Bloom Filters for Streaming Duplicate Detection
Bloom filters are probabilistic data structures that test whether an element is in a set — with false positives but no false negatives. In streaming, they detect duplicate events with minimal memory: check if event ID is in the bloom filter before processing.
Overview
Bloom filters are probabilistic data structures that test whether an element is in a set — with false positives but no false negatives. In streaming, they detect duplicate events with minimal memory: check if event ID is in the bloom filter before processing.
Key Points
- Streaming databases like RisingWave provide sub-second data freshness with SQL
- PostgreSQL compatibility means existing tools and skills transfer directly
- S3-based state storage provides durability, elastic scaling, and cost efficiency
- Native CDC eliminates middleware for database-to-streaming pipelines
- Iceberg integration bridges real-time serving with historical analytics
Architecture
The core pattern in all streaming architectures is:
Sources (CDC, Kafka) → Streaming Database (RisingWave) → Serving (PG protocol) + Storage (Iceberg)
This provides both real-time views (sub-second, via materialized views) and historical analytics (via Iceberg + Trino/DuckDB).
Implementation
-- The universal streaming pattern
CREATE SOURCE data_stream (...) WITH (connector='kafka', ...);
CREATE MATERIALIZED VIEW real_time_view AS SELECT ... FROM data_stream ...;
-- Query: SELECT * FROM real_time_view; (always fresh, sub-100ms)
Frequently Asked Questions
How does this relate to RisingWave?
RisingWave is a PostgreSQL-compatible streaming database that handles the processing and serving layers. It's open source (Apache 2.0), stores state on S3, and supports native CDC — making it the simplest path to production streaming.
Do I need Kafka for this?
Not always. RisingWave supports native CDC from PostgreSQL and MySQL without Kafka. For event-driven architectures with multiple consumers, Kafka remains valuable as a central event bus.
