30_Goel_Spanner

January 5, 2018 | Author: Anonymous | Category: Engineering & Technology, Computer Science, Data Management
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Spanner: Google’s Globally-Distributed Database By

- James C. Corbett, Jeffrey Dean, Michael Epstein, Andrew Fikes, Christopher Frost, JJ Furman Sanjay Ghemawat, Andrey Gubarev, Christopher Heiser, Peter Hochschild, Wilson Hsieh, Sebastian Kanthak, Eugene Kogan, Hongyi Li, Alexander Lloyd, Sergey Melnik, David Mwaura, David Nagle, Sean Quinlan, Rajesh Rao, Lindsay Rolig, Yasushi Saito, Michal Szymaniak, Christopher Taylor, Ruth Wang, Dale Woodford Published in Proceedings of OSDI 2012

Speaker-Mugdha Goel

What is Spanner? • It is a system to distribute data at a global scale and support externally consistent distributed transactions. • With Spanner, Google can offer a web service to a worldwide audience, but still ensure that something happening on the service in one part of the world doesn’t contradict what’s happening in another. • It automatically migrates data across machines and data centers to balance load in case of failures. • Scalable, Multi-version, and synchronously-replicated database.

Need and Evolution • • • •

• • • • •

Spanner was made for high availability. Need for consistent data across the globe. Read and write without being crushed by huge latencies. Having data located as per the Clients need. Evolution Spanner has evolved from a Bigtable-like versioned key-value store into a temporal multi-version database. Spanner is the successor to Google's Megastore system. Data is stored in Semi-relational tables and handles faster read-writes. Google's 'F1' advertising backend is using Spanner. Gmail, Picasa, Google Calendar, the Android Market and its AppEngine cloud all use Megastore, making them potential candidates for a Spanner upgrade.

Features • The replication configurations for data can be dynamically controlled at a fine grain level by applications and this process is transparent. • Applications can control the location of data. • Provides externally Consistent Read-writes. • Provides globally consistent reads across the database at a timestamp. • Provides an implementation of the TrueTime API.

Implementation

Spanserver Software Stack • BigTable based implementation has following mapping: (key:string, timestamp:int64) -> string

Spanner’s Data Model • Need for Schematized Semi-relational tables and synchronous replication - Megastore (replication,despite its relatively poor write throughput). • A semi-relational data model which provides support for synchronous • Need for an SQL like query language – Dremel(an interactive data analysis tool) • 2 phase commit had availability problems in Bigtable. Paxos mitigates the problems. • Underneath uses a Distributed File system known as Colossus. Structure • An application creates one or more databases in a universe. • Each database can contain an unlimited number of schematized tables. • Every table has an ordered set of one or more primary-key columns. • Primary keys form the name for a row. • Table defines a mapping from the primary-key columns to the nonprimary-key columns.

Example CREATE TABLE Users { uid INT64 NOT NULL, email STRING

} PRIMARY KEY (uid), DIRECTORY; CREATE TABLE Albums { uid INT64 NOT NULL, aid INT64 NOT NULL,

name STRING } PRIMARY KEY (uid, aid), INTERLEAVE IN PARENT Users ON DELETE CASCADE;

TrueTime Method

Returns

TT.now()

TTinterval: [earliest; latest]

TT.after(t)

True if t has definitely passed

TT.before(t)

True if t has definitely not arrived

TTinterval - Interval with bounded time uncertainty(Endpoints are of TTstamp type).

tabs(e) – denotes absolute time for an event e. for an invocation enow, tt = TT.now(), tt.earliest
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