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John Cieslewicz

John Cieslewicz

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    F1 Lightning: HTAP as a Service
    Ian James Rae
    Jeff Naughton
    Jeremy David Wood
    Jiacheng Yang
    Jun Ma
    Jun Xu
    Junxiong Zhou
    Kelvin Lau
    Qiang Zeng
    Xi Zhao
    Yuan Gao
    Zhan Yuan
    Ziyang Chen
    VLDB, VLDB Endowment (2020), ??-??
    Preview abstract The ongoing and increasing interest in HTAP (Hybrid Transactional and Analytical Processing) systems documents the intense interest from data owners in simultaneously running transactional and analytical workloads over the same data set. Much of the reported work on HTAP has arisen in the context of “green field” systems, answering the question “if we could design a system for HTAP from scratch, what would it look like?” While there is great merit in such an approach, and a lot of valuable technology has been developed with it, we found ourselves facing a different challenge: one in which there is a great deal of transactional data already existing in several transactional systems, heavily queried by an existing federated engine that does not “own” the transactional systems, supporting both new and legacy applications that demand transparent fast queries and transactions from this combination. This paper reports on our design and experiences with F1 Lightning, a system we built and deployed to meet this challenge. We describe our design decisions, some details of our implementation, and our experience with the system in production for some of Google's most demanding applications. View details
    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. View details
    F1: A Distributed SQL Database That Scales
    Bart Samwel
    Ben Handy
    Mircea Oancea
    Kyle Littlefield
    David Menestrina
    Stephan Ellner
    Ian Rae
    Traian Stancescu
    VLDB (2013)
    Preview abstract F1 is a distributed relational database system built at Google to support the AdWords business. F1 is a hybrid database that combines high availability, the scalability of NoSQL systems like Bigtable, and the consistency and usability of traditional SQL databases. F1 is built on Spanner, which provides synchronous cross-datacenter replication and strong consistency. Synchronous replication implies higher commit latency, but we mitigate that latency by using a hierarchical schema model with structured data types and through smart application design. F1 also includes a fully functional distributed SQL query engine and automatic change tracking and publishing. View details
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