Memory Management in Stream Processing Engines

Stream processing engines must balance memory between operator state, buffers, caches, and the JVM heap (for Java-based systems). RisingWave's Rust-based architecture avoids JVM garbage collection issues, while its S3-based state eliminates local memory pressure from large state.

Overview

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.