** Talk Given at OSCon 2010 ** We often have clients approach us looking for help in scaling their systems, and all too often their long term vision is a mixed reality based on the approaches read about on popular blogs trying to solve very different problems. Hey, scaling your database can be difficult enough by itself, you don't want to get tripped up by not understanding where you're going. In Database Scalability Patterns we will attempt to distill all of the information/hype/discussions around scaling databases, and break down the common patterns we've seen dealing with scaling databases. "Buzzwords" we'll cover (and hopefully debuzz) include: Vertical Scaling Horizontal Partitioning Horizontal Scaling Read Slaves Multi-Master Vertical Partitioning Federated Data Storage More important than just describing what these things are (although that's a good first step), we'll also discuss along the way different points in the life-cycle of your database when you need to be thinking about the different options in front of you. We'll factor in the types of application that your working on (think OLTP vs OLAP, or Social Networking vs. Corporate Application), the environment you'll be working on (Scaling "in the cloud" is very different from DIY in the datacenter), and we will talk about the types of tools you'll need to accomplish these goals (All replication systems are not the same, and some won't help at all).

1. Database Scalability
{Patterns}
/ Robert Treat
Wednesday, July 21, 2010

2. robert treat
omniti
Wednesday, July 21, 2010

3. robert treat
omniti
postgres
Wednesday, July 21, 2010

4. robert treat
omniti
postgres
oracle - mysql
Wednesday, July 21, 2010

5. robert treat
omniti
postgres
oracle - mysql
mssql - sqlite - nosql
Wednesday, July 21, 2010

6. What are “Database Scalability Patterns?”
Wednesday, July 21, 2010

7. Part Design Patterns
Part Application Life-Cycle
Wednesday, July 21, 2010

8. Part Design Patterns
Part Application Life-Cycle
Wednesday, July 21, 2010

9. Part Design Patterns
Part Application Life-Cycle
Wednesday, July 21, 2010

10. Part Design Patterns
Part Application Life-Cycle
Wednesday, July 21, 2010

11. MyFirstDatabase
Wednesday, July 21, 2010

12. Vertical Partitioning
Wednesday, July 21, 2010

13. Vertical Scaling
Wednesday, July 21, 2010

14. Vertical Scaling
Wednesday, July 21, 2010

15. Vertical Scaling
Wednesday, July 21, 2010

16. Federated Data Storage
“sharding” Horizontal Partitioning
Read Slaves
Multi-Master
Horizontal Scaling
Wednesday, July 21, 2010

17. Read Slaves / Master - Slave
Scale Read Load
Wednesday, July 21, 2010

18. Read Slaves / Master - Slave
Scale Read Load
writes go here!
Wednesday, July 21, 2010

19. Read Slaves / Master - Slave
Scale Read Load
writes go here!
reads go here (or here) (or here)
Wednesday, July 21, 2010

20. Read Slaves / Master - Slave
Scale Read Load
database writes
data to slaves writes go here!
slave db slave db slave db
reads go here (or here) (or here)
Wednesday, July 21, 2010

21. Read Slaves / Master - Slave
Scale Read Load
app writes data
everywhere writes go here!
memcached memcached memcached
reads go here (or here) (or here)
Wednesday, July 21, 2010

22. Read Slaves / Master - Slave
Scale Read Load
• Typically
• Full Copy of Data
On Each Node
• Asynchronous
Wednesday, July 21, 2010

23. Read Slaves / Master - Slave
Scale Read Load
• Typically • Consider
• Full Copy of Data • Partial Copy
On Each Node • Synchronous
• Asynchronous • Don’t use a RDBMS?
Wednesday, July 21, 2010

24. Read Slaves / Master - Slave
Scale Read Load
• Typically • Consider
• Full Copy of Data • Partial Copy
On Each Node • Synchronous
• Asynchronous • Don’t use a RDBMS?
Requires Application Changes
Wednesday, July 21, 2010

25. Read Slaves / Master - Slave
Scale Read Load
• Typically • Consider
• Full Copy of Data • Partial Copy
On Each Node • Synchronous
• Asynchronous • Don’t use a RDBMS?
Requires Application Changes
“easy”
Wednesday, July 21, 2010

26. Scaling Writes
Wednesday, July 21, 2010

27. Scaling Writes
“not easy”
Wednesday, July 21, 2010

28. Multi-Master
many different ways to implement this,
few that actually work in production
Wednesday, July 21, 2010

29. Multi-Master
many different ways to implement this,
few that actually work in production
write to any node, database syncs data
Wednesday, July 21, 2010

30. Multi-Master
many different ways to implement this,
few that actually work in production
write to any node, database syncs data
can reduce cpu, doesn’t reduce i/o
Wednesday, July 21, 2010

31. Multi-Master
many different ways to implement this,
few that actually work in production
write to any node, database syncs data
can reduce cpu, doesn’t reduce i/o
failover solution
not a scalability solution
Wednesday, July 21, 2010

32. Horizontal Partitioning
“sharding” “SOA”
Wednesday, July 21, 2010

33. Horizontal Partitioning
“sharding” “SOA”
Wednesday, July 21, 2010

34. Horizontal Partitioning
• Divide schema by job operations
• Move each piece to own server
• Duplicate some data as needed
Wednesday, July 21, 2010

35. Horizontal Partitioning
items
• Divide schema by job operations
• Move each piece to own server
• Duplicate some data as needed
forums
users
Wednesday, July 21, 2010

36. Horizontal Partitioning
items
• Divide schema by job operations
• Move each piece to own server
• Duplicate some data as needed
forums
• You must separate dependencies
in the app code first!
users
Wednesday, July 21, 2010

37. Horizontal Partitioning
items
• Divide schema by job operations
• Move each piece to own server
• Duplicate some data as needed
forums
• You must separate dependencies
in the app code first!
users
Each node is a new instance of vertical scaling
Wednesday, July 21, 2010

38. Horizontal Scaling
• data split across servers based on algorithm
• data dropped into buckets (multiple?)
app
magic hash algorithm
Wednesday, July 21, 2010

39. Horizontal Scaling
• data split across servers based on algorithm
• data dropped into buckets (multiple?)
magic hash algorithm app
Wednesday, July 21, 2010

40. Horizontal Scaling
• data split across servers based on algorithm
• data dropped into buckets (multiple?)
•someone must keep track of data, and provide
lookup services
magic hash algorithm app
Wednesday, July 21, 2010

41. Universal Truths of Scaling Databases
Vertical Scalability is Helpful for Every Pattern
Even in a horizontally scaled, fully distributed
database, the number of nodes needed is
affected by vertical scalability
Wednesday, July 21, 2010

42. Universal Truths of Scaling Databases
New Nodes Are Never Free
• Add points of failure
• Add management costs
• Add complexity to architecture
• Add complexity to your app code
Wednesday, July 21, 2010

43. MyFirstDB
V
Vertical Partitioning
V
Vertical Scaling
V
Read Slaves
V
Horizontal Partitioning
Wednesday, July 21, 2010

44. tips
Wednesday, July 21, 2010

45. tips
plan for layered data sources
Wednesday, July 21, 2010

46. tips
plan for layered data sources
read / write connections in code
Wednesday, July 21, 2010

47. tips
plan for layered data sources
read / write connections in code
use schemas to separate services
Wednesday, July 21, 2010

48. THANKS!
more:
xzilla.net
@robtreat2
omniti.com/surge
(scalability conference)
Wednesday, July 21, 2010