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F1 Query: Declarative Querying at Scale
Bart Samwel
Ben Handy
Jason Govig
Chanjun Yang
Daniel Tenedorio
Felix Weigel
David G Wilhite
Jiacheng Yang
Jun Xu
Jiexing Li
Zhan Yuan
Qiang Zeng
Ian Rae
Anurag Biyani
Andrew Harn
Yang Xia
Andrey Gubichev
Amr El-Helw
Orri Erling
Allen Yan
Mohan Yang
Yiqun Wei
Thanh Do
Colin Zheng
Somayeh Sardashti
Ahmed Aly
Divy Agrawal
Shivakumar Venkataraman
PVLDB (2018), pp. 1835-1848
Preview 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 (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 transforming data from multiple data sources into formats more suitable for analysis and reporting. 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 that Google uses to manage its 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.
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