Bloom Filters

In general, the worse case scenario when searching through a data set is when the datum being searched for doesn’t exist. In this case, the complete data storage needs to be searched before it’s possible to conclude that the datum cannot be found. If only there was a way to eliminate the need to perform unnecessary searches when we know the data won’t be found. Fortunately, there is a data structure that allows you to do just that. This data structure is knows as a “Bloom Filter”.
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Functional programming using bind

Since the introduction of the STL (Standard Template Library) the use of functors has been a prevalent part of writing C++. Most of the STL algorithms require the use of a functor. For example, the std::transform requires a function object that, given an input of the current value of the current position, it will return a new value what will be used to modify the current value.
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Lowest Common Ancestor (BST)

The Binary Search Tree (BST) is a tree like data structure that allows for the quick lookup of data. They work by storing inserted data in a tree like structure that uses the “divide and conquer” approach to locate data. Each node in the tree has, at most, two children. The left hand side (lhs) node will always contain a value that is less than it’s parent. The right hand side (rhs) node will always contain a value that is greater-than or equal to it’s parent.

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LRU Cache Implementation

One of the problems any developer will eventually have to resolve is one of latency; specifically, being able to retrieve and process data in a timely fashion.  This issue can come in many guises but they generally manifest as needing to read data from a backing store that cannot deliver the high performance needed by the application. This can be a tricky problem to solve but the general method is to implement some form of caching. The remainder of this article will discuss one caching mechanism, called the LRU Cache.

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