In the rapidly evolving landscape of big data, stream processing has become increasingly important. A number of frameworks have emerged to aid in this process, including RisingWave and Apache Flink, two popular distributed stream processing systems in the open-source world. While both of these systems offer low-latency query processing over continuously ingested streaming data, they each have distinct features and benefits. This article compares RisingWave and Apache Flink to help you decide which one is right for your needs.
Apache Flink | RisingWave | |
---|---|---|
Version | 1.17 | Latest version |
License | Apache License 2.0 | Apache License 2.0 |
System category | Stream processing framework | Streaming database |
Real-time pipeline orchestration | Complex | Simple |
Architecture | MapReduce-style | Cloud-native |
Native API | Java, Scala, Python, SQL | SQL |
Client libraries | None | Java, Python, Node.js, and more |
State management | RocksDB in local machine; periodically checkpointed to S3 | Native storage persisted in S3 or equivalent storage |
Query serving | DataSet and Table API, Apache Flink Table Store, batch mode execution | Support concurrent ad-hoc SQL query serving |
Correctness | Support exactly-once semantics and out-of-order processing | Support exactly-once semantics, out-of-order processing, and snapshot read |
Integrations and tooling | Big-data ecosystem | Big-data ecosystem, cloud ecosystem, and PostgreSQL ecosystem |
Learning curve | Steep | Extremely shallow |
Maintenance cost | High | Low |
Performance cost | High | Low |
Typical use cases | Streaming ETL, streaming analytics | Streaming ETL, streaming analytics, online serving |
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