l12 session state and distributation strategies
DESCRIPTION
One of the most critical design decisions on enterprise programming is where to keep the state. As we talked about in the lecture on Concurrency, session state is the state that is maintained between requests. A session starts when the user first hits the enterprise system, and lasts until the user signs out or times out. In this lecture we look at the session state and explore three design patterns on where to store the session state. The second topic in this lecture is how to distribution the applications. The primary reason we want to do that is to get more performance and handle more load. Most enterprise applications have lots of users, some hundreds of thousands. The only way to cope with such load is to scale the application. Scalability is how much more load an application can take if more resources are added. We will look at two ways to scale, one is by load balancing and the other by clustering. Video of this lecture are found here: http://www.olafurandri.com/?page_id=2762TRANSCRIPT
Lecture 13Session State and Distribution Strategies
Session State
Reading Fowler 6– Session State
Fowler 17– Session State Patterns
Agenda Session State– Business transactions
Session State Patterns– Client Session State– Server Session State– Database Session State
Business Transactions Transactions that expand more than one
request– User is working with data before they are
committed to the database• Example: User logs in, puts products in a shopping
cart, buys, and logs out– Where do we keep the state between
transactions?
State Server with state vs. stateless server– Stateful server must keep the state between
requests Problem with stateful servers– Need more resources, limit scalability
Stateless Servers Stateless servers scale much better Use fewer resources
Example:– View book information– Each request is separate
Stateful Servers Stateful servers are the norm Not easy to get rid of them
Problem: they take resources and cause server affinity
Example:– 100 users make request every 10 second, each
request takes 1 second– One stateful object per user– Object are Idle 90% of the time
Session State State that is relevant to a session– State used in business transactions and belong
to a specific client– Data structure belonging to a client– May not be consistent until they are persisted
Session is distinct from record data– Record data is a long-term persistent data in a
database – Session state might en up as record data
Question:Where do you store the session?
EXCERISE
Ways to Store Session State We have three players– The client using a web browser– The Server running the web application and
domain– The database storing all the data
Ways to Store Session State Three basic choices– Client Session State (456)– Server Session State (458)– Database Session State (462)
Client Session StateStore session state on the client
How It Works– Desktop applications can store the state in
memory– Web solutions can store state in cookies, hide it
in the web page, or use the URL– Data Transfer Object can be used– Session ID is the minimum client state– Works well with REST
Client Session State When to Use It– Works well if server is stateless– Maximal clustering and failover resiliency
Drawbacks– Does not work well for large amount of data– Data gets lost if client crashes– Security issues
Server Session StateStore session state on a server in a
serialized form
How It Works– Session Objects – data structures on the server
keyed to session Id Format of data– Can be binary, objects or XML
Where to store session– Application server, file or local database
Server Session State Specific Implementations– HttpSession– Stateful Session Beans – EJB
When to Use It– Simplicity, it is easy to store and receive data
Drawbacks– Data can get lost if server goes down– Clustering and session migration becomes
difficult– Space complexity (memory of server)– Inactive sessions need to be cleaned up
Database Session StateStore session data as committed data in the
database
How It Works– Session State stored in the database– Can be stored as temporary data to distinguish from
committed record data Pending session data
– Pending session data might violate integrity rules– Use of pending field or pending tables
• When pending session data becomes record data it is save in the real tables
Database Session State When to Use It– Improved scalability – easy to add servers– Works well in clusters– Data is persisted, even if data centre goes
down Drawbacks– Database becomes a bottleneck– Need of clean up procedure of pending data
that did not become record data – user just left
What about dead sessions? Client session– No our problem
Server session– Web servers will send inactive message upon
timeout Database session– Need to be clean up– Retention routines
Caching Caching is temporary data that is kept in
memory between requests for performance reasons– Not session data– Can be thrown away and retrieved any time
Saves the round-trip to the database Can become stale or old and out-dated– Distributed caching is one way to solve that
Practical Example Client session– For preferences,
user selections Server session – Used for browsing and
caching– Logged in customer
Database– “Legal” session– Stored, tracked, need to survive between
sessions
We are building an application for processing development grants. The application is complicated and users can login any time and continue work on their application. What design pattern would we use for storing the session?
A) Client Session StateB) Server Session StateC) Database Session StateD) No state required
QUIZ
✔
Distribution Strategies
Reading Fowler 7– Distribution Strategies
Fowler 15– Distribution Patterns– Remote Façade (388)
Agenda Distributed Architectures– Remote and Local Interfaces– Where You Have to Distribute– Remote Façade
Scalablity DEMO
Distributed Architecture Distribute processing by placing objects
different nodes
Distributed Architecture Distribute processing by placing objects on
different nodes Benefits– Load is distributed between different nodes
giving overall better performance– It is easy to add new nodes– Middleware products make calls between
nodes transparent
But is this true?
Distributed Architecture Distribute processing by placing objects
different nodes
“This design sucks like an inverted hurricane” – Fowler
Fowler’s First Law of Distributed Object Design: Don't Distribute your objects!
Remote and Local Interfaces Local calls– Calls between components on the same node
are local Remote calls– Calls between components on different
machines are remote Objects Oriented programming– Promotes fine-grained objects
Remote and Local Interfaces Local call within a process is very, very fast Remote call between two processes is order-of-
magnitude s l o w e r– Marshalling and un-marshalling of objects– Data transfer over the network
With fine-grained object oriented design, remote components can kill performance
Example– Address object has get and set method for each
member, city, street, and so on– Will result in many remote calls
Remote and Local Interfaces With distributed architectures, interfaces
must be course-grained– Minimizing remote function calls
Example– Instead of having getters and setters for each
field, bulk assessors are used
Example Sun Application Model– “The Canonical Architecture”
Entity Beans– Each bean maps to row in the database– find methods returns
Collection of Remote interfaces
Example Result– Architecture that does not perform very well
Suggested solution was– Use session beans to call entity beans
Distributed Architecture Better distribution model– Load Balancing or Clustering the application
involves putting several copies of the same application on different nodes
Where You Have to Distribute As architect, try to eliminate as many
remote call as possible– If this cannot be archived choose carefully
where the distribution boundaries lay Distribution Boundaries– Client/Server– Server/Database– Web Server/Application Server– Separation due to vendor differences– There might be some genuine reason
Optimizing Remote Calls We know remote calls are expensive How can we minimize the cost of remote
calls? The overhead is– Marshaling or serializing data– Network transfer
Put as much data into the call– Course grained call
Remote Façade
Remote FaçadeProvides a coarse-grained facade on
fine-grained objects to improve efficiency over a network
The façade is a thin wrapper that provides coarse-grained interface to a system– In an object-oriented model, you do best with
small objects that have small methods– Can cause great deal of interaction between
objects and method invocations
Remote Façade How It Works– Allows efficient remote access with coarse-
grained interface– Façade will use the fine-grained object to build
and return object like Data Transfer Object– Should not contain any domain logic
Remote Façade When to Use It– Whenever you need remote access to fine
grained object model– Most common use is between UI and domain
model
Remote Façade Remote method invocation are expensive– Performance killer
JVM
Entity
Entity
Session Entity
Session
ClientRMI
Remote Façade Coarse grained interface
JVM
Client Entity
Entity
Session Entity
Session
RMILocal calls
RemoteFaçade
Remote Façade Benefits– Net traffic is reduced– Transactions are closer to the database
Drawbacks– Limitations on object oriented programming– Solution is based on limitations of the network
Interfaces for Distribution XML over HTTP is a common interface– XML is structured and allows for lot of data– XML is common format, well known– HTTP is common and esay to use
XML has overhead– Parsing and manipulation of strings is
expensive– Overhead if not needed
Approches like REST are more efficient– Use HTTP right
Scalability
Scaling the application Today’s web sites must handle multiple
simulations users Examples:– All web based apps must handle several users– mbl.is handles ~180.000 users/day– Betware must handle up to 100.000
simultaneous users
The World we Live in Average number of tweets per day
58 million Total number of minutes spent on
Facebook each month 700 billion SnapChat has five million daily
active users who send 200 million photos per day.
Instagram has over 150 million users on the platform and1 billion likes happening each day
Scalability Scalability is the measure of how adding
resource (usually hardware) affects the performance– Vertical scalability (up) – increase server
power– Horizontal scalability (out) – increase the
servers Session migration – Move the session for one server to another
Server affinity– Keep the session on one server and make the
client always use the same server
Scalability Example
Load Distribution Use number of machines to handle requests Load Balancer directs all
request to particular server– All requests in one session go
to the same server– Server affinity
Benefits– Load can be increased– Easy to add new pairs– Uptime is increased
Drawbacks– Database is a bootleneck
Clustering Distributing components– Each node has one
component– Increased performance
is not guaranteed
Using cluster– Have all components
in each node and use local calls
Clustering With clustering, servers
are connected togetheras they were a singlecomputer– Request can be handled
by any server– Sessions are stored on
multiple servers– Servers can be added and
removed any time Problem is with state
– State in application servers reduces scalability– Clients become dependant on particular nodes
Clustering State Application functionality– Handle it yourself, but this is complicated, not
worth the effort Shared resources– Well-known pattern (Database Session State)– Problem with bottlenecks limits scalablity
Clustering Middleware– Several solutions, for example JBoss, Terracotta
Clustering JVM or network– Low levels, transparent to applications
Scalability Example
Measuring Scalability The only meaningful way to know about
system’s performance is to measure it Performance Tools can help this process– Give indication of scalability– Identify bottlenecks
Example tool: LoadRunner
Example tool: JMeter
Which is true when you are clustering your application?
A) Make sure all requests goes to the same machineB) Deploy each component on separate machine to distribute
loadC) You try to minimize network traffic to avoid latency
problemsD) Deploy the whole solution on many machines
QUIZ
Real World Examples: Betware Iceland Data Center
ISP1 ISP2Hardware
firewall
Loadbalancer
16 port 2GbpsSAN switch
QLogic
12 x 300GBSAS 15K
24 x 300GBSAS 15K
IBM BladeChassis
Pair of eachserver on
separate blade
CMSDB
BackupSoftware
System DB
Summary Session State– Business transactions
Session State Patterns– Client Session State– Server Session State– Database Session State
Distribution Strategies– How to distribute