This document discusses data storage solutions and introduces MemSQL, an in-memory distributed relational database. It provides a brief history of data storage from files to centralized online services. New data types and volumes require new tools like NoSQL databases but existing relational databases can also address these needs. MemSQL is presented as an in-memory distributed SQL database that is ACID compliant, horizontally scalable, compatible with MySQL, and designed for real-time analytics. A demo shows MemSQL outperforming SQL Server for a slow query by syncing data into MemSQL from SQL Server using change data capture. The key lessons are that mistakes will happen with new tools and knowing limitations is important, and when to abandon an approach.

1. Data Storage
Moving Away from a Single
System Solution
Steve Perry

2. A brief history of data
storage
• File Storage
• Documents
• Spreadsheets
• Images
• Emails
• Databases

3. The rise of the database
• Relational data models first proposed in 1970
• By the mid 80’, computing hardware and
become powerful enough to allow the wide
deployment of relational databases
• By the early 90’s relational databases had
achieved total dominance

4. Data in the modern world
• Move towards the centralisation
• Users are encouraged to live their lives online
• Where you would have stored picture and MP3s on your
local hard drive you now use Instagram or Spotify
• This has given rise to new problems due to the volume
of data, the speed at which it is required or its
semistructured nature.
• Many of these issues can be solved in tradition relation
database but new products are also available

5. The new breed of data
storage
• New problems need new tools
• Mongo
• Couchbase
• Neo4j
• Hadoop
• These new tools address specific issues
• But do they have to replace your existing tools?

6. What is MemSQL?
• In memory distributed relational database
• Fully ACID compliant
• Scales horizontally
• Compatible with the MySql wire protocol
• Designed to provide real-time analytics

7. Demo
• A SQL Server 2012 database with the
AdventureWorks database
• A 3 machine MemSQL cluster
• An in-house Java sync program running on my
local instance

8. So what just happened?
1. We had a slow running query in SQL Server
2. We span up a MemSQL cluster using docker.The cluster
comprised of one aggregator and two leaf nodes.
3. We turned Change Data Capture on in SQL server
4. We started our Java sync service on my local instance
5. We synced all of the data from SQL Server into MemSql
6. We ran our slow running query in Memsql and found that it ran
much faster
7. We then saw how CDC and the sync service were able to
ensure that the cache was always up to date.

9. What we learnt
• You are going to make mistakes… Lots of
mistakes.
• If you are using a bleeding edge product, its
imperative that you know its limitations.
• Know at what point it is time to cut your losses

10. About Me
• Email - sperry1985@gmail.com
• Skype - Live:Steve.Perry
• Twitter - @DefiantDBA
• Head of Database Development at Kurtosys
System