Jawad Srour’s Post

📅 **Day 123: N Stack Implementation** **Understanding the Problem:** We are given 'N' stacks and an array of size 'S'. We need to implement a class 'NStack' that supports the following operations: 1. **push(x, m):** Pushes element 'x' into the 'm'th stack. Returns true if successful, false otherwise. 2. **pop(m):** Pops the top element from the 'm'th stack. Returns the popped element if the stack is not empty, -1 otherwise. **Approach:** - We maintain an array to store the top index of each stack. - Another array 'next' is used to store the index of the next available spot in the array. - The 'arr' array stores the actual elements. - Initially, all elements in the 'next' array point to the next index. - To push an element, we find the next available spot, update the 'next' pointer, and update the top index of the stack. - To pop an element, we update the 'next' pointer, update the top index of the stack, and return the popped element. **Implementation:** - Implement the class 'NStack' with the required methods for pushing and popping elements from the stacks. - Use arrays to maintain the stack data structure efficiently. **Complexity Analysis:** - Time Complexity: O(1) for both push and pop operations since they involve constant-time operations. - Space Complexity: O(S) for maintaining the arrays. This implementation efficiently handles pushing and popping elements from multiple stacks. 🎉 #Stack #DataStructures #DSADay123 #AlgorithmInsights

Behold the elegance of the Linked List! A foundational data structure in DSA, it's a dynamic collection of nodes where each node points to the next, forming a sequence. Efficient for insertion and deletion, Linked Lists offer flexibility and scalability. Dive deep into its structure, traverse through nodes, and witness the beauty of pointer connections. Let's journey through the nodes of knowledge! 💡 #DataStructures #LinkedLists #codingjourney #softwareengineer #softwaredeveloper #frontenddeveloper

Convex engineer Ian Macartney shares how to use Convex's Bulk Edit feature, where you can seamlessly apply patches to all documents without downtime or code deployment. Say goodbye to scheduled maintenance interruptions and hello to efficient data transformations! Learn more here: https://lnkd.in/gGtmjGFW #database #backend #realtime #developercommunity

All these decoupled architectures in Rust for Web getting me deep into shared behaviors! If you want to follow me in my journey to building distributed and decentralized systems in Rust, send me a connection request! #rust #rustlang #rustforweb https://lnkd.in/gC-8xuVF

Regulars of my LinkedIn feed will know how regularly in awe I am of my fellow Thoughtworkers remarkable industry thought-leadership. Congratulations to my amazing colleague Unmesh Joshi on his new book, Patterns of Distributed Systems. Just because distributed systems are increasingly ubiquitous doesn't mean that they are getting easier to design and build - they can and do still pose significant challenges for developers grappling with their complexity. That’s where Unmesh's new book and it's design patterns can help — whether you are an enterprise architect, data architect or software developer working on distributed systems, you will find templated ways to solve common problems regardless of their specific implementation. https://lnkd.in/ea3eGXUB

𝐂𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 🚀 When it comes to optimizing code performance, selecting the right data structure is key. Let's break down the differences: 🔹 Arrays: Ideal for fast access by index, but resizing can be costly. 🔹 Tuples: Immutable and great for fixed-size collections, but less flexible for dynamic changes. 🔹 Trees: Efficient for hierarchical data and searching, but complex operations may have higher time complexity. 🔹 Stacks: Perfect for last-in-first-out (LIFO) operations, such as function calls or undo mechanisms. 🔹 Linked Lists: Excellent for frequent insertions and deletions, but accessing elements is slower compared to arrays. 🔹 Hashmaps: Offers fast lookups (O(1) average case), but may suffer from collisions impacting performance. Choose wisely based on your specific use case to ensure optimal performance for your applications! 💡 #تونس_أفضل #DataStructures #Performance

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"

#snsinstitutions #snsdesignthinkers #designthinking Title: "Essentials of Data Structures" Data structures are the building blocks of efficient software, organizing and managing data to enhance computational performance. Two primary categories exist: primitive (e.g., integers) and composite (e.g., arrays). Arrays store elements in contiguous memory, while linked lists connect elements via pointers, facilitating dynamic memory usage. Stacks and queues enforce specific rules for element manipulation, and trees and graphs offer hierarchical and networked data representations. Their applications span database management, algorithm design, compiler construction, and operating systems. Understanding time and space complexity is crucial, as it influences algorithm efficiency. Common operations involve traversal, insertion, deletion, and searching. Choosing the right structure depends on the problem, considering factors like data modification frequency and memory constraints. Mastery of data structures empowers developers to craft efficient, scalable code, a fundamental skill in the world of software

"Why Legacy Observability Tools are So $!&%# Expensive" Check out this article by RT Insights for an interesting take on why this problem persists. Despite the availability of solutions, unnecessary budget is still being allocated to outdated methods. #observeinc #sre Link to article: https://lnkd.in/dWtA3bKG

In our latest blog post, #software engineer Christopher S. looks at the value of pluggability when migrating vectors between #vectordatabases, and why our team prefers Qdrant. https://lnkd.in/egqNEk2Y