Our paper “Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing” has been accepted at NSDI’2012. This is Matei’s brainchild and a joint work of a lot of people including, but not limited to, TD, Ankur, Justin, Murphy, and professors Ion Stoica, Scott Shenker, and Michael Franklin. Unlike many other systems papers, Spark is actively developed and used by many people. You can also download and use it in no time to solve all your problems; well, at least the ones that require analyzing big data in little time. We focus on the concept of resilient distributed datasets or RDDs in this paper, and show how we can perform fast, in-memory iterative and interactive jobs with low-overhead fault-tolerance.
We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative algorithms and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a restricted form of shared memory, based on coarse-grained transformations rather than fine-grained updates to shared state. However, we show that RDDs are expressive enough to capture a wide class of computations, including current specialized programming models for iterative jobs like Pregel. We have implemented RDDs in a system called Spark, which we evaluate through a variety of benchmarks and user applications.
The NSDI’2012 PC accepted 30 out of 169 papers. On other news, this time Berkeley will have a big presence at NSDI with several other papers. Go Bears!!!