Efficient Set Construction Techniques

💡𝗚𝗼𝗹𝗮𝗻𝗴 𝗧𝗶𝗽: 𝗨𝘀𝗲 𝗺𝗮𝗽[𝘀𝘁𝗿𝗶𝗻𝗴]𝘀𝘁𝗿𝘂𝗰𝘁{} 𝗳𝗼𝗿 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗦𝗲𝘁-𝗹𝗶𝗸𝗲 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀!🔔 Go doesn’t have a built-in set type, but you can easily create one using a map with empty struct values. map[string]struct{} is an efficient way to represent a set in Go, as struct{} has zero memory overhead. 👀 𝗪𝗵𝘆 𝘂𝘀𝗲 𝗺𝗮𝗽[𝘀𝘁𝗿𝗶𝗻𝗴]𝘀𝘁𝗿𝘂𝗰𝘁{} 𝗳𝗼𝗿 𝗦𝗲𝘁𝘀? 🔹 𝗠𝗲𝗺𝗼𝗿𝘆 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: struct{} occupies no memory, unlike other values. 🔹 𝗙𝗮𝘀𝘁 𝗟𝗼𝗼𝗸𝘂𝗽𝘀: Constant time complexity (O(1)) for checking existence, adding, or deleting. 🔹 𝗡𝗼 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀: Ensures all elements in the collection are unique, just like a traditional set. 🔎 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗺𝗮𝗽[𝘀𝘁𝗿𝗶𝗻𝗴]𝘀𝘁𝗿𝘂𝗰𝘁{}: ➖ 𝗨𝗻𝗶𝗾𝘂𝗲 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀: When you need to store unique values (like tags or identifiers). ➖ 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝘁 𝗟𝗼𝗼𝗸𝘂𝗽𝘀: When frequent existence checks are required, such as filtering or deduplication. ➖ 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 𝗨𝘀𝗲: When managing a large collection where memory usage is a concern. 🔐 Using map[string]struct{} to create a set-like structure in Go is an efficient, idiomatic way to handle unique collections. Add your opinion? --- For more Golang content, consider following, like and repost ➡️✔️♻️ #go #golang #softwareengineering #softwaredevelopment #gophers

Discover a powerful Java Collections technique: Collections.newSetFromMap(). This method creates a Set backed by any Map implementation, enabling custom behaviors like thread-safe sets or memory-efficient collections. For example, using ConcurrentHashMap provides a thread-safe Set, ideal for concurrent applications, while WeakHashMap supports weakly referenced elements for caching. This approach is invaluable for scenarios requiring specialized set behavior, offering flexibility beyond standard HashSet or TreeSet. #Java

#DAY188 of 𝗗𝗦𝗔 : 𝗗𝗶𝘀𝗷𝗼𝗶𝗻𝘁 𝗦𝗲𝘁 — 𝗨𝗻𝗶𝗼𝗻 𝗯𝘆 𝗥𝗮𝗻𝗸 𝗮𝗻𝗱 𝗨𝗻𝗶𝗼𝗻 𝗯𝘆 𝗦𝗶𝘇𝗲 When solving problems involving connected components like Kruskal’s Algorithm, cycle detection, or network merging — Disjoint Set (aka DSU or Union-Find) is the go-to technique. But plain DSU can be slow. That's where optimizations like **𝗨𝗻𝗶𝗼𝗻 𝗯𝘆 𝗥𝗮𝗻𝗸** 𝗮𝗻𝗱 **𝗨𝗻𝗶𝗼𝗻 𝗯𝘆 𝗦𝗶𝘇𝗲** come into play. --- 🔹 𝗧𝗵𝗲 𝗗𝗶𝘀𝗷𝗼𝗶𝗻𝘁 𝗦𝗲𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗪𝗲 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻 𝘁𝘄𝗼 𝗮𝗿𝗿𝗮𝘆𝘀: * `parent[]`: stores the representative (ultimate parent) of each node * `rank[]` or `size[]`: helps us decide which tree to attach to which, minimizing depth This improves performance, especially when paired with **Path Compression**. -- 🔹 𝗦𝘁𝗲𝗽-𝗯𝘆-𝗦𝘁𝗲𝗽: 𝗨𝗻𝗶𝗼𝗻 𝗯𝘆 𝗥𝗮𝗻𝗸 1. Initialize each node as its own parent and set their rank to 0 2. To join `u` and `v`, first find their ultimate parents 3. If they are already the same, do nothing 4. Otherwise, attach the tree with smaller rank under the one with larger rank 5. If both have the same rank, attach one under the other and increment the rank This keeps trees flatter, improving future lookup speed. 🔹 𝗨𝗻𝗶𝗼𝗻 𝗯𝘆 𝗦𝗶𝘇𝗲 An alternate strategy is using size instead of rank. Steps are nearly the same — the key difference is: * We attach the smaller sized tree under the larger one * Update the size of the new root This can sometimes be more efficient when node depth doesn't directly impact tree size. --- 🔸 𝗥𝗲𝗮𝗹-𝗟𝗶𝗳𝗲 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 You're building a social network where you keep merging friend groups. Instead of checking each person’s list, DSU efficiently tells you if two users are already in the same group — in near constant time. --- 🔹 𝗧𝗶𝗺𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 With both union by rank/size and path compression, operations are nearly O(1) — specifically, O(α(n)) where α is the inverse Ackermann function. #codingjourney #problemsolving #disjointset #unionfind #unionbyrank #unionbysize #pathcompression #graphalgorithms #connectedcomponents #dsu #kruskalsalgorithm #dsaproblems #competitivecoding #cplusplus #techgrind #learntocode

Mind Refreshing: C++ std::unordered_set data-structure. The std::unordered_set class template in C++ is a dynamic-size container that offers unique functionality compared to other containers. Unlike std::deque or std::list, which organize their elements into a sequence of blocks or linked lists respectively, std::unordered_set internally organizes its elements into a hash table, allowing for efficient insertion, deletion, and search operations. The three main operations that std::unordered_set supports are element access, modifiers, and capacity. 🔵 Element Access: Element access refers to the ability to read or write the elements of the set using various methods, such as find() and count(). These methods enable efficient access to elements within the set, regardless of size. It’s important to note that, unlike std::deque or std::list, std::unordered_set does not support sequential access, so methods like front(), back(), operator[], and at() are not available. 🔵 Modifiers: Modifiers are operations that change the content or state of the set, such as insert(), erase(), and clear(). These methods allow for adding or removing elements from the set, maintaining its efficient structure. 🔵 Capacity: Capacity refers to the ability to query the size of the set, such as size(), max_size(), and empty(). These methods provide information about the number of elements in the set, the maximum number of elements that the set can hold, and whether the set is empty. 🔵 Bucket Interface: The bucket interface in std::unordered_set includes methods like bucket_count(), max_bucket_count(), bucket_size(n), and bucket(k). These methods provide information about the number of buckets, the maximum number of buckets that the unordered_set can hold, the number of elements in bucket n, and the bucket number where the element with the key k is located. 🔵 Hash Policy: Hash policy in std::unordered_set can be queried or set using methods like load_factor() and max_load_factor(). These methods provide information about the current load factor and allow setting the maximum load factor of the unordered_set. The load factor influences when the rehashing occurs. ✅ One of the benefits of using std::unordered_set in C++ is its efficient insertion, deletion, and search operations, making it suitable for scenarios where elements need to be added, removed, or searched frequently. This capability can be handy in applications requiring dynamic data structures with flexible storage requirements and fast access times. Reference: https://lnkd.in/g7Q6w_vi #cpp #cppprogramming #datastructure #programming #coding #computerscience #cplusplus #dailybiteofcpp