System Design | Design Key Value Store | Pt10 | Handling Permanent Failures | Merkle Tree
Welcome to Software Interview Prep! Our channel is dedicated to helping software engineers prepare for coding interviews and land their dream jobs. We provide expert tips and insights on everything from data structures and algorithms to system design and behavioral questions. Whether you're just starting out in your coding career or you're a seasoned pro looking to sharpen your skills, our videos will help you ace your next coding interview. Join our community of aspiring engineers and let's conquer the tech interview together! -------------------------------------------------- A Merkle tree, also known as a hash tree, is a binary tree data structure that is used to efficiently and securely verify the integrity and consistency of large sets of data. It was invented by Ralph Merkle in 1979. In a Merkle tree, the data is organized in a hierarchical structure where each leaf node represents a block of data or a hash value of the underlying data. The non-leaf nodes, also known as internal nodes, store the hash value of their child nodes. The root node of the tree, often called the Merkle root, contains the hash value of the entire data set. Here's how a Merkle tree is constructed: 1. Data Segments: The data set is divided into fixed-size segments or blocks. Each block is assigned a unique identifier. 2. Leaf Nodes: Each leaf node of the tree represents a data segment. The leaf nodes store either the actual data or the hash value of the data segment. If the data segments are large, it is common to store the hash value to save space. 3. Hash Calculation: Starting from the leaf nodes, the hash value of each data segment is computed using a cryptographic hash function, such as SHA-256. The hash values are then used as inputs to compute the hash values of the parent nodes. 4. Parent Nodes: The hash values of the leaf nodes are combined pairwise to compute the hash values of the parent nodes. This process continues until a single root node is reached, which represents the root hash or Merkle root of the entire data set. Merkle trees provide several benefits: 1. Data Integrity Verification: By comparing hash values, Merkle trees allow for efficient and secure verification of data integrity. By comparing a small number of hash values, one can determine if any individual block of data has been tampered with. 2. Efficient Proof Generation: Merkle trees enable the generation of proofs for the inclusion or absence of specific data blocks. Proofs can be generated with a logarithmic number of hash calculations, making them efficient for verification. 3. Incremental Updates: Merkle trees can efficiently handle incremental updates to the data set. When new data is added or modified, only a portion of the tree needs to be recomputed, minimizing computational overhead. Merkle trees are widely used in various applications, including blockchain technology, file systems, data synchronization, and distributed systems. They provide a robust and efficient mechanism for ensuring data integrity, detecting tampering or corruption, and enabling efficient verification and validation of large datasets.
Welcome to Software Interview Prep! Our channel is dedicated to helping software engineers prepare for coding interviews and land their dream jobs. We provide expert tips and insights on everything from data structures and algorithms to system design and behavioral questions. Whether you're just starting out in your coding career or you're a seasoned pro looking to sharpen your skills, our videos will help you ace your next coding interview. Join our community of aspiring engineers and let's conquer the tech interview together! -------------------------------------------------- A Merkle tree, also known as a hash tree, is a binary tree data structure that is used to efficiently and securely verify the integrity and consistency of large sets of data. It was invented by Ralph Merkle in 1979. In a Merkle tree, the data is organized in a hierarchical structure where each leaf node represents a block of data or a hash value of the underlying data. The non-leaf nodes, also known as internal nodes, store the hash value of their child nodes. The root node of the tree, often called the Merkle root, contains the hash value of the entire data set. Here's how a Merkle tree is constructed: 1. Data Segments: The data set is divided into fixed-size segments or blocks. Each block is assigned a unique identifier. 2. Leaf Nodes: Each leaf node of the tree represents a data segment. The leaf nodes store either the actual data or the hash value of the data segment. If the data segments are large, it is common to store the hash value to save space. 3. Hash Calculation: Starting from the leaf nodes, the hash value of each data segment is computed using a cryptographic hash function, such as SHA-256. The hash values are then used as inputs to compute the hash values of the parent nodes. 4. Parent Nodes: The hash values of the leaf nodes are combined pairwise to compute the hash values of the parent nodes. This process continues until a single root node is reached, which represents the root hash or Merkle root of the entire data set. Merkle trees provide several benefits: 1. Data Integrity Verification: By comparing hash values, Merkle trees allow for efficient and secure verification of data integrity. By comparing a small number of hash values, one can determine if any individual block of data has been tampered with. 2. Efficient Proof Generation: Merkle trees enable the generation of proofs for the inclusion or absence of specific data blocks. Proofs can be generated with a logarithmic number of hash calculations, making them efficient for verification. 3. Incremental Updates: Merkle trees can efficiently handle incremental updates to the data set. When new data is added or modified, only a portion of the tree needs to be recomputed, minimizing computational overhead. Merkle trees are widely used in various applications, including blockchain technology, file systems, data synchronization, and distributed systems. They provide a robust and efficient mechanism for ensuring data integrity, detecting tampering or corruption, and enabling efficient verification and validation of large datasets.