RedisSMQ Benchmarks

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A comprehensive benchmarking suite for RedisSMQ message queue system, measuring throughput, latency, and end-to-end performance under various workloads.

📊 Overview

This benchmark suite provides tools to measure RedisSMQ performance across different scenarios:

1. Producer Throughput - Measure message production rates
2. Consumer Throughput - Measure message consumption rates
3. End-to-End Throughput - Measure complete system performance with concurrent producers and consumers

Note: These benchmarks are for performance testing and should be run in a controlled environment. Always test with production-like data and conditions.

🚀 Quick Start

Prerequisites

• Node.js 20+
• Redis server running (default: localhost:6379)

Installation

npm install redis-smq__TAG_SUFFIX__ redis-smq-common__TAG_SUFFIX__ redis-smq-benchmarks__TAG_SUFFIX__ --save

⚙️ Configuration

Benchmarks can be configured using environment variables:

| Variable | Description | Default | |-----------------------|-----------------------------------|------------| | BENCH_MESSAGES | Total messages to process | 100000 | | BENCH_PRODUCERS | Number of producer worker threads | 0 | | BENCH_CONSUMERS | Number of consumer worker threads | 0 | | BENCH_SHOW_PROGRESS | Benchmark progress reporting | 0 | | REDIS_HOST | Redis server host | localhost | | REDIS_PORT | Redis server port | 6379 | | REDIS_DB | Redis server database | 0 |

Example Configuration

# Run with custom configuration
REDIS_HOST=127.0.0.1 \
BENCH_PRODUCERS=10 \
BENCH_CONSUMERS=5 \
npx redis-smq-benchmarks

📈 Benchmark Types

1. Producer Throughput Benchmark

Measures how quickly producers can enqueue messages.

Features:

• Even distribution of messages across producers
• Real-time progress reporting
• Individual producer statistics
• Aggregate throughput calculation

Usage:

BENCH_MESSAGES=50000 BENCH_PRODUCERS=4 npx redis-smq-benchmarks

2. Consumer Throughput Benchmark

Measures how quickly consumers can process messages.

Features:

• Pre-fills queue with messages
• Even distribution across consumers
• Individual consumer statistics
• Complete consumption tracking

Usage:

BENCH_MESSAGES=50000 BENCH_CONSUMERS=4 npx redis-smq-benchmarks

3. End-to-End Throughput Benchmark

Measures complete system performance with concurrent producers and consumers.

Features:

• Concurrent producer and consumer execution
• Production vs consumption phase tracking
• System backlog monitoring
• Complete throughput analysis

Usage:

BENCH_MESSAGES=100000 BENCH_PRODUCERS=8 BENCH_CONSUMERS=8 npx redis-smq-benchmarks

📊 Output Metrics

Each benchmark provides detailed metrics:

Individual Worker Metrics

• Messages processed per worker
• Time taken per worker
• Throughput per worker (messages/second)

Aggregate Metrics

• Total messages processed
• Total time elapsed
• Overall throughput
• Average time per worker

E2E-Specific Metrics

• Production throughput
• Consumption throughput
• System backlog (produced vs consumed)
• End-to-end latency

📊 Sample Output

Starting end-to-end throughput benchmark...
Queue: benchmarking/queue-1767808640925 | Messages: 100000 | Producers: 10 | Consumers: 5
Setting up producers and consumers...

========== E2E BENCHMARK COMPLETE ==========
Production Phase:
  Total produced: 100000
  Production time: 3.35s
  Production throughput: 29824 msg/s

Consumption Phase:
  Total consumed: 100000
  Consumption time: 6.37s
  Consumption throughput: 15689 msg/s

End-to-End:
  Total time: 6.37s
  Overall throughput: 15689 msg/s
  System backlog: 0 messages
  Status: All messages processed successfully ✓
============================================

🏗️ Architecture

Design Principles

1. Worker-Based Architecture: Each producer/consumer runs in its own worker thread
2. Even Distribution: Messages are evenly distributed across all workers
3. Real-time Monitoring: Progress updates during execution
4. Graceful Shutdown: Clean termination of all resources
5. Extensible: Easy to add new benchmark types

Performance Tuning Tips

1. Worker Count: Increase worker count to leverage multi-core CPUs
2. Redis Configuration: Ensure Redis has sufficient memory and connection limits
3. Network: Local Redis provides the best performance. Network latency impacts throughput

📊 Interpreting Results

Key Metrics to Monitor

1. Throughput Plateau: Point where adding more workers doesn't increase throughput
2. CPU Utilization: Monitor CPU usage during benchmarks
3. Memory Usage: Watch for memory leaks in long-running tests

License

RedisSMQ Benchmarks is released under the MIT License.