F1 query
-
Published:2018-08
Issue:12
Volume:11
Page:1835-1848
-
ISSN:2150-8097
-
Container-title:Proceedings of the VLDB Endowment
-
language:en
-
Short-container-title:Proc. VLDB Endow.
Author:
Samwel Bart1, Cieslewicz John1, Handy Ben1, Govig Jason1, Venetis Petros1, Yang Chanjun1, Peters Keith1, Shute Jeff1, Tenedorio Daniel1, Apte Himani1, Weigel Felix1, Wilhite David1, Yang Jiacheng1, Xu Jun1, Li Jiexing1, Yuan Zhan1, Chasseur Craig1, Zeng Qiang1, Rae Ian1, Biyani Anurag1, Harn Andrew1, Xia Yang1, Gubichev Andrey1, El-Helw Amr1, Erling Orri1, Yan Zhepeng1, Yang Mohan1, Wei Yiqun1, Do Thanh1, Zheng Colin1, Graefe Goetz1, Sardashti Somayeh1, Aly Ahmed M.1, Agrawal Divy1, Gupta Ashish1, Venkataraman Shiv1
Abstract
F1 Query is a stand-alone, federated query processing platform that executes SQL queries against data stored in different file-based formats as well as different storage systems at Google (e.g., Bigtable, Spanner, Google Spreadsheets, etc.). F1 Query eliminates the need to maintain the traditional distinction between different types of data processing workloads by simultaneously supporting: (i) OLTP-style point queries that affect only a few records; (ii) low-latency OLAP querying of large amounts of data; and (iii) large ETL pipelines. F1 Query has also significantly reduced the need for developing hard-coded data processing pipelines by enabling declarative queries integrated with custom business logic. F1 Query satisfies key requirements that are highly desirable within Google: (i) it provides a unified view over data that is fragmented and distributed over multiple data sources; (ii) it leverages datacenter resources for performant query processing with high throughput and low latency; (iii) it provides high scalability for large data sizes by increasing computational parallelism; and (iv) it is extensible and uses innovative approaches to integrate complex business logic in declarative query processing. This paper presents the end-to-end design of F1 Query. Evolved out of F1, the distributed database originally built to manage Google's advertising data, F1 Query has been in production for multiple years at Google and serves the querying needs of a large number of users and systems.
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Cited by
26 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. BigLake: BigQuery's Evolution toward a Multi-Cloud Lakehouse;Companion of the 2024 International Conference on Management of Data;2024-06-09 2. Characterizing a Memory Allocator at Warehouse Scale;Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3;2024-04-27 3. Krypton: Real-Time Serving and Analytical SQL Engine at ByteDance;Proceedings of the VLDB Endowment;2023-08 4. Progressive Partitioning for Parallelized Query Execution in Google's Napa;Proceedings of the VLDB Endowment;2023-08 5. Presto: A Decade of SQL Analytics at Meta;Proceedings of the ACM on Management of Data;2023-06-13
|
|