Katt Risen

Build The Keyword

Find online business ideas that will bring you customers on auto-pilot

Product of the day #1 on Product Hunt

Founder

nocode-exits.com (acquired)

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Product of the day #3 on Product Hunt

100DaysOfNoCode

Practicioner in Residence

Creator in Residence

No-Code Guru (Acquired)

A wise tool advisor for beginner No-Code makers

Maker

MY STORY

+ Before 2022
Reading business books and listening to startup podcasts. Always lurking, never doing.
Always full of ideas and dreams, but never the skills to act on it.

+ February 2022
Learned about No-Code and joined the 100 Days of No-Code Bootcamp

+ April 2022
Launched my first side project made with No-Code on Product Hunt

+ June 2022
Started doing No-Code freelance gigs
Gave my first No-Code workshop

+ August 2022
First side project got acquired

+ Augustus 2022
Started www.nocode-exits.com

+ 2023
Quit my job to go full time No-Code. Freelancing and building a portfolio of tiny products made with No-Code

+ 2024
Started www.buildthekeyword.com

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Product of the day #3 on Product Hunt

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

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.

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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.

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API Protocols

REST (Representational State Transfer)
What it is: An architectural style for designing networked applications that use stateless HTTP methods (GET, POST, PUT, DELETE, etc.).
How it works: Resources are identified by URLs; operations are performed using standard HTTP verbs; responses are typically JSON or XML.
When to use: Public APIs, services requiring broad compatibility and simplicity; scalable web services with cache-friendly GET requests.
Key takeaway: Strong in simplicity and ubiquity, but can lead to inconsistent designs if not standardized.

EDA (Event-Driven Architecture)
What it is: A design where components communicate primarily through events.
How it works: Producers emit events; consumers react to events through event brokers or message queues (asynchronous, decoupled).
When to use: Real-time updates, complex workflows, scalable microservices that react to changes (e.g., inventory, telemetry).
Key takeaway: Improves responsiveness and scalability but adds complexity in event choreography and eventual consistency.

EDI (Electronic Data Interchange)
What it is: A standardized format for exchanging business documents (orders, invoices, etc.) between organizations.
How it works: Using predefined message formats and protocols (like EDIFACT, ANSI X12) to automate B2B data exchange.
When to use: B2B integrations with trading partners, supply chain automation.
Key takeaway: Highly standardized but can be rigid and domain-specific.

ESI / SSE (Server-Sent Events)
Note: SSE is a simple standard for server-to-client push over HTTP.
What it is: A unidirectional streaming protocol from server to browser/client.
How it works: Client opens a long-held HTTP connection; server sends events as text streams.
When to use: Real-time dashboards, live feed updates where only the server-to-client direction is needed.
Key takeaway: Lightweight alternative to WebSockets for simple streaming needs.

WebSockets
What it is: A full-duplex protocol that provides a persistent connection between client and server.
How it works: After an initial HTTP handshake, a continuous, low-latency channel is established for bi-directional communication.
When to use: Real-time collaboration apps, chat, live gaming, trading platforms requiring real-time data streams.
Key takeaway: Low-latency, two-way communication; more complex to scale and secure than REST.


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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.

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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.

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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.


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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).

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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.

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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.

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