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RisingWave
Real-time streaming platform with native Iceberg lakehouse support
RisingWave delivers ultra‑low‑latency stream processing, instant SQL queries, and built‑in Apache Iceberg management, enabling millions of events per second with PostgreSQL‑compatible and Python interfaces.
Overview
Highlights
Sub‑100 ms end‑to‑end freshness with 10‑20 ms materialized view latency
Native Apache Iceberg integration for continuous ingestion and table maintenance
PostgreSQL‑compatible SQL and Python DataFrame APIs for familiar development
Object‑storage‑first architecture with elastic disk cache for cost‑effective scaling
Pros
- Ultra‑low latency suitable for real‑time dashboards
- Simplified operations: no manual state tuning, built‑in table maintenance
- Seamless compatibility with existing Postgres tools and ecosystem
- Cost‑efficient storage using S3 and optional local cache
Considerations
- Optimized for S3‑compatible object storage; other storage may need extra configuration
- Advanced custom state backends are limited compared to some specialized stream processors
- Enterprise support primarily via RisingWave Cloud; community still growing
- Learning curve for Iceberg concepts for teams new to lakehouse formats
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Fit guide
Great for
- Teams needing sub‑second analytics for dashboards or monitoring
- Organizations building real‑time feature pipelines for machine learning
- Companies adopting an open‑format lakehouse with Apache Iceberg
- Developers who prefer SQL or Python interfaces without managing separate stream and batch systems
Not ideal when
- Workloads that require on‑premise block storage without object‑storage compatibility
- Scenarios demanding highly customized state backends or low‑level stream operator control
- Small projects where a full‑featured stream engine adds unnecessary complexity
- Environments with strict vendor‑locked licensing requirements
How teams use it
Live financial dashboards
Deliver sub‑second price updates and analytics to traders, ensuring decisions are based on the freshest market data.
Fraud detection alerts
Continuously enrich transaction streams, apply real‑time rules, and trigger alerts within milliseconds of suspicious activity.
ML feature store
Merge batch and streaming data into consistent feature tables, feeding models with up‑to‑date inputs without separate pipelines.
Iceberg lakehouse ingestion
Stream data directly into Apache Iceberg tables, automatically handling compaction and cleanup for downstream analytics.
Tech snapshot
Rust92%
Java3%
Python2%
Shell1%
TypeScript1%
Go1%
Frequently asked questions
How does RisingWave store state and tables?
All state, materialized views, and tables are persisted in S3 (or compatible object storage) with optional elastic disk cache to accelerate hot data access.
Can I use RisingWave with existing PostgreSQL clients?
Yes, it speaks the PostgreSQL wire protocol, so tools like psql, JDBC, and any Postgres driver work out of the box.
What programming languages are supported for queries?
SQL via any PostgreSQL client and a Python DataFrame‑style API for programmatic access.
Is there a managed service available?
RisingWave Cloud provides a fully managed deployment; otherwise you can run it via Docker, Helm, or the standalone installer.
Does RisingWave handle table maintenance automatically?
Yes, it performs compaction, small‑file optimization, vacuum, and snapshot cleanup for Iceberg tables without external pipelines.
Project at a glance
Active- Stars
- 8,728
- Watchers
- 8,728
- Forks
- 724
LicenseApache-2.0
Repo age3 years old
Last commityesterday
Primary languageRust
Last synced yesterday