building scalable websites for the cloud

1

Scalable Web ApplicationsReference Architectures and Best Practices

Brian Adler, PS Architect

2

Scalable Web Application

3

Scalable Web Application• What?

• An application built on an architecture that can adapt to changing conditions

4

Scalable Web Application• What?

• An application layered on an architecture that can adapt to changing conditions

• Why?• Traffic and load patterns are unpredictable

• Viral or flash-mob events can result in very dynamic conditions

• Availability and Reliability• Application must be distributed to increase likelihood of end-user accessibility

• Overprovision• Under-utilized resources == wasted $$$

• Underprovision• Missed opportunities – users unable to access your site/product• Don’t be a victim of your own success

5

This bed is too big. This bed is too small…

6

Cloud Resource Model• Dynamically provision the resources you need to meet the

current demand• Demand goes up, resources are added• Demand goes down, resources are removed

• In true “utility computing” fashion, only pay for what you use, when you use it

7

But this bed is just right

8

Scalable Web Application• When?

• No time like the present

9

Scalable Web Application• When?

• No time like the present

• How?• Stay tuned…

10

Reference Architecture

11

Load Balancing Tier

12

Load Balancing• ELB or not ELB. That is the question.

• No SSL termination on the ELB (*)• Can load balance at the TCP level, but that eliminates sticky sessions for

secure connections (*)• (*) No longer the case as of mid-October 2010

• Can scale to handle large amounts of traffic, but slow to ramp-up• Only need one• (RightScale has a technical white paper on load balancing solutions that

can be provided if desired)

13

Load Balancing• ELB or not ELB. That is the question.

• No SSL termination on the ELB (*)• Can load balance at the TCP level, but that eliminates sticky sessions for

secure connections (*)• (*) No longer the case as of mid-October 2010

• Can scale to handle large amounts of traffic, but slow to ramp-up• Only need one• (RightScale has a technical white paper on load balancing solutions that

can be provided if desired)

• HAProxy + Apache• Can handle SSL termination on the load balancer• Allows for sticky sessions with secure connections• Each instance can handle a specific amount of traffic with no ramp-up time• Each instance can only handle a specific amount of traffic• Addition of load balancers is possible, but requires DNS modifications

14

Load Balancing• Load Balancer + Application server

• Possible, and good for test and dev• Not a best practice for a production environment

• Traffic spikes can cause instance to perform both load balancing and application functions…poorly

15

Load Balancing• Load Balancer + Application server

• Possible, and good for test and dev• Not a best practice for a production environment

• Traffic spikes can cause instance to perform both load balancing and application functions…poorly

• Recommendation: Minimum of two load balancers• Each load balancer should be in a different availability zone (AZ) to

increase reliability and availability• RightScale testing has shown that m1.large is a good choice for load

balancers• Due to 100K-120K packet-per-second limit, larger instances do not provide much gain in

throughput• Roughly 5K responses/second can be handled by m1.large• With the 5K threshold in mind, select the number of load balancers required to handle your

peak traffic

16

Application Server Tier• Puts the “scalable” in a scalable application• True autoscaling a must in any dynamic/unpredictable

environment

17

Application Server Tier• Autoscaling

• Fully automated server launch based on autoscaling triggers• No manual intervention (can be challenging in certain environments, i.e.

Windows)• Download and install application code from common repository to ensure

identical configuration of all servers in the tier

18

Application Server Tier• Autoscaling

• Fully automated server launch based on autoscaling triggers• No manual intervention (can be challenging in certain environments, i.e.

Windows)• Download and install application code from common repository to ensure

identical configuration of all servers in the tier

• Triggers• Common

• CPU idle• Free memory• System load

• Custom• Web server connections• Application-specific metrics

19

Application Server Tier• When to scale?

• Conservatively. Both up and down

20



• Up• Allow adequate lead time for new servers to become operational• Before system is negatively impacted• Look for trends in activity and react early• Worst that can happen: Charged for an extra instance hour

21



• Up• Allow adequate lead time for new servers to become operational• Before system is negatively impacted• Look for trends in activity and react early• Worst that can happen: Charged for an extra instance hour

• Down• When system has been underutilized for a consistent, consecutive period

of time• Scale down fewer servers than in a scale-up event• Again, only downside is an extra hour of instance charge• Better safe than sorry

22

Application Server Tier• Array considerations

23


• Weight the array across all availability zones (not regions)• Increases reliability of application• NOTE: Traffic within an AZ on private IPs is free. Traffic between AZs incurs a per-gigabyte

charge• Traffic between regions is charged at public Internet rates

24




• Set minimums and maximums appropriately• Minimum can assist in cost savings in times of low usage• Maximum can limit overall cost exposure

25





• Instance size• m1.large is typically a good choice for array-based servers in a production environment

• m1.smalls (and even micro instances) can be used in test and development environments

• Every application is different, so run load tests and benchmarks to find the optimal solution for your environment

26





• Instance size• m1.large is typically a good choice for array-based servers in a production environment

• m1.smalls (and even micro instances) can be used in test and development environments

• Every application is different, so run load tests and benchmarks to find the optimal solution for your environment

• Code Deployment• Updated code can be pushed to all servers in an array via a single click of a button

27

Caching Tier• Caching can dramatically decrease the load on the database

• Particularly in read-intensive applications

• Costs of caching• Application complexity/modification• Additional instance hours to support the cache

28

Caching Tier• Best practice is to have a separate, dedicated caching tier

• Caching can be implemented on each application server• Prevents the use of a distributed cache• Local cache should only be used by the co-resident application server

• Application complexities• Database performance degradation

29




• Instance Size and Count• Determine memory caching footprint

• Select instance size and count that spreads the load over several servers• Prevents loss of entire cache if a single instance fails• Distribute caching servers across AZs for reliability• Overprovision if possible

• Provide capacity for system to grow to fully utilize cache (budget permitting)

30




• Instance Size and Count• Determine memory caching footprint

• Select instance size and count that spreads the load over several servers• Prevents loss of entire cache if a single instance fails• Distribute caching servers across AZs for reliability• Overprovision if possible

• Provide capacity for system to grow to fully utilize cache (budget permitting)

• Manually scaling caching servers is possible but non-trivial• Involves application and database performance degradation• Time To Lives (TTLs)

• Always set to expire

31

Caching Tier• Write-intensive applications

• Don’t see as large a performance gain as read-intensive apps• Memcached can be modified to perform lazy writes of data objects to the

database• Data can be lost in case of caching server crash• Not a recommended best practice, but can be (and has been) done• Tradeoff of performance versus end-user experience

32

Caching Tier• Write-intensive applications

• Don’t see as large a performance gain as read-intensive apps• Memcached can be modified to perform lazy writes of data objects to the

database• Data can be lost in case of caching server crash• Not a recommended best practice, but can be (and has been) done• Tradeoff of performance versus end-user experience

• Third-party providers• Vendor solutions exist that allow dynamic memcached scaling

33

Database Tier• Numerous database architecture options exist• No “one size fits all” solution, so testing and benchmarking are

critical to determine proper configuration

34

Database Tier• Masters and Slave(s)

• Multiple Slaves if budget permits• Distribute Master and Slave(s) across AZs• Always use same instance size for Master and Slaves

35



• Data Storage• EBS volumes for data store• Never use ephemeral storage for persistent data• Back up Master and Slaves frequently• Upload snapshots to S3 or some other persistent, redundant storage

36



• Data Storage• EBS volumes for data store• Never use ephemeral storage for persistent data• Back up Master and Slaves frequently• Upload snapshots to S3 or some other persistent, redundant storage

• Instance Size• Varies greatly based on the nature of the application and site traffic• Load testing and benchmarking can assist in identifying a reasonable

initial size

37

Database Scaling

38

Database Scaling• Vertical

• “Grow” or “shrink” a database server from one instance size to another

39

Database Scaling• Vertical

• “Grow” or “shrink” a database server from one instance size to another

• Horizontal• Add additional servers to spread the database load

40

Database Vertical/Horizontal Scaling

RightScale Makes Vertical or Horizontal Scaling Easier

SmallType A

SmallType B

SmallType B

SmallType B

SmallType A

SmallType A

LargeType A

LargeType B

HorizontalScaling

VerticalScaling

More servers of same types

Same quantity of larger servers

41

Horizontal Database Scaling• Addition of read Slaves

• Effective for read-intensive applications• Only writes need to access the master• Replication lag to slaves must be considered

42

Horizontal Database Scaling• Addition of read Slaves

• Effective for read-intensive applications• Only writes need to access the master• Replication lag to slaves must be considered

• Effective mechanism is to use MySQL Proxy

43

Horizontal Database Scaling• Sharding

• Concept is to partition the database into distinct, non-overlapping pieces• “Horizontal slicing” of the database tables into groups of rows• Forethought required in setting up shards since cross-shard joins are

resource intensive

44

Horizontal Database Scaling

Before Sharding

45

Horizontal Database Scaling

After Sharding

46

Horizontal Database Scaling• Master-Master

• Two (or more) Master DBs• Any Master can modify any database object• Replication lag can result in database inconsistencies• Poorly-designed applications can cause data object collisions and leave

databases in indeterminate state• Not a recommended best practice, nor supported by RightScale

47

Horizontal Database Scaling• NoSQL solutions

• Many options exist – SimpleDB, Cassandra, Membase, CouchDB, MongoDB, Riak, etc.

• Basically a Key/Value store• No complex operations between data objects (no relational operations)• Multiple nodes can be used to implement a distributed data store

• Coordinated backup and recovery can be challenging

• Some RightScale customers are beginning to use some NoSQL solutions in specific use cases.

48

So What’s Best?

49

So What’s Best?• As is typical in many technology discussions…

50


“It depends”

51


• Many scalable environments share some common underlying architecture concepts

“It depends”

52



• Every application is different. As such, there is no “one size fits all”

“It depends”

53



• Every application is different. As such, there is no “one size fits all”

• Components of a reference architecture such as this can be used as a starting point, with tweaks and modifications made per the unique characteristics of the application itself, or the load and traffic patterns it experiences

“It depends”

54

Scalable Web Applications

Q&ARightScale.com/Conference

(Presentations available next week)

[email protected]

RightScale.com/whitepapers

building scalable websites for the cloud

Technology

application server tierwhen

load balancingelb

application server tierputs

application serverpossible

application code

load balancerallows

load patterns

load balancersdue