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Download IgniteARCHITECTURAL BACKBONE
CORE FEATURES
DATA PLACEMENT & PROCESSING
OPERATIONAL EXCELLENCE
ADDITIONAL USE CASES
Download IgniteARCHITECTURAL BACKBONE
CORE FEATURES
DATA PLACEMENT & PROCESSING
OPERATIONAL EXCELLENCE
ADDITIONAL USE CASES
Event Stream Processing
And Enrichment
With Apache Ignite
Fast OR Consistent? Choose Both.
Enrich high-throughput event streams with consistent reference data
Enrich high-throughput event streams with consistent reference data
The Trade-off Problem
Traditional event stream architectures force an impossible choice: in-memory caches for speed but stale data, or relational databases for consistency but high network round-trip latency. Stream processors need both low-latency lookups AND strong consistency for reference data enrichment.
Cache invalidation complexity creates operational burden. Eventual consistency risks processing events with outdated reference data. Database queries add latency that breaks real-time processing requirements.
How Apache Ignite Solves This
Apache Ignite eliminates the latency-consistency trade-off through memory-first architecture with ACID guarantees
Low-Latency Lookups
Memory-first architecture with partition-aware routing delivers microsecond-to-millisecond lookups. RecordView API provides direct partition access without coordinator overhead, delivering significant latency reduction for reference data enrichment.
Strong Consistency
ACID guarantees with consensus replication eliminate cache invalidation complexity. Stream processors always read consistent reference data. No eventual consistency windows. Colocation support enables local joins for further performance optimization.
Architecture Pattern
Event Enrichment Without Cache Invalidation
Stream processors read reference data directly from Apache Ignite using partition-aware routing for low-latency lookups with ACID consistency.
Integration Pattern: Streaming platforms process events, enriching each event by looking up reference data in Apache Ignite through RecordView API.
Consistency Model: Consensus replication ensures writes to reference data propagate to all replicas with strong consistency. No eventual consistency delays.
Performance Characteristics: Memory-first architecture delivers microsecond-to-millisecond lookup latency at high throughput. Partition-aware routing eliminates coordinator overhead.
When This Pattern Works
This architecture pattern is best for:
- High-throughput event stream enrichment
- Reference data that changes infrequently or requires strong consistency
- Real-time processing where cache staleness creates business risk
- Systems where cache invalidation complexity becomes operational burden
Example Use Cases:
- Financial Trading: Enrich order events with current instrument data, margin requirements, and risk parameters
- E-commerce: Enrich clickstream events with product catalog, pricing, and inventory status
- Fraud Detection: Enrich transaction events with customer profiles, risk scores, and historical patterns
Key Benefits
Eliminate Cache Invalidation
ACID guarantees replace cache invalidation complexity. Stream processors read consistent reference data without cache warming, TTL tuning, or invalidation logic. Updates propagate through consensus replication, not cache invalidation messages.
Low-Latency At Scale
Memory-first storage delivers microsecond-to-millisecond latency for reference data lookups. Partition-aware routing bypasses coordinator overhead. Horizontal scalability handles throughput growth without latency degradation.
Strong Consistency
Consensus replication ensures reference data updates propagate with strong consistency. No eventual consistency windows. Stream processors never enrich events with stale reference data.
System Consolidation
Single platform replaces separate caching and database systems for reference data. Reduces infrastructure complexity and operational overhead. Eliminates synchronization between cache and database layers.
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Explore More Use CasesLearn about other Apache Ignite use cases
Use Cases Overview