RisingWave vs Apache Flink - RisingWave: Open-Source Streaming Database

Both RisingWave and Apache Flink are designed for building real-time stream processing applications. But they differ drastically in design, user experiences, and cost efficiency.

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.

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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.

Choose Friction-Less Path to Stream Processing

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.

RisingWave comes with automated state management, allowing you to focus on building your application logic. RisingWave handles the complexity behind the scenes, ensuring seamless state management and fault tolerance.

RisingWave’s architecture is designed to easily handle complex queries, such as joins, aggregations, and time windowing functions. You can execute these queries efficiently without getting stuck, making your real-time applications more powerful and reliable.

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.

Flink involves manual and intricate state management, which can bog down development and add unnecessary complexity, increasing the risk of errors and inefficiencies.

Users can easily get stuck with complex queries like joins, aggregations, and time windowing functions, leading to performance bottlenecks and frustration due to Flink's intricate architecture.

RisingWave

Flink

Reduce the Complexity of Your Event-Driven Architecture

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.

What Makes RisingWave
the Clear Choice for Cost Efficiency?

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.

Stream Processing Made Easy

More Reasons to Move to RisingWave

While Apache Flink offers flexibility, RisingWave's ease of use and cost-efficiency have led to its increasing preference among enterprises and fast-growing companies over Apache Flink for stream processing.

	Apache Flink	RisingWave
License	Apache License 2.0	Apache License 2.0
System category	Stream processors	Streaming database
Architecture	MapReduce-style Coupled compute-storage	Cloud-native Decoupled compute-storage
Programming API	Java, Scala, Python, SQL	SQL + UDF (Python, Java, and more)
Client libraries	-	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	Support 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, snapshot read, and consistent read
Integrations and tooling	Big-data ecosystem	Big-data ecosystem, cloud ecosystem, and PostgreSQL ecosystem
Learning curve	Steep (Flink-specific interface)	Extremely shallow (PostgreSQL-Like experience)
Failure recovery	Minutes to hours (depending on specific system configuration)	Instant
Dynamic scaling	Stop the world	Transparent and instant
Performance cost	High	Low (especially when handling complex queries like joins)
Typical use cases	Streaming ETL, streaming analytics	Streaming ETL, streaming analytics, online serving

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