@inproceedings{1439ae31ef5844c4bd08011ece6708b4,
title = "Fast concurrent data sketches",
abstract = "Data sketches are approximate succinct summaries of long data streams. They are widely used for processing massive amounts of data and answering statistical queries about it. Existing libraries producing sketches are very fast, but do not allow parallelism for creating sketches using multiple threads or querying them while they are being built. We present a generic approach to parallelising data sketches efficiently and allowing them to be queried in real time, while bounding the error that such parallelism introduces. Utilising relaxed semantics and the notion of strong linearisability we prove our algorithm{\textquoteright}s correctness and analyse the error it induces in two specific sketches. Our implementation achieves high scalability while keeping the error small. We have contributed one of our concurrent sketches to the open-source data sketches library.",
author = "Arik Rinberg and Alexander Spiegelman and Edward Bortnikov and Eshcar Hillel and Idit Keidar and Lee Rhodes and Hadar Serviansky",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computing Machinery.; 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2020 ; Conference date: 22-02-2020 Through 26-02-2020",
year = "2020",
month = feb,
day = "19",
doi = "10.1145/3332466.3374512",
language = "الإنجليزيّة",
isbn = "978-1-4503-6818-6",
series = "Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP",
pages = "117--129",
booktitle = "PPoPP 2020 - Proceedings of the 2020 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming",
}