Both RisingWave and Apache Flink are designed for building real-time stream processing applications.
Flink specializes in stream processing, but it relies on other datastores for serving real-time data, which may not be optimized for that purpose. In contrast, RisingWave is built on Unified batch and stream processing architecture with built-in serving layer. This is crucial because it enables data processing within a single framework and reducing complexity and cost.
RisingWave was created during the cloud era. By adopting a modern decoupled compute and storage architecture.
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RisingWave | Apache Flink | |
---|---|---|
License | Apache License 2.0 | Apache License 2.0 |
System category | Streaming database | Stream processors |
Architecture | Cloud-native Decoupled compute-storage | MapReduce-style Coupled compute-storage |
Programming API | SQL + UDF (Python, Java, and more) | Java, Scala, Python, SQL |
Client libraries | Java, Python, Node.js, and more | - |
State management | Native storage persisted in S3 or equivalent storage | RocksDB in local machine; periodically checkpointed to S3 |
Query serving | Support concurrent ad-hoc SQL query serving | Support batch mode execution |
Correctness | Support exactly-once semantics, out-of-order processing, snapshot read, and consistent read | Support exactly-once semantics and out-of-order processing |
Integrations and tooling | Big-data ecosystem, cloud ecosystem, and PostgreSQL ecosystem | Big-data ecosystem |
Learning curve | Extremely shallow (PostgreSQL-Like experience) | Steep (Flink-specific interface) |
Failure recovery | Instant | Minutes to hours (depending on specific system configuration) |
Dynamic scaling | Transparent and instant | Stop the world |
Performance cost | Low (especially when handling complex queries like joins) | High |
Typical use cases | Streaming ETL, streaming analytics, online serving | Streaming ETL, streaming analytics |
License | Apache License 2.0 |
System category | Streaming database |
Architecture | Cloud-native Decoupled compute-storage |
Programming API | SQL + UDF (Python, Java, and more) |
Client libraries | Java, Python, Node.js, and more |
State management | Native storage persisted in S3 or equivalent storage |
Query serving | Support concurrent ad-hoc SQL query serving |
Correctness | Support exactly-once semantics, out-of-order processing, snapshot read, and consistent read |
Integrations and tooling | Big-data ecosystem, cloud ecosystem, and PostgreSQL ecosystem |
Learning curve | Extremely shallow (PostgreSQL-Like experience) |
Failure recovery | Instant |
Dynamic scaling | Transparent and instant |
Performance cost | Low (especially when handling complex queries like joins) |
Typical use cases | Streaming ETL, streaming analytics, online serving |