Axoniq Conference Day 2 | 2025 Slaying Three Dragons: Caching, AI and Analytics at Scale

ASSIST shares battle-tested lessons from production: solving deadlocks, achieving 100% cache hit rates with cache size ONE, building AI recommendations from event history, and creating real-time analytics. Watch as Richard and Ondrej reveal how they transformed an automotive spare parts eShop into a high-performance, ML-powered system while supporting millions of events.

🎯 Speakers:

Richard Bouška and Ondrej Halata - ASSIST Software Development House (50 developers specializing in event-driven systems, DDD, CQRS)

🐉 The Three Dragons Slayed:

Dragon 1: Circular Dependency Deadlocks
Problem: 10 threads, 5-minute timeout, 3 retries = 20 minutes of silence
Root Cause: Aggregates emitting commands directly
Solution: Choreography pattern with sagas, event-driven communication
Result: Quick deployment, application restructured
Dragon 2: Hydration Performance Crisis
Problem: Sequence generator aggregate with 25K daily events
Numbers: 320M events without snapshots, 6.2M with snapshots
Solution: Consistent hashing plus Caffeine cache
Result: 700ms to 8ms globally distributed, cache size ONE with 100% hit rate
Dragon 3: Tiered Storage Optimization
Problem: Growing event count, older tiers slower
Discovery: Power law distribution, older events less visited
Solution: Aggressive caching stabilized server open segments
Result: Flat performance with growing events since July

💻 Tech Stack:

Axon Framework 4 and Server Cluster
Caffeine Cache for aggregate caching
Consistent Hashing for routing stability
PostgreSQL for analytical projections
Grafana for real-time dashboards
Word2Vec neural network for ML
AWS infrastructure with S3 and SQS

🚀 Performance Breakthrough:

Before Optimization: 700ms per sequence ID
With Snapshots (every 500): 70ms
With Caffeine Cache: 8ms
Cache Hit Rate: 100% with size ONE
Other Aggregates: 80-90% hit rates with 1,000 entries

🎯 The Unicorn Moment:

Server Open Segments chart shows breakthrough - flat performance since July despite growing events. Power law distribution means older events rarely accessed. Cache handles hot aggregates, tiered storage cost-effective for cold data.

🎓 Key Lessons:

Consistent hashing critical but watch topology changes
Axonic 2000 error provides retry compensation
Power law distribution = aggressive caching wins
Event history enables ML without data engineering
Real-time analytics trivial with projection services
Observability standard tooling at Asasis

🚫 What NOT To Do:

❌ Commands from aggregates (use choreography)
❌ Ignoring consistent hashing topology changes
❌ Forgetting snapshots on long-living aggregates
❌ Missing cache opportunities (power law applies!)
❌ Waiting 24 hours for analytics (project real-time!)