Jeff Shute
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SQL Has Problems. We Can Fix Them: Pipe Syntax In SQL
Shannon Bales
Matthew Brown
Jean-Daniel Browne
Brandon Dolphin
Romit Kudtarkar
Andrey Litvinov
Jingchi Ma
John Morcos
Michael Shen
David Wilhite
Xi Wu
Lulan Yu
Proc. VLDB Endow. (2024), pp. 4051-4063 (to appear)
Preview abstract
SQL has been extremely successful as the de facto standard language for working with data. Virtually all mainstream database-like systems use SQL as their primary query language. But SQL is an old language with significant design problems, making it difficult to learn, difficult to use, and difficult to extend. Many have observed these challenges with SQL, and proposed solutions involving new languages. New language adoption is a significant obstacle for users, and none of the potential replacements have been successful enough to displace SQL.
In GoogleSQL, we’ve taken a different approach - solving SQL’s problems by extending SQL. Inspired by a pattern that works well in other modern data languages, we added piped data flow syntax to SQL. The results are transformative - SQL becomes a flexible language that’s easier to learn, use and extend, while still leveraging the existing SQL ecosystem and existing userbase. Improving SQL from within allows incrementally adopting new features, without migrations and without learning a new language, making this a more productive approach to improve on standard SQL.
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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.
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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.
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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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High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads
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.
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Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing
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.
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F1: A Distributed SQL Database That Scales
Bart Samwel
Ben Handy
Chad Whipkey
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.
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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.
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F1 - The Fault-Tolerant Distributed RDBMS Supporting Google's Ad Business
Mircea Oancea
Stephan Ellner
Ben Handy
Bart Samwel
Chad Whipkey
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.
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