The hottest Data Structures Substack posts right now

And their main takeaways
Category
Top Technology Topics

Is your bucket leaky?

Freelance Footprints • 8 HN points • 20 Feb 24
🕹 Technology Algorithms Web Development Data Structures Networking Software Engineering
  1. The leaky bucket algorithm helps manage the rate of requests a web application can handle. It uses the idea of a bucket that can fill up and overflow if too many requests come in at once.
  2. In this algorithm, there are two key settings: the maximum number of requests allowed at a time and the rate at which requests are processed. This controls how quickly requests are dealt with and prevents overload.
  3. The leaky bucket algorithm is widely used in tech, such as by companies like SeatGeek for their waiting room systems, to ensure smooth user experiences without exceeding server limits.

[Solution]Problem 44: Longest Substring Without Repeating Characters (Meta / Facebook)

Technology Made Simple • 19 implied HN points • 01 Jul 22
🕹 Technology Programming Algorithms Data Structures Web Development
  1. The problem discussed is about finding the length of the longest substring without repeating characters.
  2. Approaching a coding problem by carefully considering the data structures to use, like dictionaries and sets, can be crucial in developing an efficient solution.
  3. Building a clear path to figure out the start and end points of the substring can lead to a more organized and effective algorithm implementation.

[Solution]Problem 42: Length of the longest subarray where all its elements are distinct (Microsoft)

Technology Made Simple • 19 implied HN points • 17 Jun 22
🕹 Technology Math Combinatorics Data Structures Algorithms
  1. When faced with a problem involving combinatorics, taking the time to read and analyze the question can provide crucial insights for a more efficient solving approach
  2. In the context of building max heaps from a list of integers, understanding the structure of heaps as complete binary trees allows for simplification of the problem into calculating combinations and values for left and right subtrees
  3. Calculating the values for the left subtree, such as height and number of nodes, can lead to a shift in the coding task complexity from generating heaps to conducting combinatorics

Problem 41: Create your own Datastructure with O(1) Time complexity (Dropbox)

Technology Made Simple • 19 implied HN points • 09 Jun 22
🕹 Technology Data Structures
  1. Creating a data structure with O(1) time complexity involves operations like adding, decrementing, and finding maximum and minimum values efficiently.
  2. Special requests can help boost newsletter visibility by encouraging readers to engage or provide feedback.
  3. Engaging with readers and asking for their input on future topics can lead to more interactive content creation.
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Why Math Mondays

Technology Made Simple • 19 implied HN points • 14 Mar 22
🚌 Education Math Interview Preparation Knowledge Data Structures Algorithms
  1. Understanding the importance of Math in preparation can help with solving problems better.
  2. Knowledge about subjects, problem interpretation, and connections between topics are crucial in problem-solving.
  3. Math Mondays aim to show how mathematical knowledge can enhance problem-solving skills and help make connections between different concepts.

#48

The Nibble • 7 implied HN points • 24 Feb 24
🕹 Technology AI Crypto Web Development Software Development Data Structures
  1. Feedback is being sought for the newsletter content to better cater to readers' interests.
  2. Interesting developments in the tech world include potential $7 trillion AI chip funding, Reddit IPO unique share distribution, and US Tech getting into the Indian market.
  3. Noteworthy advancements in AI technologies, such as new models like LLM streaming and fast inference engines, are shaping diverse industries.

Bloom Filters and Beyond: An Illustrated Introduction and Implementation

Confessions of a Code Addict • 5 HN points • 05 Sep 23
🕹 Technology Data Structures Programming Algorithms Python Applications
  1. Bloom filters are efficient data structures for quick searches in large datasets and minimize memory usage, with a probabilistic approach to determining membership
  2. Bloom filters use hash functions and bit vectors to store data item membership information while conserving memory by not storing actual items
  3. Counting Bloom Filters are an extension that allow item deletion but come with weaknesses such as handling hash collisions and counter overflow, providing an advanced data handling tool

A Nibble of Quadtrees in Rust

Get Code • 7 implied HN points • 22 Feb 23
🕹 Technology Data Structures WebAssembly Image Processing
  1. Quadtrees are data structures where each non-leaf node has exactly four children and are used to represent properties of two-dimensional space.
  2. Quadtrees are used for performance reasons, like optimizing collision detection in simulations with many moving objects.
  3. Implementing region quadtrees in Rust involves subdividing the tree based on error thresholds and region lengths to efficiently represent images.

Introduction to Hash Tables

Photon-Lines Substack • 2 HN points • 09 Jul 23
🕹 Technology Data Structures Algorithms Hashing Computing Programming
  1. Hash tables allow for efficient storage and retrieval of key-value pairs using hash functions.
  2. Real-world applications of hash tables include dictionaries, caching systems, database indexing, and symbol tables in compilers.
  3. Good hash functions must be efficient, deterministic, and ensure a uniform distribution of generated keys to avoid collisions.

Range Partitioning: Zero to One

aspiring.dev • 0 implied HN points • 17 Mar 24
🕹 Technology Databases Distributed Systems Data Structures Software Engineering Cloud Computing
  1. Range partitioning splits data into key ranges to improve performance and scalability. This method helps databases manage heavy loads by distributing data efficiently.
  2. Unlike hash partitioning, range partitioning allows for easier scaling. You can adjust the number of ranges as needed without the hassle of rewriting data.
  3. While range partitioning is powerful, it can be tricky to implement and may struggle with very sequential workloads. Planning is necessary to avoid creating performance hotspots.

Mastering Data at Scale: A Young Professional's Guide to Partitioning and Replication

DataSketch’s Substack • 0 implied HN points • 29 Feb 24
🕹 Technology Data Management Database Systems Information Architecture Data Structures Performance optimization
  1. Partitioning is like organizing a library into sections, making it easier to find information. It helps speed up searches and makes handling large amounts of data simpler.
  2. Replication means making copies of important data, like having extra copies of popular books in a library. This ensures data is safe and can be accessed quickly.
  3. Using strategies like hashing and range-based partitioning allows for better performance and scalability of data systems. This means your data can grow without slowing things down.

Object-Oriented Programming in LM

Andrew’s Substack • 0 implied HN points • 17 Oct 24
🕹 Technology Programming Software Data Structures Abstraction Modeling
  1. LM does not have a traditional object model, class model, or inheritance model, but it can represent some object-oriented features.
  2. The 'Diamond Problem' in inheritance can be avoided in LM by using plural type notation, which clearly shows type relationships.
  3. LM supports features like object subtyping, runtime types, and aspect-oriented programming, making it versatile despite its assembly-like nature.

A Walk-Through of String Search Algorithms

Photon-Lines Substack • 0 implied HN points • 22 Nov 24
🕹 Technology Algorithms Computing Data Structures Software Development Programming
  1. String search algorithms are important for everyday tasks like searching in browsers and filtering emails. They help make these tasks fast and easy, saving us time and effort.
  2. The Boyer-Moore algorithm is popular because it skips unnecessary comparisons by starting the search from the end of the pattern. This makes it much faster than simpler methods.
  3. The Robin-Karp algorithm uses hashing to represent patterns and text, which speeds up the search process. It's especially useful when you need to find multiple patterns quickly.

Trie (Keyword Tree) Prefix Tree

Better Engineers • 0 implied HN points • 27 May 20
🕹 Technology Data Structures Algorithms Programming Software Development Information Retrieval
  1. A Trie is a special data structure that helps store and retrieve strings efficiently by organizing them based on their prefixes. This makes searching and inserting words faster.
  2. Tries are useful in many applications, like predictive text and autocomplete features, because they allow quick access to stored words and their prefixes.
  3. While Tries have advantages over hash tables, such as no key collisions, they can require more memory and may perform slower when accessing stored data on slower devices.

Internal Storage Design of Modern Key-value Database Engines [Part 2]

Practical Data Engineering Substack • 0 implied HN points • 09 Aug 23
🕹 Technology Database Systems Data Structures Software Engineering Cloud Computing
  1. Sorted Segment files, or SSTables, help databases manage data more efficiently by keeping key-value records in order. This sorting makes searching and accessing data faster.
  2. In-memory storage, called Memtables, acts like a buffer that groups new data before it's saved to disk. This keeps data organized and speeds up how quickly new information can be written.
  3. Using a structure called the LSM-Tree helps optimize how databases write and read data. It focuses on reducing the time and effort it takes to handle a lot of updates and inserts, which is common in many apps.

Internal Storage Design of Modern Key-value Database Engines [Part 1]

Practical Data Engineering Substack • 0 implied HN points • 05 Aug 23
🕹 Technology Databases Software Data Structures Engineering
  1. Key-value stores use a simple model where each piece of data has a unique key and its associated value. This makes them great for fast lookups, especially when you only need to search by key.
  2. The log-structured data design helps improve writing speed by storing data in order and delaying updates until they're batched together. This means the system can handle many writes quickly.
  3. Many modern key-value stores are inspired by early successes like Amazon's DynamoDB and Google's BigTable. These systems have shaped how newer ones are built to be efficient and scalable.