Yingjun Wu discusses the architecture and design patterns of streaming databases, challenges like real-time data enrichment, managing data order with Watermark technology, and the future of streaming databases with the potential impact of Amazon S3 Express One Zone.
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Quick takeaways
Streaming databases simplify processing streaming data by offering a familiar database experience and addressing cost efficiency challenges.
Traditional stream processing systems have limitations in use and cost efficiency, requiring users to understand complex concepts and provision unnecessary resources.
Building a streaming database involves trade-offs, such as choosing between remote object stores and local machines impacting performance and maintenance requirements.
Deep dives
Streaming Databases: Overview and Fundamentals
Streaming databases provide a database-like experience for processing streaming data, allowing data to be stored within the system. They address challenges faced by traditional stream processing systems, which can be complex and costly to use. Streaming databases like Rising Wave aim to simplify processing streaming data by offering a familiar database experience for users without the need to understand internal workings of the system.
Challenges Faced by Stream Processing Systems
The key challenges faced by existing stream processing systems such as Apache Flink and Spark Streaming include difficulties in use and lack of cost efficiency. Users often need to understand complex concepts and write code specific to these systems. Additionally, traditional stream processing systems may not be cost-efficient as they require provisioning of multiple computer nodes even when workload fluctuates, leading to unnecessary expenses.
Cost Efficiency and Trade-offs in Streaming Databases
Streaming databases like Rising Wave aim to address the ease of use and cost efficiency challenges in processing streaming data. By providing a Postgres-compatible system, users can leverage their existing knowledge and tools to work with Rising Wave. The trade-off for achieving cost efficiency lies in the system's design and data storage mechanisms, such as using object stores like S3, impacting latency and access speeds.
Trade-offs in Building a Streaming Database
Building a streaming database like Rising Wave involves trade-offs, especially from the vendor's perspective. Developing a system that can handle streaming data processing with efficiency and low latency requires significant effort and time. The choice between storing data in remote object stores like S3 or local machines impacts the system's performance, resilience, and maintenance requirements.
Handling Low Latency and Dynamic Workloads
Achieving low latency in streaming databases involves incremental computation to process data efficiently. Systems like Rising Wave adapt to dynamic workloads by provisioning resources based on demand, enabling dynamic scaling without the overhead of managing excessive computing nodes. The ability to process streaming data in real-time ensures responsiveness to fluctuating workloads, crucial for use cases like real-time analytics and monitoring.
Yingjun Wu, founder of RisingWave Labs and previously a software engineer at Amazon Web Services and researcher at IBM Almaden Research Center, speaks with SE Radio host Brijesh Ammanath about streaming databases. After considering the benefits and unique challenges, they delve into the architecture and design patterns of streaming databases, as well as the evolution and security considerations. Yingjun also talks about the future of streaming databases, including the potential impact that Amazon S3 Express One Zone will have on the streaming landscape, and how the unified batch and streaming might evolve in the database world. Brought to you by IEEE Computer Society and IEEE Software magazine.
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