Best of Data Structures — July 2025

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    Article
    Avatar of collectionsCollections·44w

    Mastering Data Structures and Algorithms for Coding Interviews

    A comprehensive guide covering essential data structures and algorithms needed for coding interview success. Key topics include arrays, hashmaps, linked lists, binary search, graph traversal algorithms, and dynamic programming. The guide emphasizes pattern recognition over random problem solving, recommending a structured approach of mastering 100-150 problems across common coding patterns like sliding window and two pointers.

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    Article
    Avatar of francofernandoThe Polymathic Engineer·45w

    How to Tackle Coding Interviews

    A structured 7-step framework for succeeding in coding interviews: listen carefully to understand the problem, create meaningful examples, start with brute force solutions, optimize using BUD methodology (bottlenecks, unnecessary work, duplicated work), walk through the algorithm before coding, write clean modular code with descriptive variables, and thoroughly test the solution. The approach emphasizes communication, systematic problem-solving, and demonstrating debugging skills when issues arise.

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    Video
    Avatar of developedbyeddevelopedbyed·44w

    Leetcode is not scary, it's actually really fun

    LeetCode problems are actually enjoyable brain training exercises rather than scary interview hurdles. The key is approaching them as puzzles that build problem decomposition skills applicable to real-world programming. Starting with structured roadmaps like arrays and hashing, then gradually building intuition through practice makes the experience rewarding. Three classic problems demonstrate different approaches: two-sum shows optimization from O(n²) to O(n) using hash maps, contains duplicate leverages sets for efficient lookups, and valid anagram can be solved through character counting or string sorting techniques.

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    Video
    Avatar of TechWithTimTech With Tim·44w

    How I Mastered Data Structures and Algorithms

    A practical guide to mastering data structures and algorithms efficiently for coding interviews. The approach emphasizes choosing an easy language like Python, learning theory quickly without over-studying, practicing 75-100 quality problems instead of hundreds, and simulating real interview conditions with mock interviews and whiteboard practice. The key is quality over quantity, proper preparation methodology, and building confidence through thorough practice.

  5. 5
    Article
    Avatar of thedataengineerThe Data Engineer·43w

    We stopped relying on bloom filters and now sort our ClickHouse primary key on a resource fingerprint. It cut our log query scans to 0.85% of blocks

    A development team optimized ClickHouse log query performance by replacing bloom filter skip indexes with a deterministic resource fingerprint approach. They sort their primary key on a hash of cluster, namespace, and pod information, which groups logs from the same source together. This change reduced block scanning from nearly 100% to just 0.85% (222 out of 26,135 blocks) for single namespace queries, significantly improving I/O and latency. The team is now exploring ClickHouse's native JSON column type to further optimize GROUP BY operations.

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    Article
    Avatar of hnHacker News·47w

    People Keep Inventing Prolly Trees

    Prolly trees, a data structure combining Merkle trees with content-defined chunking, have been independently invented at least four times since 2009 under different names. Starting with bup in 2009, then Noms in 2015 (coining "prolly tree"), French researchers in 2019 ("Merkle Search Trees"), and DePaul University in 2020 ("Content-Defined Merkle Trees"). These structures provide history independence, efficient diffing, structural sharing, and self-balancing properties, making them valuable for version control systems and distributed applications. The repeated independent discovery suggests strong demand for this technology in modern software development.

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    Article
    Avatar of do_communityDigitalOcean Community·45w

    How to Use a Priority Queue in Python

    Priority queues in Python can be implemented using the heapq module for single-threaded applications or queue.PriorityQueue for thread-safe operations. The heapq module provides a min-heap implementation with O(log n) time complexity for push/pop operations, while queue.PriorityQueue offers built-in synchronization for multithreaded environments. Max-heaps can be simulated by negating values or using custom comparison methods. Priority queues are essential for task scheduling, pathfinding algorithms, and any scenario requiring ordered processing based on element priority.

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    Video
    Avatar of codeheadCodeHead·43w

    Top 10 DSAs YOU NEED TO KNOW For Your Coding Interview

    A comprehensive guide covering the 10 most essential data structures and algorithms for coding interviews, including arrays, hashmaps, stacks, queues, binary search, recursion, trees, heaps, and graphs. Each concept is explained with practical examples and common interview problem patterns, designed to help developers recognize and solve algorithmic challenges efficiently during technical interviews.

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