Rene Yibowei

Cryptography

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Modern databases achieve high speed and efficiency through the use of advanced data structures and indexing techniques. This includes hash indexes for fast insertion and lookup, B-Trees for optimized reads and writes, skip lists for fast operations on dynamic datasets, memtables for efficient writes, and SSTables for optimized storage of key-value pairs. Additional structures like inverted indexes, Bloom filters, bitmap indexes, R-trees, and Write-Ahead Logs further enhance performance and reliability by improving search capabilities, reducing memory usage, speeding up complex queries, and ensuring data consistency even during crashes.

10 Data Structures That Make Databases Fast and Scalable

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Very effectively written and visualised. R trees are mainly used in spatial data queries e.g - finding overlapping geo location, search a city or place.
This reduces a lot of calculation required otherwise.

t’s always impressive how the right data structures can transform databases into fast, scalable powerhouses. A reminder of how much brilliance goes into what we often take for granted

It is a nice article, but I could only read the first two without a subscription :(