Application patterns

Application Patterns tomi vanek

February 2014

• User Interaction

• Services

• Persistence – scaling, locking, cache

• Real-time processing

• High Volume Batch Processing

Agenda

User Interaction

MVC / MVP / MV-VM

View Controller Action

View Presenter Action

View-Model Action

Model-View-Controller (MVC)

MVC Variant: Page Flow

Page flow with asynchronous

commit of order

Simple synchronous MVC

implementation

MVC: Page Flow Example

In-Memory Server Session State

Persistent Session State

Serialized Session State in View

Client Session State in Local Storage

Services

Method invocation / Resource manipulation

• Services: – Strongly typed interface

contract.

– Broad range of aspects are standardized.

– Implemented in integration infrastructure and technologies.

– Service discoverability and lifetime management.

• But: – Limits in scalability.

– Expensive extensibility.

• Resources:

– Simple data exploration.

– Simple extensibility.

– Simple data consumer

• But:

– Limited schema

definition.

– No standards for

complex functionalities /

scenarios.

Business Layer Remote Procedure Call

Presentation

Presentation Layer

Business Tier Data Tier

External

Business Module

Resources Data Layer Business Layer

External

System

Repository

Integration Gateway

Service

Relational Database

Compact Business Layer with Active Records

Application Tier

Dynamic

Presentation Module

Presentation Layer Resources Domain Layer

Presentation

Data Tier

External

System

Integration Gateway

Relational Database

REST / WS Business Layer

Presentati

External

Business Module

Resources Data Layer Business Layer Presentation Layer

External

System

Repository

Integration Gateway

Service

Relational Database C

troller

Asynchronous Business Layer

Presentation

External

Business Module

External

System

Repository Relational Database

Integration Gateway

Service

Push Notification Business Layer

Presentation

Business Tier

Business Module

Subject Register

Service

Data synchronization

Persistence - Scaling

Database Scaling: Master – Slave

Internet

Load Balancer

Slaves

Web Servers

Master

Write Read

Replicate

Database Scaling: Active - Passive

Internet

Load Balancer

Passive

Web Servers

Failed

Active Instance

Passive to Active

Instance

Replicate

Read / Write

Database Scaling: Cluster

Internet

Load Balancer

Virtual Database

Cluster

Controller

Virtual Database

Cluster

Controller

Database Scaling

Load Balancer

Shard Resolver

Web Servers

Product 1 .. n

Product n+1 .. o

Product o+1 .. p

Locking

Optimistic Locking

Pessimistic Locking

Read-through / Write-through Cache

Pre-fetch

Publisher

Real-time processing Integration Patterns

Reference model

Messaging Idioms

• Channels

• Message Construction

• Rooting

• Transformation

Messaging Design Patterns

• Pipes and Filters

• Claim Check

• Scatter-Gather

Integration Application Architecture Design Patterns

Pipes and filters

Pipeline

Claim-check

Claim-check for secure communication

Scatter-Gather with Publish-Subscribe Input Channel

Scatter-Gather with Recipient List

Scale-Out Batch High Volume Batch Processing

Batch Application Elements

Launcher Job Step

Reader

Processor

Writer

Job Repository

Batch reference model

Scaling

Job with Parallel Steps

Step with parallel threads

Partitioning

Remote chunking

Discussion

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