Presto is an open source distributed query engine that supports much of the SQL analytics workload at Facebook. Presto is designed to be adaptive, flexible, and extensible. It supports a wide variety of use cases with diverse characteristics. These range from user-facing reporting applications with sub-second latency requirements to multi-hour ETL jobs that aggregate or join terabytes of data. Presto's Connector API allows plugins to provide a high performance I/O interface to dozens of data sources, including Hadoop data warehouses, RDBMSs, NoSQL systems, and stream processing systems. In this paper, we outline a selection of use cases that Presto supports at Facebook. We then describe its architecture and implementation, and call out features and performance optimizations that enable it to support these use cases. Finally, we present performance results that demonstrate the impact of our main design decisions.

In this paper we describe a two step process for reverse engineering the software architecture of a system directly from its source code. The first step involves clustering the modules from the source code into abstract structures called subsystems. The second step involves reverse engineering the subsystem-level relations using a formal (and visual) architectural constraint language. We use search techniques to accomplish both of these steps, and have implemented a suite of integrated tools to support the reverse engineering process. Through a case study, we demonstrate how our tools can be used to extract the software architecture of an open-source software package from its source code without having any a priori knowledge about its design.

Collections of general purpose networked workstations offer processing capability that often rivals or exceeds supercomputers. Since networked workstations are readily available in most organizations, they provide an economic and scalable alternative to parallel machines. The authors discuss how individual nodes in a computer network can be used as a collection of connected processing elements to improve the performance of a software engineering tool that we developed. Our tool, called Bunch, automatically clusters the structure of software systems into a hierarchy of subsystems. Clustering helps developers understand complex systems by providing them with high-level abstract (clustered) views of the software structure. The algorithms used by Bunch are computationally intensive and, hence, we would like to improve our tool's performance in order to cluster very large systems. The paper describes how we designed and implemented a distributed version of Bunch, which is useful for clustering large systems.