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Himani Apte

Himani Apte

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    F1 Query: Declarative Querying at Scale
    John Cieslewicz
    Goetz Graefe
    Petros Venetis
    Bart Samwel
    Jeff Shute
    Himani Apte
    Ahmed Aly
    Thanh Do
    Craig Chasseur
    Ashish Gupta
    Keith Peters
    Somayeh Sardashti
    Jiexing Li
    Jiacheng Yang
    Chanjun Yang
    Jason Govig
    Andrew Harn
    Zhan Yuan
    Daniel Tenedorio
    Colin Zheng
    Allen Yan
    Orri Erling
    Yang Xia
    Qiang Zeng
    Divy Agrawal
    Jun Xu
    Mohan Yang
    Andrey Gubichev
    Felix Weigel
    Yiqun Wei
    Ben Handy
    Anurag Biyani
    Ian Rae
    Amr El-Helw
    Shivakumar Venkataraman
    David G Wilhite
    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
    Shasta: Interactive Reporting at Scale
    Apurv Gupta
    Divy Agrawal
    Jun Xu
    Shivakumar Venkataraman
    Jeffrey D. Ullman
    Ben Handy
    Stephan Ellner
    Alejandro Estrella-Balderrama
    Karl Schnaitter
    Stephan Gudmundson
    Gokul Nath Babu Manoharan
    Bart Samwel
    Himani Apte
    Sridatta Chegu
    Nitin Gangahar
    Chad Whipkey
    Larysa Aharkava
    SIGMOD, San Francisco, CA (2016) (to appear)
    Preview abstract We describe Shasta, a middleware system built at Google to support interactive reporting in complex user-facing applications related to Google’s Internet advertising business. Shasta targets applications with challenging requirements: First, user query latencies must be low. Second, underlying transactional data stores have complex “read-unfriendly” schemas, placing significant transformation logic between stored data and the read-only views that Shasta exposes to its clients. This transformation logic must be expressed in a way that scales to large and agile engineering teams. Finally, Shasta targets applications with strong data freshness requirements, making it challenging to precompute query results using common techniques such as ETL pipelines or materialized views. Instead, online queries must go all the way from primary storage to userfacing views, resulting in complex queries joining 50 or more tables. Designed as a layer on top of Google’s F1 RDBMS and Mesa data warehouse, Shasta combines language and system techniques to meet these requirements. To help with expressing complex view specifications, we developed a query language called RVL, with support for modularized view templates that can be dynamically compiled into SQL. To execute these SQL queries with low latency at scale, we leveraged and extended F1’s distributed query engine with facilities such as safe execution of C++ and Java UDFs. To reduce latency and increase read parallelism, we extended F1 storage with a distributed read-only in-memory cache. The system we describe is in production at Google, powering critical applications used by advertisers and internal sales teams. Shasta has significantly improved system scalability and software engineering efficiency compared to the middleware solutions it replaced. View details
    F1: A Distributed SQL Database That Scales
    Ben Handy
    David Menestrina
    Traian Stancescu
    Mircea Oancea
    Ian Rae
    Kyle Littlefield
    Stephan Ellner
    John Cieslewicz
    Bart Samwel
    Jeff Shute
    Himani Apte
    Radek Vingralek
    Chad Whipkey
    Eric Rollins
    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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