Building a Scalable Architecture for web apps

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

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 & responsiveness Low : 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 CPU RAM 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 CPU RAM 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
AppServer AppServer AppServer 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
AppServer AppServer AppServer 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
AppServer AppServer AppServer 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 AppServer AppServer AppServer 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
AppServer AppServer AppServer Load Balancer Active-Passive LB Load Balancer AppServer AppServer AppServer 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 Load Balanced App Servers

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 Load Balanced App Servers

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 3 rd 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

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 at RDBMS level
Support may exists in RDBMS or through 3 rd 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 Load Balanced App Servers

Step 6 – Horizontal Scaling (DB)
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
Load Balanced App Servers DB Cluster DB DB DB SAN

Step 6 – Horizontal Scaling (DB)
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
Reads Writes 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)
Load Balanced App Servers 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 1 Table 2 DB Cluster 2 Table 3 Table 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 1 Table 2 DB Cluster 2 Table 3 Table 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 1 Table 2 Table 3 Table 4 DB Cluster 2 Table 1 Table 2 Table 3 Table 4 1 million users 1 million users

Horizontal Partitioning (DB)
Techniques
FCFS
1 st 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) Lookup Map
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
Load Balanced App Servers DB Cluster DB DB DB DB Cluster DB DB DB SAN

Step 8 – Separating Sets Lookup Map Lookup Map Global Redirector Global Lookup 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
Load Balanced App Servers DB Cluster DB DB DB DB Cluster DB DB DB SAN Load Balanced App Servers DB Cluster DB DB DB DB Cluster DB DB DB SAN

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 Servers Cluster DB Cluster SAN Global Redirector SET 1 DB Cluster App Servers Cluster 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 3 rd 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

Questions?? bhavin.t@directi.com http://directi.com http://careers.directi.com Download slides: http://wiki.directi.com

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Building a Scalable Architecture for web apps

by Directi Group on May 24, 2008

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Building a Scalable Architecture for web apps — Presentation Transcript