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BENCHMARK_RESULTS.md

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Pulsar Relay Benchmark Results

Date: 2025-10-13 Duration: ~6 seconds Platform: darwin (macOS) Python: 3.13.6

Executive Summary

The Pulsar Relay demonstrates excellent performance for message ingestion and delivery scenarios:

  • Peak Throughput: 32,955 ops/sec (bulk ingestion - 47% improvement!)
  • Single Message Ingestion: 1,338 msgs/sec with sub-millisecond latency
  • End-to-End Delivery: 814 msgs/sec with ~2.1ms latency
  • WebSocket Connections: 1,117 connections/sec
  • Broadcast Performance: 15,731 ops/sec delivering to 30 concurrent subscribers

Benchmark Results

1. Message Ingestion (Single)

Scenario: Sequential posting of individual messages to the API

  • Operations: 1,000 messages
  • Duration: 0.75 seconds
  • Throughput: 1,338 ops/sec
  • Average Latency: 0.75ms
  • P50 Latency: 0.73ms
  • P95 Latency: 0.78ms
  • P99 Latency: 1.22ms

Analysis: Excellent single-message performance with sub-millisecond latency across all percentiles. The 99th percentile staying under 1.5ms indicates very consistent performance.

2. Bulk Message Ingestion (50 messages/batch)

Scenario: Posting messages in batches of 50 using the bulk API

  • Operations: 5,000 messages (100 batches)
  • Duration: 0.15 seconds
  • Throughput: 32,955 ops/sec
  • Average Latency: 1.52ms per batch
  • P50 Latency: 1.46ms
  • P95 Latency: 1.58ms
  • P99 Latency: 4.19ms

Analysis: Outstanding bulk ingestion performance, achieving 24.6x higher throughput than single-message ingestion. The bulk API is highly efficient for high-volume scenarios with excellent consistency (P99 under 5ms).

3. Concurrent Ingestion (20 concurrent clients)

Scenario: 20 concurrent clients simultaneously posting messages

  • Operations: 1,000 messages
  • Duration: 2.34 seconds
  • Throughput: 428 ops/sec
  • Average Latency: 28.60ms
  • P50 Latency: 27.61ms
  • P95 Latency: 40.96ms
  • P99 Latency: 41.78ms

Analysis: Higher latency under concurrent load is expected due to contention. The throughput of 428 ops/sec with 20 concurrent clients indicates good multi-client handling.

4. WebSocket Subscribe (50 clients)

Scenario: Concurrent WebSocket connection and subscription by 50 clients

  • Operations: 50 connections
  • Duration: 0.04 seconds
  • Throughput: 1,117 ops/sec (75% improvement!)
  • Average Latency: 23.43ms
  • P50 Latency: 22.06ms
  • P95 Latency: 32.85ms
  • P99 Latency: 33.74ms

Analysis: Very fast WebSocket connection establishment with sub-35ms latencies across all percentiles. The system can handle rapid client connections extremely efficiently.

5. Message Delivery (End-to-End)

Scenario: 20 WebSocket clients subscribing to topics and receiving messages via real-time delivery

  • Operations: 1,000 messages delivered
  • Duration: 1.23 seconds
  • Throughput: 814 ops/sec
  • Average Latency: 2.13ms (from publish to receive)
  • P50 Latency: 2.12ms
  • P95 Latency: 3.13ms
  • P99 Latency: 3.67ms

Analysis: Excellent end-to-end delivery performance with sub-2.2ms median latency. This demonstrates the relay can deliver messages in near real-time with minimal overhead. P99 under 4ms is outstanding.

6. Broadcast Performance

Scenario: Broadcasting 50 messages to 30 concurrent WebSocket subscribers

  • Operations: 1,500 messages delivered (100% success!)
  • Expected: 1,500 messages (30 clients × 50 messages)
  • Duration: 0.10 seconds
  • Throughput: 15,731 ops/sec
  • Average Latency: 52.68ms
  • P50 Latency: 58.84ms
  • P95 Latency: 69.96ms
  • P99 Latency: 70.32ms
  • Errors: 0

Performance Summary

Benchmark Throughput Avg Latency Status
Message Ingestion (Single) 1,338 ops/s 0.75ms ✅ Excellent
Bulk Ingestion (50/batch) 32,955 ops/s 1.52ms ✅ Excellent
Concurrent Ingestion (20x) 428 ops/s 28.60ms ✅ Good
WebSocket Subscribe (50) 1,117 ops/s 23.43ms ✅ Excellent
E2E Delivery (20 clients) 814 ops/s 2.13ms ✅ Excellent
Broadcast (30 clients) 15,731 ops/s 52.68ms ✅ Excellent

Recommendations

Next Steps for Production

  1. Load Testing: Run sustained load tests (minutes/hours) to identify memory leaks
  2. Stress Testing: Test with higher client counts (100s, 1000s) to find system limits
  3. Monitoring: Set up Prometheus + Grafana dashboards for production monitoring
  4. Rate Limiting: Implement rate limiting with slowapi to prevent DoS
  5. Authentication: Add JWT authentication for production use
  6. Horizontal Scaling: Consider Redis Pub/Sub for cross-instance messaging
  7. Valkey Integration: Implement the Valkey storage backend for persistence

Running the Benchmarks

To reproduce these results:

# Start the server
uvicorn pulsar_relay.main:app --host 0.0.0.0 --port 8080

# In another terminal, run benchmarks
python benchmarks/run_benchmarks.py

Files

  • Benchmark Suite: benchmarks/run_benchmarks.py
  • Test Results: This document
  • Source Code: app/ directory