Eleke Great’s Post

🚀 API Architecture Styles - Quick Overview!🚀 🔧 SOAP: XML-based, enterprise-level, and strict, operating over HTTP/HTTPS, perfect for high-security enterprise solutions. 💡 REST: The backbone of scalable APIs, leveraging standard HTTP methods like GET, POST, and DELETE. 📊 GraphQL: Empower clients to query exactly the data they need—efficient and customizable API querying. ⚡ gRPC: High-performance, using HTTP/2 for remote procedure calls, ideal for low-latency, high-speed communications. 🔗 Webhooks: Asynchronous event-driven callbacks, enabling server-to-server communication in real-time. #APIs #SoftwareArchitecture #REST #GraphQL #gRPC #Webhooks #linkedincreator #continouslearning #linkedin #devops #fullstack

How to improve API performance? Here are 5 tips: 1. 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 Pagination is a common optimization technique when result sets are large. By streaming results back to the client in pages, service responsiveness can be improved. 2. 𝗔𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗼𝘂𝘀 𝗟𝗼𝗴𝗴𝗶𝗻𝗴 Synchronous logging writes to disk on every API call, slowing down the system. With asynchronous logging, logs are first sent to a lock-free buffer and control is immediately returned. The buffer contents are then flushed periodically to disk, significantly reducing I/O overhead. 3. 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 Frequently accessed data can be cached for fast retrieval. Clients can query the cache first instead of hitting the database directly every time. For cache misses, the database can be queried as a fallback. In-memory caches like Redis provide faster data access compared to databases. 4. 𝗣𝗮𝘆𝗹𝗼𝗮𝗱 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 Request and response payloads can be compressed using algorithms such as gzip to reduce transmitted data volume. This speeds up upload and download times. 5. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹𝗶𝗻𝗴 Opening and closing database connections has significant overhead. Using a pool of open connections avoids this. The connection pool manages lifecycle events internally. What other performance optimization tricks have you found useful for APIs? #Design #Technology #API #Developers

In designing modern software systems, various components play crucial roles in ensuring efficiency, scalability, and reliability. Understanding these components is essential for creating robust and scalable architectures. Below are short notes on some system design components: 🌟 Load Balancer: Distributes incoming traffic across multiple servers to enhance availability and performance. Example: Nginx, HAProxy. 🌟 API (Application Programming Interface): Enables different software systems to communicate and integrate with each other. Example: REST APIs, GraphQL. 🌟 Monitoring System: Tracks system health and performance, providing alerts to identify and resolve issues. Example: Codegiant, Prometheus. 🌟 Web Server: Handles HTTP requests and serves web content to users. Example: Apache, Nginx. 🌟 Analytics: Analyzes data to understand user behavior and support data-driven decisions. Google Analytics, Mixpanel, Apache Spark. 🌟 Full-Text Search: Allows quick and accurate search through large text bodies using keywords. Example: Elasticsearch, Apache Solr. 🌟 CDN (Content Delivery Network): Delivers web content from distributed servers to reduce latency and improve load times. Example: Cloudflare, Akamai. 🌟 Distributed Storage: Spans multiple servers to provide high availability, fault tolerance, and scalability for data storage. Example: Amazon S3, Google Cloud Storage. 🌟 Cache: Temporarily stores frequently accessed data for quick retrieval to improve performance. Example: Redis, Memcached. 🌟 Cloud: Provides scalable, flexible, and cost-efficient resources and storage over the Internet. Example: Amazon Web Services (AWS), Microsoft Azure. 🌟 Database: Organizes and stores data, ensuring efficient access and retrieval. Example: MySQL, PostgreSQL. 🌟 Messaging System: Facilitates reliable communication between distributed systems or components. Example: Apache Kafka, RabbitMQ. Effective system design relies on the integration and optimal configuration of various components such as load balancers, APIs, and databases. Each element serves a distinct purpose, from enhancing performance and reliability to facilitating communication and data management. Elevate your software development process with Codegiant, an all-in-one platform designed to streamline project management, code hosting, and CI/CD pipelines. With its intuitive interface and robust features, Codegiant helps teams collaborate more effectively and deliver high-quality software faster. Experience the power of seamless integration and enhanced productivity by exploring Codegiant today. https://hubs.li/Q02J3qTx0 #systemdesign #software #cloud

Analyzing nginx logs of your website can provide you great insights. If only that was easy. Check the cool blog from Sampath Siva Kumar Boddeti to learn how to get it done in under 10 minutes. He even touches how to parse and redact data in logs in real time. Read the full blog at https://lnkd.in/gCv3hkFS #Nginx #Observability #LogManagement #OpenSource #DevOps

𝐃𝐚𝐲 21: 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐲: 𝐃𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐚 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐖𝐞𝐛 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 🖥️ Welcome to Day 21 of our System Design series! Today, we'll guide you through designing a scalable web application. 𝐒𝐭𝐞𝐩-𝐛𝐲-𝐒𝐭𝐞𝐩 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐃𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐚 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐖𝐞𝐛 𝐀𝐩𝐩 𝐒𝐭𝐞𝐩 1: 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬 𝐆𝐚𝐭𝐡𝐞𝐫𝐢𝐧𝐠 𝑰𝒅𝒆𝒏𝒕𝒊𝒇𝒚 𝑪𝒐𝒓𝒆 𝑭𝒆𝒂𝒕𝒖𝒓𝒆𝒔 : Determine essential features like user authentication, data storage, and content delivery. 𝑼𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝑼𝒔𝒂𝒈𝒆 𝑷𝒂𝒕𝒕𝒆𝒓𝒏𝒔: Estimate user numbers, peak traffic times, and interaction types. 𝐒𝐭𝐞𝐩 2: 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝑴𝒐𝒏𝒐𝒍𝒊𝒕𝒉𝒊𝒄 𝒗𝒔. 𝑴𝒊𝒄𝒓𝒐𝒔𝒆𝒓𝒗𝒊𝒄𝒆𝒔: Choose between a monolithic architecture (simpler but less scalable) or microservices (more scalable but complex). 𝐒𝐭𝐞𝐩 3: 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠 𝑷𝒖𝒓𝒑𝒐𝒔𝒆: Distribute traffic to prevent server overload. 𝑻𝒐𝒐𝒍𝒔: Use AWS Elastic Load Balancer, Nginx, or HAProxy. 𝐒𝐭𝐞𝐩 4: 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 𝑷𝒖𝒓𝒑𝒐𝒔𝒆: Reduce database load and speed up responses. 𝑻𝒆𝒄𝒉𝒏𝒊𝒒𝒖𝒆𝒔: Client-side, server-side (Redis, Memcached), and CDNs (Cloudflare). 𝐒𝐭𝐞𝐩 5: 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐃𝐞𝐬𝐢𝐠𝐧 𝐚𝐧𝐝 𝐒𝐡𝐚𝐫𝐝𝐢𝐧𝐠 𝑷𝒖𝒓𝒑𝒐𝒔𝒆: Enhance performance by distributing data. 𝑻𝒆𝒄𝒉𝒏𝒊𝒒𝒖𝒆𝒔: Vertical scaling and horizontal scaling (sharding). 𝐒𝐭𝐞𝐩 6: 𝐀𝐬𝐲𝐧𝐜𝐡𝐫𝐨𝐧𝐨𝐮𝐬 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝑷𝒖𝒓𝒑𝒐𝒔𝒆: Improve user experience by offloading tasks. 𝑻𝒐𝒐𝒍𝒔: Use RabbitMQ or Kafka. 𝐒𝐭𝐞𝐩 7: 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 𝑷𝒖𝒓𝒑𝒐𝒔𝒆: Observe performance and log events. 𝑻𝒐𝒐𝒍𝒔: Use Prometheus for monitoring and the ELK stack for logging. 𝐒𝐭𝐞𝐩 8: 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝑷𝒖𝒓𝒑𝒐𝒔𝒆: Protect application and user data. 𝑻𝒆𝒄𝒉𝒏𝒊𝒒𝒖𝒆𝒔: Implement robust authentication (OAuth 2.0), role-based access control, and encrypt data in transit and at rest (HTTPS). 🔗 Stay Connected: Follow this series to deepen your understanding of system design. Got questions or thoughts? Drop a comment below! #SystemDesign #ScalableWebApp #LoadBalancing #Caching #DatabaseSharding #PerformanceOptimization

🚀 𝐃𝐚𝐲 𝟒𝟎 - 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐕𝐨𝐥𝐮𝐦𝐞𝐬: 𝐞𝐦𝐩𝐭𝐲𝐃𝐢𝐫 𝐚𝐧𝐝 𝐡𝐨𝐬𝐭𝐏𝐚𝐭𝐡 🚀 Today, I dove deep into the world of Kubernetes volumes and learned about two key types: emptyDir and hostPath. These volumes provide different functionalities, allowing containers to manage data effectively during their lifecycle. 🔹𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: 𝟏. 𝐞𝐦𝐩𝐭𝐲𝐃𝐢𝐫: Used for temporary storage, great for tasks like caching or intermediate data processing. It lasts as long as the Pod runs but is deleted once the Pod is gone. 𝟐. 𝐡𝐨𝐬𝐭𝐏𝐚𝐭𝐡: Maps a directory from the host Node’s filesystem into the container, making it ideal for persistent storage like logs or configuration files that need to survive Pod restarts. 🔹𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: 1. emptyDir for data processing and session storage. 2.hostPath for storing logs or database files that need to persist across Pod restarts. 🔹𝐓𝐚𝐬𝐤𝐬 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐞𝐝: 1. Created and deployed a Pod with an emptyDir volume, verified file creation, and observed how data behaves during Pod restarts. 2. Set up a hostPath volume to map a directory from the host machine into a Pod, storing data persistently on the Node. Check out my latest newsletter for a deep dive into these concepts! 🚀👇 Let’s keep learning and growing! If you have any questions, drop a comment, and we can discuss.🚀 Happy Learning!😊 #𝟗𝟎𝐃𝐚𝐲𝐬𝐎𝐟𝐃𝐞𝐯𝐎𝐩𝐬 #Kubernetes #DevOps #Containers #CloudComputing Kedar Pattanshetti #TechLearning #emptyDir #hostPath #PersistentStorage #CloudNative #K8s #LearningJourney #DevOpsCommunity Shubham Londhe TrainWithShubham

EP7: GraphQL vs. REST: Which is Better for APIs? ** REST (Representational State Transfer):** - Structure: REST is an architectural style that uses standard HTTP methods like GET, POST, PUT, and DELETE. - Endpoints: It relies on multiple endpoints for different resources. - Data Retrieval: With REST, you often fetch more data than needed because each endpoint returns a fixed structure. - Caching: REST has strong support for caching via HTTP headers. - Error Handling: Utilizes standard HTTP status codes for error responses. Pros of REST: - Simple and easy to use. - Widespread adoption and familiarity. - Good for simple, CRUD-based applications. Cons of REST: - Over-fetching or under-fetching of data. - Multiple round trips to the server may be required for complex queries. - Changes in API versioning can be cumbersome. **GraphQL:** - Structure: GraphQL is a query language for your API and a server-side runtime that executes queries. - Endpoints: It uses a single endpoint to fetch data. - Data Retrieval: Allows clients to request exactly the data they need, avoiding over-fetching or under-fetching. - Caching: Caching can be more complex compared to REST, though solutions are evolving. - Error Handling: Errors are returned in the response body with detailed information. Pros of GraphQL: - More efficient data retrieval. - Reduces the number of requests to the server. - Strongly typed schema improves the developer experience. - Flexible and adaptable to evolving data needs. Cons of GraphQL: - Can be more complex to implement initially. - Requires additional tooling for caching and performance optimization. - Learning curve for those unfamiliar with it. ### Which is Better for APIs? The choice between GraphQL and REST depends on the specific needs of your application: - **Use REST if**: - Your application is simple and primarily CRUD-based. - You need to leverage HTTP caching mechanisms. - You prefer a simpler setup with widespread community support. - **Use GraphQL if**: - Your application requires complex queries and interactions. - You want to minimize the amount of data transferred over the network. - You need flexibility and efficiency in data retrieval. Ultimately, both GraphQL and REST have their strengths and can be used effectively depending on the use case. Some applications even use a hybrid approach, employing both GraphQL and REST where each is most effective. Check it out!! https://lnkd.in/gN9iYCNY

#GraphQL is transforming how we think about #APIs, offering flexibility and efficiency that REST often can’t match. This is particularly the case where high volume, variable, and complex data interactions are required. Network and complex, hybrid infrastructure automation is a perfect example. That’s why we built #Infrahub ’s API using GraphQL. This BetaNews article highlights why GraphQL is becoming a serious contender in API development. Worth a read. https://bit.ly/4jkXMZ6

💻 REST vs. GraphQL: What’s the Difference? 🚀 In the world of APIs, understanding the distinctions between REST and GraphQL is crucial. 🔹 What is REST? REST, or Representational State Transfer, is an API architecture using HTTP methods like GET, POST, PUT, and DELETE. This approach allows easy client-server interaction for data retrieval and manipulation. With its simplicity and long-standing presence, REST remains a solid choice for many applications. 🔹 How to Use REST? - Set up endpoints (e.g., /users, /products). - Employ HTTP methods for operations. - Send requests to the server and receive responses in JSON format. 🔹 What is GraphQL? GraphQL stands out as a newer API query language that offers enhanced flexibility. Clients can request precise data, avoiding unnecessary information. Instead of multiple endpoints, GraphQL streamlines requests through a single endpoint with specified queries. 🔹 How to Use GraphQL? - Establish a single GraphQL endpoint (e.g., /graphql). - Define data requirements through queries. - Retrieve only the requested fields in the response. 🔹 Benefits: - REST: Simple setup, widespread support, built-in caching. - GraphQL: Enhanced flexibility, efficiency, clear schema structure. Both REST and GraphQL serve as powerful tools for API development. While REST offers simplicity, GraphQL provides precision. The choice between them depends on your specific use case! 💡 #APIs #REST #GraphQL #WebDevelopment #TechPost #React #Node #AWS