-7f=dir:///mnt/ssd1/store",
    "80-ff=dir:///mnt/ssd2/store"
]

Frontend Performance Tuning

Build Optimization

For production deployment, build the frontend with optimization:

npm run build

This creates optimized assets in the dist directory.

Serving Static Assets

Use efficient methods to serve the frontend:

  1. Content Delivery Network (CDN): Deploy static assets to a CDN
  2. HTTP/2: Enable HTTP/2 in your web server
  3. Compression: Enable gzip or Brotli compression
  4. Caching: Configure appropriate cache headers

Example Nginx configuration:

server {
    listen 443 ssl http2;
    server_name your-domain.example.com;

    root /path/to/rfs/frontend/dist;
    index index.html;

    gzip on;
    gzip_types text/plain text/css application/javascript application/json;

    location /assets/ {
        expires 1y;
        add_header Cache-Control "public, max-age=31536000, immutable";
    }

    location / {
        try_files $uri $uri/ /index.html;
    }
}

Network Optimization

Bandwidth Management

  1. Quality of Service (QoS): Prioritize RFS traffic if needed
  2. Traffic Shaping: Limit bandwidth usage during peak times
  3. Compression: Enable compression for HTTP-based stores

Latency Reduction

  1. Geographic Proximity: Use storage backends close to users
  2. Connection Pooling: Maintain persistent connections to backends
  3. DNS Optimization: Ensure fast DNS resolution for backend hostnames

Monitoring and Profiling

System Monitoring

Monitor system resources to identify bottlenecks:

# CPU and memory usage
top

# Disk I/O
iostat -x 1

# Network usage
iftop

RFS Profiling

Enable debug logging to identify performance issues:

# Enable debug logging
rfs --debug mount -m flist.fl /mount/point

FL Server Monitoring

Monitor the FL server using standard tools:

# Process monitoring
ps aux | grep "rfs server"

# Log monitoring
tail -f rfs-server.log

Performance Testing

Benchmarking Tools

Use these tools to benchmark RFS performance:

  1. fio: For disk I/O benchmarking

    fio --name=test --filename=/mount/point/testfile --direct=1 --rw=randread --bs=4k --size=1G --numjobs=4 --runtime=60 --group_reporting

  2. dd: For simple throughput testing

    dd if=/mount/point/largefile of=/dev/null bs=1M count=1000

  3. time: For measuring command execution time

    time rfs unpack -m flist.fl ~/extracted

Performance Metrics

Key metrics to monitor:

  1. Mount Time: Time to mount an flist
  2. First Access Time: Time to first access a file (cold cache)
  3. Subsequent Access Time: Time to access a file again (warm cache)
  4. Throughput: Data transfer rate for large files
  5. IOPS: Operations per second for small files

Common Performance Issues and Solutions

Slow Mount Operations

Symptoms:

  • Long delay when mounting flists
  • High CPU usage during mount

Solutions:

  • Use smaller flists with fewer files
  • Ensure fast metadata access (SSD storage)
  • Pre-warm the cache for frequently accessed flists

Slow First Access

Symptoms:

  • Long delay when accessing a file for the first time
  • Normal performance for subsequent access

Solutions:

  • Use storage backends with lower latency
  • Increase parallel download count
  • Pre-warm the cache for important files

Network Bottlenecks

Symptoms:

  • Good performance with local stores, poor with remote stores
  • Network saturation during file access

Solutions:

  • Use more efficient storage backends
  • Implement sharding across multiple backends
  • Consider local caching proxies for remote backends

High CPU Usage

Symptoms:

  • CPU saturation during RFS operations
  • Slow performance despite fast storage and network

Solutions:

  • Reduce parallel download count
  • Use more efficient storage backends
  • Distribute load across multiple instances

Memory Limitations

Symptoms:

  • Increasing memory usage over time
  • Swapping during heavy usage

Solutions:

  • Limit parallel operations
  • Ensure sufficient RAM for your workload
  • Consider memory-optimized instances for large deployments

Advanced Performance Techniques

Custom Kernel Parameters

Tune kernel parameters for better performance:

# Increase the maximum number of open files
sysctl -w fs.file-max=1000000

# Increase the maximum number of inotify watches
sysctl -w fs.inotify.max_user_watches=524288

# Optimize network parameters
sysctl -w net.core.somaxconn=4096
sysctl -w net.ipv4.tcp_max_syn_backlog=4096

FUSE Optimization

Optimize FUSE mount options:

# Mount with optimized FUSE options
rfs mount -m flist.fl -o big_writes,max_read=131072,max_write=131072 /mount/point

Custom Compilation

Compile RFS with performance optimizations:

# Build with optimizations
RUSTFLAGS="-C target-cpu=native -C opt-level=3" cargo build --release

Conclusion

Performance tuning is an iterative process. Start with the recommendations in this guide, monitor performance, identify bottlenecks, and make targeted improvements.

Remember that the optimal configuration depends on your specific use case, hardware, and network environment. What works best for one deployment may not be ideal for another.

Next Steps

For more information about related topics, see: