RisingWave vs Apache Flink: Which Streaming Platform Should You Choose?
RisingWave is a PostgreSQL-compatible streaming database that uses SQL instead of Java, with up to 10x lower infrastructure cost than Flink. Compare architecture, performance, cost, and ease of use.
highlights
RisingWave is Remarkably Easier to Use and Significantly More Cost-efficient than Flink
10x
Cost Efficiency
RisingWave is highly optimized for performance and efficiency, especially with complex queries like joins, aggregations, and time windowing.
<10s
Dynamic Scaling
RisingWave supports near-instantaneous dynamic scaling without any service interruptions. Not minutes or hours.
PostgreSQL
RisingWave speaks PostgreSQL-style SQL, enabling users to dive into stream processing in much the same way as operating a PostgreSQL database.
Why is RisingWave easier to use than Apache Flink?
RisingWave eliminates the friction that slows down Flink development. Say goodbye to:
Steep Learning Curves
Manual State Management
Endless Query Optimizations
RisingWave
With PostgreSQL compatibility, RisingWave allows you to build applications effortlessly using a familiar PostgreSQL-style experience. Developers can hit the ground running without needing to learn a new system from scratch.
Apache Flink
Requires extensive training and expertise, with a complex API and steep learning curve that slows down development, making it challenging for those not already familiar with its intricacies.
RisingWave
Flink
How does RisingWave simplify streaming architecture compared to Flink?
RisingWave unifies stream processing and data serving in a single system, eliminating the need for separate serving databases that Flink deployments require.
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 a unified batch and stream processing architecture with a built-in serving layer. This enables data processing within a single framework, reducing both complexity and cost.
How much cheaper is RisingWave compared to Apache Flink?
RisingWave delivers up to 10x cost efficiency over Flink by using S3-compatible object storage instead of provisioned local SSDs, scaling compute resources dynamically in seconds, and eliminating JVM overhead.
RisingWave
RisingWave was created during the cloud era. By adopting a modern decoupled compute and storage architecture.
RisingWave achieves better elasticity and cost efficiency. In particular RisingWave persists its data in S3 or other compatible cloud storage services.- RisingWave can handle complex streaming joins over large time windows and recover from failures in seconds, not minutes or hours.
- The new architecture also allows each component to be optimized separately, reducing resource waste and avoiding task overload.
Apache Flink
As a computing framework born during the Hadoop-dominant big-data era, the architecture of Flink was heavily influenced by the MapReduce paradigm. The coupled compute and storage architecture enables Flink to achieve high parallelism and scalability.
However, this very architecture can give rise to concerns regarding execution costs. Due to the nature of its local state storage, e.g. RocksDB, Flink needs to scale large enough to handle large streaming joins and other stateful stream processing tasks.
However, this very architecture can give rise to concerns regarding execution costs. Due to the nature of its local state storage, e.g. RocksDB, Flink needs to scale large enough to handle large streaming joins and other stateful stream processing tasks.
As a computing framework born during the Hadoop-dominant big-data era, the architecture of Flink was heavily influenced by the MapReduce paradigm. The coupled compute and storage architecture enables Flink to achieve high parallelism and scalability.
However, this very architecture can give rise to concerns regarding execution costs. Due to the nature of its local state storage, e.g. RocksDB, Flink needs to scale large enough to handle large streaming joins and other stateful stream processing tasks.
However, this very architecture can give rise to concerns regarding execution costs. Due to the nature of its local state storage, e.g. RocksDB, Flink needs to scale large enough to handle large streaming joins and other stateful stream processing tasks.
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Feature-by-Feature Comparison
How does RisingWave compare to Apache Flink?
RisingWave outperforms Flink in ease of use, cost efficiency, and failure recovery, while matching Flink's correctness guarantees. Both support exactly-once semantics — but RisingWave does it with SQL, not Java.
| 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 |
RisingWave
| 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 |
Frequently Asked Questions
Common questions about choosing between RisingWave and Apache Flink
Is RisingWave a drop-in replacement for Apache Flink?
Can RisingWave handle the same data scale as Apache Flink?
Does RisingWave support exactly-once semantics?
Do I need to know Java to use RisingWave?
Can RisingWave connect to my existing Kafka cluster?
How does RisingWave handle failure recovery compared to Flink?
How much cheaper is RisingWave than Apache Flink?
What programming languages does RisingWave support?
