The hottest Data Structures Substack posts right now

And their main takeaways
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#48

The Nibble 2 implied HN points 24 Feb 24
  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.
Low Latency Trading Insights 117 implied HN points 11 Feb 24
  1. The requirements for a rate-limiting algorithm include precise event counting, fast performance especially during market turbulence, and minimal impact on cache memory.
  2. Creating a rate-limiting algorithm using a multimap for counting events has inefficiencies; a better solution involves enhancements for optimal performance.
  3. A bounded approximation approach for rate limiting achieves memory efficiency by assuming a minimum time precision and implementing a clever advance-and-clear mechanism.
The ZenMode 15 HN points 10 Feb 24
  1. Caching like Redis stores frequently used data for faster retrieval, improving response times, reducing database load, and leading to cost-effectiveness in running high-traffic applications.
  2. Redis is fast due to in-memory storage, optimized data structures, reduced I/O operations, single-threaded architecture, and event-driven design, but has limitations like limited capacity and issues with data persistence.
  3. Choosing the right caching system, like Redis, requires considering factors like data size, access patterns, consistency requirements, and fault tolerance for high availability and durability.
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Confessions of a Code Addict 46 HN points 14 Sep 23
  1. Python uses Bloom filters in its string data structure to speed up certain string processing functions like strip and splitlines.
  2. The unique Bloom filter implementation in CPython uses an unsigned long type to represent the bit vector, making storing and querying items more efficient.
  3. CPython determines the position in the bit vector for adding and querying characters by using the lower n-bits of the character, avoiding costly hash computations.
Data Engineering Central 157 implied HN points 13 Mar 23
  1. Understanding Data Structures and Algorithms is important for becoming a better engineer, even if you may not use them daily.
  2. Linked Lists are a linear data structure where elements are not stored contiguously in memory but are linked using pointers.
  3. Creating a simple Linked List in Rust involves defining nodes with values and pointers to other nodes, creating a LinkedList to hold these nodes, and then linking them to form a chain.
Confessions of a Code Addict 5 HN points 05 Sep 23
  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
Get Code 7 implied HN points 22 Feb 23
  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.