Eric Rollins
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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: 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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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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