🛠️ What is GitHub Actions?

GitHub Actions is a feature of GitHub that lets you create automated workflows for your software projects—right inside your GitHub repository.

📌 What can you do with GitHub Actions?

You can build CI/CD pipelines (Continuous Integration / Continuous Deployment), such as:

• ✅ Automatically test code (e.g. with PHPUnit, Jest, Pytest)

• 🛠️ Build your app on every push or pull request

• 🚀 Automatically deploy (e.g. to a server, cloud platform, or DockerHub)

• 📦 Create releases (e.g. zip packages or version tags)

• 🔄 Run scheduled tasks (cronjobs)

🧱 How does it work?

GitHub Actions uses workflows, defined in a YAML file inside your repository:

• Typically stored as .github/workflows/ci.yml

• You define events (like push, pull_request) and jobs (like build, test)

• Each job consists of steps, which are shell commands or prebuilt actions

Example: Simple CI Workflow for Node.js

name: CI

on: [push]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: '20'
      - run: npm install
      - run: npm test

🧩 What are "Actions"?

An Action is a single reusable step in a workflow. You can use:

• Prebuilt actions (e.g. actions/checkout, setup-node, upload-artifact)

• Custom actions (e.g. shell scripts or Docker-based logic)

You can explore reusable actions in the GitHub Marketplace.

💡 Why use GitHub Actions?

• Saves time by automating repetitive tasks

• Improves code quality through automated testing

• Enables consistent, repeatable deployments

• Integrated directly in GitHub—no need for external CI tools like Jenkins or Travis CI

Docker Compose is a tool that lets you define and run multi-container Docker applications using a single configuration file. Instead of starting each container manually via the Docker CLI, you can describe all your services (like a web app, database, cache, etc.) in a docker-compose.yml file and run everything with a single command.

In short:

Docker Compose = Project config + Multiple containers + One command to run it all

Example: docker-compose.yml

version: '3.9'
services:
  web:
    build: .
    ports:
      - "5000:5000"
    volumes:
      - .:/code
  redis:
    image: "redis:alpine"

This file:

• Builds and runs a local web app container

• Starts a Redis container from the official image

• Automatically networks the two containers

Common Commands:

docker-compose up        # Start all services in the foreground
docker-compose up -d     # Start in detached (background) mode
docker-compose down      # Stop and remove containers, networks, etc.

Benefits of Docker Compose:

✅ Easy setup for multi-service applications
✅ Version-controlled config (great for Git)
✅ Reproducible development environments
✅ Simple startup/shutdown of entire stacks

Typical Use Cases:

• Local development with multiple services (e.g., web app + DB)

• Integration testing with full stack

• Simple deployment workflows (e.g., via CI/CD)

Contentful is a headless content management system (headless CMS). It allows businesses to manage content centrally and deliver it flexibly to various channels—such as websites, apps, or digital displays—via APIs.

What does “Headless” mean?

Traditional CMS platforms (like WordPress) handle both content management and content presentation (e.g., rendering on a website). A headless CMS separates the content backend from the presentation frontend—hence the term “headless,” as the “head” (the frontend) is removed.

Key features of Contentful:

• API-first: Content is accessed via REST or GraphQL APIs.

• Flexible content modeling: You can define your own content types (e.g., blog posts, products, testimonials) with customizable fields.

• Multi-language support: Well-suited for managing multilingual content.

• Cloud-based: No server maintenance needed.

• Integration-friendly: Works well with tools like React, Vue, Next.js, Shopify, SAP, etc.

Who is Contentful for?

• Companies with multiple delivery channels (websites, apps, smartwatches, etc.)

• Teams that want to develop frontend and backend separately

• Large brands with global content needs

• Developer teams seeking a scalable and flexible CMS

A Prepared Statement is a programming technique, especially used when working with databases, to make SQL queries more secure and efficient.

1. How does a Prepared Statement work?

It consists of two steps:

1. Prepare the SQL query with placeholders
   Example in SQL:

SELECT * FROM users WHERE username = ? AND password = ?

• (Some languages use :username or other types of placeholders.)

• Bind parameters and execute
  The real values are bound later, for example:

$stmt->bind_param("ss", $username, $password);
$stmt->execute();

2. Advantages

✅ Protection against SQL injection:
User input is treated separately and safely, not directly inserted into the SQL string.

✅ Faster with repeated use:
The SQL query is parsed once by the database server and can be executed multiple times efficiently (e.g., in loops).

3. Example in PHP using MySQLi

$conn = new mysqli("localhost", "user", "pass", "database");
$stmt = $conn->prepare("SELECT * FROM users WHERE email = ?");
$stmt->bind_param("s", $email); // "s" stands for string
$email = "example@example.com";
$stmt->execute();
$result = $stmt->get_result();

In short:

A Prepared Statement separates SQL logic from user input, making it a secure (SQL Injection) and recommended practice when dealing with databases.

💡 What is an Entity Manager?

An Entity Manager is a core component of ORM (Object-Relational Mapping) frameworks, especially in Java (JPA – Java Persistence API), but also in other languages like PHP (Doctrine ORM).

📦 Responsibilities of an Entity Manager:

1. Persisting:

   • Saves a new object (entity) to the database.

   • Example (Doctrine): $entityManager->persist($user);

2. Finding/Loading:

   • Retrieves an object by its ID or other criteria.

   • Example: $entityManager->find(User::class, 1);

3. Updating:

   • Tracks changes to objects and writes them to the database (usually via flush()).

4. Removing:

   • Deletes an object from the database.

   • Example: $entityManager->remove($user);

5. Managing Transactions:

   • Begins, commits, or rolls back transactions.

6. Handling Queries:

   • Executes custom queries, often using DQL (Doctrine Query Language) or JPQL.

🔁 Entity Lifecycle:

The Entity Manager tracks the state of entities:

• managed (being tracked),

• detached (no longer tracked),

• removed (marked for deletion),

• new (not yet persisted).

🛠 Example with Doctrine (PHP):

$user = new User();
$user->setName('Max Mustermann');

$entityManager->persist($user); // Mark for saving
$entityManager->flush();        // Write to DB

✅ Summary:

The Entity Manager is the central component for working with database objects — creating, reading, updating, deleting. It abstracts SQL and provides a clean, object-oriented way to interact with your data layer.

A Join Point is a concept from Aspect-Oriented Programming (AOP).

Definition:

A Join Point is a specific point during the execution of program code where additional behavior (called an aspect) can be inserted.

Typical examples of Join Points:

• Method calls

• Method executions

• Field access (read/write)

• Exception handling

Context:

In AOP, cross-cutting concerns (like logging, security, or transaction management) are separated from the main business logic. These concerns are applied at defined points in the program flow — the Join Points.

Related terms:

• Pointcut: A way to specify which Join Points should be affected (e.g., "all methods starting with save").

• Advice: The actual code that runs at a Join Point (e.g., "log this method call").

• Aspect: A combination of Pointcut(s) and Advice(s) — the full module that implements a cross-cutting concern.

Example (in Spring AOP):

@Before("execution(* com.example.service.*.*(..))")
public void logBeforeMethod(JoinPoint joinPoint) {
    System.out.println("Calling method: " + joinPoint.getSignature().getName());
}

→ This logs a message before every method call in a specific package. The joinPoint.getSignature() call provides details about the actual Join Point.

Aspect-Oriented Programming (AOP) is a programming paradigm focused on modularizing cross-cutting concerns—aspects of a program that affect multiple parts of the codebase and don't fit neatly into object-oriented or functional structures.

💡 Goal:

Typical cross-cutting concerns include logging, security checks, error handling, transaction management, or performance monitoring. These concerns often appear in many classes and methods. AOP allows you to write such logic once and have it automatically applied where needed.

🔧 Key Concepts:

• Aspect: A module that encapsulates a cross-cutting concern.

• Advice: The actual code to be executed (e.g., before, after, or around a method call).

• Join Point: A point in the program flow where an aspect can be applied (e.g., method execution).

• Pointcut: A rule that defines which join points are affected (e.g., "all methods in class X").

• Weaving: The process of combining aspects with the main program code—at compile-time, load-time, or runtime.

🛠 Example (Java with Spring AOP):

@Aspect
public class LoggingAspect {
    @Before("execution(* com.example.service.*.*(..))")
    public void logBeforeMethod(JoinPoint joinPoint) {
        System.out.println("Calling method: " + joinPoint.getSignature().getName());
    }
}

This code automatically logs a message before any method in the com.example.service package is executed.

✅ Benefits:

• Improved modularity

• Reduced code duplication

• Clear separation of business logic and system-level concerns

❌ Drawbacks:

• Can reduce readability (the flow isn't always obvious)

• Debugging can become more complex

• Often depends on specific frameworks (e.g., Spring, AspectJ)

Assertions are programming constructs used to check assumptions about the state of a program. An assertion tests whether a specific condition is true—if it isn't, an error is typically raised and the program stops.

x = 10
assert x > 0   # passes
assert x < 5   # raises AssertionError, since x is not less than 5

Purpose of Assertions:

• They help with debugging: you can verify that certain conditions in code hold true during development.

• They document implicit assumptions, e.g., “At this point, the list must have at least one item.”

• They are mainly used during development—assertions are often disabled in production code.

Key Difference from Regular Error Handling:

Assertions are meant to catch programmer errors, not user input or external failures. For example:

• assert age > 0 → inappropriate if age comes from user input.

• Instead, use: if age <= 0: raise ValueError("Age must be positive.")

Design by Contract (DbC) is a concept in software development introduced by Bertrand Meyer. It describes a method to ensure the correctness and reliability of software by defining clear "contracts" between different components (e.g., methods, classes).

Core Principles of Design by Contract

In DbC, every software component is treated as a contract party with certain obligations and guarantees:

1. Preconditions
   Conditions that must be true before a method or function can execute correctly.
   → Responsibility of the caller.

2. Postconditions
   Conditions that must be true after the execution of a method or function.
   → Responsibility of the method/function.

3. Invariant (Class Invariant)
   Conditions that must always remain true throughout the lifetime of an object.
   → Responsibility of both the method and the caller.

Goal of Design by Contract

• Clear specification of responsibilities.

• More robust and testable software.

• Errors are detected early (e.g., through contract violations).

Example in Pseudocode

class BankAccount {
    private double balance;

    // Invariant: balance >= 0

    void withdraw(double amount) {
        // Precondition: amount > 0 && amount <= balance
        if (amount <= 0 || amount > balance) throw new IllegalArgumentException();

        balance -= amount;

        // Postcondition: balance has been reduced by amount
    }
}

Benefits

• Clear contracts reduce misunderstandings.

• Easier debugging, as violations are detected immediately.

• Supports defensive programming.

Drawbacks

• Requires extra effort to define contracts.

• Not directly supported by all programming languages (e.g., Java and C++ via assertions, Python with decorators; Eiffel supports DbC natively).

Perl Compatible Regular Expressions (PCRE) are a type of regular expression syntax and engine that follows the powerful and flexible style of the Perl programming language. They offer advanced features that go beyond the basic regular expressions found in many older systems.

Why "Perl Compatible"?

Perl was one of the first languages to introduce highly expressive regular expressions. The PCRE library was created to bring those capabilities to other programming languages and tools, including:

• PHP

• Python (similar via the re module)

• JavaScript (with slight differences)

• pcregrep (a grep version supporting PCRE)

• Editors like VS Code, Sublime Text, etc.

Key Features of PCRE:

✅ Lookahead & Lookbehind:

• (?=...) – positive lookahead

• (?!...) – negative lookahead

• (?<=...) – positive lookbehind

• (?<!...) – negative lookbehind

✅ Non-greedy quantifiers:

• *?, +?, ??, {m,n}?

✅ Named capturing groups:

• (?P<name>...) or (?<name>...)

✅ Unicode support:

• \p{L} matches any kind of letter in any language

✅ Assertions and anchors:

• \b, \B, \A, \Z, \z

✅ Inline modifiers:

• (?i) for case-insensitive

• (?m) for multiline matching, etc.

(?<=\buser\s)\w+

This expression matches any word that follows "user " using a lookbehind assertion.

Summary:

PCRE are like the "advanced edition" of regular expressions — highly powerful, widely used, and very flexible. If you're working in an environment that supports PCRE, you can take advantage of rich pattern matching features inspired by Perl.