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CAP Theorem - Debunking Myths

The CAP theorem is a widely recognized idea in the field of distributed systems. It represents three key concepts: Consistency, Availability, and Partition Tolerance. While most of us are familiar with its definition, the devil lies in the details. In this discussion, we'll clarify common myths and misunderstandings. We'll start by explaining the CAP theorem in detail, and then explore various scenarios that may challenge the common interpretation of it. CAP theorem also known as Brewer's theorem states that any distributed data store can provide only two of the following three guarantees: Consistency: For every read request, the system should provide the most recent write or an error. Note that this consistency is different from the consistency of the ACID theorem Availability: For every request, the system should provide a response, even if it’s not the latest data. In other words, all non-failing (healthy) nodes in the distributed system return a valid ...

Understanding Merkle Tree

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...

Event Driven Architecture - SAGA Pattern (Part-1 : Choreography Model)

The Saga pattern is a distributed transactional pattern used in microservices architecture to maintain data consistency across multiple services. It helps manage long-running transactions involving multiple services by breaking them down into smaller, more manageable work units. There is a famous Database per Service pattern in the Microservice world. Under this paradigm, each service maintains its own dedicated database. Some business transactions, however, span multiple services so we need a mechanism to implement transactions that span through services. Take, for instance, the scenario of placing an online order, which involves actions like inventory verification and item reservation till payment completion. Since services such as Orders, Inventory, and Payment operate on separate databases, the application cannot simply use a local ACID transaction. 2 Phase Commit Protocol is one of the options being used for ensuring transactions across services. However, it has se...

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