This document discusses database scalability. It defines scalability and distinguishes it from performance. It describes two methods of scaling databases: vertical scaling by adding resources to a single node, and horizontal scaling by adding new nodes and using sharding to distribute data. It provides examples of MySQL Cluster and MongoDB for horizontal scaling, noting advantages and disadvantages of each. Caching techniques are also presented as a way to reduce load on databases and enable scaling.

1. XXL WEB
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2. ABOUT US, ABOUT YOU?
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Ömer Taşkın
Software
Engineer@GG
Software
Engineer@GG

3. OUTLINE
• WHY XXL WEB?
• What is Scalability in Theory?
• Scalability & Performance Issue
• How to scale?
• What is Scalability in Practice?
• Horizontal (Scale Out)
• Vertical (Scale Up)

4. WHY XXL WEB?
Scalability in WEB domain

5. XXL
web
What is scalability?
• What is scalability?
• What is not scalability?
• Scalability != performance, but..

6. XXL
web
Scalability & Performance
Only scalability does not increase the performance,
but increasing performance make scaling easier.

7. XXL
web
Scalability & Performance
Question: 100 room, 100 person
1- 1 P per room
2- 100 P in one room
What’s your choice?

8. XXL
web
Scalability & Performance

9. XXL
web
Language-oriented?
After 3 days + 10 min.. JJ Then, click ‘make it scalable’.

10. XXL
web
What can I do sometimes?
• Optimize your code,
• Design for scaling! “It won’t scale, if it’s not to scale”.
• A couple of language oriented examples.
• Performance is must for Scaling!
• Use caching Mechanisms
• And surely.. Scale your DB!

11. Caching Techniques
•
Store entire page
•
Store parts of a page
•
Store SQL queries
•
Store Complex Results
•
And Yours

12. Why Cache?
•
Kind a short-cut
•
Reducing number of requests
•
In fact, it’s for not scaling in DB layer
•
Ok, what if cache is burn out?
•
Then scale it!

13. That is not all..

14. Scalability @ DB Layer
With a basic example that shows DB things for scaling

15. A scaling scenario
• User actions should be logged.
• Probably records will be bigger than we guess.

16. A scaling scenario
•
Scalability Types
•
Horizontal (Scale Out)
•
Vertical (Scale Up)

17.
VERTICAL SCALABILITY (SCALE UP)
•
Adding resources to single node in a system
•
•
•
•
Bandwidth
Disc √
CPU
Memory

18. VERTICAL SCALABILITY (SCALE UP)
•
It’s not enough, isn’t it?

19. HORIZONTAL SCALABILITY (SCALE out)
•
Adding new node to system

20. HORIZONTAL SCALABILITY (SCALE out)
•
Need a sharding mechanism!

21. HORIZONTAL SCALABILITY (SCALE out)
•
First attempt:
Write your own sharding KK

22. HORIZONTAL SCALABILITY (SCALE out)

23. HORIZONTAL SCALABILITY (SCALE out)
Disadvantages of MySQL Cluster
• Does not supports add / remove node while cluster is up.
• Alter queries does not run on every node. Alter queries have to
run for every node.

24. HORIZONTAL SCALABILITY (SCALE out)
•
What we need?
• Have to be transactional?
• Must data be warranty?

25. HORIZONTAL SCALABILITY (SCALE out)
•
Second attempt
•
MongoDB!

26. HORIZONTAL SCALABILITY (SCALE out)

27. HORIZONTAL SCALABILITY (SCALE out)
•
Disadvantages of mongodb
• Does not supports transaction
• Does not supports data guarantee ( >= Mongo 1.8)

28. Wake Up!

29. Here we are!
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30. REFERANCES
http://www.oracle.com/technetwork/articles/dsl/white-php-part1-355135.html
http://docs.mongodb.org/manual/sharding/
http://dev.mysql.com/doc/refman/5.0/en/mysql-cluster.html
http://dev.mysql.com/doc/refman/5.0/en/mysql-cluster-limitations.html
http://bobiler.org
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