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This document discusses effective practices for dependency injection (DI). It begins with a quick DI refresher and then provides guidelines for DI such as: explicitly defining dependencies, injecting exactly what is needed, preferring constructor injection, avoiding work in constructors, and avoiding direct dependencies on the injector. It also discusses testing code using DI, applying DI to existing code, and techniques for migrating code to use DI such as bottom-up or top-down approaches.
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Presented by FASTRAC International Advisor Sam Chittick at the Muslim Mindanao Autonomy Roundtable Discussion Series at the Senate of the Philippines on Aug. 17, 2015.
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This document discusses effective practices for dependency injection (DI). It begins with a quick DI refresher and then provides guidelines for DI such as: explicitly defining dependencies, injecting exactly what is needed, preferring constructor injection, avoiding work in constructors, and avoiding direct dependencies on the injector. It also discusses testing code using DI, applying DI to existing code, and techniques for migrating code to use DI such as bottom-up or top-down approaches.
The document discusses dependency injection and inversion of control principles using the Microsoft Unity framework. It begins by describing problems with rigid and fragile code that is difficult to maintain and change. It then covers dependency injection and inversion of control patterns, using Unity to resolve dependencies and decouple classes. The document provides examples of constructor injection, property injection, and lifetime managers in Unity. It concludes by noting some limitations of Unity and references for further reading.
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Presented by FASTRAC International Advisor Sam Chittick at the Muslim Mindanao Autonomy Roundtable Discussion Series at the Senate of the Philippines on Aug. 17, 2015.
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This document provides an introduction to design patterns, including their motivation, history, definition, categories, and examples. Some key points:
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In software engineering, a design pattern is a general reusable solution to a commonly occurring problem within a given context in software design
Patterns are formalized best practices that the programmer must implement in the application.
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The first one is dependency injection. This covers putting the variables that a class needs to function preferably inside a constructor.
The second one is the factory pattern. A factory moves the responsibility of instantiating an object to a third-party class.
The third one is dependency injection. This allows us to place a class' dependencies at one time, making it easy to come back and see what the class needs to survive.
Finally, we discuss the chain of responsibility. This allows complex operations to be handled by a chain of classes. Each class in the chain determines whether it is capable of handling the request and, if so, it returns the result.
This document discusses three design patterns: Adapter, Bridge, and Composite.
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The recorded video is available here: https://amsterdam2014.drupal.org/session/content-staging-drupal-8-continued
The document discusses software design patterns and how they can help address common problems that arise during design. It describes three main categories of patterns - creational, structural, and behavioral - and provides examples of specific patterns like Singleton, Factory, Adapter, and Bridge. The key benefits of design patterns are that they provide proven, reusable solutions to general problems and help create flexible, modular designs that can more easily adapt to changes.
Within DDD, the idea is to allocate all logic (as much as feasible) to the entity POJOs. This often includes logic of the following categories: accessing state (setters, getters, navigation and change of object graph), validating the state, calculation of derived attributes (calculating order sum of an order) and event processing (action-logic triggered by changes to the state of an entity, such as producing a shipment note when the order state is changed to “ready-for-shipment”). With large systems, there are usually context-specific (e.g. use-case-specific) needs that influence what methods we add to the domain classes. This creates problems over time when there are a lot of context-specific change requests that require new versions of a shared domain model implementation. With dynamic languages it is straightforward to separate these concerns without sacrificing the sought-after programming model of "intelligent pojos". There is also a new architecture (DCI) that take the reasoning further, towards a formal architecture for separation of concerns that is driven by the same core problem: how to get the fruit of DDD without the downside of a fragile domain model? This presentation illustrates pragmatic CDI with the power of a dynamic language (Groovy).
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This document provides an overview of key concepts in Java programming including:
- Java programs are compiled to bytecode that runs on the Java Virtual Machine (JVM).
- Java supports classes, objects, inheritance, interfaces, and exceptions.
- Multithreading allows programs to perform multiple tasks simultaneously through threads.
- Synchronization is used to coordinate access to shared resources across threads to avoid race conditions.
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This document provides an overview of design patterns, which are proven solutions to common problems in software design. It discusses different types of patterns like creational, structural, and behavioral patterns. It then gives examples of some common patterns like Iterator, Strategy, and Factory Method. The Factory Method pattern allows defining an interface for creating objects but letting subclasses decide which objects to instantiate. The Strategy pattern defines a family of algorithms, puts each of them in a separate class, and makes their objects interchangeable.
In software engineering, a design pattern is a general reusable solution to a commonly occurring problem within a given context in software design
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Many Apache Kafka use cases take advantage of Kafka’s ability to integrate multiple heterogeneous systems for stream processing and real-time machine learning scenarios. But Kafka also exists in a rich ecosystem of related but complementary stream processing technologies and tools, particularly from the open-source community. In this talk, we’ll take you on a tour of a selection of complementary tools that can make Kafka even more powerful. We’ll focus on tools for stream processing and querying, streaming machine learning, stream visibility and observation, stream meta-data, stream visualisation, stream development including testing and the use of Generative AI and LLMs, and stream performance and scalability. By the end you will have a good idea of the types of Kafka “superhero” tools that exist, which are my favourites (and what superpowers they have), and how they combine to save your Kafka applications development universe from swamploads of data stagnation monsters!
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Data Collection and Analysis with GenAI enables organizations to gather, analyze, and visualize vast amounts of supplier data, identifying key performance indicators and trends. Predictive analytics forecast future supplier performance, mitigating risks and seizing opportunities. Supplier segmentation allows for tailored management strategies, optimizing resource allocation. Automated scorecards and reporting provide real-time insights, enhancing transparency and tracking progress. Collaboration is fostered through GenAI-powered platforms, driving continuous improvement. NLP analyzes unstructured feedback, uncovering deeper insights into supplier relationships. Simulation and scenario planning tools anticipate supply chain disruptions, supporting informed decision-making. Integration with existing systems enhances data accuracy and consistency. McKinsey estimates GenAI could deliver $2.6 trillion to $4.4 trillion in economic benefits annually across industries, revolutionizing procurement processes and delivering significant ROI.
Baha Majid WCA4Z IBM Z Customer Council Boston June 2024.pdfBaha Majid
IBM watsonx Code Assistant for Z, our latest Generative AI-assisted mainframe application modernization solution. Mainframe (IBM Z) application modernization is a topic that every mainframe client is addressing to various degrees today, driven largely from digital transformation. With generative AI comes the opportunity to reimagine the mainframe application modernization experience. Infusing generative AI will enable speed and trust, help de-risk, and lower total costs associated with heavy-lifting application modernization initiatives. This document provides an overview of the IBM watsonx Code Assistant for Z which uses the power of generative AI to make it easier for developers to selectively modernize COBOL business services while maintaining mainframe qualities of service.
Orca: Nocode Graphical Editor for Container OrchestrationPedro J. Molina
Tool demo on CEDI/SISTEDES/JISBD2024 at A Coruña, Spain. 2024.06.18
"Orca: Nocode Graphical Editor for Container Orchestration"
by Pedro J. Molina PhD. from Metadev
What is Continuous Testing in DevOps - A Definitive Guide.pdfkalichargn70th171
Once an overlooked aspect, continuous testing has become indispensable for enterprises striving to accelerate application delivery and reduce business impacts. According to a Statista report, 31.3% of global enterprises have embraced continuous integration and deployment within their DevOps, signaling a pervasive trend toward hastening release cycles.
5. 3
DESIGN PATTERNS
In software engineering, a design pattern is a general
repeatable solution to a commonly occurring problem in
software design. A design pattern isn't a nished design that
can be transformed directly into code. It is a description or
template for how to solve a problem that can be used in many
different situations.
14. 7 . 2
7 . 3
Java Optimisation : Bill Pugh Singleton Implementation
public class BillPughSingleton {
private BillPughSingleton(){}
private static class SingletonHelper{
private static final BillPughSingleton INSTANCE
= new BillPughSingleton();
}
public static BillPughSingleton getInstance(){
return SingletonHelper.INSTANCE;
}
}
15. 8 . 1
De ne an interface for creating an object but let subclasses
decide which class to instanciate. The factory method lets a
class defer instantiation to subclasses.
FACTORY METHOD
16. 8 . 2
Basically, we create object without exposing the creation
logic and refer to newly created object using a common
interface.
20. 8 . 5
9 . 1
Provides an interface for creating families of related or
dependent objects without specifying their concrete classes.
This pattern provides an encapsulation mechanism to a group
of individual factories with a common theme.
ABSTRACT FACTORY
29. 10 . 4
11 . 1
Separate the construction of a complex object from its
representation so that the same construction processes can
create different representations.
BUILDER
38. 13 . 4
14 . 1
Use sharing to support large numbers of ne grained objects
ef ciently.
FLYWEIGHT
Flyweight pattern tries to reuse already existing similar kind
objects by storing them and creates new object only when no
matching object is found.
39. 14 . 2
Example : In a Role Playing Game, we have salesman Non
Playable Characters :
They don't move.
Event reactions are the same.
Only speci c caracteristics (prices, items, skin) vary.
48. 15 . 5
16 . 1
Provide a uni ed interface to a set of interfaces in a system.
Facade de nes a higher-level interface that makes the
subsystem easier to use. this pattern adds an interface to
existing system to hide its complexities.
FACADE
49. 16 . 2
Facade pattern hides the complexities of the system by
involving a single class which provides simpli ed methods
required by client and delegates calls to methods of existing
system classes.
This is the basic principle of an API...
50. 17 . 1
Attach additionnal responsibilities to an object dynamically.
Decorators provide a exible alternative to subclassing for
extending functionality.
DECORATOR
Instead of modifying the existing functionalities, we will
extend them.
One class takes in another class, both of which extend the
same abstract class, and adds functionality.
55. 17 . 5
17 . 6
Real example :
//First open an inputstream of it:
FileInputStream fis = new FileInputStream("/objects.gz");
//We want speed, so let's buffer it in memory:
BufferedInputStream bis = new BufferedInputStream(fis);
//The file is gzipped, so we need to ungzip it:
GzipInputStream gis = new GzipInputStream(bis);
//we can now use the read method.
56. 18 . 1
Convert the interface of a class into another interface that
client expect. The adapter pattern lets classes work together
that couldn't otherwise because of incompatible interfaces.
ADAPTER
65. 19 . 3
20
GANG OF FOUR
Behavioral patterns :
Observer, Template Method, Command, Iterator, State,
Mediator, Strategy, Chain of Responsibility, Visitor,
Interpreter, Memento.
66. 21 . 1
De ne a one-to-many dependency between objects so that
when one object changes state, all its dependents are noti ed
and updated automatically.
OBSERVER
67. 21 . 2
Example : A Graphical User Interface with 3 visualizations of
a number : binary, octal and hexadecimal. Whenever the
number changes, these visualizations must change too.
73. 22 . 1
De ne the skeleton of an algorithm in an operation, deferring
some steps to subclasses. The template method lets
subclasses rede ne certains steps of an algorithm without
changing the algorithm's structure.
TEMPLATE METHOD
74. 22 . 2
Well ... It's the purpose of an abstract class.
75. 23 . 1
Encapsulate a request as an object, thereby letting you
parameterize clients with different requests, queue or log
request, and support undoable operations.
COMMAND
81. 23 . 6
24 . 1
Provide a way to access the elements of an aggregate object
sequentially without exposing its underlying representation.
ITERATOR
82. The concerned classes (ex : People) return a subclasse
(PeopleIterator) which implements the Iterator interface.
interface Iterator<T> {
boolean hasNext();
T next();
//first();
// ...
}
83. 24 . 2
24 . 3
Remember : In Java,
List are Iterable<T>.
Set are Iterable<T>.
Arrays are not even if the foreach is well de ned.
ArrayList<String> list = new ArrayList<>();
String[] array = new String[1];
list.add("test1")
array[0] = "test1"
list.add("test2")
array[1] = "test2"
for(String s : list){
System.out.println(s);
}
for(String s : array){
System.out.println(s);
}
84. 25 . 1
Allow an object to alter its behaviour when its internal state
changes. The object will appear to change its class.
STATE
Example : Screen in a video game
87. 25 . 3
26 . 1
De ne an object that encapsulates how a set of objects
interacts. The mediator pattern promotes loose coupling by
keeping objects from referring to each other explicitly, and it
lets you vary their interaction independently.
MEDIATOR
90. 26 . 3
27 . 1
De ne a family of algorithms, encapsulate each one, and
make them interchangeable. The strategy pattern lets the
algorithm vary independently from client to client.
STRATEGY
93. 27 . 3
28 . 1
Avoid coupling the sender of a request to its receiver by
giving more than one object a chance to handle the request.
Chain the receiving objects and pass the request along the
chain until an object handles it.
CHAIN OF RESPONSIBILITY
97. 28 . 4
29 . 1
Represent an operation to be performed on the elements of
an object structure. The visitor pattern lets you de ne a new
operation without changing the classes of the elements on
which it operates.
VISITOR
101. 29 . 4
30 . 1
Given a language, de ne a representation for its grammar
along with an interpreter that uses the representation to
interpret sentences in the language.
INTERPRETER
Example : SQL parsing
105. 30 . 4
31 . 1
Without violating encapsulation, capture and externalize an
object's internal state so that the object can be restored to
this state later.
MEMENTO