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.
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RisingWave is an open-source distributed SQL streaming database. Specifically, RisingWave enables users to manage and process stream data using a PostgreSQL-like approach, performing continuous real-time stream processing, in addition to offering data storage and ad-hoc query access. Built from scratch using Rust, RisingWave adopts a decoupled compute-storage architecture to optimize for high-throughput and low-latency stream processing in the cloud.
Apache Flink is an open-source distributed stream processing framework. Flink features a distributed dataflow engine that supports parallel, pipelined, and iterative execution of data processing programs. Initially inspired by the classic MapReduce paradigm, Flink exposes its lower-level APIs in different programming languages and offers a SQL wrapper for users to craft and manage computing jobs. For detailed information about Apache Flink, including its architecture, use cases, benefits, and limitations, please refer to Understanding Apache Flink.
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.
POSTGRESQL
RisingWave speaks PostgreSQL-style SQL, enabling users to dive into stream processing in much the same way as operating a PostgreSQL database.
STREAMING JOINS
RisingWave can efficiently process overly complex SQL queries like multi-stream joins in production environment, with no concerns about system crashes.
ELASTIC
RisingWave supports near-instantaneous dynamic scaling without any service interruptions, and can recover from failure in seconds, not minutes or hours.
10X
RisingWave achieves 10X cost-efficiency compared with Apache Flink, especially when handling complex and fluctuated workloads.
When developing applications using Flink, users often need to connect multiple instances of a stream processing engine with multiple instances of message queues to express complex logic. To query the results, users must export the stream processing results to a dedicated downstream database and perform queries there. This architecture is complex, incurs high operational costs, and requires users to take responsibility for the consistency of computation results across systems.
When using RisingWave, users only need to focus on constructing materialized views and can reduce development complexity by splitting complex logic into multiple cascading materialized views. RisingWave guarantees the consistency, persistence, and high concurrent query access of materialized views. Users only need to manage a RisingWave cluster, as RisingWave ensures the consistency between different materialized views.
More reasons to move to RisingWave
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
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
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.
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.
RisingWave
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.
Apache Flink
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.