Matteo Interlandi - Databricks

Matteo Interlandi

Scientist, Microsoft CISL

Matteo Interlandi recently joined Microsoft CISL as a Research Scientist. Prior to joining Microsoft, Matteo was Postdoctoral Scholar at the University of California, Los Angeles. His research lies in between databases, distributed systems and declarative languages. In particular, he loves to build systems and tools that make it easier to design and implement data-driven distributed applications.
Matteo obtained his PhD from the University of Modena and Reggio Emilia, Italy.



Debugging Big Data Analytics in Apache Spark with BigDebugSummit 2017

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 Session hashtag: #SFr8

Lazy Join Optimizations Without Upfront StatisticsSummit 2017

Modern Data-Intensive Scalable Computing (DISC) systems such as Apache Spark do not support sophisticated cost-based query optimizers because they are specifically designed to process data that resides in external storage systems (e.g. HDFS), or they lack the necessary data statistics. Consequently, many crucial optimizations, such as join order and plan selection, are presently out-of-scope in these DISC system optimizers. Yet, join order is one of the most important decisions a cost-optimizer can make because wrong orders can result in a query response time that can become more than an order-of-magnitude slower compared to the better order. Session hastag: #SFr4