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Performance Guide

KaflowSQL Performance: Sub-millisecond joins, 100K+ events/second, minimal operational overhead

Benchmark Results

Throughput Benchmarks

Scenario Events/Second Join Hit Rate Latency P99 Memory Usage
Simple Enrichment 150K+ 95% < 1ms 500MB
Multi-topic Join 100K+ 85% < 2ms 1.2GB
High-dimensional Join 80K+ 90% < 3ms 2.5GB
IoT High-Volume 250K+ 75% < 0.5ms 800MB

Tested on Mac M3 MAX (8-core CPU, 32GB RAM) - production servers typically perform 20-30% better

Real-World Performance

Financial Services

# Transaction enrichment pipeline
Volume: 500K transactions/minute
Join Rate: 98% (user profiles + merchant data)
P99 Latency: < 2ms
Memory: 1.8GB (10M user profiles, 50K merchants)
Deployment: Single container per region
Recovery Time: < 30 seconds

E-commerce Platform

# Real-time personalization
Volume: 1M+ page views/hour
Join Rate: 92% (user profiles + product catalog)
P99 Latency: < 1.5ms
Memory: 2.2GB (5M users, 100K products)
State Size: 15GB on disk (RocksDB)
Cache Hit Rate: 94%

IoT Manufacturing

# Sensor data enrichment
Volume: 2M+ sensor readings/hour
Join Rate: 88% (device metadata + config)
P99 Latency: < 0.8ms
Memory: 600MB (50K devices)
TTL Strategy: 5-minute rolling window
Throughput Scaling: Linear up to 8 cores

Performance Characteristics

Latency Breakdown

Total Processing Time (P99): < 2ms
├── Kafka Consume: 0.2ms
├── Deserialization: 0.1ms
├── Hash Lookup: 0.05ms
├── Join Processing: 0.3ms
├── SQL Execution: 0.5ms
├── Serialization: 0.15ms
└── Kafka Produce: 0.7ms

Memory Usage Patterns

  • Hot State (Hash Tables): 60% of total memory
  • RocksDB Cache: 25% of total memory
  • Processing Buffers: 10% of total memory
  • Go Runtime: 5% of total memory

Throughput Scaling

1 CPU Core:   ~30K events/second
2 CPU Cores:  ~55K events/second  
4 CPU Cores:  ~100K events/second
8 CPU Cores:  ~180K events/second
16 CPU Cores: ~250K events/second (diminishing returns)

Architecture Performance

Hash-Based Sharding

KaflowSQL uses 256 internal shards for concurrent processing:

// Simplified sharding logic
shard := xxhash64(joinKey) % 256
processor := shardProcessors[shard]

Benefits:

  • Parallel Processing: 256 concurrent join operations
  • Memory Locality: Related data co-located in same shard
  • Lock-Free Design: Per-shard processing eliminates contention

Zero-Copy Processing

Events are stored as pointers, not copied:

type JoinRow struct {
    LeftPtr  *Event    // Pointer to original event
    RightPtr *Event    // Pointer to joined event  
    Presence uint8     // Bitmask for available fields
}

Performance Impact:

  • 50% less memory allocation
  • 30% faster processing due to cache efficiency
  • Reduces GC pressure by minimizing temporary object creation

RocksDB Integration

  • LSM Trees: Optimized for write-heavy workloads
  • Block Cache: Configurable memory cache (default 25% of heap)
  • Compression: LZ4 compression for 60% space savings
  • Write Batching: Micro-batches for optimal throughput

DuckDB SQL Engine

  • Vectorized Execution: SIMD optimizations for analytics
  • Columnar Storage: Efficient for aggregations and analytics
  • JIT Compilation: Runtime query optimization
  • Memory Mapping: Zero-copy data access

Optimization Guide

Memory Optimization

TTL Tuning

# Aggressive TTL for high-volume scenarios
state:
  events:
    default_ttl: 5m        # Short retention
    overrides:
      high_volume_topic: 2m # Even shorter for specific topics
  dimensions:
    - static_lookups       # Never expire reference data

Emission Strategy

# Immediate emission for high throughput
emission:
  type: immediate    # Don't wait for complete joins
  
# Smart emission for data quality
emission:
  type: smart
  require: [critical_field]  # Only wait for essential fields

Throughput Optimization

Kafka Configuration

kafka:
  consumer:
    fetchMinBytes: 1048576      # 1MB fetch batches
    fetchMaxWait: 100           # 100ms max wait
    maxPollRecords: 10000       # Large poll batches
  producer:
    batchSize: 1048576          # 1MB producer batches  
    lingerMs: 5                 # Small linger for latency
    compression: "lz4"          # Fast compression

State Management

state:
  rocksdb:
    writeBufferSize: 67108864   # 64MB write buffer
    maxWriteBufferNumber: 3     # 3 write buffers
    blockSize: 65536            # 64KB blocks
    blockCache: 268435456       # 256MB block cache

Latency Optimization

Pipeline Configuration

# Minimize processing latency
name: low_latency_pipeline
window: 30s              # Short global window

emission:
  type: immediate        # Don't wait for complete data

state:
  events:
    default_ttl: 30s     # Minimal state retention

Hardware Recommendations

  • CPU: High single-thread performance > many cores
  • Memory: 32GB+ for production workloads
  • Storage: NVMe SSD for RocksDB (3000+ IOPS)
  • Network: Low-latency connection to Kafka (< 1ms RTT)

Monitoring & Metrics

Key Performance Indicators

Throughput Metrics

  • kaflowsql_events_processed_total: Events processed counter
  • kaflowsql_events_per_second: Current processing rate
  • kaflowsql_join_success_rate: Percentage of successful joins

Latency Metrics

  • kaflowsql_processing_duration_histogram: End-to-end latency
  • kaflowsql_join_lookup_duration: State lookup latency
  • kaflowsql_sql_execution_duration: DuckDB query execution time

Memory Metrics

  • kaflowsql_memory_usage_bytes: Total memory consumption
  • kaflowsql_state_size_bytes: In-memory state size
  • kaflowsql_rocksdb_cache_usage: RocksDB cache utilization

State Metrics

  • kaflowsql_state_entries_total: Total state entries
  • kaflowsql_state_evictions_total: TTL-based evictions
  • kaflowsql_join_hit_rate: State lookup success rate

Performance Monitoring

Grafana Dashboard Queries

# Processing rate
rate(kaflowsql_events_processed_total[5m])

# Join success rate
kaflowsql_join_success_rate * 100

# P99 latency
histogram_quantile(0.99, kaflowsql_processing_duration_histogram)

# Memory usage
kaflowsql_memory_usage_bytes / (1024^3)  # GB

Alerting Thresholds

# Performance degradation alerts
- alert: HighLatency
  expr: histogram_quantile(0.99, kaflowsql_processing_duration_histogram) > 0.005
  for: 2m

- alert: LowThroughput  
  expr: rate(kaflowsql_events_processed_total[5m]) < 10000
  for: 5m

- alert: LowJoinRate
  expr: kaflowsql_join_success_rate < 0.8
  for: 1m

Troubleshooting Performance Issues

High Latency

Symptoms

  • P99 latency > 5ms
  • Increasing processing delays
  • Kafka consumer lag growing

Diagnosis

# Check RocksDB statistics
curl http://localhost:8080/metrics | grep rocksdb

# Monitor memory usage
curl http://localhost:8080/metrics | grep memory

# Check join hit rates
curl http://localhost:8080/metrics | grep join_hit_rate

Solutions

  1. Increase RocksDB cache: More memory for hot data
  2. Optimize TTL settings: Reduce state retention
  3. Scale horizontally: Deploy multiple instances
  4. Tune Kafka consumers: Larger fetch batches

Low Throughput

Symptoms

  • Processing rate < expected throughput
  • CPU utilization < 70%
  • Memory usage stable but low performance

Diagnosis

# Check consumer lag
kafka-consumer-groups --bootstrap-server localhost:9092 \
  --group kaflowsql --describe

# Monitor CPU per shard
curl http://localhost:8080/debug/pprof/profile

Solutions

  1. Increase consumer parallelism: More Kafka partitions
  2. Optimize SQL queries: Simpler joins and filters
  3. Adjust emission strategy: Use immediate emission
  4. Tune batch sizes: Larger processing batches

Memory Issues

Symptoms

  • Memory usage growing continuously
  • OOM kills in containerized environments
  • Slow state lookups

Diagnosis

# Check state size growth
curl http://localhost:8080/metrics | grep state_size

# Monitor TTL effectiveness
curl http://localhost:8080/metrics | grep evictions

Solutions

  1. Reduce TTL values: More aggressive state cleanup
  2. Optimize join keys: Better data distribution
  3. Use dimensions wisely: Only for truly static data
  4. Increase memory limits: Scale container resources

Benchmarking Your Deployment

Load Testing Setup

1. Generate Test Data

# Use built-in data generator
./fakegen \
  --topics user_events,user_profiles \
  --rate 50000 \
  --duration 10m \
  --brokers localhost:9092

2. Monitor During Test

# Real-time metrics
watch -n 1 'curl -s http://localhost:8080/metrics | grep -E "(events_per_second|join_hit_rate|processing_duration)"'

3. Analyze Results

# Export metrics for analysis
curl http://localhost:8080/metrics > benchmark_results.txt

# Generate performance report
python scripts/analyze_performance.py benchmark_results.txt

Performance Testing Checklist

  • Baseline Performance: Single-topic, no joins
  • Join Performance: 2-topic, simple joins
  • Multi-Join Performance: 3+ topics, complex queries
  • High-Volume Testing: Maximum sustainable throughput
  • Latency Testing: P99 latency under load
  • Memory Stress Testing: Large state scenarios
  • Recovery Testing: Performance after restart
  • Long-Running Testing: 24+ hour stability

Best Practices Summary

Configuration

  • Use immediate emission for maximum throughput
  • Use smart emission for data quality requirements
  • Set TTL based on business requirements, not technical limits
  • Configure RocksDB cache to 25-50% of available memory

Deployment

  • Deploy one instance per Kafka partition for optimal parallelism
  • Use NVMe storage for RocksDB data
  • Monitor join hit rates to validate pipeline effectiveness
  • Set up comprehensive alerting on key performance metrics

Operations

  • Profile regularly using built-in pprof endpoints
  • Test performance before production deployment
  • Monitor state growth and adjust TTL accordingly
  • Scale horizontally rather than vertically when possible

Need help optimizing your KaflowSQL deployment? Check our troubleshooting guide or join the discussion.