GUIDE

Kafka Stream Processing with SQL

Q: Can RisingWave consume from multiple Kafka topics simultaneously?

Yes. You can create multiple sources from different Kafka topics and join them together in materialized views using standard SQL. RisingWave handles offset management, consumer group coordination, and exactly-once processing automatically.

Q: Does RisingWave support Kafka exactly-once semantics?

Yes. RisingWave provides exactly-once semantics end-to-end — from Kafka consumption through processing to sink output. This is handled automatically without manual transaction configuration or idempotency key management.

Q: How does RisingWave handle Kafka schema evolution?

RisingWave integrates with Confluent Schema Registry and supports Avro, Protobuf, and JSON schemas. When schemas evolve, RisingWave handles backward-compatible changes automatically. For breaking changes, you can update the source definition.

Q: Can I sink processed results back to Kafka?

Yes. RisingWave supports Kafka as both a source and a sink. You can consume from one set of topics, process and transform the data with SQL, and write results back to different Kafka topics — all with exactly-once guarantees.

Process Apache Kafka streams using standard SQL instead of Java consumers. RisingWave ingests Kafka topics, transforms data with SQL, creates materialized views, and sinks results — all with exactly-once semantics.

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SQL

No Java Required

Write Kafka processing pipelines in SQL instead of hundreds of lines of Java consumer code

Exactly-Once

End-to-End

Automatic exactly-once semantics from Kafka consumption through processing to sink output

Multi-Topic

SQL JOINs

Join multiple Kafka topics with database tables using standard SQL JOIN syntax

1-2 Weeks

Time to Production

Deploy Kafka processing pipelines in days, not the 6-12 weeks typical with Java consumers

Comparison

What Kafka stream processing patterns does RisingWave support?

RisingWave supports all common Kafka stream processing patterns — real-time aggregations, stream-stream joins, stream-table joins, windowed computations, event deduplication, and CDC enrichment — all expressed as SQL queries. Each pattern that would require hundreds of lines of Java becomes a single SQL statement.

Capability	Kafka Streams	ksqlDB	Flink SQL	RisingWave
Language	Java API	KSQL	SQL	PostgreSQL SQL
State Management	RocksDB (manual)	Managed	RocksDB (tunable)	Automatic
Exactly-Once	Manual config	Yes	Manual config	Automatic
Query Serving	No	Pull queries	No	Full PostgreSQL
Multi-Source Joins	Kafka only	Kafka only	Multi-source	Multi-source
Operational Cost	High	Medium	High	Low
Time to Production	6-12 weeks	2-4 weeks	4-8 weeks	1-2 weeks

→Real-time aggregations: COUNT, SUM, AVG over tumbling, hopping, and session windows
→Stream-stream joins: join two Kafka topics on matching keys with time-bounded windows
→Stream-table enrichment: join Kafka events with dimension tables from PostgreSQL CDC
→Event deduplication: use materialized views with DISTINCT ON to deduplicate events
→Pattern detection: identify sequences of events using window functions and self-joins
→Fan-out processing: one Kafka source feeding multiple materialized views for different consumers

How It Works

How does RisingWave process Kafka streams with SQL?

RisingWave connects to Kafka topics as streaming sources, letting you write standard SQL to transform, aggregate, join, and window your event data. Results are maintained as materialized views that update incrementally with each new Kafka message. No Java code, no consumer management, no state store configuration — just SQL.

Native Kafka Connector

CREATE SOURCE connects to any Kafka cluster. Supports Avro, Protobuf, JSON, and CSV formats with Schema Registry integration.

SQL Transformations

Filter, map, aggregate, and join Kafka streams using standard SQL. Window functions, CTEs, and subqueries all supported.

Automatic State Management

No RocksDB tuning or state store configuration. RisingWave manages all intermediate state with automatic checkpointing.

Kafka Sink Output

Write processed results back to Kafka topics, or sink to PostgreSQL, Elasticsearch, Redis, S3, and 20+ destinations.

The Problem

Why is processing Kafka streams with Java so complex?

Processing Kafka streams with Java requires writing consumer applications, managing offsets, handling deserialization, implementing windowing logic, managing state stores, and dealing with rebalancing. Even simple aggregations demand hundreds of lines of code, specialized Kafka expertise, and weeks of development before reaching production.

→Write and maintain Java consumer applications with complex configuration for every processing task
→Manually manage consumer offsets, commit strategies, and exactly-once transaction boundaries
→Implement windowing, aggregation, and join logic from scratch using low-level APIs
→Configure and tune RocksDB state stores for stateful operations in Kafka Streams
→Handle consumer group rebalancing, partition assignment, and backpressure manually
→Hire Java engineers with Kafka expertise — a rare and expensive skill combination

Frequently Asked Questions

Can RisingWave consume from multiple Kafka topics simultaneously?

Does RisingWave support Kafka exactly-once semantics?

How does RisingWave handle Kafka schema evolution?

Can I sink processed results back to Kafka?

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