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
4. Evolution
60s 70s 80s 90s 00s
IBM
nframes
mited
ering or
traction
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
iOS
Android
HTML5
Browsers
Apps
API
Cloud
NoSQL
10s
5. 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
7. 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?
Login
Catalog
search
List of
results
Select
products
put into
cart
Buy
cart
8. State
▪ Server with state vs. stateless server
– Stateful server must keep the state between requests
▪ Problem with stateful servers
– Need more resources, limit scalability
Client 1
Client 2
Client 3
Stateful Server Stateless Server
Client 1
Client 2
Client 3
Data 1
Data 2
Data 2
9. Stateless Servers
▪ Stateless servers scale much better
▪ Use fewer resources
▪ Example:
– View book information
– Each request is separate
▪ REST was designed to be stateless
10. 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
11. 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
13. Ways to Store Session State
▪ We have three players
– The client using a web browser or app
– The Server running the web application and domain
– The database storing all the data
Client Server Database
14. Ways to Store Session State
▪ Three basic choices
– Client Session State
– Server Session State
– Database Session State
Client Server Database
15. 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 - Representational State Transfer
16. 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
17. Server Session State
Store session state on a server in a
serialised 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 or in-memory database
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
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
– Minimising remote function calls
▪ Service Architecture has to have course-grained APIs and combine
several objects
– Avoid fine-grained interfaces
▪ 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
Order
Application
Order
Application
Order
Application
Order
Application
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
36. Term microservices is sometimes used, but is misleading
Has nothing to do with lines of code
How big is a service?
Example definition:
Balance between integration points and size
Time: Can be rewritten in one iteration (2 weeks)
Features: All things that belong together
37. Loose Coupling
When services are loosely coupled, a change in one
service should not require a change in another
A loosely coupled service knows as little about the
services with which it collaborates
Source: Building Microservices
38. High Cohesion
We want related behaviour to sit together, and unrelated
to sit elsewhere
Group together stuff the belongs together, as in SRP
If you want to change something, it should change in one
place, as in DRY
Source: Building Microservices
39. Bounded Context
Concept that comes from Domain-driven Design (DDD)
Any given domain contains multiple bounded contexts,
and within each are “models” or “things” (or “objects”)
that do not need to be communicated outside
that are shared with other bounded contexts
The shared objects are define the explicit interface to the
bounded context
Source: Building Microservices
41. 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 sufficiently course grained
43. 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
44.
45. 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
▪ Number of messages sent by WhatsApp: 30 billion
46. 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?
47. 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
49. Scaling Applications
In the Internet world you want to build web
sites that gets lots of users and massive
hit per second
But how can you cope with such load?
Browser
HTTP
Server
Application Database
50. 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
51. 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
52. 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
53. Scale Cube
X scaling: duplicate the system
Z
scaling:Partition
the
data
Yscaling:PartitiontheApplication
54. 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
55. 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
56. 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
60. 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
61. 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
62. 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
63. 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
65. 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
66. 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
67. 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
68. 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
69. 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
70. 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?
71. 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
72.
73. 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
74. 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
76. 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
79. 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
