Merkle Trees
In this tutorial, we are going to discuss about Merkle trees. Let’s understand how Dynamo uses Merkle trees for anti-entropy operations. Anti-entropy through Merkle trees is a technique commonly used in distributed systems to ensure data consistency across multiple nodes.
As we know, Dynamo uses vector clocks to remove conflicts while serving read requests. Now, if a replica falls significantly behind others, it might take a very long time to resolve conflicts using just vector clocks. It would be nice to be able to automatically resolve some conflicts in the background. To do this, we need to quickly compare two copies of a range of data residing on different replicas and figure out exactly which parts are different.
Merkle Trees Overview
A Merkle tree (also known as a hash tree) is a tree structure where:
- Leaf nodes contain cryptographic hashes of individual data blocks.
- Non-leaf nodes contain cryptographic hashes of their child nodes.
- The root node contains a hash that represents the entire dataset.
This structure allows a system to efficiently verify the integrity and consistency of large datasets by comparing the root hash values of two trees.
What are Merkle trees?
A replica can contain a lot of data. Naively splitting up the entire data range for checksums is not very feasible; there is simply too much data to be transferred. Therefore, Dynamo uses Merkle trees to compare replicas of a range. A Merkle tree is a binary tree of hashes, where each internal node is the hash of its two children, and each leaf node is a hash of a portion of the original data.
Comparing Merkle trees is conceptually simple:
- Compare the root hashes of both trees.
- If they are equal, stop.
- Recurse on the left and right children.
Ultimately, this means that replicas know precisely which parts of the range are different, and the amount of data exchanged is minimized.
Merits and demerits of Merkle trees
The principal advantage of using a Merkle tree is that each branch of the tree can be checked independently without requiring nodes to download the entire tree or the whole data set. Hence, Merkle trees minimize the amount of data that needs to be transferred for synchronization and reduce the number of disk reads performed during the anti-entropy process.
The disadvantage of using Merkle trees is that many key ranges can change when a node joins or leaves, and as a result, the trees need to be recalculated.
How Merkle Trees Help with Anti-Entropy
Merkle trees enhance the anti-entropy process by enabling nodes to compare data more efficiently:
- Instead of comparing all the data across nodes, the nodes first compare their Merkle tree root hashes.
- If the root hashes match, the data is identical, and no further action is needed.
- If the root hashes differ, the nodes recursively compare the hashes of child nodes to identify the exact blocks of data that are different.
- Once the differences are found, only the non-matching data blocks are transmitted, reducing the amount of data exchanged during synchronization.
Advantages of Using Merkle Trees for Anti-Entropy
- Efficiency: By comparing hashes instead of raw data, Merkle trees minimize the amount of data transmitted between nodes.
- Scalability: Merkle trees scale well with large datasets, as only parts of the tree that differ need to be traversed and transmitted.
- Integrity: Cryptographic hashing ensures that data integrity is maintained during the comparison process.
Use Cases
Merkle trees and anti-entropy processes are widely used in systems like:
- Cassandra and DynamoDB: Distributed databases that use Merkle trees for efficient anti-entropy processes.
- Git: Version control system where Merkle trees are used to compare different versions of data.
- Blockchain: Bitcoin and Ethereum use Merkle trees to verify transactions.
In summary, anti-entropy through Merkle trees is a powerful mechanism for maintaining data consistency in distributed systems while minimizing network and computational overhead.
That’s all about the Merkle Trees. If you have any queries or feedback, please write us email at contact@waytoeasylearn.com. Enjoy learning, Enjoy system design..!!