Intelligent People. Uncommon Ideas. 1 Building a Scalable Architecture for Web Apps - Part I (Lessons Learned @ Directi) By Bhavin Turakhia CEO, Directi

Intelligent People. Uncommon Ideas.

11

Building a Scalable Architecture for Web Apps - Part I

(Lessons Learned @ Directi)

By Bhavin Turakhia CEO, Directi

(http://www.directi.com | http://wiki.directi.com | http://careers.directi.com)

Licensed under Creative Commons Attribution Sharealike Noncommercial

http://www.directi.com/

http://wiki.directi.com/

http://careers.directi.com/

http://careers.directi.com/

22Creative Commons Sharealike Attributions Noncommercial

Agenda

• Why is Scalability important

• Introduction to the Variables and Factors

• Building our own Scalable Architecture (in incremental steps) Vertical Scaling Vertical Partitioning Horizontal Scaling Horizontal Partitioning … etc

• Platform Selection Considerations

• Tips


Why is Scalability Important in a Web 2.0 world

• Viral marketing can result in instant successes

• RSS / Ajax / SOA pull based / polling type XML protocols - Meta-data > data Number of Requests exponentially grows with user base

• RoR / Grails – Dynamic language landscape gaining popularity

• In the end you want to build a Web 2.0 app that can serve millions of users with ZERO downtime


The Variables

• Scalability - Number of users / sessions / transactions / operations the entire system can perform

• Performance – Optimal utilization of resources

• Responsiveness – Time taken per operation

• Availability - Probability of the application or a portion of the application being available at any given point in time

• Downtime Impact - The impact of a downtime of a server/service/resource - number of users, type of impact etc

• Cost

• Maintenance Effort

High: scalability, availability, performance & responsivenessLow: downtime impact, cost & maintenance effort


The Factors

• Platform selection

• Hardware

• Application Design

• Database/Datastore Structure and Architecture

• Deployment Architecture

• Storage Architecture

• Abuse prevention

• Monitoring mechanisms

• … and more


Lets Start …

• We will now build an example architecture for an example app using the following iterative incremental steps – Inspect current Architecture Identify Scalability Bottlenecks Identify SPOFs and Availability Issues Identify Downtime Impact Risk Zones Apply one of -

• Vertical Scaling

• Vertical Partitioning

• Horizontal Scaling

• Horizontal Partitioning

Repeat process


Step 1 – Lets Start …

Appserver & DBServer


Step 2 – Vertical Scaling

Appserver, DBServer

CPU

CPURAM RAM


Step 2 - Vertical Scaling

• Introduction Increasing the hardware resources

without changing the number of nodes Referred to as “Scaling up” the Server

• Advantages Simple to implement

• Disadvantages Finite limit Hardware does not scale linearly

(diminishing returns for each incremental unit)

Requires downtime Increases Downtime Impact Incremental costs increase

exponentially

Appserver, DBServer

CPU

CPURAM RAM

CPU

CPU

RAM RAM


Step 3 – Vertical Partitioning (Services)

AppServer

DBServer

• Introduction Deploying each service on a separate node

• Positives Increases per application Availability Task-based specialization, optimization and

tuning possible Reduces context switching Simple to implement for out of band

processes No changes to App required Flexibility increases

• Negatives Sub-optimal resource utilization May not increase overall availability Finite Scalability


Understanding Vertical Partitioning

• The term Vertical Partitioning denotes – Increase in the number of nodes by distributing the

tasks/functions Each node (or cluster) performs separate Tasks Each node (or cluster) is different from the other

• Vertical Partitioning can be performed at various layers (App / Server / Data / Hardware etc)


Step 4 – Horizontal Scaling (App Server)

AppServer AppServer AppServer

Load Balancer

DBServer

• Introduction Increasing the number of nodes of

the App Server through Load Balancing

Referred to as “Scaling out” the App Server


Understanding Horizontal Scaling

• The term Horizontal Scaling denotes – Increase in the number of nodes by replicating the nodes Each node performs the same Tasks Each node is identical Typically the collection of nodes maybe known as a cluster

(though the term cluster is often misused) Also referred to as “Scaling Out”

• Horizontal Scaling can be performed for any particular type of node (AppServer / DBServer etc)


Load Balancer – Hardware vs Software

• Hardware Load balancers are faster• Software Load balancers are more customizable• With HTTP Servers load balancing is typically combined

with http accelerators


Load Balancer – Session Management

• Sticky Sessions Requests for a given user are

sent to a fixed App Server Observations

• Asymmetrical load distribution (especially during downtimes)

• Downtime Impact – Loss of session data


Load Balancer

Sticky Sessions

User 1 User 2



• Central Session Store Introduces SPOF An additional variable Session reads and writes

generate Disk + Network I/O Also known as a Shared

Session Store Cluster


Load Balancer

Session Store

Central Session Storage



• Clustered Session Management Easier to setup No SPOF Session reads are instantaneous Session writes generate Network

I/O Network I/O increases

exponentially with increase in number of nodes

In very rare circumstances a request may get stale session data

• User request reaches subsequent node faster than intra-node message

• Intra-node communication fails AKA Shared-nothing Cluster


Load Balancer

Clustered Session Management



• Sticky Sessions with Central Session Store Downtime does not cause loss

of data Session reads need not

generate network I/O

• Sticky Sessions with Clustered Session Management No specific advantages

Sticky Sessions


Load Balancer

User 1 User 2



• Recommendation Use Clustered Session Management if you have –

• Smaller Number of App Servers

• Fewer Session writes Use a Central Session Store elsewhere Use sticky sessions only if you have to


Load Balancer – Removing SPOF

• In a Load Balanced App Server Cluster the LB is an SPOF

• Setup LB in Active-Active or Active-Passive mode Note: Active-Active nevertheless

assumes that each LB is independently able to take up the load of the other

If one wants ZERO downtime, then Active-Active becomes truly cost beneficial only if multiple LBs (more than 3 to 4) are daisy chained as Active-Active forming an LB Cluster


Load Balancer

Active-Passive LB

Load Balancer


Load Balancer

Active-Active LB

Load Balancer

Users

Users


Step 4 – Horizontal Scaling (App Server)

DBServer

• Our deployment at the end of Step 4

• Positives Increases Availability and

Scalability No changes to App required Easy setup

• Negatives Finite Scalability

Load Balanced App Servers


Step 5 – Vertical Partitioning (Hardware)

DBServer

• Introduction Partitioning out the Storage

function using a SAN

• SAN config options Refer to “Demystifying Storage” at

http://wiki.directi.com -> Dev University -> Presentations

• Positives Allows “Scaling Up” the DB Server Boosts Performance of DB Server

• Negatives Increases Cost


SAN

http://wiki.directi.com/


Step 6 – Horizontal Scaling (DB)

DBServer

• Introduction Increasing the number of DB nodes Referred to as “Scaling out” the DB

Server

• Options Shared nothing Cluster Real Application Cluster (or Shared

Storage Cluster)DBServer DBServer


SAN


Shared Nothing Cluster

• Each DB Server node has its own complete copy of the database

• Nothing is shared between the DB Server Nodes

• This is achieved through DB Replication at DB / Driver / App level or through a proxy

• Supported by most RDBMs natively or through 3rd party software

DBServer

Database

DBServer

Database

DBServer

Database

Note: Actual DB files maybe stored on a central SAN


Replication Considerations

• Master-Slave Writes are sent to a single master which replicates the data to

multiple slave nodes Replication maybe cascaded Simple setup No conflict management required

• Multi-Master Writes can be sent to any of the multiple masters which replicate

them to other masters and slaves Conflict Management required Deadlocks possible if same data is simultaneously modified at

multiple places


Replication Considerations• Asynchronous

Guaranteed, but out-of-band replication from Master to Slave Master updates its own db and returns a response to client Replication from Master to Slave takes place asynchronously Faster response to a client Slave data is marginally behind the Master Requires modification to App to send critical reads and writes to

master, and load balance all other reads

• Synchronous Guaranteed, in-band replication from Master to Slave Master updates its own db, and confirms all slaves have updated

their db before returning a response to client Slower response to a client Slaves have the same data as the Master at all times Requires modification to App to send writes to master and load

balance all reads


Replication Considerations

• Replication at RDBMS level Support may exists in RDBMS or through 3rd party tool Faster and more reliable App must send writes to Master, reads to any db and critical reads

to Master

• Replication at Driver / DAO level Driver / DAO layer ensures

• writes are performed on all connected DBs

• Reads are load balanced

• Critical reads are sent to a Master

In most cases RDBMS agnostic Slower and in some cases less reliable


Real Application Cluster

• All DB Servers in the cluster share a common storage area on a SAN

• All DB servers mount the same block device

• The filesystem must be a clustered file system (eg GFS / OFS)

• Currently only supported by Oracle Real Application Cluster

• Can be very expensive (licensing fees)

DBServer

SAN

DBServer DBServer

Database


Recommendation

• Try and choose a DB which natively supports Master-Slave replication

• Use Master-Slave Async replication

• Write your DAO layer to ensure writes are sent to a single DB reads are load balanced Critical reads are sent to a

master

DBServer

DBServer

DBServer


Writes & Critical Reads

Other Reads



• Our architecture now looks like this

• Positives As Web servers grow, Database

nodes can be added DB Server is no longer SPOF

• Negatives Finite limit


DB Cluster

DB DB DB

SAN



• Shared nothing clusters have a finite scaling limit Reads to Writes – 2:1 So 8 Reads => 4 writes 2 DBs

• Per db – 4 reads and 4 writes 4 DBs

• Per db – 2 reads and 4 writes 8 DBs

• Per db – 1 read and 4 writes

• At some point adding another node brings in negligible incremental benefit

Read

sW

rites

DB1 DB2


Step 7 – Vertical / Horizontal Partitioning (DB)

• Introduction Increasing the number of DB

Clusters by dividing the data

• Options Vertical Partitioning - Dividing

tables / columns Horizontal Partitioning - Dividing by

rows (value)


DB Cluster

DB DB DB

SAN


Vertical Partitioning (DB)

• Take a set of tables and move them onto another DB Eg in a social network - the users

table and the friends table can be on separate DB clusters

• Each DB Cluster has different tables

• Application code or DAO / Driver code or a proxy knows where a given table is and directs queries to the appropriate DB

• Can also be done at a column level by moving a set of columns into a separate table

App Cluster

DB Cluster 1

Table 1Table 2

DB Cluster 2

Table 3Table 4


Vertical Partitioning (DB)

• Negatives One cannot perform SQL joins or

maintain referential integrity (referential integrity is as such over-rated)

Finite Limit

App Cluster

DB Cluster 1

Table 1Table 2

DB Cluster 2

Table 3Table 4


Horizontal Partitioning (DB)

• Take a set of rows and move them onto another DB Eg in a social network – each DB

Cluster can contain all data for 1 million users

• Each DB Cluster has identical tables

• Application code or DAO / Driver code or a proxy knows where a given row is and directs queries to the appropriate DB

• Negatives SQL unions for search type queries

must be performed within code

App Cluster

DB Cluster 1

Table 1Table 2Table 3Table 4

DB Cluster 2

Table 1Table 2Table 3Table 4

1 million users 1 million users


Horizontal Partitioning (DB)

• Techniques FCFS

• 1st million users are stored on cluster 1 and the next on cluster 2 Round Robin Least Used (Balanced)

• Each time a new user is added, a DB cluster with the least users is chosen

Hash based• A hashing function is used to determine the DB Cluster in which the

user data should be inserted Value Based

• User ids 1 to 1 million stored in cluster 1 OR

• all users with names starting from A-M on cluster 1 Except for Hash and Value based all other techniques also require

an independent lookup map – mapping user to Database Cluster This map itself will be stored on a separate DB (which may further

need to be replicated)


Step 7 – Vertical / Horizontal Partitioning (DB)


DB Cluster

DB DB DB

DB Cluster

DB DB DB

LookupMap

SAN


• Positives As App servers grow, Database

Clusters can be added

• Note: This is not the same as table partitioning provided by the db (eg MSSQL)

• We may actually want to further segregate these into Sets, each serving a collection of users (refer next slide


Step 8 – Separating Sets


DB Cluster

DB DB DB

DB Cluster

DB DB DB

LookupMap

SAN


DB Cluster

DB DB DB

DB Cluster

DB DB DB

LookupMap

SAN

Global RedirectorGlobalLookup

Map

SET 1 – 10 million users SET 2 – 10 million users

• Now we consider each deployment as a single Set serving a collection of users


Creating Sets

• The goal behind creating sets is easier manageability• Each Set is independent and handles transactions for a

set of users• Each Set is architecturally identical to the other• Each Set contains the entire application with all its data

structures• Sets can even be deployed in separate datacenters• Users may even be added to a Set that is closer to them

in terms of network latency


Step 8 – Horizontal Partitioning (Sets)

App ServersCluster

DB Cluster

SAN

Global Redirector

SET 1

DB Cluster

App ServersCluster

DB Cluster

SAN

SET 2

DB Cluster


• Positives Infinite Scalability

• Negatives Aggregation of data across sets

is complex Users may need to be moved

across Sets if sizing is improper Global App settings and

preferences need to be replicated across Sets


Step 9 – Caching

• Add caches within App Server Object Cache Session Cache (especially if you are using a Central Session

Store) API cache Page cache

• Software Memcached Teracotta (Java only) Coherence (commercial expensive data grid by Oracle)


Step 10 – HTTP Accelerator

• If your app is a web app you should add an HTTP Accelerator or a Reverse Proxy

• A good HTTP Accelerator / Reverse proxy performs the following – Redirect static content requests to a lighter HTTP server (lighttpd) Cache content based on rules (with granular invalidation support) Use Async NIO on the user side Maintain a limited pool of Keep-alive connections to the App Server Intelligent load balancing

• Solutions Nginx (HTTP / IMAP) Perlbal Hardware accelerators plus Load Balancers


Step 11 – Other cool stuff

• CDNs• IP Anycasting• Async Nonblocking IO (for all Network Servers)• If possible - Async Nonblocking IO for disk• Incorporate multi-layer caching strategy where required

L1 cache – in-process with App Server L2 cache – across network boundary L3 cache – on disk

• Grid computing Java – GridGain Erlang – natively built in


Platform Selection Considerations

• Programming Languages and Frameworks Dynamic languages are slower than static languages Compiled code runs faster than interpreted code -> use

accelerators or pre-compilers Frameworks that provide Dependency Injections, Reflection,

Annotations have a marginal performance impact ORMs hide DB querying which can in some cases result in poor

query performance due to non-optimized querying

• RDBMS MySQL, MSSQL and Oracle support native replication Postgres supports replication through 3rd party software (Slony) Oracle supports Real Application Clustering MySQL uses locking and arbitration, while Postgres/Oracle use

MVCC (MSSQL just recently introduced MVCC)

• Cache Teracotta vs memcached vs Coherence


Tips

• All the techniques we learnt today can be applied in any order

• Try and incorporate Horizontal DB partitioning by value from the beginning into your design

• Loosely couple all modules• Implement a REST-ful framework for easier caching• Perform application sizing ongoingly to ensure optimal

utilization of hardware

Intelligent People. Uncommon Ideas.

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Intelligent People. Uncommon Ideas. 1 Building a Scalable Architecture for Web Apps - Part I (Lessons Learned @ Directi) By Bhavin Turakhia CEO, Directi