Many important “big data” applications need to process data arriving in real time. However, current programming models for distributed stream processing are relatively low-level, often leaving the user to worry about consistency of state across the system and fault recovery. Furthermore, the models that provide fault recovery do so in an expensive manner, requiring either hot replication or long recovery times. We propose a new programming model, discretized streams (D-Streams), that offers a high-level functional programming API, strong consistency, and efﬁcient fault recovery. D-Streams support a new recovery mechanism that improves efﬁciency over the traditional replication and upstream backup solutions in streaming databases: parallel recovery of lost state across the cluster. We have prototyped D-Streams in an extension to the Spark cluster computing framework called Spark Streaming, which lets users seamlessly intermix streaming, batch and interactive queries.
National Science Foundation
Expeditions in Computing