Scalable Web Architectures: Common Patterns and Approaches

1. Scalable Web Architectures Common Patterns & Approaches Cal Henderson

2.
Hello

3. Scalable Web Architectures?
What does scalable mean?
What’s an architecture?
An answer in 12 parts

4.
1.
Scaling

5. Scalability – myths and lies
What is scalability?

What is scalability not ?

What is scalability not ?
Raw Speed / Performance
HA / BCP
Technology X
Protocol Y

So what is scalability?

So what is scalability?
Traffic growth
Dataset growth
Maintainability

10. Today
Two goals of application architecture:
Scale
HA

11. Today
Three goals of application architecture:
Scale
HA
Performance

12. Scalability
Two kinds:
Vertical (get bigger)
Horizontal (get more)

13. Big Irons Sunfire E20k $450,000 - $2,500,000 36x 1.8GHz processors PowerEdge SC1435 Dualcore 1.8 GHz processor Around $1,500

14. Cost vs Cost

15. That’s OK
Sometimes vertical scaling is right
Buying a bigger box is quick (ish)
Redesigning software is not
Running out of MySQL performance?
Spend months on data federation
Or, Just buy a ton more RAM

16. The H & the V
But we’ll mostly talk horizontal
Else this is going to be boring

17.
2.
Architecture

18. Architectures then?
The way the bits fit together
What grows where
The trade-offs between good/fast/cheap

19. LAMP
We’re mostly talking about LAMP
Linux
Apache (or LightHTTPd)
MySQL (or Postgres)
PHP (or Perl, Python, Ruby)
All open source
All well supported
All used in large operations
(Same rules apply elsewhere)

20. Simple web apps
A Web Application
Or “Web Site” in Web 1.0 terminology
Interwebnet App server Database

21. Simple web apps
A Web Application
Or “Web Site” in Web 1.0 terminology
Interwobnet App server Database Cache Storage array AJAX!!!1

22. App servers
App servers scale in two ways:

23. App servers
Really well

24. App servers
Really well
Quite badly

25. App servers
Sessions!
(State)
Local sessions == bad
When they move == quite bad
Centralized sessions == good
No sessions at all == awesome!

26. Local sessions
Stored on disk
PHP sessions
Stored in memory
Shared memory block (APC)
Bad!
Can’t move users
Can’t avoid hotspots
Not fault tolerant

27. Mobile local sessions
Custom built
Store last session location in cookie
If we hit a different server, pull our session information across
If your load balancer has sticky sessions, you can still get hotspots
Depends on volume – fewer heavier users hurt more

28. Remote centralized sessions
Store in a central database
Or an in-memory cache
No porting around of session data
No need for sticky sessions
No hot spots
Need to be able to scale the data store
But we’ve pushed the issue down the stack

29. No sessions
Stash it all in a cookie!
Sign it for safety
$data = $user_id . ‘-’ . $user_name;
$time = time();
$sig = sha1($secret . $time . $data);
$cookie = base64(“$sig-$time-$data”);
Timestamp means it’s simple to expire it

30. Super slim sessions
If you need more than the cookie (login status, user id, username), then pull their account row from the DB
Or from the account cache
None of the drawbacks of sessions
Avoids the overhead of a query per page
Great for high-volume pages which need little personalization
Turns out you can stick quite a lot in a cookie too
Pack with base64 and it’s easy to delimit fields

31. App servers
The Rasmus way
App server has ‘shared nothing’
Responsibility pushed down the stack
Ooh, the stack

32. Trifle

33. Trifle Sponge / Database Jelly / Business Logic Custard / Page Logic Cream / Markup Fruit / Presentation

34. Trifle Sponge / Database Jelly / Business Logic Custard / Page Logic Cream / Markup Fruit / Presentation

35. App servers

36. App servers

37. App servers

38. Well, that was easy
Scaling the web app server part is easy
The rest is the trickier part
Database
Serving static content
Storing static content

39. The others
Other services scale similarly to web apps
That is, horizontally
The canonical examples:
Image conversion
Audio transcoding
Video transcoding
Web crawling
Compute!

40. Amazon
Let’s talk about Amazon
S3 - Storage
EC2 – Compute! (XEN based)
SQS – Queueing
All horizontal
Cheap when small
Not cheap at scale

41.
3.
Load Balancing

42. Load balancing
If we have multiple nodes in a class, we need to balance between them
Hardware or software
Layer 4 or 7

43. Hardware LB
A hardware appliance
Often a pair with heartbeats for HA
Expensive!
But offers high performance
Many brands
Alteon, Cisco, Netscalar, Foundry, etc
L7 - web switches, content switches, etc

44. Software LB
Just some software
Still needs hardware to run on
But can run on existing servers
Harder to have HA
Often people stick hardware LB’s in front
But Wackamole helps here

45. Software LB
Lots of options
Pound
Perlbal
Apache with mod_proxy
Wackamole with mod_backhand
http://backhand.org/wackamole/
http ://backhand.org/mod_backhand/

46. Wackamole

47. Wackamole

48. The layers
Layer 4
A ‘dumb’ balance
Layer 7
A ‘smart’ balance
OSI stack, routers, etc

49.
4.
Queuing

50. Parallelizable == easy!
If we can transcode/crawl in parallel, it’s easy
But think about queuing
And asynchronous systems
The web ain’t built for slow things
But still, a simple problem

51. Synchronous systems

52. Asynchronous systems

53. Helps with peak periods

54. Synchronous systems

57.
5.
Relational Data

58. Databases
Unless we’re doing a lot of file serving, the database is the toughest part to scale
If we can, best to avoid the issue altogether and just buy bigger hardware
Dual Opteron/Intel64 systems with 16+GB of RAM can get you a long way

59. More read power
Web apps typically have a read/write ratio of somewhere between 80/20 and 90/10
If we can scale read capacity, we can solve a lot of situations
MySQL replication!

60. Master-Slave Replication

61. Master-Slave Replication Reads and Writes Reads

70.
6.
Caching

71. Caching
Caching avoids needing to scale!
Or makes it cheaper
Simple stuff
mod_perl / shared memory
Invalidation is hard
MySQL query cache
Bad performance (in most cases)

72. Caching
Getting more complicated…
Write-through cache
Write-back cache
Sideline cache

73. Write-through cache

74. Write-back cache

75. Sideline cache

76. Sideline cache
Easy to implement
Just add app logic
Need to manually invalidate cache
Well designed code makes it easy
Memcached
From Danga (LiveJournal)
http://www.danga.com/memcached/

77. Memcache schemes
Layer 4
Good: Cache can be local on a machine
Bad: Invalidation gets more expensive with node count
Bad: Cache space wasted by duplicate objects

78. Memcache schemes
Layer 7
Good: No wasted space
Good: linearly scaling invalidation
Bad: Multiple, remote connections
Can be avoided with a proxy layer
Gets more complicated
Last indentation level!

79.
7.
HA Data

80. But what about HA?

81. But what about HA?

82. SPOF!
The key to HA is avoiding SPOFs
Identify
Eliminate
Some stuff is hard to solve
Fix it further up the tree
Dual DCs solves Router/Switch SPOF

83. Master-Master

84. Master-Master
Either hot/warm or hot/hot
Writes can go to either
But avoid collisions
No auto-inc columns for hot/hot
Bad for hot/warm too
Unless you have MySQL 5
But you can’t rely on the ordering!
Design schema/access to avoid collisions
Hashing users to servers

85. Rings
Master-master is just a small ring
With 2 nodes
Bigger rings are possible
But not a mesh!
Each slave may only have a single master
Unless you build some kind of manual replication

86. Rings

87. Rings

88. Dual trees
Master-master is good for HA
But we can’t scale out the reads (or writes!)
We often need to combine the read scaling with HA
We can simply combine the two models

89. Dual trees

90. Cost models
There’s a problem here
We need to always have 200% capacity to avoid a SPOF
400% for dual sites!
This costs too much
Solution is straight forward
Make sure clusters are bigger than 2

91. N+M
N+M
N = nodes needed to run the system
M = nodes we can afford to lose
Having M as big as N starts to suck
If we could make each node smaller, we can increase N while M stays constant
(We assume smaller nodes are cheaper)

92. 1+1 = 200% hardware

93. 3+1 = 133% hardware

94. Meshed masters
Not possible with regular MySQL out-of-the-box today
But there is hope!
NBD (MySQL Cluster) allows a mesh
Support for replication out to slaves in a coming version
RSN!

95.
8.
Federation

96. Data federation
At some point, you need more writes
This is tough
Each cluster of servers has limited write capacity
Just add more clusters!

97. Simple things first
Vertical partitioning
Divide tables into sets that never get joined
Split these sets onto different server clusters
Voila!
Logical limits
When you run out of non-joining groups
When a single table grows too large

98. Data federation
Split up large tables, organized by some primary object
Usually users
Put all of a user’s data on one ‘cluster’
Or shard, or cell
Have one central cluster for lookups

99. Data federation

100. Data federation
Need more capacity?
Just add shards!
Don’t assign to shards based on user_id!
For resource leveling as time goes on, we want to be able to move objects between shards
Maybe – not everyone does this
‘ Lockable’ objects

101. The wordpress.com approach
Hash users into one of n buckets
Where n is a power of 2
Put all the buckets on one server
When you run out of capacity, split the buckets across two servers
Then you run out of capacity, split the buckets across four servers
Etc

102. Data federation
Heterogeneous hardware is fine
Just give a larger/smaller proportion of objects depending on hardware
Bigger/faster hardware for paying users
A common approach
Can also allocate faster app servers via magic cookies at the LB

103. Downsides
Need to keep stuff in the right place
App logic gets more complicated
More clusters to manage
Backups, etc
More database connections needed per page
Proxy can solve this, but complicated
The dual table issue
Avoid walking the shards!

104. Bottom line
Data federation is how large applications are scaled

105. Bottom line
It’s hard, but not impossible
Good software design makes it easier
Abstraction!
Master-master pairs for shards give us HA
Master-master trees work for central cluster (many reads, few writes)

106.
9.
Multi-site HA

107. Multiple Datacenters
Having multiple datacenters is hard
Not just with MySQL
Hot/warm with MySQL slaved setup
But manual (reconfig on failure)
Hot/hot with master-master
But dangerous (each site has a SPOF)
Hot/hot with sync/async manual replication
But tough (big engineering task)

108. Multiple Datacenters

109. GSLB
Multiple sites need to be balanced
Global Server Load Balancing
Easiest are AkaDNS-like services
Performance rotations
Balance rotations

110.
10.
Serving Files

111. Serving lots of files
Serving lots of files is not too tough
Just buy lots of machines and load balance!
We’re IO bound – need more spindles!
But keeping many copies of data in sync is hard
And sometimes we have other per-request overhead (like auth)

112. Reverse proxy

113. Reverse proxy
Serving out of memory is fast!
And our caching proxies can have disks too
Fast or otherwise
More spindles is better
We stay in sync automatically
We can parallelize it!
50 cache servers gives us 50 times the serving rate of the origin server
Assuming the working set is small enough to fit in memory in the cache cluster

114. Invalidation
Dealing with invalidation is tricky
We can prod the cache servers directly to clear stuff out
Scales badly – need to clear asset from every server – doesn’t work well for 100 caches

115. Invalidation
We can change the URLs of modified resources
And let the old ones drop out cache naturally
Or prod them out, for sensitive data
Good approach!
Avoids browser cache staleness
Hello Akamai (and other CDNs)
Read more:
http://www.thinkvitamin.com/features/webapps/serving-javascript-fast

116. Reverse proxy
Choices
L7 load balancer & Squid
http://www.squid-cache.org/
mod_proxy & mod_cache
http://www.apache.org/
Perlbal and Memcache?
http://www.danga.com/

117. High overhead serving
What if you need to authenticate your asset serving?
Private photos
Private data
Subscriber-only files
Two main approaches
Proxies w/ tokens
Path translation

118. Perlbal backhanding
Perlbal can do redirection magic
Client sends request to Perbal
Perlbl plugin verifies user credentials
token, cookies, whatever
tokens avoid data-store access
Perlbal goes to pick up the file from elsewhere
Transparent to user

Doesn’t keep database around while serving
Doesn’t keep app server around while serving
User doesn’t find out how to access asset directly

121. Permission URLs
But why bother!?
If we bake the auth into the URL then it saves the auth step
We can do the auth on the web app servers when creating HTML
Just need some magic to translate to paths
We don’t want paths to be guessable

123. Permission URLs (or mod_perl)

Downsides
URL gives permission for life
Unless you bake in tokens
Tokens tend to be non-expirable
We don’t want to track every token
Too much overhead
But can still expire
Upsides
It works
Scales nicely

125.
11.
Storing Files

126. Storing lots of files
Storing files is easy!
Get a big disk
Get a bigger disk
Uh oh!
Horizontal scaling is the key
Again

127. Connecting to storage
NFS
Stateful == Sucks
Hard mounts vs Soft mounts, INTR
SMB / CIFS / Samba
Turn off MSRPC & WINS (NetBOIS NS)
Stateful but degrades gracefully
HTTP
Stateless == Yay!
Just use Apache

128. Multiple volumes
Volumes are limited in total size
Except (in theory) under ZFS & others
Sometimes we need multiple volumes for performance reasons
When using RAID with single/dual parity
At some point, we need multiple volumes

129. Multiple volumes

130. Multiple hosts
Further down the road, a single host will be too small
Total throughput of machine becomes an issue
Even physical space can start to matter
So we need to be able to use multiple hosts

131. Multiple hosts

132. HA Storage
HA is important for assets too
We can back stuff up
But we tend to want hot redundancy
RAID is good
RAID 5 is cheap, RAID 10 is fast

133. HA Storage
But whole machines can fail
So we stick assets on multiple machines
In this case, we can ignore RAID
In failure case, we serve from alternative source
But need to weigh up the rebuild time and effort against the risk
Store more than 2 copies?

134. HA Storage

135. Self repairing systems
When something fails, repairing can be a pain
RAID rebuilds by itself, but machine replication doesn’t
The big appliances self heal
NetApp, StorEdge, etc
So does MogileFS (reaper)

136.
12.
Field Work

137. Real world examples
Flickr
Because I know it
LiveJournal
Because everyone copies it

138. Flickr Architecture

139. Flickr Architecture

140. LiveJournal Architecture

141. LiveJournal Architecture

142. Buy my book!

143. Or buy Theo’s

144. The end!

145. Awesome!
These slides are available online:
iamcal.com/talks/

Scalable Web Architectures: Common Patterns and Approaches

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Scalable Web Architectures: Common Patterns and Approaches

techdude

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