Leadership advisor. Executive coach. Tech founder on a mission to add life to people's years.

With over 20 years leading teams in global investment banking and hedge funds, I bring deep expertise in banking, derivatives, trading, risk management, and institutional finance.

Today, I lead the IZUN GROUP with a mission to democratize access to next-generation financial products - leveraging blockchain, DeFi, and digital asset infrastructure to build inclusive, user-centric solutions for both retail and institutional markets.

I thrive at the convergence of traditional finance (TradFi) and Web3, and bring a strategic focus on:

Digital Asset Strategy & Tokenized Finance

Crypto Derivatives Trading & Market Structure

Fintech Product Innovation & Go-to-Market

Institutional-Grade Risk & Governance

I’m passionate about building the financial systems of tomorrow - secure, transparent, and universally accessible.

Gilles Dahan

Hi there! 👋,
I'm Sina Riahi and 29 years old
I am a full-stack developer with 5 years of experience in website design and application development in the field of business (CRM, ASS, WMS, HMS, GMS).
I spend my free time posting on LinkedIn (for clean code and the best code and new features, etc.) and sample projects on GitHub.
I like details and team spirit.
Creation is the ultimate beauty.
I am a creator who creates every moment with infinite joy.
I will learn and with patience and determination in the challenges that I will face, I will turn them into experiences

Sina Riyahi

API Roadmap

1. API Roadmap
APIs are among the first things software engineers should learn, well before more complex topics such as distributed systems, cloud computing, and others. They are the basis of all development today, and they will help you understand how software systems work and how to use proper programming languages and frameworks.

Whether you're a beginner or an experienced developer looking to learn about APIs, this API learning roadmap will help you understand the key concepts and technologies.

Introduction to APIs
Before diving deep into API development, it's crucial to understand the fundamentals. This section covers the basic building blocks that form the foundation of API knowledge, from core protocols to different APIs used in modern software development.

Basics: Learn about HTTP protocol, DNS, URLs, and more.
API Definition: An API is a set of protocols, routines, and tools for building software applications. It specifies how software components should interact.

API Types:
🌐 Public APIs: Open for use by external developers (e.g., Twitter API)
🔒 Private APIs: Used internally within an organization
🔑 Partner APIs: Shared with specific business partners
🧩 Composite APIs: Combine multiple data or service APIs

2. API Architectures
Understanding different API architectural styles is essential, as each serves specific use cases and has advantages and trade-offs. These architectures form the core of your API's structure and client interaction.

REST (Representational State Transfer): A widely used architectural style for web APIs
GraphQL: A query language for APIs that allows clients to request specific data
SOAP (Simple Object Access Protocol): A protocol for exchanging structured data
gRPC: A high-performance, open-source framework developed by Google
WebSockets: Enables full-duplex, real-time communication between client and server
Webhook: Allows real-time notifications and event-driven architecture

3. API Security
Security is one of the most critical parts of API development. This section covers essential security concepts and best practices that protect your API from unauthorized access and potential threats while ensuring secure data transmission.

Authentication: Basic, OAuth 2.0, JSON Web Tokens (JWT)
Authorization: Controlling access rights to resources
Rate Limiting: Preventing abuse by limiting the number of requests
Encryption: Protecting data in transit using HTTPS
Best practices: OWASP Top 10 Security Risks


Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

TOP 6 Architectural Pattern

1. Monolithic Architecture
💡Description:
A monolithic architecture is a traditional software design where all components of an application—user interface, business logic, data access, etc.—are tightly integrated into a single, unified unit.

🛎Where It Is Used:

Small to medium-sized applications
When rapid development is needed without complex deployment processes
Legacy systems that haven't been modularized

2. Controller-Worker Architecture
💡Description:
This pattern separates the system into a controller that manages incoming requests and delegates tasks to worker components that perform specific operations asynchronously or synchronously.

🛎Where It Is Used:

Systems requiring task delegation and background processing
Web servers handling user requests with background jobs (e.g., job queues)
Distributed systems with decoupled processing

3. Microservices Architecture
💡Description:
An approach where an application is broken down into small, independent services, each responsible for a specific business capability, communicating over network protocols like HTTP or messaging queues.

🛎Where It Is Used:

Large, complex applications requiring scalability and agility
Organizations adopting DevOps practices and continuous deployment
Cloud-native applications

4. Model-View-Controller (MVC)
💡Description:
A pattern that separates an application into three interconnected components:

Model: Manages data and business logic
View: Handles the presentation layer (UI)
Controller: Processes user input, interacts with Model, and selects the View

🛎Where It Is Used:

Web applications (e.g., frameworks like Django, Ruby on Rails, ASP.NET MVC)
Desktop applications with UI components

5. Event-Driven Architecture
💡Description:
An architecture where components communicate asynchronously through events. When an event occurs, it is published to an event bus or message broker, and interested components subscribe to relevant events.

🛎Where It Is Used:

Real-time applications (e.g., chat, stock trading)
Microservices communicating asynchronously
IoT systems and systems requiring loose coupling and responsiveness

6. Layered Architecture
💡Description:
An architecture where the system is divided into layers with specific responsibilities, such as presentation, business logic, data access, etc. Each layer communicates only with adjacent layers.

🛎Where It Is Used:

Enterprise applications with clear separation of concerns
Systems requiring maintainability and modularity


Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭 and repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

RESTful API Design 

Designing a RESTful API involves several key principles and best practices to ensure that the API is efficient, scalable, and easy to use. 

1.Resource Identification

-Use nouns to represent resources: Resources should be identified using nouns, not verbs. For example:
  - `/users` for a collection of users
  - `/users/{id}` for a specific user
  - `/products` for a collection of products

2.HTTP Methods

Use standard HTTP methods to perform operations on resources:

-GET: Retrieve data from the server (safe and idempotent).
-POST: Create a new resource (not idempotent).
-PUT: Update an existing resource (idempotent).
-PATCH: Partially update an existing resource (idempotent).
-DELETE: Remove a resource (idempotent).

3.Status Codes

Use appropriate HTTP status codes to indicate the outcome of an API request:

-200 OK: Successful GET, PUT, or PATCH request.
-201 Created: Successful POST request that creates a resource.
-204 No Content: Successful DELETE request.
-400 Bad Request: The request could not be understood or was missing required parameters.
-401 Unauthorized: Authentication failed or user does not have permissions.
-403 Forbidden: Server understood the request but refuses to authorize it.
-404 Not Found: Resource not found.
-500 Internal Server Error: An error occurred on the server.

4.Versioning

Version your API to ensure backward compatibility. Common practices include:

-URI Versioning: e.g., `/v1/users`
-Query Parameter Versioning: e.g., `/users?version=1`
-Header Versioning: Use custom headers to specify the API version.

5.Filtering, Sorting, and Pagination

Allow clients to filter, sort, and paginate results:

-Filtering: Use query parameters to filter resources, e.g., `/users?age=30&status=active`.
- Sorting: Allow sorting using query parameters, e.g., `/products?sort=price`.
-Pagination: Use query parameters for pagination, e.g., `/users?page=2&limit=10`.

6.Hypermedia as the Engine of Application State (HATEOAS)

Provide links to related resources in the response to guide users on possible actions. For example:

```json
{
  "id": 1,
  "name": "John Doe",
  "links": [
    { "rel": "self", "href": "/users/1" },
    { "rel": "friends", "href": "/users/1/friends" }
  ]
}
```
7.Security

Implement security measures such as:

-Authentication: Use token-based authentication (e.g., JWT) or OAuth 2.0.
-Authorization: Ensure users have permission to access or modify resources.
-HTTPS: Use HTTPS to encrypt data in transit.

8.Documentation

Provide clear and comprehensive API documentation. Tools like Swagger/OpenAPI can help automate documentation generation based on your API design.

9.Error Handling

Provide meaningful error messages and error codes in the response body:

```json
{
  "error": {
    "code": "USER_NOT_FOUND",
    "message": "The user with the specified ID does not exist."
  }
}
```

Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭 and repost♻️, thank you🌹🙏

Microservice Best Practices

💡Microservices architecture offers a way to build scalable and maintainable applications by breaking down monolithic applications into smaller, independent services.


🪛Single Responsibility Principle (SRP):

✅️Each microservice should have one responsibility or focus area. This leads to simpler services that are easier to understand, test, and maintain. For example, a service handling user authentication should not also be responsible for managing user preferences.

🔧Stateless:

✅️Microservices should be stateless, meaning they do not maintain any client context between requests. This allows services to be easily replicated and scaled. Any necessary state should be managed externally, such as in a database or cache.

📈Micro Frontends:

✅️Similar to microservices, micro frontends involve breaking down the frontend application into smaller, independent pieces. Each team can develop, deploy, and scale their own frontend components, promoting a more modular and flexible architecture.

🛎Separate Data Store:

✅️Each microservice should have its own database or data store. This encapsulation helps to ensure that services are independent and reduces the risk of tight coupling. It also allows teams to choose the best data storage solution for their specific service needs.

🧲Asynchronous Communication:

✅️Use asynchronous communication methods where possible (e.g., message queues, event streams) to decouple services. This can improve resilience and scalability, as services do not need to wait for responses and can continue processing other tasks.

📎Containerization:

✅️Use containers (e.g., Docker) to package microservices, ensuring that they can run consistently across different environments. Container orchestration tools like Kubernetes can help manage the lifecycle of these containers, including scaling and failover.

🔎Orchestration:

✅️Utilize orchestration tools to manage microservice deployment and scaling. Kubernetes is a popular choice for orchestrating containerized applications, providing features like service discovery, load balancing, and automated deployment.

📎Separation of Build and Deploy:

✅️Establish a clear distinction between build and deploy processes. Continuous integration and continuous deployment (CI/CD) pipelines can help automate the build process while allowing different teams to deploy their services independently.

🛡Domain-Driven Design (DDD):

✅️Apply DDD principles to define the boundaries of microservices based on business domains. Identify bounded contexts and ensure that each microservice corresponds to a specific domain model, leading to more cohesive and focused services.

Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭 and repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

What does API Gateway do ?

💡 An API Gateway is a crucial component in modern microservices architectures. It acts as a single entry point for clients to interact with various backend services and provides a wide range of functionalities to manage, secure, and optimize API requests.

✅HTTP Request Handling: The API Gateway receives incoming HTTP requests from clients and routes them to the appropriate backend services.

✅Parameter Validation: It can validate incoming request parameters (e.g., query parameters, headers, body) to ensure they meet specific criteria before passing them to the backend services.

✅Allow List/Deny List: The API Gateway can enforce security policies by allowing or blocking traffic from specific IP addresses or ranges, helping to protect against malicious requests.

✅Authentication/Authorization: It manages user authentication (verifying identity) and authorization (checking permissions) to ensure that only authenticated and authorized users can access specific resources.

✅Rate Limiting: The API Gateway can enforce rate limits on how many requests a client can make in a specified timeframe, protecting backend services from being overwhelmed by too many requests.

✅Dynamic Routing: It can route requests to different backend services based on various criteria, such as request paths, headers, or other parameters.

✅Service Discovery: The API Gateway can integrate with service discovery mechanisms to dynamically find and communicate with backend services without hardcoding their addresses.

✅Protocol Conversion: It can facilitate communication between clients and services that use different protocols (e.g., converting HTTP requests to gRPC), enabling interoperability.

✅Error Handling: The API Gateway can manage errors by providing standardized error responses and handling retries or fallbacks when backend services fail.

✅Logging and Monitoring: It can log API requests and responses, providing insights into usage patterns, performance metrics, and potential issues. This data can be used for monitoring and debugging.

✅Circuit Breaker: The API Gateway can implement circuit breaker patterns to prevent requests from being sent to a failing service, thereby improving system resilience and providing fallback mechanisms.

✅Caching: It can cache responses to common requests, reducing latency and load on backend services by serving cached data for repeat requests.

🔎 an API Gateway plays a vital role in managing and orchestrating microservices, providing security, performance, and reliability features that enhance the overall architecture of an application.

Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭 and repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

TOP 5 Message Brokers

1. RabbitMQ
🔎Overview: RabbitMQ is an open-source message broker that uses the Advanced Message Queuing Protocol (AMQP). It supports multiple messaging protocols and is known for its flexibility and ease of use.

🔦Key Features:
✅Reliable messaging with acknowledgments and persistent messages.
✅Flexible routing capabilities through exchanges.
✅Support for multiple programming languages.
✅Strong community support and extensive documentation.

2. Apache Kafka
🔎Overview: Kafka is a distributed streaming platform designed for high-throughput, fault-tolerant messaging. It's primarily used for building real-time data pipelines and streaming applications.

🔦Key Features:
✅High throughput and scalability, capable of handling large volumes of data.
✅Durability through distributed log storage.
✅Supports stream processing through Kafka Streams.
✅Integration with various big data tools and systems.

3. ActiveMQ
🔎Overview: ActiveMQ is an open-source message broker that supports the Java Message Service (JMS) and multiple protocols. It's widely used in enterprise environments.

🔦Key Features:
✅Supports various messaging protocols (AMQP, MQTT, STOMP, etc.).
✅Provides both point-to-point and publish-subscribe messaging models.
✅Offers features like message persistence, transactions, and security.
✅Good integration with Java applications and enterprise frameworks.

4. IBM MQ
🔎Overview: IBM MQ (formerly known as WebSphere MQ) is a robust message broker designed for enterprise-level applications, offering high reliability and security.

🔦Key Features:
✅Strong support for transactional messaging.
✅Built-in security features, including encryption and authentication.
✅Integration with IBM's ecosystem and other enterprise systems.
✅Support for a variety of programming languages and platforms.

5. NATS
🔎Overview: NATS is a lightweight, high-performance messaging system designed for cloud-native applications and microservices architectures.

🔦Key Features:
✅Simple design with a focus on performance and low latency.
✅Supports publish-subscribe and request-reply messaging patterns.
✅Easy to deploy and manage with minimal configuration.
✅Built-in clustering and support for distributed systems.

💡 Each of these message brokers has its own strengths and ideal use cases. RabbitMQ is great for flexibility and ease of integration, Kafka excels in high-throughput and streaming scenarios, ActiveMQ is robust for enterprise applications, IBM MQ is suitable for mission-critical applications, and NATS is excellent for lightweight, cloud-native architectures. The choice of a message broker often depends on specific requirements such as performance, scalability, and the messaging patterns needed for your application.

Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭 and repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

6 Popular API architecture styles

1. gRPC
💡Description: gRPC (gRPC Remote Procedure Calls) is a high-performance, open-source framework developed by Google that uses HTTP/2 for transport, Protocol Buffers as the interface description language, and provides features such as authentication, load balancing, and more.

🔎Use Cases: Ideal for microservices, inter-service communication, and scenarios requiring high throughput and low latency.

2. SOAP (Simple Object Access Protocol)
💡Description: SOAP is a protocol for exchanging structured information in the implementation of web services. It relies on XML and typically uses HTTP or SMTP for message transmission.

🔎Use Cases: Often used in enterprise-level applications requiring high security, ACID-compliant transactions, and formal contracts (WSDL).

3. GraphQL
💡Description: GraphQL is a query language for APIs and a runtime for executing those queries by using a type system. It allows clients to request exactly the data they need and nothing more.

🔎Use Cases: Best for applications with complex data requirements and for scenarios where clients need flexibility in data retrieval.

4. Webhooks
💡Description: Webhooks are user-defined HTTP callbacks that are triggered by specific events. When an event occurs, the source application makes an HTTP request to the URL configured for the webhook.

🔎Use Cases: Commonly used for real-time notifications and integrations between different services, such as payment processing, version control systems, and CI/CD pipelines.

5. REST (Representational State Transfer)
💡Description: REST is an architectural style that uses standard HTTP methods (GET, POST, PUT, DELETE) to interact with resources represented as URLs. It is stateless and has a uniform interface.

🔎Use Cases: Widely used for web services and APIs due to its simplicity, scalability, and ease of integration with web technologies.

6. WebSocket
💡Description: WebSocket is a communication protocol that provides full-duplex communication channels over a single TCP connection. It allows for real-time data transfer between a client and server.

🔎Use Cases: Ideal for applications requiring real-time updates, such as chat applications, live notifications, online gaming, and collaborative tools.

🕯These API architecture styles cater to different needs and use cases, allowing developers to choose the most suitable approach for their applications.

Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭 and repost♻️, thank you🌹🙏 

#Csharp #EFCore #dotnet #dotnetCore

8 Must-Used Distributed System Patterns

💡Distributed systems are complex architectures that require careful design to ensure reliability, scalability, and maintainability.

💡Event Sourcing: This pattern involves storing the state of a system as a sequence of events. Instead of persisting the current state, every change is recorded as an event, which can be replayed to reconstruct the state at any point in time. This provides a robust audit trail and allows for flexibility in how the system evolves.

💡Sharding: Sharding is the practice of partitioning data across multiple databases or servers to distribute load and improve performance. Each shard holds a subset of the data, allowing for parallel processing and reducing the strain on any single database or server.

💡Leader Election: This pattern is used to designate a single node as the coordinator among a group of nodes in a distributed system. The leader is responsible for managing tasks, making decisions, and coordinating activities. Leader election algorithms ensure that a leader is chosen and that a new leader can be elected if the current one fails.

💡Pub/Sub (Publish/Subscribe): In this messaging pattern, publishers send messages to a topic without knowledge of subscribers, and subscribers receive messages from topics they are interested in without knowledge of publishers. This decouples the components of the system, allowing for more scalable and flexible architectures.

💡Sidecar Pattern: The sidecar pattern involves deploying a secondary application alongside a primary application to extend its capabilities. This pattern is often used in microservices architectures, where the sidecar can handle cross-cutting concerns such as logging, monitoring, and service discovery without modifying the primary application.

💡Ambassador Pattern: Similar to the sidecar pattern, the ambassador pattern involves deploying a proxy service alongside a primary service. However, the ambassador is primarily used to manage communication between a service and external systems, such as APIs or databases, handling tasks like authentication, routing, or data transformations.

💡CQRS (Command Query Responsibility Segregation): This pattern separates the read and write operations of a system. Commands modify the state, while queries retrieve data. This allows for optimization of each side independently and can improve performance and scalability, especially in systems with complex business logic.


Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭 and repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

6 COMMON WAYS TO BUILD APIS

REST
What it is: Representational State Transfer; a stateless, resource-based approach using HTTP.

How it works: A client (often a browser or app) sends HTTP requests to a server; the server responds with resources (usually in JSON or XML).
Key details from the diagram:
Client and server exchange HTTP requests and responses.
Emphasizes the request/response model over a single connection.

GraphQL
What it is: A query language and runtime for APIs that lets clients request exactly the data they need.

How it works: A client talks to a GraphQL server (often via HTTP) which queries a graph of services/databases; the server resolves the query and returns precisely the requested data.
Key details from the diagram:
Client communicates with a central GraphQL node (often a gateway) that routes to multiple backend services or databases (depicted as a cluster of servers).
Flexible data retrieval in a single request, reducing over-fetching.

WebSocket
What it is: A protocol that provides a persistent, full-duplex connection between client and server, enabling real-time communication.

How it works: After an initial handshake over HTTP, a single long-lived connection is kept open, allowing both sides to send messages anytime.
Key details from the diagram:
Shows a client establishing a web socket connection to the server, then messages flow back and forth over the same connection.

gRPC
What it is: A high-performance RPC framework built on HTTP/2 with protocol buffers (protobuf) for serialization.

How it works: A gRPC client makes a “proto req” to a gRPC server, which returns a “proto res.” Multiple servers can be involved behind a gRPC server.
Key details from the diagram:
Client -> Proto Req -> Proto Res -> GRPC Server, with a chain-like flow to multiple nodes.

MQTT
What it is: A lightweight publish/subscribe messaging protocol designed for low-bandwidth, high-latency or unreliable networks (IoT).

How it works: There are publishers (devices producing messages), subscribers (clients consuming messages), and a broker that routes messages based on topics.
Key details from the diagram:
Shows a broker in the center connected to publishers and subscribers via topics.

Serverless
What it is: An execution model where you run code in response to events without managing servers (functions-as-a-service).

How it works: A client makes a request (REQ); the system triggers a serverless function (API) that processes the request and returns a response (RES).
Key details from the diagram:
Shows a client calling an API, which is implemented via serverless functions. The API is managed by a platform (no server you manage directly).

Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭 and repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

Top 8 API Protocols

SOAP (Simple Object Access Protocol):
💡 Description: A protocol for exchanging structured information in web services. It relies on XML for message format and usually operates over HTTP or SMTP.
🔦 Use Cases: Enterprise-level applications requiring high security and transactions, such as banking and payment processing.

REST (Representational State Transfer):
💡Description: An architectural style that uses HTTP requests to perform CRUD (Create, Read, Update, Delete) operations on resources. It is stateless and can return data in various formats, including JSON and XML.
🔦Use Cases: Web services and APIs for web applications, mobile apps, and microservices.

GraphQL:
💡Description: A query language for APIs and a runtime for executing those queries. Clients can request exactly the data they need, reducing over-fetching and under-fetching of data.
🔦Use Cases: Applications requiring flexible data fetching, such as social media platforms and complex applications with diverse data needs.

gRPC (gRPC Remote Procedure Calls):
💡Description: A high-performance framework that uses HTTP/2 for transport and Protocol Buffers as the interface description language. It supports bi-directional streaming and is suitable for microservices communication.
🔦Use Cases: Real-time applications, inter-service communication in microservices architectures, and systems requiring efficient communication.

WebSocket:
💡Description: A protocol that enables full-duplex communication channels over a single TCP connection. It is designed for real-time applications.
🔦Use Cases: Chat applications, live notifications, online gaming, and real-time collaboration tools.

Webhook:
💡Description: A lightweight way for apps to communicate with each other in real-time. A webhook sends data from one application to another when a specific event occurs.
🔦Use Cases: Triggering actions in applications based on events, such as payment processing, form submissions, and deployment notifications.

MQTT (Message Queuing Telemetry Transport):
💡Description: A lightweight messaging protocol optimized for low-bandwidth, high-latency, or unreliable networks. It uses a publish-subscribe model.
🔦Use Cases: IoT applications, remote sensor monitoring, and telemetry systems.

AMQP (Advanced Message Queuing Protocol):
💡Description: An open standard for message-oriented middleware that enables reliable messaging between clients and servers. It supports queuing, routing, and delivery guarantees.
🔦Use Cases: Enterprise messaging systems, financial transactions, and applications requiring reliable message delivery.

🔎 Each of these protocols has its strengths and is suited for different use cases, depending on requirements like performance, data format, and the complexity of interactions.


Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭 and repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

Top 4 Microservices Patterns

💡Microservices patterns are reusable solutions to common problems encountered when developing and deploying microservices-based applications.

🔎By following these patterns, you can improve the design, development, and operation of your microservices.

1. API Composition Pattern
🔦Overview: This pattern involves aggregating data from multiple microservices through a single API endpoint. It acts as a facade to provide a simplified interface for clients.

🛎Use Case: Useful when a client needs to aggregate information from different services in one request. Instead of the client calling multiple services, it can make a single API call to the composition service.

✅️Key Benefits:
Reduces the number of calls the client has to make.
Centralizes the logic for integrating multiple services.

2. Database Per Service Pattern
🔦Overview: Each microservice has its own dedicated database. This means that services do not share databases and manage their own data storage independently.

🛎Use Case: Suitable for systems needing high availability and flexibility, allowing each service to scale and evolve independently.

✅️Key Benefits:
Service autonomy is maintained; each service can choose the database technology best suited for its needs.
Changes in one service’s database schema won't impact others.

3. Event Sourcing and CQRS (Command 🔦Query Responsibility Segregation)
Overview: This pattern involves separating the reading of data (queries) and the writing of data (commands) into different models. Event sourcing tracks changes as a sequence of events rather than just storing the current state.

🛎Use Case: Ideal for applications that require complex business logic or need to maintain a detailed history of changes.

✅️Key Benefits:
Improves performance by allowing optimized models for reading and writing.
Facilitates historical audits and the investigation of past states.

4. Shared Database Pattern
🔦Overview: Unlike the database per service approach, this pattern allows multiple services to share a single database. The services read from and write to the same database, often resulting in tight coupling.

🛎Use Case: Common in smaller applications or systems where the complexity of managing separate databases outweighs the benefits.

✅️Key Benefits:
Simplicity in database management as there's only one database schema.
Easier to implement initially, reducing the overhead of managing multiple databases.

Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

Architectural Styles

💡Architectural styles are essential frameworks that guide the structure and organization of software systems.

1. Monolithic Architecture
🔎Definition: A monolithic architecture is a traditional model where all components of an application are integrated into a single, unified codebase. This includes the user interface, business logic, and data access layers.

🔦Characteristics:
✅Single Codebase: All functionalities are encapsulated in one codebase, making deployment simple.
✅Tight Coupling: Components are highly interdependent, making it difficult to isolate and update parts of the application.
✅Scalability Challenges: Scaling often requires duplicating the entire application rather than specific components.
✅Development Speed: Initially, it can be faster to develop as everything is in one place, but this can lead to challenges as the application grows.

🕯️Use Cases: Small to medium-sized applications, startups, or projects with straightforward requirements.

2. N-Tier Architecture
🔎Definition: N-Tier architecture, also known as multi-tier architecture, organizes an application into layers, each with specific responsibilities. Typically, this includes presentation, business logic, and data access layers.

🔦Characteristics:
✅Separation of Concerns: Each layer is responsible for a specific aspect of the application, promoting modularity and easier maintenance.
✅Scalability: Different layers can be scaled independently depending on demand.
✅Interoperability: Layers can communicate through well-defined interfaces, which can facilitate integration with other systems or services.
✅Flexibility: Changes to one layer can be made with minimal impact on others, allowing for easier updates and enhancements.

🕯️Use Cases: Web applications, enterprise applications, and systems that require clear separation of responsibilities.

3. Microservices Architecture
🔎Definition: Microservices architecture is a modern approach that structures an application as a collection of small, loosely coupled services. Each service is independently deployable and focuses on a specific business capability.

🔦Characteristics:
✅Decentralized Data Management: Each microservice can manage its own database, allowing for flexibility in technology choices.
✅Independent Deployment: Services can be deployed and scaled independently, enabling faster delivery and updates.
✅Resilience: The failure of one service does not necessarily impact the entire system, enhancing overall reliability.
✅Polyglot Programming: Different services can be built using different programming languages or technologies best suited for their tasks.

🕯️Use Cases: Large applications, cloud-native systems, and organizations that prioritize continuous delivery and scalability.

Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

Batch vs Stream vs Message Queue

💡Batch processing, stream processing, and message queues are three different paradigms for handling data and processing workloads in computing. Each has its own use cases, advantages, and challenges.

Batch Processing
💡Definition: Batch processing involves collecting a large volume of data over a period of time and processing it all at once. It is typically done on a scheduled basis, such as daily, hourly, or at specific intervals.

🧲Characteristics:
🔧Latency: High latency since data is aggregated and processed in bulk.
🔧Use Cases: Ideal for scenarios like data warehousing, periodic reporting, ETL (Extract, Transform, Load) processes, and analytics where immediate results are not required.
🔧Frameworks/Tools: Apache Hadoop, Apache Spark (in batch mode), and traditional RDBMS systems.

✅️Advantages:
✔️Efficient for processing large volumes of data.
✔️Easier to manage and optimize for performance since all data is available at once.

🛎Challenges:
✏️Not suitable for real-time processing or applications requiring immediate feedback.
✏️Potential for data staleness if processing intervals are too long.

Stream Processing
💡Definition: Stream processing involves handling data in real-time as it arrives. Data is processed continuously and immediately, allowing for real-time analytics and event-driven applications.

🧲Characteristics:
🔧Latency: Low latency, with data processed in real-time or near real-time.
🔧Use Cases: Suitable for real-time analytics, monitoring, fraud detection, and applications where immediate insights are critical (e.g., IoT data processing, social media feeds).
🔧Frameworks/Tools: Apache Kafka, Apache Flink, Apache Storm, and AWS Kinesis.

✅️Advantages:
✔️Enables real-time insights and immediate action.
✔️Can handle high-velocity data streams efficiently.

🛎Challenges:
✏️Complexity in managing state and ensuring reliability.
✏️Requires careful design to handle out-of-order data and failures.

Message Queues
💡Definition: A message queue is a communication method used in distributed systems where messages are sent from producers to consumers via a queue. This decouples the message sender (producer) and receiver (consumer), allowing for asynchronous processing.

🧲Characteristics:
🔧Latency: Varies; generally low, but depends on the system design and message processing speed.
🔧Use Cases: Commonly used for decoupling microservices, buffering requests, and handling load spikes. Useful for task queues and event-driven architectures.
🔧Frameworks/Tools: RabbitMQ, Apache Kafka (also used as a stream processor), Amazon SQS, and Azure Service Bus.

✅️Advantages:
✔️Decouples components, promoting scalability and flexibility.
✔️Provides reliability and durability of messages, allowing for asynchronous processing.

Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭 and repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

Kafka Architecture

💡Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant, and scalable data streaming. It was originally developed at LinkedIn and later open-sourced as an Apache project. Kafka is widely used for building real-time data pipelines and streaming applications.

🔎Components of Kafka Architecture

🛎Producers:
✅️Producers are applications that publish (write) messages to Kafka topics. They can send messages to one or more partitions of a topic.

✅️Producers are responsible for deciding which message goes to which partition in a topic, typically using a key-based partitioning strategy.

🛎Topics:
✅️A topic is a category or feed name to which records are published. Topics are partitioned for scalability.

✅️Each topic can have multiple partitions, which allows Kafka to handle a massive volume of messages and enables parallel processing.

🛎Partitions:
✅️Each topic is divided into partitions, which are ordered sequences of messages. Partitions allow Kafka to scale horizontally by distributing load across multiple brokers.

✅️Each message within a partition has a unique offset, which is an integer value that defines the position of the message.

🛎Brokers:
✅️A Kafka broker is a server that stores data and serves client requests. A Kafka cluster consists of multiple brokers that work together.

✅️Each broker can handle multiple partitions, and each partition can be replicated across multiple brokers for fault tolerance.

🛎Consumers:
✅️Consumers are applications that read (consume) messages from Kafka topics. They subscribe to one or more topics and process the messages.

✅️Consumers can be part of a consumer group, which allows for load balancing among multiple consumers. Each message is delivered to only one consumer in the group.

🛎Consumer Groups:
✅️A consumer group is a group of consumers that work together to consume messages from a set of topics. Each consumer in the group is assigned a unique set of partitions to read from.

✅️This enables horizontal scaling of message consumption.

🛎ZooKeeper:
✅️Apache ZooKeeper is used in older versions of Kafka to manage distributed brokers and maintain metadata about topics, partitions, and consumer offsets. However, newer versions of Kafka are moving towards a Kafka-native metadata management system (KRaft mode), reducing the reliance on ZooKeeper.

🛎Kafka Connect:
✅️Kafka Connect is a tool for integrating Kafka with other systems. It provides a way to ingest data from various sources into Kafka and export data from Kafka to external systems.

✅️It supports various connectors for databases, key-value stores, search indexes, and file systems.

🛎Kafka Streams:
✅️Kafka Streams is a library for building real-time applications and microservices that process data stored in Kafka. It allows for transformations, aggregations, and joins of streaming data.

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

Cookies and sessions

💡Cookies and sessions are both mechanisms used to store data in web applications, but they have different purposes, characteristics, and use cases.

Cookies
🔎Definition: Cookies are small pieces of data stored on the client-side (user's browser) by the web server. They are sent to the server with each HTTP request.

🗂Storage: Cookies can store data for a long time, typically set with an expiration date. They can persist even after the browser is closed.

🔧Capacity: The size of cookies is limited (usually around 4KB).

📈Data Accessibility: Cookies are accessible through JavaScript (unless marked as HttpOnly) and can be read by both the client and server.

🔦Use Cases:

✅️User preferences (e.g., language, theme).
✅️Tracking users for analytics or advertising.
✅️Storing session identifiers to manage user sessions.
🛎Security: Cookies can be secured using attributes like Secure (only sent over HTTPS), HttpOnly (not accessible via JavaScript), and SameSite (controls cross-site request behavior).

Sessions
🔎Definition: Sessions are server-side storage mechanisms used to keep track of user data across multiple requests. A session ID is typically stored in a cookie or passed in the URL.

🗂Storage: Session data is stored on the server, and the session ID is sent to the client (often as a cookie).

🔧Capacity: Sessions can store significantly more data compared to cookies, as the limit is generally based on server capacity.

📈Data Accessibility: Session data is not accessible via JavaScript and is only available on the server-side.

🔎Use Cases:

✅️Authenticating users (e.g., maintaining a logged-in state).
✅️Storing temporary data during a user's visit to a website (e.g., shopping cart contents).
🛎Security: Sessions can be more secure than cookies in terms of sensitive data storage since the data is not stored on the client-side. However, session hijacking is a concern, so secure practices (like using HTTPS) are essential.

🧲Cookies are primarily for storing small amounts of data on the client-side and can persist across sessions.
🧲Sessions are for managing user data on the server-side for the duration of a session, with typically larger storage capacity and better security for sensitive information.

💡In many web applications, cookies and sessions are used together to create a seamless user experience while managing user authentication and state.

Want to know more? Follow me or connect🥂

Please don't forget to like❤️ and comment💭 and repost♻️

x.com/sina_riyahi
medium.com/@Sina-Riyahi
Instagram.com/Cna_Riyahi
github.com/sinariyahi

taplio

The Battle

Gilles Dahan

Sina Riyahi

Followers

Posts

Engagements

Likes

Performance ScoreSina Riyahi's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Gilles Dahan

Sina Riyahi

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Sina Riyahi's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreSina Riyahi's LinkedIn Performance