Is NoSQL the Future of Data Storage?

By Gary ShortDeveloper Express

Introduction

• Gary Short• Technical Evangelist for Developer Express• C# MVP• gary@garyshort.org• www.garyshort.org• @garyshort.

Where Does NoSQL Originate?

• 1998– OS relational database• Didn’t expose an SQL interface

• Created by Carlo Strozzi– Said the NoSQL movement• “departs from the relational model altogether...”• “...should have been called ‘NoREL”.

More Recently...

• Eric Evans reintroduced the term in 2009– Johan Oskarsson (last.fm)• Event to discuss OS distributed databases

• This labels growing number datastores– Open source– Non-relational– Distributed– (often) don’t guarantee ACID.

Atlanta 2009

• No:sql(east) conference• Billed as “conference of no-rel datastores”• Worst tag line ever– SELECT fun, profit FROM real_world WHERE rel=false.

Not Ant-RDBMS

Key Attributes of NoSQL Databases

• Don’t require fixed table schemas• Non-relational• (Usually) avoid join operations• Scale horizontally– Adding more nodes to a storage system.

What Does the Taxonomy Look Like?

Document Store

• Apache Jackrabbit• CouchDB• MongoDB• SimpleDB• XML Databases– MarkLogic Server– eXist.

Document What?

• Okay think of a web page...– Relational model requires column/tag– Lots of empty columns– Wasted space

• Document model just stores the pages as is– Saves on space– Very flexible.

Graph Storage

• AllegroGraph• Core Data• Neo4j• DEX• FlockDB.

Which Means?

• Graph consists of– Node (‘stations’ of the graph)– Edges (lines between them)

• FlockDB– Created by the Twitter folks– Nodes = Users– Edges = Nature of relationship between nodes.

Key/Value Stores

• On disk• Cache in Ram• Eventually Consistent

– Weak Definition• “If no updates occur for a period, eventually all updates will

propagate through the system and all replicas will be consistent”

– Strong Definition• “for a given update and a given replica eventually either the

update reaches the replica or the replica retires”

• Ordered– Distributed Hash Table allows lexicographical processing.

Object Databases

• Db4o• GemStone/S• InterSystems Caché• Objectivity/DB• ZODB.

Okay got it, Now Let’s Compare Some Real World Scenarios

You Need Constant Consistency

• You’re dealing with financial transactions• You’re dealing with medical records• You’re dealing with bonded goods• Best you use a RDMBS .

You Need Horizontal Scalability

• You’re working across defined timezones• You’re Aggregating large quantities of data• Maintaining a chat server (Facebook chat)• Use NoSQL.

Up in the Clouds Baby

• If you are using Azure or AWS– Compare costs of Azure Storage or SimpleDB to

SQL Azure or Elastic RDBMS• Could be cheaper for your scenario.

It’s all About the iPhone!

Frequently Written Rarely Read

• Think web counters and the like• Every time a user comes to a page = ctr++• But it’s only read when the report is run• Use NoSQL (key-value storage).

I Got Big Data!

• Think weather stats• Satellite Images• Maps• Use NoSQL ( Something like Hadoop).

Binary Baby!

• If you are YouTube• Flickr• Twitpic• Spotify• NoSQL (Amazon S3).

Here Today Gone Tomorrow

• Transient data like..– Web Sessions– Locks– Short Term Stats• Shopping cart contents

• Use NoSQL (Memcache).

Data Replication

• Same data in two or more locations– Music Library• Web browser• iPone App

• NoSQL (CouchDB).

Hit me Baby One More Time!

• High Availability– High number of important transactions• Online gambling• Pay Per view

– Ahem!

• Online Auction

• NoSQL (Cassandra – automatic clustering).

Give me a Real World Example

• Twitter– The challenges• Needs to store many graphs

– Who you are following– Who’s following you– Who you receive phone notifications from etc

• To deliver a tweet requires rapid paging of followers• Heavy write load as followers are added and removed• Set arithmetic for @mentions (intersection of users).

What Did They Try?

• Relational Databases• Key-Value storage of denormalized lists• Did it work?– Nope!• Either good at

– Handling the write load– Or paging large amounts of data– But not both .

What Did They Need?

• Simplest possible thing that would work• Allow for horizontal partitioning• Allow write operations to– Arrive out of order– Or be processed more than once

• Failures should result in redundant work– Not lost work!

The Result was FlockDB

• Stores graph data• Not optimised for graph traversal operations• Optimised for large adjacency lists– List of all edges in a graph• Key is the edge value a set of the node end points

• Optimised for fast read and write• Optimised for page-able set arithmetic.

How Does it Work?

• Stores graphs as sets of edges between nodes• Data is partitioned by node– All queries can be answered by a single partition

• Write operations are idempotent– Can be applied multiple times without changing

the result• And commutative– Changing the order of operands doesn’t change

the result.

Commutative Writes Help Bring up Partitions

• Partition can receive write traffic immediately• Receive dump of data in the background• Live for read as soon as the dump is complete.

Performance?

• Currently store 13 billion edges• 20K writes / second• 100K reads / second.

Lessons Learned?

• Use aggressive timeouts– Cut a client loose after timeout expired– Let it try again on another app server

• Use same code path for error and normal ops– Error requests are periodically retried

• Instrument.

Punchline?

• Under all the bells and whistles...– Its MySQL .

So is this the Future?

• Yes!• And No!

Questions?

• Contact me– gary@garyshort.org– @garyshort

Coming up…P/X001Understanding and Preventing SQL Injection AttacksKevin KlineP/L001SSIS FieldnotesDarren GreenP/L002The (Geospatial) Shapes of Things to ComeSimon MunroP/L005End to End Master Data Management with SQL Server Master Data ServicesJeremy KashelP/T007Understanding Microsoft Certification in SQL ServerChris Testa-O'Neill

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