TagSniff

Abstract

Although big data processing has become dramatically easier over the last decade, there has not been matching progress over big data debugging. It is estimated that users spend more than 50% of their time debugging their big data applications, wasting machine resources and taking longer to reach valuable insights. One cannot simply transplant traditional debugging techniques to big data. In this paper, we propose the TagSniff model, which can dramatically simplify data debugging for dataflows (the de-facto programming model for big data). It is based on two primitives -- tag and sniff -- that are flexible and expressive enough to model all common big data debugging scenarios. We then present Snoopy -- a general purpose monitoring and debugging system based on the TagSniff model. It supports both online and post-hoc debugging modes. Our experimental evaluation shows that Snoopy incurs a very low overhead on the main dataflow, 6% on average, as well as it is highly responsive to system events and users instructions.