Presto is an open-source distributed SQL query engine that supports analytics workloads involving multiple exabyte-scale data sources. Presto is used for low-latency interactive use cases as well as long-running ETL jobs at Meta. It was originally launched at Meta in 2013 and donated to the Linux Foundation in 2019. Over the last ten years, upholding query latency and scalability with the hyper growth of data volume at Meta as well as new SQL analytics requirements have raised impressive challenges for Presto. A top priority has been ensuring query reliability does not regress with the shift towards smaller, more elastic container allocation, which requires queries to run with substantially smaller memory headroom and can be preempted at any time. Additionally, new demands from machine learning, privacy, and graph analytics have driven Presto maintainers to think beyond traditional data analytics. In this paper, we discuss several successful evolutions in recent years that have improved Presto latency as well as scalability by several orders of magnitude in production at Meta. Some of the notable ones are hierarchical caching, native vectorized execution engines, materialized views, and Presto on Spark. With these new capabilities, we have deprecated or are in the process of deprecating various legacy query engines so that Presto becomes the single piece to serve interactive, ad-hoc, ETL, and graph processing workloads for the entire data warehouse.

Studies show that about 50% of web search is for information exploration purpose, where a user would like to investigate, compare, evaluate, and synthesize multiple relevant results. Due to the absence of general tools that can effectively analyze and differentiate multiple results, a user has to manually read and comprehend potentially large results in an exploratory search. Such a process is time consuming, labor intensive and error prone. With meta information embedded, keyword search on structured data provides the potential for automating or semi-automating the comparison of multiple results. In this demo we present a system XSACT for differentiating search results on structured data. XSACT takes as input a set of structured results, and outputs a Differentiation Feature Set (DFS) for each result to highlight their differences within a size bound. The problem of generating DFSs with maximal differences is proved to be NP-hard. XSACT adopts efficient algorithms for DFS generation, and features a user-friendly interface that effectively interacts with the users to help them compare search results.

Bit-stuffing is the procedure of introducing an extra bit in the transmitted stream after a fixed number of consecutive ones or zeros. This paper demonstrates that, for conventional unstuffing, the BER experienced by individual bits within a stuffed frame dramatically and monotonically degrades with its positional-index. We also show that when knowledge of the number of stuffed bits is applied to maximum a posteriori (MAP) bit-unstuffing (MAPBU) (through a BCJR algorithm), this trend is significantly mitigated.

Bit-stuffing is used in various communications protocols, typically as a way to remove ambiguity in the detection of control flags or as a method to aid clock recovery. Typically a demodulator makes hard symbol-by-symbol decisions without incorporating the knowledge that the data source is bit-stuffed. A demodulator making symbol-by-symbol decisions is clearly not optimum for a source that has been bit-stuffed. In this paper we investigate an optimum approach. We develop the maximum-likelihood sequence detector (MLSD) for a bit-stuffed data source. We start by modeling the transmitted signal as a Markov process. We then proceed to calculate approximate performance bounds for the MLSD. We then develop a trellis receiver structure and perform simulations using the Viterbi decoder algorithm.