code.store’s Post

🚀 Why the Stack is Lightning Fast in Memory Access & Best Practices for Developers 🚀 Hello #linkedincommunity, today we'll talk about stack-based memory allocation. The stack holds a place of honor for its efficiency and speed. But what makes the stack so incredibly fast for memory access? After answering that question, we will talk about "when" use the stack. 🔍 Understanding the Stack's Speed The stack is a Last In, First Out (LIFO) data structure, used extensively for managing function calls, local variables, and control flow. Its speed stems from several key characteristics: 1. Contiguous Memory Allocation: Stack allocations happen in a contiguous block of memory. This means that moving up and down the stack is a matter of adjusting the stack pointer (increment or decrement operations), making both allocation and deallocation operations extremely fast. 2. Cache Friendliness: Due to its sequential nature and the locality of reference, the stack tends to be cache-friendly. Modern CPUs excel at prefetching and caching memory that is accessed in predictable patterns, further enhancing the stack's performance. 🛠️ Best Practices for Using the Stack 1. Limit Stack Usage for Small, Short-Lived Variables: The stack is ideal for temporary variables and function call management. Keep your stack usage to small, short-lived data to avoid stack overflow errors (stack size is small). 2. Understand Stack Size Limitations: Most environments have a fixed stack size. Be mindful of this limit to prevent overflow, especially with recursive functions (never forget the base case!) or allocating large data structures on the stack. 3. Opt for Heap Allocation for Large or Dynamic Data: For large datasets or dynamically sized data structures, use the heap. This helps in maintaining the efficiency of the stack and ensures that larger data requirements are managed appropriately. 4. Profile Your Applications: Always profile your applications to understand your stack usage. This can help in identifying bottlenecks and ensuring that your memory usage patterns are optimized for performance. #datastructuresandalgorithms #stackoverflow #memorymanagement #softwareengineering #cleanarchitecture

Leveraging SWR for Efficient Data Fetching: A Deep Dive into Use Cases and Integration with ISR Architecture In this article, we’ll delve into how to use SWR and explore how it can be integrated with Incremental Static Regeneration (ISR), a powerful feature provided by Next.js for static site generation. https://lnkd.in/dt5uSsc7 #publicissapient #publicisgroupe #nextjs #swr #frontenddevelopment #serversiderendering #clientsiderendering #serverside #clientside

How to Use Nx Workspace for Building Monorepo Frontend Applications at Scale The tool itself is based on best practices developed at Google to scale thousands of applications across thousands of developers in a single monorepo-style application. However, you don’t need to operate at Google’s scale to see the benefits of Nx. It is a very powerful tool for projects of all sizes. Read More: https://lnkd.in/dtTSXqFa #webappdevelopment #webapp #appdevelopment

Do you want to learn how to implement #couchbase  transactions with @Transactional annotation in a #springboot application? Read the blog from our customer #trendyol https://lnkd.in/eWi9fX2G

The general idea to avoid dual writes is to split the process into multiple steps. If you only want to replicate data between your services or inform other services that an event has occurred, you can use the outbox pattern with a change data capture implementation. If you need to implement a consistent write operation that involves multiple services, then you can use the saga pattern. #microservicesarchitecture #microservices #orchestration #designpatterns

While dltHub is releasing new features, Willi Müller took the time to compare the creation (and I would say also the maintenance) of an API extraction using the dlt REST API source and Airbyte low-code CDK. The post guides you through the implementation process showing how to do it and the differences step by step. If you want to use dlt to extract data from an API and there isn't an already existing verified source, this is a must read. If you are just curious about the outcome, well, it's less code, more features, and you can run it whenever you want. Which one? Oh...

Hello LinkedIn community! 🚀💻 Wondering how to boost code performance? Here are some key strategies to optimize your code and enhance efficiency: Profile and Analyze: Start by profiling your code to identify performance bottlenecks. Tools like [specific tool] can provide valuable insights. Optimize Algorithms: Review and refine your algorithms for better time and space complexity. Small changes can make a significant impact. Cache Smartly: Utilize caching mechanisms strategically to store and retrieve data efficiently, reducing redundant computations. Parallelize Tasks: Explore opportunities for parallel computing to execute multiple tasks concurrently, improving overall execution speed. Choose Efficient Data Structures: Opt for data structures that align with the specific needs of your application, promoting faster data retrieval and manipulation. Minimize I/O Operations: Reduce unnecessary I/O operations, such as file reads and writes, to enhance overall system performance. Use Proper Indexing: Ensure databases are properly indexed to expedite data retrieval, especially in large datasets. Memory Management: Be mindful of memory usage. Optimize data structures and free up memory when it's no longer needed. Update Dependencies: Regularly update libraries and dependencies to leverage performance improvements and bug fixes. Testing and Profiling: Rigorously test your optimized code and profile it again to confirm improvements. Implementing these strategies can lead to a significant boost in code performance. Share your thoughts and experiences on optimizing code! 💬 What strategies have you found most effective? Let's discuss it! #CodeOptimization #ProgrammingTips #SoftwareDevelopment #PerformanceImprovement #TechStrategies #CodingEfficiency #DeveloperCommunity #LinkedInDiscussion #OptimizeCode"

Want to build a data platform and consider Airbyte Low-code CDK or dltHub's new REST API Source toolkit to build many custom data ingestions? Check out our comparison with a practical case study: https://lnkd.in/gqQgAJqF

Often, people ask "How does Airbyte compare to dltHub?" 🤷 ✨Here✨, we discuss the strengths and weaknesses of Airbyte's Low-Code CDK vs. dltHub's REST API Source toolkit. In a practical case study, we implement a Zoom connector pulling webinar and meeting data and discuss: - features: custom authorization and error handling - implementation: Python vs. YAML vs. GUI - execution: platform vs. library What would you choose for your data platform in 2024?

Do you use Airbyte and wondering how dlt's REST API connector compares? Untitled Data Company did a comparison between the 2, check it out below. In my book, the comparison is not even necessary - do you use python? then use python. Don't use python? then be dependent on what others can do for you.