Japie Teunissen’s Post

Hello LinkedIn Community 🙌🏻 !! In today’s world of scalable architectures and microservices messaging queues play an important role in inter-process or inter-microservice communication. So let’s try to understand what messaging queues are 👇🏻 👉🏻 Messaging queues commonly used as brokers are components used for communication . It uses queues to exchange messages between multiple services. It is a producer/consumer model, whereby "producers" push messages into "queues", and "consumers" can listen for new messages coming into the queue. 👉🏻 As soon as the consumer acknowledges the message from the queue the message is removed from the queue. This model is asynchronous in nature as interactions of the components are independent to each other. 🌟 Messaging Queues not only makes the architecture Highly Scalable, they also increases the robustness by caching the data making it highly reliable and recoverable. #coding #systemdesign #kafka #queue #software #engineer #architecture

𝐇𝐨𝐰 𝐭𝐨 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐀𝐏𝐈 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 Harness the power of peak API performance with these expert strategies designed to supercharge your digital interactions. From smart pagination to seamless asynchronous operations, discover how to elevate your API's efficiency and responsiveness: Join my friend's vibrant DevOps community ⏬ https://lnkd.in/g7fEqzj3  Pagination - Efficient Data Transfer: Implement server-side pagination. - Client Navigation: Use intuitive query parameters like `page` and `pageSize`. - Clear Documentation: Provide guidance in your API documentation.  Seamless Asynchronous Logging - Asynchronous Logging: Log activities asynchronously. - Reduce Overhead: Use message queues or dedicated services for background processing.  Connection Pooling - Optimized Operations: Employ strategic connection pooling. - Stable Management: Ensure swift and stable database interaction.  Advanced Caching Techniques - Fast Access: Use in-memory systems like Redis or Memcached. - Reduce Strain: Employ HTTP caching directives.  Dynamic Load Balancing - Resilient API: Implement intelligent load balancing. - Balanced Load: Distribute incoming requests across multiple servers.  Payload Compression - Streamlined Transmission: Utilize compression techniques like GZIP, Brotli, or Zstandard. - Quick Interactions: Reduce transfer times for efficient API interactions.   Follow Ankit Kumar for more content credit: Ashish Sahu #python #softwareengineering #systemdesign

Embracing CQRS for Scalable Architecture In traditional systems, a single data model or DTO often handles both reads and writes, which works fine for basic CRUD. But as application complexity grows, this one-size-fits-all approach becomes a bottleneck. Enter CQRS (Command Query Responsibility Segregation) — a pattern that introduces separate models for querying and writing. This separation allows for more flexible data handling, tailored to specific operations, and prevents data loss during parallel operations. By adopting CQRS, applications can scale better and address the complexities of modern system architectures. It's a game-changer for those looking to enhance performance and flexibility. #CQRS #Architecture #Scalability #SoftwareEngineering

Exploring the Power of CQRS in Software Architecture! Considering Command Query Responsibility Segregation (CQRS) for your next project? Here are some scenarios where it can shine: 1. Scalability Goals: Tailor read and write workloads independently to scale efficiently, especially useful when they vary significantly. 2. Performance Boost: Optimize the read side for high-performance queries, crucial when reads outnumber writes. 3. Event Sourcing: Combine CQRS with event sourcing for a comprehensive audit trail of state changes. 4. Team Collaboration: Separate teams or developers handling writes and reads? CQRS fosters collaboration by defining clear responsibilities. 5. Flexibility and Evolution: Make system evolution smoother. Changes in one side are less likely to impact the other, crucial for large and evolving systems. Remember, while CQRS offers advantages, carefully weigh its benefits against added complexity for your specific project. #SoftwareArchitecture #CQRS #TechInnovation 🛠️💻

Distributed tracking is a powerful technique that allows you to trace requests across multiple services in a distributed system. By visualizing the complete path of a request, from its initial entry point to its final response, you gain valuable insights that can help you optimize performance and identify issues faster. Here's how it works: when a request enters the system, a unique identifier (trace ID) is assigned and attached to all subsequent calls made by the service handling the initial request. Each service or component involved in processing the request creates a "span" within the overall trace, capturing details like execution time, called functions, and any errors encountered. The spans are collected and aggregated by a central tracing system, which then builds a complete picture of the request's journey. The benefits of distributed tracking are many. Simplified debugging allows you to pinpoint the exact component or service where an issue arose within the request flow. Performance optimization is possible by analyzing the time spent in each service call, identifying bottlenecks and optimizing your distributed system. Finally, improved root cause analysis allows you to trace the root cause of problems through the entire flow of a request, rather than being limited to a single service's logs. In essence, observability provides the big picture, while distributed tracking zooms in on the specific details of request execution across various microservices. Both are crucial for effectively managing and troubleshooting distributed systems. #DistributedTracking #Observability #Microservices

The Rise of the Platform Engineer: How to Deal With the Increasing Complexity of Software https://lnkd.in/g8MTpRXt Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, The Modern DevOps Lifecycle: Shifting CI/CD and Application Architectures. DevOps — ✓ DevSecOps — ✓ Platform engineering — ?

🎉 Excited to share that I’ve just completed my certification in “Microservices: Design Patterns” by Frank P Moley 🚀 This journey covered a wide array of essential patterns including: 🔹 Decomposition Patterns: Strangler & Sidecar 🔹 Integration Patterns: Gateway, Process Aggregator, Edge 🔹 Data Patterns: Single Database Service, Shared Database Service, CQRS, Asynchronous Eventing 🔹 Operational Patterns: Log Aggregation, Metrics Aggregation, Tracing, Continuous Delivery, Service Delivery I’m thrilled to apply these insights and continue growing in the field. Check it out:  https://lnkd.in/e4KcbA2C #Microservices #DesignPatterns  #CloudComputing #DevOps #ContinuousDelivery #CQRS #DataPatterns #OperationalPatterns #Softwaredesignpatterns

12 Microservices Best Practices You Should Know 1 - Single Responsibility 2 - Containers 3 - Orchestration 4 - Resilience and Fault Tolerance 5 - Scalability 6 - CI/CD 7 - Observability 8 - Security 9 - API Gateway 10 - Stateless 11 - DB Per Service 12 - Event Driven Architecture

🔍 Fascinating deep dive into how distributed tracing is revolutionizing service dependency mapping. This post explains why static architecture diagrams fall short and how real-time tracing data can transform our understanding of complex systems. Key insights: - Move from static documentation to dynamic, data-driven architecture understanding - Enable proactive system management through real-world dependency tracking - Transform team collaboration with shared, ground-truth system insights Essential reading for anyone managing distributed systems or microservices architectures. Read the full article: https://lnkd.in/edsPS7cP #DistributedSystems #Microservices #SoftwareArchitecture #DevOps

Optimizing Test Environments and Data Flow Limiting blast radius in testing Centralizing data flow understanding Leveraging virtualization for ephemeral environments Using sandboxes and virtual stubs for efficient testing Key takeaway: Smart, stateful data virtualization can accelerate development and testing, reducing reliance on full end-to-end environments. #DevOps #TestingStrategy #DataManagement #VirtualizationTechniques