Database Scalability Patterns

•

24 likes•11,330 views

** 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).

Technology

Database Scalability
{Patterns}

/ Robert Treat

Wednesday, July 21, 2010

robert treat
omniti

Wednesday, July 21, 2010

robert treat
omniti
postgres

Wednesday, July 21, 2010

robert treat
omniti
postgres
oracle - mysql

Wednesday, July 21, 2010

robert treat
omniti
postgres
oracle - mysql
mssql - sqlite - nosql

Wednesday, July 21, 2010

What are “Database Scalability Patterns?”

Wednesday, July 21, 2010

Part Design Patterns

Part Application Life-Cycle

Wednesday, July 21, 2010

MyFirstDatabase

Wednesday, July 21, 2010

Vertical Partitioning

Wednesday, July 21, 2010

Vertical Scaling

Wednesday, July 21, 2010

Federated Data Storage

“sharding” Horizontal Partitioning

Read Slaves

Multi-Master

Horizontal Scaling

Wednesday, July 21, 2010

Read Slaves / Master - Slave
Scale Read Load

Wednesday, July 21, 2010

Read Slaves / Master - Slave
Scale Read Load

writes go here!

Wednesday, July 21, 2010

Read Slaves / Master - Slave
Scale Read Load

writes go here!

reads go here (or here) (or here)
Wednesday, July 21, 2010

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

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

Read Slaves / Master - Slave
Scale Read Load

• Typically
• Full Copy of Data
On Each Node
• Asynchronous

Wednesday, July 21, 2010

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

Scaling Writes

Wednesday, July 21, 2010

Scaling Writes

“not easy”

Wednesday, July 21, 2010

Multi-Master

many different ways to implement this,
few that actually work in production

Wednesday, July 21, 2010

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

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

Horizontal Partitioning

“sharding” “SOA”

Wednesday, July 21, 2010

Horizontal Partitioning

• Divide schema by job operations
• Move each piece to own server
• Duplicate some data as needed

Wednesday, July 21, 2010

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

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

Horizontal Scaling
• data split across servers based on algorithm
• data dropped into buckets (multiple?)
app

magic hash algorithm

Wednesday, July 21, 2010

Horizontal Scaling
• data split across servers based on algorithm
• data dropped into buckets (multiple?)

magic hash algorithm app

Wednesday, July 21, 2010

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

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

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

MyFirstDB
V
Vertical Partitioning
V
Vertical Scaling
V
Read Slaves
V
Horizontal Partitioning

Wednesday, July 21, 2010

tips

plan for layered data sources

Wednesday, July 21, 2010

tips

plan for layered data sources

read / write connections in code

Wednesday, July 21, 2010

tips

plan for layered data sources

read / write connections in code

use schemas to separate services

Wednesday, July 21, 2010

THANKS!

more:
xzilla.net
@robtreat2

omniti.com/surge
(scalability conference)

Wednesday, July 21, 2010

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Database Scalability Patterns

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

Database Scalability Patterns

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