Beyond (No)SQL

Beyond (No)SQL Sarah Mei Pivotal Labs @sarahmei sarah@pivotallabs.com

Fsck CS. -DHH

Fsck CS. -DHH (paraphrased)

Photo by jasonwg (http://www.ﬂickr.com/photos/jasonwg/1382036808)

Agenda

Agenda • The data storage landscape

Agenda • The data storage landscape • Relational model & SQL

Agenda • The data storage landscape • Relational model & SQL RELATIONAL ALGEBRA

Agenda • The data storage landscape • Relational model & SQL RELATIONAL ALGEBRA • Evaluating data stores

Photo by TANAKA Juuyoh http://www.ﬂickr.com/photos/tanaka_juuyoh/3121538767/

“NoSQL”

“NoSQL” MongoDB

“NoSQL” MongoDB Memcache

“NoSQL” MongoDB Memcache Bigtable

“NoSQL” MongoDB Memcache Bigtable CouchDB

“NoSQL” MongoDB Memcache Bigtable CouchDB Cassandra

“NoSQL” MongoDB Memcache Bigtable CouchDB Cassandra Project Voldemort

“NoSQL” MongoDB Memcache Bigtable CouchDB Cassandra Project Voldemort Hbase

“NoSQL” MongoDB Memcache Bigtable CouchDB Cassandra Project Voldemort Hbase Riak

“NoSQL” MongoDB Memcache Bigtable CouchDB Cassandra Project Voldemort Hbase Riak Redis

“NoSQL” MongoDB Memcache Bigtable CouchDB Cassandra Project Voldemort Hbase Riak Redis Tokyo Cabinet

“NoSQL” MongoDB Memcache Key-value Bigtable CouchDB Cassandra Project Voldemort Hbase Riak Redis Tokyo Cabinet

“NoSQL” MongoDB Memcache Key-value Bigtable CouchDB Document Cassandra Project Voldemort Hbase Riak Redis Tokyo Cabinet

“NoSQL” MongoDB Memcache Key-value Bigtable CouchDB Document Cassandra Other Project Voldemort Hbase Riak Redis Tokyo Cabinet

Photo from mga (http://www.ﬂickr.com/photos/mgiraldo/420642350)

The Relational Model Diagram by Wikipedia user AutumnSnow (http://en.wikipedia.org/wiki/File:Relational_model_concepts.png)

Sets • No duplicates

Sets • No duplicates • Unordered

More sets

More sets • TABLE: a set of columns and a set of rows

More sets • TABLE: a set of columns and a set of rows • COLUMN: a unique name and a type

More sets • TABLE: a set of columns and a set of rows • COLUMN: a unique name and a type • ROW: a set of name-value pairs

Relational Algebra Operations you can do on tables (“relations”)

Operations • projection: subset of available columns • selection: subset of available rows • cartesian product • set union • set intersection • rename

A Join select * from foo, bar where foo.ipsum = bar.lorem First you take the cartesian product.... foo: bat ipsum bar: cat lorem me 5 X us 5 you 4 them 4 bat ipsum cat lorem me 5 us 5 = me 5 them 4 you 4 us 5 you 4 them 4

A Join select * from foo, bar where foo.ipsum = bar.lorem Then you select the rows that satisfy the join condition: bat ipsum cat lorem bat ipsum cat lorem me 5 us 5 me 5 us 5 me 5 them 4 => you 4 them 4 you 4 us 5 you 4 them 4

A Join select * from foo, bar where foo.ipsum = bar.lorem First you take the cartesian product.... foo: bat ipsum bar: cat lorem me 5 X us 5 you 4 them 4 bat ipsum cat lorem me 5 us 5 = me 5 them 4 you 4 us 5 you 4 them 4

A Join select * from foo, bar where foo.ipsum = bar.lorem First you take the cartesian product.... foo: bat ipsum bar: cat lorem me 5 X us 5 8,000,000 you 4 them 4 bat ipsum cat lorem me 5 us 5 = me 5 them 4 you 4 us 5 you 4 them 4

A Join select * from foo, bar where foo.ipsum = bar.lorem First you take the cartesian product.... foo: bat ipsum bar: cat lorem me 5 X us 5 8,000,000 100,000 you 4 them 4 bat ipsum cat lorem me 5 us 5 = me 5 them 4 you 4 us 5 you 4 them 4

A Join select * from foo, bar where foo.ipsum = bar.lorem First you take the cartesian product.... foo: bat ipsum bar: cat lorem me 5 X us 5 8,000,000 100,000 you 4 them 4 bat ipsum cat lorem me 5 us 5 = me 5 them 4 800,000,000,000 you 4 us 5 you 4 them 4

Your choices

Your choices • Scale the database

Your choices • Scale the database • Try a different approach

What SQL Gets You

What SQL Gets You • Speed

What SQL Gets You • Speed (when data is highly structured and small enough)

What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation

What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation • Relational searching

What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation • Relational searching • ACID - guaranteed full consistency

Image by captcreate (http://www.ﬂickr.com/photos/27845211@N02/2662264721)

What SQL Gets You

What SQL Gets You • Speed

What SQL Gets You • Speed (when data is highly structured and small enough)

What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation

What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation • Relational searching

What SQL Gets You • Speed (when data is highly structured and small enough) • Aggregation • Relational searching • ACID - guaranteed full consistency

What if you gave up data aggregation?

What if you gave up data aggregation? Document databases: MongoDB CouchDB Riak

What if you gave up where clauses?

What if you gave up where clauses? Key-value stores: Memcache Project Voldemort Redis Tokyo Cabinet

What if you gave up consistency?

What if you gave up consistency? Then things get interesting.

ACID & BASE

ACID & BASE • Atomicity

ACID & BASE • Atomicity • Consistency

ACID & BASE • Atomicity • Consistency • Isolation

ACID & BASE • Atomicity • Consistency • Isolation • Durability

ACID & BASE • Atomicity • Basically Available • Consistency • Isolation • Durability

ACID & BASE • Atomicity • Basically Available • Consistency • Soft state • Isolation • Durability

ACID & BASE • Atomicity • Basically Available • Consistency • Soft state • Isolation • Eventually consistent • Durability

Photo by Marcus Vegas (http://www.ﬂickr.com/photos/vegas/413159909)

Fully ACID Fully BASE Photo by Marcus Vegas (http://www.ﬂickr.com/photos/vegas/413159909)

Fully ACID Fully BASE Redis Photo by Marcus Vegas (http://www.ﬂickr.com/photos/vegas/413159909)

Fully ACID Fully BASE CouchDB Redis Photo by Marcus Vegas (http://www.ﬂickr.com/photos/vegas/413159909)

Fully ACID Fully BASE CouchDB Redis Bigtable Photo by Marcus Vegas (http://www.ﬂickr.com/photos/vegas/413159909)

Questions to ask about data • Where can I compromise aggregation? • Where can I compromise where clauses? • Where can I compromise consistency? Where can I localize consistency?

CAP Theorem Pick any two:

CAP Theorem Pick any two: • Consistency

CAP Theorem Pick any two: • Consistency • Availability

CAP Theorem Pick any two: • Consistency • Availability • Partition tolerance

Summary

Summary Every system at scale will have to compromise consistency at some level.

Summary Every system at scale will have to compromise consistency at some level. Do it mindfully.

Questions? Twitter: @sarahmei Email: sarah@pivotallabs.com Slides: http://bit.ly/9xS2PK Please rate this talk! http://bit.ly/9MCtX9

http://www.sarahmei.com	502
http://en.oreilly.com	58
http://www.slideshare.net	36
http://static.slidesharecdn.com	2
http://newsblur.com	1

Beyond (No)SQL

by Sarah Mei

Accessibility

Categories

Upload Details

Usage Rights

5 Embeds 599

Statistics

Beyond (No)SQL Presentation Transcript

Beyond (No)SQL

by Sarah Mei

Accessibility

Categories

Upload Details

Usage Rights

5 Embeds 599

Statistics

Beyond (No)SQL Presentation Transcript

Let LinkedIn power your SlideShare experience

Let LinkedIn power your SlideShare experience

Customize SlideShare content based on your interests

Keep up to date when your LinkedIn contacts post on SlideShare