Amelia Stone’s Post

"It’s becoming clear that workflow-centric approaches and database-centric approaches are on a collision course. The primary reason for this is that while application state and database state are logically distinct, they are dependent on each other, and a system that doesn’t cover both is complex to get right and to debug." -- https://lnkd.in/ebgN-MgV Although the authors (Yoko Li, Martin Casado, and Satish Talluri at Andreessen Horowitz) don't mention #EventSourcing in their article, I could not stop thinking of the fact that event sourcing sit right at the intersection of workflow-centric and at the same time database-centric approaches to develop modern applications. What do you think? #Software #SoftwareDevelopment #ModernApplications #Innovation

Designing Scalable and Performant Retool Applications for Large Organizations One of the key aspects of building scalable Retool applications is optimizing query performance. Retool provides several features and best practices to minimize latency and ensure efficient data retrieval.... https://lnkd.in/eTS_ddit

The tale of how overengineering my open-source project was actually fun and paid back later 🧵 At the time I wrote the first line of the code of Dekart (webgl map vis for BigQuery and Snowflake) I was quite obsessed CQS/CQRS backend design ideas. In short, CQRS (Command Query Responsibility Separation) is a principle that (in contrast to common REST/CRUD) separates the handling of read and write operations into distinct models. Applying CQRS to a tool like Dekart might seem like an overkill. But I had a lot of fun doing it 😃 Dekart is built on top of gRPC with server streaming support. Every change is an RPC call which does not return data. Frontend subscribed to gRPC stream, which shoots application state on each update. 3 years later, we are using Dekart more than before. Not being able to edit the same report together became annoying. In a recent release, Dekart, finally, allows multiple users to edit the same report: when one user updates the report, another user magically sees the update ✨ I’m sure your BI tool cannot do it (let me know in comment if it can!) 💡 With the CQRS model, all race conditions are resolved by design and user live updates are free. It took me exactly one evening to add the live collaboration feature. 👉 Reference article by Martin Fowler https://lnkd.in/e8Jzdwpz 👉 If you are looking for lightweight alternative to CARTO with live collaboration feature, check latest Dekart release https://lnkd.in/eVckEfWh

🌟 Dive into the world of software architectural patterns! Here are 6️⃣ must-know patterns every software architect should master: Credits : Nelson Djalo 1️⃣ Event-Driven Architecture: 🔗 Decoupled Components and Asynchronous Communication. 🎯 Ideal for scalable and responsive systems triggered by events. 2️⃣ Layered Architecture: 📊 Hierarchical Structure with Presentation, Business Logic, and Data Layers. 🎯 Common in enterprise apps, enhances maintainability and modular development. 3️⃣ Monolithic Architecture: 🏛️ Unified Codebase and Deployment. 🎯 Best for smaller applications or simplicity-focused projects, but may pose scaling challenges. 4️⃣ Microservices Architecture: 🔀 Distributed System with Independent Services. 🎯 Perfect for large systems, improves scalability, fault isolation, and independent service development. 5️⃣ Model-View-Controller (MVC): 🖥️ Segregates Concerns into Model, View, and Controller. 🎯 Found in web apps, enhances code organization and maintenance by separating UI logic. 6️⃣ Master-Slave Architecture: 👨💼 Centralized Control with Multiple Worker Nodes. 🎯 Ubiquitous in distributed computing, optimizes parallel processing and load balancing. Which pattern are you most intrigued by? Share your thoughts below! 💬 #SoftwareArchitecture #TechTalk #PatternsInTech 🚀

8 Design Patterns for Low-Latency Applications 🚀 In the age of real-time data, low-latency apps are crucial. From trading platforms to gaming, minimizing latency is key. Here are essential design patterns to ensure efficient, rapid response times. 1- Event-Driven Architecture 📅 - Components react to events rather than being called. - Event Producers, Consumers, and Brokers (Kafka, RabbitMQ) ensure real-time, asynchronous communication. - Decouples components for scalable, maintainable systems. 2- Microservices Architecture 🏗️ - Break down applications into smaller, loosely coupled services. - Independent deployment, scaling, and updating. - Enhances scalability and fault isolation. 3- Caching 🗄️ - Store frequently accessed data in high-speed storage. - Use In-Memory Caching (Redis, Memcached) and CDNs. - Significantly reduces data retrieval time and offloads database queries. 4- Asynchronous Processing ⏳ - Decouple tasks for asynchronous processing. - Utilize Message Queues (RabbitMQ, Kafka) and Job Queues. - Improves responsiveness by offloading time-consuming tasks. 5- Load Balancing ⚖️ - Distribute network traffic across multiple servers. - Use Hardware and Software Load Balancers (HAProxy, NGINX). - Ensures high availability, reliability, and optimal resource utilization. 6- Data Partitioning and Sharding 📊 - Divide large datasets for independent processing. - Horizontal Partitioning (Sharding) and Vertical Partitioning. - Improves performance and scalability. 7- Edge Computing 🌐 - Process data closer to the source. - Deploy edge servers or devices for real-time processing. - Reduces latency and enhances real-time decision-making. 8- Optimized Data Structures and Algorithms 📈 - Use efficient data structures and algorithms. - Choose lower time complexity algorithms and fast-access data structures. - Ensures faster operations and minimal computational overhead. 🏁 By leveraging these design patterns, developers can build systems that meet the demands of modern, latency-sensitive applications. Optimize your architecture for the best performance! 💡 #SoftwareDevelopment #RealTime #Programming https://lnkd.in/dEEATfzd

Looking to optimize your application's #performance? Give DataDog's APM a try 🚀 In our latest article, ORIL's Lead Software Engineer, Eugenii Samarskyi, explores how #DataDog can pinpoint bottlenecks, cut operational expenses, and elevate application efficiency ⬇ #oril #softwaredevelopment #appperformance #data

Building a Performant, Maintainable, and Resilient Application Creating an application that stands the test of time requires strategic planning and best practices. Here are key components to focus on: 1. Architecture & Design - Modular Architecture: Break down functionality into discrete, reusable modules. - Microservices: Independent, deployable services for scalability and fault isolation. - Design Patterns: Use proven patterns like MVC and Singleton for cleaner code. 2. Performance Optimization - Efficient Algorithms: Choose algorithms and data structures wisely. - Caching: Use Redis or Memcached to reduce redundant computations. - Asynchronous Processing: Leverage message queues for background tasks. - Load Balancing: Distribute traffic across multiple servers. 3. Maintainability - Clean Code: Follow SOLID principles and maintain readable code. - Documentation: Keep comprehensive documentation for onboarding and troubleshooting. - Version Control: Use Git with a clear branching strategy. - Code Reviews: Ensure quality through peer reviews. 4. Resilience - Error Handling: Implement robust error management. - Redundancy: Introduce redundancy to minimize downtime. - Automated Testing: Use unit, integration, and end-to-end tests. - Monitoring & Logging: Set up real-time monitoring with tools like Prometheus and Grafana. 5. Scalability - Horizontal Scaling: Add instances to handle increased load. - Database Scaling: Use sharding, replication, and NoSQL solutions. - API Rate Limiting: Protect your app from being overwhelmed by excessive requests. By focusing on these components, you can build applications that are efficient, easy to maintain, and resilient to challenges. #SoftwareDevelopment #TechLeadership #BestPractices #Scalability #PerformanceOptimization

𝐊𝐢𝐜𝐤𝐢𝐧𝐠 𝐎𝐟𝐟 𝐎𝐮𝐫 𝐒𝐞𝐫𝐢𝐞𝐬 𝐨𝐧 𝐌𝐞𝐭𝐚𝐦𝐨𝐝𝐞𝐥𝐬: 𝐓𝐡𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥 𝐂𝐨𝐫𝐞 𝐨𝐟 𝐍𝐨-𝐂𝐨𝐝𝐞🚀 Dive into the core of enterprise no-code applications as we explore the critical role of metamodels. These are the brain behind the scenes: 🛠️ Architectural Core: Metamodels are the foundational architecture for no-code platforms, defining the framework within which Business Engineers build applications. 🗺️ Blueprints for Development: Metamodels detail what is achievable within a no-code platform, effectively shaping its capabilities and creative freedom. ⚙️ Customization & Expressive Power: Metamodels' structure is key. It enables tailored, scalable solutions and a platform's ability to model diverse business processes, blending industry-specific adaptability with broad applicability. 🤔 Ever Hit a Wall? Have you found your no-code platform's potential limited or had to resort to traditional coding for certain functionalities? These challenges often point to limitations in the platform's metamodel. 👥 Who Are Business Engineers?: They're tech-savvy IT professionals who bridge technology and business. 🔍 Next in Series: Application models and metadata. If you want to learn more immediately, read https://lnkd.in/dMaPpXtu. #NoCode #ModelDriven #Metamodels #Metadata #ApplicationModels #EnterpriseSolutions #EnterpriseApplications #SoftwareDevelopment #BusinessEngineers

The Outbox Pattern: A Game-Changer in Application Messaging The outbox pattern is a messaging pattern that has revolutionized the way applications handle message-based communication. It provides a reliable and scalable approach to decoupling components within an application, allowing them to communicate asynchronously. In this section, we will explore the fundamentals of the outbox pattern and its significance in modern application development. At its core, the outbox pattern involves the use of an outbox table in a database to store messages that need to be sent by an application. Instead of directly sending messages when an event occurs, the application writes the messages to the outbox table. Another component, known as the message dispatcher, is responsible for reading the messages from the outbox table and sending them to their intended recipients. This decoupling of the message sending process from the event generation process brings several benefits. #outboxpattern #softwarearchitecture #softwareengineering #softwaredevelopment #softwaredeveloper #softwaredesign #sqs #dynamodb #eventbridge

Want to build a low-latency applications in Go ?? Here's a step-by-step approach to building low-latency applications in Go: 1. Define Requirements and Use Cases:   - Clearly define the latency requirements for your application. What is an acceptable response time for different operations?   - Identify the use cases and components that require low latency. 2. Choose the Right Architecture:   - Design your application with a modular and well-separated architecture.   - Consider microservices or service-oriented architecture to isolate functionalities and optimize performance. 3. Concurrency with Goroutines:   - Utilize goroutines to achieve concurrent execution of tasks.   - Design your application to take advantage of multiple CPU cores for parallel processing. 4. Effective Channel Usage:   - Use channels for communication and synchronization between goroutines.   - Utilize buffered channels to improve throughput and reduce contention. 5. Minimize Lock Contention:   - Use fine-grained locking strategies to minimize contention between goroutines.   - Consider using read-write locks for cases where multiple goroutines can read simultaneously. 6. Optimize I/O Operations:   - Use non-blocking I/O operations to prevent blocking on network or file operations.   - Utilize the `io.Reader` and `io.Writer` interfaces for efficient I/O processing. 7. Memory Management:   - Minimize memory allocations and deallocations to reduce garbage collection impact.   - Reuse objects using the `sync.Pool` or similar mechanisms. 8. Profiling and Benchmarking:   - Use the built-in profiling tools like `pprof` to identify performance bottlenecks.   - Write benchmarks to measure the performance of critical code paths. 9. Network Communication:   - Use high-performance libraries like `fasthttp` for network operations.   - Optimize network communication by reducing unnecessary data transfers. 10. Optimize Critical Paths:   - Identify the critical paths in your application that contribute the most to latency.   - Focus your optimization efforts on these critical paths. 11. Caching and Memoization:   - Implement caching mechanisms to store frequently accessed data.   - Use memoization to avoid recomputation of expensive operations. 12. Load Testing:   - Perform load testing to simulate real-world usage and identify performance bottlenecks.   - Test how your application behaves under high load conditions. 13. Regular Monitoring:   - Implement monitoring tools to track latency and other performance metrics.   - Use monitoring tools like Prometheus and Grafana to visualize and analyze data. 14. Tuning Operating System Parameters:   - Adjust operating system parameters such as file descriptor limits and TCP settings to optimize network performance. #Golang #LowLatency #PerformanceOptimization #SoftwareArchitecture #Concurrency #CodeOptimization #Microservices #HighPerformance