Jeff Shute

Jeff Shute

Authored Publications
Google Publications
Other Publications
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    Dremel: A Decade of Interactive SQL Analysis at Web Scale
    Andrey Gubarev
    Dan Delorey
    Geoffrey Michael Romer
    Hossein Ahmadi
    Jing Jing Long
    Matt Tolton
    Mosha Pasumansky
    Narayanan Shivakumar
    Sergey Melnik
    Slava Min
    Theo Vassilakis
    PVLDB(2020), pp. 3461-3472
    Preview abstract Google's Dremel was one of the first systems to combine a set of architectural principles that have become a common practice in today's cloud-native analytical systems, such as disaggregated storage and compute, in situ analysis, and columnar storage for semistructured data. In this paper, we discuss how these ideas evolved in the past decade and became the foundation for Google BigQuery. View details
    F1 Lightning: HTAP as a Service
    Goetz Graefe
    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
    Petros Venetis
    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
    Goetz Graefe
    Somayeh Sardashti
    Ahmed Aly
    Divy Agrawal
    Ashish Gupta
    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
    High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads
    Ashish Gupta
    Workshop on Business Intelligence for the Real Time Enterprise (BIRTE), Springer(2015) (to appear)
    Preview abstract Google’s Ads Data Infrastructure systems run the multi- billion dollar ads business at Google. High availability and strong consistency are critical for these systems. While most distributed systems handle machine-level failures well, handling datacenter-level failures is less common. In our experience, handling datacenter-level failures is critical for running true high availability systems. Most of our systems (e.g. Photon, F1, Mesa) now support multi-homing as a fundamental design property. Multi-homed systems run live in multiple datacenters all the time, adaptively moving load between datacenters, with the ability to handle outages of any scale completely transparently. This paper focuses primarily on stream processing systems, and describes our general approaches for building high availability multi-homed systems, discusses common challenges and solutions, and shares what we have learned in building and running these large-scale systems for over ten years. View details
    Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing
    Ashish Gupta
    Fan Yang
    Jason Govig
    Adam Kirsch
    Kelvin Chan
    Kevin Lai
    Shuo Wu
    Sandeep Dhoot
    Abhilash Kumar
    Mingsheng Hong
    Jamie Cameron
    Masood Siddiqi
    David Jones
    Andrey Gubarev
    Shivakumar Venkataraman
    Divyakant Agrawal
    VLDB(2014)
    Preview abstract Mesa is a highly scalable analytic data warehousing system that stores critical measurement data related to Google's Internet advertising business. Mesa is designed to satisfy a complex and challenging set of user and systems requirements, including near real-time data ingestion and queryability, as well as high availability, reliability, fault tolerance, and scalability for large data and query volumes. Specifically, Mesa handles petabytes of data, processes millions of row updates per second, and serves billions of queries that fetch trillions of rows per day. Mesa is geo-replicated across multiple datacenters and provides consistent and repeatable query answers at low latency, even when an entire datacenter fails. This paper presents the Mesa system and reports the performance and scale that it achieves. 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
    Preview abstract We introduce a protocol for schema evolution in a globally distributed database management system with shared data, stateless servers, and no global membership. Our protocol is asynchronous—it allows different servers in the database system to transition to a new schema at different times—and online—all servers can access and update all data during a schema change. We provide a formal model for determining the correctness of schema changes under these conditions, and we demonstrate that many common schema changes can cause anomalies and database corruption. We avoid these problems by replacing corruption-causing schema changes with a sequence of schema changes that is guaranteed to avoid corrupting the database so long as all servers are no more than one schema version behind at any time. Finally, we discuss a practical implementation of our protocol in F1, the database management system that stores data for Google AdWords. View details
    F1 - The Fault-Tolerant Distributed RDBMS Supporting Google's Ad Business
    Mircea Oancea
    Stephan Ellner
    Ben Handy
    Bart Samwel
    Xin Chen
    Beat Jegerlehner
    Kyle Littlefield
    Phoenix Tong
    SIGMOD(2012)
    Preview abstract Many of the services that are critical to Google’s ad business have historically been backed by MySQL. We have recently migrated several of these services to F1, a new RDBMS developed at Google. F1 implements rich relational database features, including a strictly enforced schema, a powerful parallel SQL query engine, general transactions, change tracking and notification, and indexing, and is built on top of a highly distributed storage system that scales on standard hardware in Google data centers. The store is dynamically sharded, supports transactionally-consistent replication across data centers, and is able to handle data center outages without data loss. The strong consistency properties of F1 and its storage system come at the cost of higher write latencies compared to MySQL. Having successfully migrated a rich customerfacing application suite at the heart of Google’s ad business to F1, with no downtime, we will describe how we restructured schema and applications to largely hide this increased latency from external users. The distributed nature of F1 also allows it to scale easily and to support significantly higher throughput for batch workloads than a traditional RDBMS. With F1, we have built a novel hybrid system that combines the scalability, fault tolerance, transparent sharding, and cost benefits so far available only in “NoSQL” systems with the usability, familiarity, and transactional guarantees expected from an RDBMS. View details
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