"实现水平扩展的Spark并行数据库技术——Citus在Spark中的应用"

The paper "Horizontally Scalable Relational Databases with Spark" discusses the use of Citus, a horizontally scalable relational database system, along with Apache Spark for data processing and analysis. Citus is built on top of standard Postgres and allows for sharding data across multiple nodes, making it ideal for live analytics and multi-tenant applications. By creating an extension with Citus, users can benefit from its scalability and flexibility without the need for a separate forked database system. The integration of Citus with Spark offers a powerful solution for data processing workflows. The process typically involves ingesting data into Apache Kafka, manipulating and transforming it using Spark, and then leveraging Citus to serve live traffic. This approach enables users to seamlessly handle large volumes of data, apply machine learning models, and efficiently store key-value pairs for real-time applications. Overall, the combination of Citus and Spark provides a comprehensive solution for building scalable, high-performance databases that can process and serve data in a distributed environment. With its open-source nature and commercial support available, Citus offers a versatile option for organizations looking to optimize their data processing infrastructure and drive profitability through advanced analytics and real-time data services.