Is NoSQL the Future of Data Storage? By Gary Short Developer Express

Introduction Gary Short Technical Evangelist for Developer Express C# MVP

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 (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 –

Coming up… P/X001 Understanding and Preventing SQL Injection Attacks Kevin Kline P/L001 SSIS Fieldnotes Darren Green P/L002 The (Geospatial) Shapes of Things to Come Simon Munro P/L005 End to End Master Data Management with SQL Server Master Data Services Jeremy Kashel P/T007 Understanding Microsoft Certification in SQL Server Chris Testa-O'Neill # SQLBITS