RisingWave vs Apache Flink
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.
Why developers choose RisingWave over Apache Flink
Both RisingWave and Apache Flink are designed for building real-time stream processing applications. 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.
RisingWave speaks PostgreSQL-style SQL, enabling users to dive into stream processing in much the same way as operating a PostgreSQL database.
RisingWave can efficiently process overly complex SQL queries like multi-stream joins in production environment, with no concerns about system crashes.
RisingWave supports near-instantaneous dynamic scaling without any service interruptions, and can recover from failure in seconds, not minutes or hours.
RisingWave achieves 10X cost-efficiency compared with Apache Flink, especially when handling complex and fluctuated workloads.
More reasons to move to RisingWave
RisingWave is a distributed SQL streaming database. It allows users to handle stream data using a SQL database approach, performing continuous real-time stream processing, along with data storage and ad-hoc query access functionalities. On top of that, RisingWave mainly focuses on reinventing stream processing by improving two aspects: ease of use and cost efficiency.
Apache Flink is a distributed stream processing framework designed for high performance and scalability. It was inspired by the idea of MapReduce, in which developers can build complex streaming applications using low-level APIs, with a wide array of operators for data transformation. Flink utilizes embarrassingly parallelism, enabling efficient, scalable, and robust data processing pipelines, tailored to handle the demands of real-time stream processing.
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. With that 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.
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.
RisingWave abstracts away unnecessary low-level details and allows users to write PostgreSQL-style SQL. In addition RisingWave integrates to a diverse range of cloud systems and the PostgreSQL ecosystem, making it straightforward to incorporate into existing infrastructures.
Flink provides users with fine-grained low-level control over the streaming pipeline. With its Java APIs, users create their stream processing applications by adding stream processors one after one. Flink is deeply integrated with existing big data ecosystems, such as Hadoop and Zookeeper. Users can set up and configure Flink on top of their existing Hadoop-based infrastructures.