Saurabh S.’s Post

🚀 Achievement Unlocked: Optimized Code for Lightning-fast Performance! 🚀 Today, I successfully optimized a critical piece of code in my company's Order History Page reducing its execution time from a 2.5-3 seconds down to a blazing-fast 400-500 ms! 💡 What I Learned: 1. Use Separate Models: I realized the importance of using separate models with only a few columns per use case. This approach significantly improved performance, especially when dealing with many One-to-Many relationships and eagerly fetched data. 2. Leverage Native Queries: I also found that using native queries and extracting data into Data Transfer Objects (DTOs) helped enhance performance by reducing database load and optimizing data retrieval. Key Takeaway: Optimal performance isn't just about writing efficient code; it's about understanding your data model and choosing the right strategies to handle it effectively. fun fact: we are not able to prove Optimized code is faster than normal one LOL 🤣 I'm thrilled to have achieved such a significant performance boost and look forward to applying these strategies in future projects! 💪 #CodeOptimization #PerformanceBoost #SoftwareEngineering

Another top learning content for a Software Crafter: The Hunt for the Missing Data Type (https://lnkd.in/g_hkFg2z)

🔑 Unlocking the Potential of Data Structures: Key Insights 🔍 In the realm of software engineering, data structures are the backbone that supports efficient data management and algorithm performance. 🚀 Let’s dive into the analysis of data structures and uncover the best keys to mastering them: Array of Opportunities 📊 Arrays provide a solid foundation with their simplicity and direct access. They are the go-to for indexing data and building blocks for more complex structures. Linked Lists: The Dynamic Duo 🔗 Singly or doubly, linked lists offer flexibility in memory allocation and are ideal for dynamic data sets where modification is frequent. Hashing Out Efficiency 📈 Hash tables are the champions of quick retrieval, making them perfect for high-performance lookups with their unique key-value pairing. Trees: Branching Out to New Heights 🌳 Binary search trees, AVL trees, and B-trees, each have their own use-cases, balancing the act between ordered data and optimized search times. Graphs: The Interconnected Web 🕸️ Graphs excel in representing complex relationships, providing pathways to solve problems like network flows and social network analyses. Understanding and analyzing these data structures is crucial for any developer looking to optimize code and solve problems efficiently. The key 🔑 is to choose the right structure for the right task and to implement it with precision and care. 🌟 Stay curious, keep learning, and always strive for elegant code solutions. Happy coding! 💻 Feel free to customize this draft to better fit your personal style or specific audience. Happy posting! 😄 #spweb #spwebdevs #dsa #datastructures

🚀 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝗞𝗲𝘆 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗘𝘃𝗲𝗿𝘆 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗠𝗮𝘀𝘁𝗲𝗿! 🚀 As a developer, understanding essential data structures is crucial for building efficient and scalable applications. Here are some must-know data structures: 𝗟𝗶𝗻𝗸𝗲𝗱𝗟𝗶𝘀𝘁 𝗦𝘁𝗮𝗰𝗸 𝗤𝘂𝗲𝘂𝗲 𝗛𝗲𝗮𝗽 𝗧𝗿𝗲𝗲 𝗦𝘂𝗳𝗳𝗶𝘅 𝗧𝗿𝗲𝗲 𝗚𝗿𝗮𝗽𝗵 𝗥-𝗧𝗿𝗲𝗲 𝗩𝗲𝗿𝘁𝗲𝘅 𝗕𝘂𝗳𝗳𝗲𝗿 𝗦𝗸𝗶𝗽𝗹𝗶𝘀𝘁 𝗛𝗮𝘀𝗵𝗶𝗻𝗱𝗲𝘅 𝗦𝗦𝗧𝗮𝗯𝗹𝗲 𝗟𝗦𝗠 𝗧𝗿𝗲𝗲 𝗕-𝗧𝗿𝗲𝗲 𝗜𝗻𝘃𝗲𝗿𝘁𝗲𝗱 𝗜𝗻𝗱𝗲𝘅 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆 𝗤𝘂𝗲𝘂𝗲 Mastering these data structures opens doors to endless possibilities in software development! 💡 #DataStructures #SoftwareDevelopment #TechSkills #CodingLife #LinkedInLearning

Day 41 of #51DaysOfCode . Just tackled the "Delete Duplicates from a Sorted List" problem on LeetCode Here's my approach to efficiently remove consecutive duplicate values: 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: https://lnkd.in/eadWgE9U Given the head of a sorted linked list, remove all duplicate elements such that each element appears only once. 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: Two-Pointer Technique: Utilize two pointers: curr (current) and next (next node). Iterate Through the List: While curr is not null and next is not null: If the current node's value (curr->val) is equal to the next node's value (curr->next->val), this indicates a duplicate. Remove Duplicate Node: Skip the duplicate node by setting curr->next to point two nodes ahead (curr->next->next). This effectively removes the duplicate from the linked list. Keep Track of Non-Duplicates: If the values are not duplicates, simply advance the curr pointer to the next node to continue iterating. Return the Head: After the loop finishes iterating through the list, the head node might have been modified (if duplicates existed at the beginning). Therefore, we return the original head to maintain the linked list structure. Time Complexity: O(n), where n is the number of nodes in the list. We iterate through the list potentially once, depending on the number of duplicates. Space Complexity: O(1), as we modify the existing linked list structure in-place without using additional significant data structures. #leetcode #c++ #linkedlists #algorithms #datastructures How do you typically approach problems involving linked lists and duplicate elements? Share your strategies in the comments!

Why does some code slow down with large data sets and others not? Let's talk Big-O 📈 It's important to understand how your code performs at various input sizes. One common reason for slow performance is the choice of algorithms—specifically, their Big-O time complexity. Big-O analysis allows us to evaluate how efficiently code scales. Here's a breakdown of some key time complexities: 🟢 O(1): Constant time—optimal performance, independent of data size. 🟠 O(n): Linear time—performance decreases proportionally as data grows. 🔴 O(n²): Quadratic time—performance declines rapidly as input increases, often leading to slowdowns with large data. In my experience, optimising code for scalability requires selecting the right algorithm from the start. Efficient algorithms (like O(log n)) can make all the difference, especially in real-world applications where speed and resource usage matter. If you're aiming for performance optimisation, always consider the impact of time complexity on your code's scalability.

What makes a no-code platform truly scalable? There are two key aspects: 1 - the structural approach to application layers (stacks)   2 - the method of implementing application logic (interpretation vs. compilation) Read more about these in the Triggre blog: https://lnkd.in/eeuCVPti 

🧠 𝐓𝐞𝐜𝐡 𝐓𝐢𝐩 𝐓𝐮𝐞𝐬𝐝𝐚𝐲: 𝐒𝐮𝐩𝐞𝐫𝐜𝐡𝐚𝐫𝐠𝐞 𝐘𝐨𝐮𝐫 𝐒𝐐𝐋 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 𝐰𝐢𝐭𝐡 𝐒𝐦𝐚𝐫𝐭 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠 🚀 Follow for more software engineering tips: Aditya Singh Ever wonder why your database queries are crawling? I just slashed our app's response time by 70% with this simple trick: 👉 Strategic indexing on frequently accessed columns Here's the lowdown: 1. Identify slow queries: Used EXPLAIN ANALYZE to spot the bottlenecks. Our user search was the culprit! 2. Pick your targets: Columns in WHERE, JOIN, and ORDER BY clauses are prime candidates. 3. Create the index: ` CREATE INDEX idx_user_lastname ON users(last_name); ` 4. Before vs After: Before: 2.3 seconds After: 0.7 seconds That's a 70% improvement! 🎉 5. Don't over-index: Indexes speed up reads but slow down writes. Balance is key! 6. Maintain your indexes: Regularly review and update as your data and query patterns change. Bonus tip: Composite indexes can be magic for multi-column conditions. Remember, every millisecond counts when you're scaling! Follow for more software engineering tips: Aditya Singh What's your go-to query optimisation trick? Share below and let's learn together! 👇 #TechTipTuesday #DatabaseOptimisation #SQLTips #PerformanceTuning #SoftwareEngineering

🤔 Let's talk about #BigOnotation! 🚀 Big O notation is a fundamental concept in computer science and software development. It helps us analyze the performance and scalability of algorithms, crucial for writing efficient code. Here's a quick rundown: 1️⃣ What is Big O notation? Big O notation describes the worst-case scenario of how the runtime or space requirements of an algorithm grow as the size of the input grows. It provides a way to compare the efficiency of different algorithms in terms of time and space complexity. 2️⃣ Why does it matter? Efficient algorithms can make a huge difference in software performance. Whether you're building a simple script or a complex application, understanding Big O notation can help you write code that scales gracefully, even as your data grows. 3️⃣ Common Notations: O(1) - Constant Time: The algorithm's runtime or space requirement remains constant, regardless of input size. O(log n) - Logarithmic Time: The runtime grows logarithmically as the input size increases. O(n) - Linear Time: The runtime grows linearly with the size of the input. O(n^2) - Quadratic Time: The runtime grows quadratically with the size of the input. O(2^n) - Exponential Time: The runtime doubles with each additional element in the input. 4️⃣ How to Use Big O Notation? Understanding Big O notation allows developers to make informed decisions when choosing algorithms and data structures. By selecting the most efficient approach, you can optimize your code for performance and scalability. 5️⃣ Example: Consider searching for an element in an array. A linear search (O(n)) checks each element one by one, while a binary search (O(log n)) halves the search space with each iteration. For large datasets, the difference in performance can be significant.

Optimizing Code Performance: Techniques Every Developer Should Know In today's fast-paced tech landscape, optimizing code performance isn't just about efficiency—it's about delivering a superior user experience and reducing operational costs. Here are some key techniques every developer should consider: 1 Algorithm Efficiency: Start by choosing the right algorithms and data structures for your application. Small changes in algorithm choice can lead to significant performance gains. 2. Code Profiling: Use profiling tools to identify bottlenecks in your code. By pinpointing which parts of your application are consuming the most resources, you can prioritize optimizations effectively. 3. Reduce I/O Operations: Minimize disk reads and writes, network calls, and database queries. Caching frequently accessed data and batching operations can reduce latency and improve overall performance. 4. Memory Management: Avoid memory leaks and excessive memory usage. Use efficient data structures, manage object lifecycles carefully, and consider techniques like object pooling. 5. Concurrency and Parallelism: Leverage multi-threading, asynchronous programming, and parallel processing where applicable. Distributing workload across multiple cores can speed up execution. 6. Optimized Database Queries: Ensure your database queries are optimized with appropriate indexing, query caching, and minimizing unnecessary data retrieval. 7. Use of Compiler Optimizations: Understand and utilize compiler optimizations specific to your programming language and platform. Compiler flags and settings can often yield performance improvements. 8. Code Refactoring: Regularly review and refactor code to eliminate redundant operations, simplify complex logic, and improve readability. Cleaner code often performs better. 9. Hardware Considerations: Understand the hardware your application runs on and optimize accordingly. Tailor optimizations to leverage CPU caches, memory architecture, and network capabilities. 10. Continuous Monitoring and Iteration: Performance optimization is an ongoing process. Monitor performance metrics in production, gather feedback, and iterate on improvements. Remember, optimizing code performance isn't just about speed—it's about creating more efficient, reliable, and scalable software solutions. Embrace these techniques to deliver optimal user experiences and stay ahead in the competitive tech landscape. Ready to unlock peak performance for your software? Let's optimize together and elevate your application to the next level! 💡✨ Drop us a message to start the conversation. #SoftwareDevelopment #CodePerformance #TechTips #DeveloperTips #Optimization #SoftwareOptimization #CodePerformance #TechConsulting #DeveloperCommunity