S-Store: Streaming Meets Transaction Processing

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees

Data Ingestion for the Connected World

ABSTRACT In this paper, we argue that in many "Big Data" applications, getting data into the system correctly and at scale via traditional ETL (Extract, Transform, and Load) processes is a fundamental roadblock to being able to perform timely analytics or make real-time decisions. The best way to address this problem is to build a new architecture for ETL which takes advantage of the push-based nature of a stream processing system. We discuss the requirements for a streaming ETL engine and describe a generic architecture which satisfies those requirements. We also describe our implementation of streaming ETL using a scalable messaging system (Apache Kafka), a transactional stream processing system (S-Store), and a distributed polystore (Intel's BigDAWG), as well as propose a new time-series database optimized to handle ingestion internally