I am a fourth year Ph.D candidate in the Programming Languages and Software Engineering (PLSE) Group at University of California, Los Angeles. My research interests lie at the intersection of software engineering and big data systems. Specifically, I am interested in supporting interactive debugging in big data processing frameworks and providing efficient ways to perform automated fault localization in big data applications.
Developing Big Data Analytics often involves trial and error debugging, due to the unclean nature of datasets or wrong assumptions about data. Data scientists typically write code that implements a data processing pipeline and test it on their local workstation with a small sample data, downloaded from a TB-scale data warehouse. They cross fingers and hope that the program works in the expensive production cloud.When a job fails or they get a suspicious result, data scientists spend hours guessing at the source of the error, digging through post-mortem logs. In such cases, the data scientists may want to pinpoint the root cause of errors by investigating a subset of corresponding input records. In this talk, we presentÂ BigSift, an automated debugger for Apache Spark that data engineers and scientists can use. It takes an Apache Spark program, a user-defined test oracle function, and a dataset as input and outputs a minimum set of input records that reproduces the same test failure. BigSift combines insights from automated fault isolation in software engineering and data provenance in database systems to find a minimum set of failure-inducing inputs. It redefines data provenance for the purpose of debugging using a test oracle function and implements several unique optimizations, specifically geared towards the iterative nature of automated debugging workloads. BigSift exposes an interactive web interface where a user can monitor a big data analytics job running remotely on the cloud, write a user-defined test oracle function, and then trigger the automated debugging process. BigSift also provides a set of predefined test oracle functions, which can be used for explaining common types of anomalies in big data analytics. This debugging effort is led by UCLA Professors Miryung Kim and Tyson Condie, and produced several research papers in top Software Engineering and Database conferences. Session hashtag: #Res3SAIS
Debugging big data analytics in Data-Intensive Scalable Computing (DISC) systems is a time-consuming effort. Today’s DISC systems offer very little tooling for debugging and, as a result, programmers spend countless hours analyzing log files and performing trial and error debugging. To aid this effort, UCLA developed BigDebug, an interactive debugging tool and automated fault localization service to help Apache Spark developers in debugging big data analytics. To emulate interactive step-wise debugging without reducing throughput, BigDebug provides simulated breakpoints that enable a user to inspect a program without actually pausing the entire distributed computation. It also supports on-demand watchpoints that enable a user to retrieve intermediate data using a guard predicate and transfer the selected data on demand. To understand the flow of individual records within a pipeline of RDD transformations, BigDebug provides data provenance capability, which can help understand how errors propagate through data processing steps. To support efficient trial-and-error debugging, BigDebug enables users to change program logic in response to an error at runtime through a realtime code fix feature, and selectively replay the execution from that step. Finally, BigDebug proposes an automated fault localization service that leverages all the above features together to isolate failure-inducing inputs, diagnose the root cause of an error, and resume the workflow for only affected data and code. The BigDebug system should contribute to improving Spark developerproductivity and the correctness of their Big Data applications. This big data debugging effort is led by UCLA Professors Miryung Kim and Tyson Condie, and produced several research papers in top Software Engineering and Database conferences. The current version of BigDebug is publicly available at https://sites.google.com/site/sparkbigdebug/. Session hashtag: #SFr8