A Community Cache with Complete Information


Mania Abdi, Northeastern University; Amin Mosayyebzadeh, Boston University; Mohammad Hossein Hajkazemi, Northeastern University; Emine Ugur Kaynar, Boston University; Ata Turk, State Street; Larry Rudolph, TwoSigma; Orran Krieger, Boston University; Peter Desnoyers, Northeastern University


Kariz is a new architecture for caching data from datalakes accessed, potentially concurrently, by multiple analytic platforms. It integrates rich information from analytics platforms with global knowledge about demand and resource availability to enable sophisticated cache management and prefetching strategies that, for example, combine historical run time information with job dependency graphs (DAGs), information about the cache state and sharing across compute clusters. Our prototype supports multiple analytic frameworks (Pig/Hadoop and Spark), and we show that the required changes are modest. We have implemented three algorithms in Kariz for optimizing the caching of individual queries (one from the literature, and two novel to our platform) and three policies for optimizing across queries from, potentially, multiple different clusters. With an algorithm that fully exploits the rich information available from Kariz, we demonstrate major speedups (as much as 3×) for TPC-H and TPC-DS.

FAST '21 Open Access Sponsored by NetApp

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@inproceedings {264854,
author = {Mania Abdi and Amin Mosayyebzadeh and Mohammad Hossein Hajkazemi and Emine Ugur Kaynar and Ata Turk and Larry Rudolph and Orran Krieger and Peter Desnoyers},
title = {A Community Cache with Complete Information},
booktitle = {19th USENIX Conference on File and Storage Technologies (FAST 21)},
year = {2021},
isbn = {978-1-939133-20-5},
pages = {323--340},
url = {https://www.usenix.org/conference/fast21/presentation/abdi},
publisher = {USENIX Association},
month = feb

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