This document discusses scalability and distributed systems. It introduces the scale cube model for scaling applications horizontally across multiple servers and vertically by splitting functionality. Session state can be stored on clients, servers, or in a database. Distributed systems require coarse-grained interfaces to minimize remote calls. Eventual consistency relaxes ACID properties and guarantees availability over consistency through asynchronous replication. The CAP theorem states it is impossible to guarantee consistency, availability, and partition tolerance simultaneously in distributed systems.

1. Lecture 20
Scalability

2. Agenda
 Evolution - where are we today?
 Requirements of 21st century web applications
 Session State
 Distribution Strategies
 Scale Cube
 Eventual Consistency
– CAP Theorm
 Real World Example

3. Evolution
60s 70s 80s 90s 00s
IBM
Mainframes
Limited
layering or
abstraction
IBM, DEC
Mini-computers
Unix, VAX
“Dumb”
terminals
Screens/Files
PC, Intel,
DOS, Mac,
Unix,
Windows
Client/Server
RMDB
Windows
Internet
HTTP
Web
Browsers
Web
Applications
RMDB
Windows,
Linux
MacOS
Browsers,
Services
Domain
Applications
RMDB
10s
iOS
Android
HTML5
Browsers
Apps
API
Cloud
NoSQL

4. Motivation
 Requirements of 21st century web systems
– High availability
– Millions of simultaneous users
– Peak load of 1000s tx/sec
 Example
– What if we need to handle load of 20.000 tx/sec?
– That’s 1.2 million tx per minute

5. Session State

6. 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?

7. State
 Server with state vs. stateless server
– Stateful server must keep the state between requests
 Problem with stateful servers
– Need more resources, limit scalability

8. Stateless Servers
 Stateless servers scale much better
 Use fewer resources
 Example:
– View book information
– Each request is separate
 REST was designed to be stateless

9. 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

10. 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

11. EXCERISE
Question:
Where do you store the session?

12. 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

13. Ways to Store Session State
 Three basic choices
– Client Session State
– Server Session State
– Database Session State

14. Client Session State
Store 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

15. 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

16. Server Session State
Store 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
– Memory, application server, file or local database

17. 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

18. Database Session State
Store 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

19. 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

20. What about dead sessions?
 Client session
– Not our problem
 Server session
– Web servers will send inactive message upon timeout
 Database session
– Need to be clean up
– Retention routines

21. 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 (message driven cache) is one
way to solve that

22. Practical Example
 Client session
– For preferences,
user selections
 Server session
– Used for browsing and
caching
– Logged in customer
 Database
– “Legal” session
– Stored, trackable, need to survive between sessions

23. QUIZ
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 State
B) Server Session State
C) Database Session State
D) No state required

24. QUIZ
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 State
B) Server Session State
C) Database Session State
D) No state required
✔

25. Distribution Strategies

26. Distributed Architecture
 Distribute processing by placing objects on
different nodes

27. 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?

28. 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!

29. 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

30. 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

31. 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

32. Distributed Architecture
 Better distribution model (X scaling)
– Load Balancing or Clustering the application involves
putting several copies of the same application on
different nodes

33. 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

34. 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 enough data into the call
– Course grained call
– Use binary protocols – avoid XML

35. The Right Balance
 In Service Architecture, we want to split by
functionality (Y Scaling)
– Boundaries must be well designed – objects that work
together are grouped together
– APIs must be

36. The Scale Cube

37. Scaling the application
 Today’s web sites must handle multiple
simulations users
 Examples:
– All web based apps must handle several users
– mbl.is handles >200.000 users/day
– Betware must handle up to 100.000 simultaneous
users and 1,2 million tx/min for terminal system peak
load

39. The World we Live in
 Average number of tweets per day 500
million
 Total number of minutes spent on
Facebook each month 700 billion
 SnapChat has 100 million daily active
users who send 1 billion snaps each
day
 Instagram has over 200 million users
on the platform who send 60 million
photos per day

40. Scalability
 Scalability is the ability of a system, network, or
process to handle a growing amount of work in a
capable manner or its ability to be enlarged to
accommodate that growth
 With more load, how does the load of the
system vary?

41. 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

42. Scalability
 How is the system growth pattern – what is the
formula?

43. Amdahl’s Law
 This law is used to find the maximum expected
improvement to an overall system when only
part of the system is improved
 In parallel computing, it states that a small
portion of the program which cannot be
parallelized will limit the overall speed-up
available from parallelization

44. Amdahl’s Law
 Amdahl’s law for overall speedup
1
Overall speedup =
F
(1 – F) +
S
F = The fraction enhanced
S = The speedup of the enhanced fraction
If we make 20% of the program be 10x faster
F=0.2
S=10
1
overall speedup =
0.2
(1 – 0.2) +
10
Gives 1.22 in overall speedup
IF S = 1000, overall speedup is 1.25

45. Amdahl’s Corollary
 Make the common case fast
– Common case being defined as “most time
consuming”
40% 10x faster => 1.5625
20% 100x faster => 1.2468

46. The Optimization Process
 There is only one way to test scalability:
Measure
– Find the bottleneck (the common case)
– Hypothesize about improvement
– Make optimization – change only one thing a time
– Measure again and repeat

47. The Scaling Problem
 We need to handle number of request to our
system
 There are two ways to scale:
– Vertically or scale up: Add more capacity to your
hardware, more memory for example
– Horizontal or scale out: Add more machines

48. Scaling Up
 This is the traditional approach for many
monolithic systems
 Use a big powerful system
 Pros:
– Easy to do, easy to understand
– One memory space and one database
 Cons:
– Has very hard limits
– Does not work for the 21st century requirements

49. The Scale Cube
One
Monolithic
system
X-Axis: Horizontal Duplication
Multiple
Services
Clustered
Single data
lookups
Many System
Duplicated
Y-Axis:
Split by
Functionality
No splits
No splits
Splits by
functionality

50. Scaling Out (X scaling)
 This can work for monolithic systems if the
database requirements is not high
 Use a many machines and distribute the load
– Have one big powerful database
 Pros:
– Scales well – handles much more load
– Shared database
 Cons:
– Session management is a challenge
– Database is a bottleneck

51. 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

52. Clustering
 With clustering, servers
are connected together
as they were a single
computer
– 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

53. 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

54. Scalability Example

55. Scalability Example

56. The Scale Cube
One
Monolithic
system
X-Axis: Horizontal Duplication
Multiple
Services
Clustered
Single data
lookups
Many System
Duplicated
Y-Axis:
Split by
Functionality
No splits
No splits
Many Services
Data/User splits
Splits by
functionality
Near infinite scaling

57. Eventual Consistency

58. Transactions
 Transaction is a bounded sequence of work
– Both start and finish is well defined
– Transaction must complete on an all-or-nothing basis
 All resources are in consistent state before and
after the transaction
 Example: Database transaction
– Withdraw data from account
– Buy the product
– Update stock information
 Transactions must have ACID properties

59. ACID properties
 Atomicity
– All steps are completed successfully – or rolled back
 Consistency
– Data is consistent at the start and the end of the
transaction
 Isolation
– Transaction is not visible to any other until that transaction
commits successfully
 Durability
– Any results of a committed transaction must be made
permanent

60. Transactional Resources
 Anything that is transactional
– Use transaction to control concurrency
– Databases, printers, message queues
 Transaction must be as short as possible
– Provides greatest throughput
– Should not span multiple requests
– Long transactions span multiple request

61. Transaction Isolations and
Liveness  Transactions lock tables (or resources)
– Need to provide isolation to guarantee correctness
– Liveness suffers
– We need to control isolation
 Serializable Transactions
– Full isolation
– Transactions are executed serially, one after the other
– Benefits: Guarantees correctness
– Drawbacks: Can seriously damage liveness and
performance

62. Isolation Level
 Problems can be controlled by setting the
isolation level
– We don’t want to lock table since it reduces
performance
– Solution is to use as low isolation as possible while
keeping correctness

63. Problem
 Serialization crates scalability bottlenecks
 Applications that support fully secure
serialization of using RMDB have hard time with
scale
 Can we scarify something?
– Can we relax these requirements?

64. CAP Theorem
 States that it is impossible for a distributed
computer system to simultaneously provide all
three of the following guarantees:
– Consistency: all nodes see the same data at the
same time
– Availability: a guarantee that every request receives
a response about whether it was successful or failed
– Partition tolerance: the system continues to operate
despite arbitrary message loss or failure of part of the
system

66. ACID vs. BASE
 BASE: Basically Available, Soft state, Eventual
consistency
 Basically Available: Guarantees availability of
the database
 Soft state: The state of the system can change
over time - even without input.
 Eventual consistency: The system will
eventually become consistent over time given no
new input

67. ACID vs. BASE
 The difference has more to do with
synchronous and asynchronous messaging
 For large scale systems asynchronous caters for
the fastest and least restricted workflow

68. Asynchronous
 Eventual Consistency example
Web Layer
Requests
Approve RMDB
Msg
Q
Process

69. 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

70. Example tool: LoadRunner

71. Example tool: JMeter

72. Real World Examples:
Betware Iceland Data Center

73. ISP1 ISP2
Hardware
firewall
Load
balancer
16 port 2Gbps
SAN switch
QLogic
12 x 300GB
SAS 15K
24 x 300GB
SAS 15K
IBM Blade
Chassis
System
DB
CMS
DB
Backup
Software
Pair of each
server on
separate blade

77. Summary
 Requirements of 21st century web applications
– Availability, Eventual consistency
 Session State
– Client, Server, Database
 Distribution Strategies
– Don’t distribute fine grained object – identify
bouneries
 The Scale Cube
 Eventual Consistency
– CAP Theorm
 Real World Example