Tech With Rahul

A Merkle Tree is a cryptographic tree structure used in computer science and distributed systems to efficiently verify the integrity of large sets of data (accuracy and consistency of data over its lifecycle).  Merkle Tree, also known as Hash Tree is a tree of hash values.  It has a tree structure in which each leaf node is a hash of a small portion of the data, and each non-leaf node is a hash of its children. It is used in applications such as  NoSQL databases, Git, Cryptocurrencies,  File Systems, etc. Some key characteristics of Merkle Tree are: Binary Tree Structure:  The Merkle Tree is a binary tree, where each leaf node represents a hash of data. Leaf Nodes: The data set is divided into fixed-size blocks or "leaves". Each leaf node contains the hash of a specific data block or piece of information. Non-Leaf Nodes: Non-leaf nodes in the tree represent the hash of the concatenation of their child node's hashes.  If the number of leaves is odd...

A Bloom filter is a space-efficient probabilistic data structure used to determine whether an element is a member of a set. The filter responds to queries with either a definite No or a probable Yes. The probabilistic nature of the Bloom filter is apparent in its ' probably Yes ' responses. This implies the possibility of false positives. When the Bloom filter indicates Yes, the element might or might not be present in the set. However, if the Bloom filter returns No, then the element is definitively not present in the set. Software engineering is all about tradeoffs. When using the Bloom filter, we sacrifice a bit of accuracy and flexibility to gain a substantial boost in performance. The key consideration is identifying use cases where it's acceptable to detect an element in the set, even if it's not actually present. Let us understand the core algorithm first then we will see its real life applications: Algorithm: A key element in the construction of a Bloom Filter i...