How to Implement Merkle Patricia Tries in Ethereum
Introduction
In this practical guide, we will delve into the intricacies of Merkle Patricia Tries, which play a pivotal role in maintaining the integrity and security of data in blockchain technology. We will walk through a step-by-step implementation of a Merkle Patricia Trie, its architecture, and future trends and considerations. For a better understanding of the concept of Merkle Patricia Tries, check out our deep dive article here.
Estimated time to follow along: 25 mins - 35 mins
But before we delve in, here’s a list of things you need to have in place before you begin following this tutorial.
Pre-requisites
Knowledge of Merkle Patricia Tries (informational guide here).
Basic understanding of Ethereum.
Familiarity with Javascript.
Familiarity with some crypto terminologies.
Setting up the development environment: Visual Studio and NodeJs.
Merkle Patricia Trie Implementation
For our implementation, we’ll utilize this algorithm: In a trie, you can think of each node as a container that stores values using characters as keys. These nodes are connected, forming a tree-like structure. When you perform operations like adding, removing, or accessing data in a trie, they typically have a time complexity of O(h), where "h" represents the length of the data being stored or the depth of the tree.
Let's break down the algorithm for storing data in a typical Patricia trie:
Start with an empty container, which can be viewed as a key-value pair.
Go through the data (usually a hash or a sequence of characters) character by character.
For each character encountered, create a new empty container and associate it with the current character in the previous container.
Update your reference to the current container to the one you just created, effectively moving deeper into the trie.
Repeat these steps for each character in the data until you've created the entire branch.
Finally, at the end of this branch, store the actual data you want to associate with the label COV.
When you need to access data stored in the trie, you can simply retrieve the value associated with the COV label in the last container along the path. For deletion, you would remove the leaf container corresponding to the specific data you want to delete.
Hence, this algorithm helps efficiently organize and retrieve data within the trie. Let’s now implement the Patricia trie, but before that, make sure you’ve Visual Studio or any editor installed on your machine and have NodeJs installed.
Create your project directory and cd into it as shown:
Next, launch your VSCode editor and create a file structure similar to this:
In the cryptohelper file, we’ll create a hash function for hashing the necessary data as we proceed, as shown below.
Next, we have the transaction handler file that initializes the transaction, which is shown below:
This defines a Transaction class responsible for managing transactions. We begin by creating the sha256 hash function from the module cryptohelper. The class constructor initializes a new transaction with sender, recipient, and amount parameters, assigns it a unique ID, calculates its hash using a combination of transaction data, and maintains a transaction count. There are two static methods, getCount() and incrementCount(), for retrieving and updating the transaction count. Additionally, the calculateHash() method computes the transaction's hash, and toString() formats the transaction as a string.
To store our transaction, let’s create a transaction list file as shown below:
The
TransactionListclass has a constructor that initializes an empty array calledlist. This array is meant to store transactions.It includes a method called
addTransaction(transaction), which is used to add a new transaction to the list. When this method is called, it pushes the provided transaction object into thelistarray, effectively adding it to the list of transactions.The
TransactionListclass is exported at the end of the code using themodule.exports. This allows the class to be used externally in other parts of the codebase, making it a reusable component for managing lists of transactions.
Having created our data, which in this case is the transaction and our hashing function. Let’s implement the Patricia trie.
Here, we start by importing the necessary util module, which provides utility functions for working with objects and formatting output. Next, we define a class called PatriciaTrie. This class represents Patricia Trie’s data structure.
In the constructor method (
constructor()), we initialize an empty object as the root node of the Patricia Trie. This root node serves as the starting point for storing data.
The insertTransaction(transaction) method is used to add a transaction to the Patricia Trie. It takes a transaction object as its parameter. In the insertTransaction method, we have:
We start at the root node and obtain the hash of the given transaction.
Using a loop, we traverse the nodes of the trie based on the characters in the hash.
If a node for a character doesn't exist, we create it as an empty object.
We move to the next node and repeat this process for each character.
Lastly, we assign the provided transaction to the
"COV"property of the last node, effectively storing it.
The findTransaction(hash) method is used to retrieve a transaction from the Patricia Trie based on its hash. In the findTransaction method, we have:
We start at the root node and traverse the nodes using a loop based on the characters in the provided hash.
If at any point a node is missing, we return null to indicate that the transaction was not found.
If we successfully traverse the nodes and reach the end, we check if the
"COV"property exists in the last node.If it exists, we return the associated transaction; otherwise, we return null.
The deleteTransaction(hash) method is used to remove a transaction from the Patricia Trie based on its hash. In the deleteTransaction method, we have:
Similar to the previous methods, we start at the root node and traverse the nodes based on the characters in the provided hash.
If we encounter a missing node during traversal, we return false to indicate that the removal failed.
If we successfully traverse the nodes and reach the end, we check if the
"COV"property exists in the last node.If it exists, we delete it to remove the transaction and return true to indicate successful removal.
If the
"COV"property doesn't exist, we return false to indicate that the removal failed.We then export the
PatriciaTrieclass, making it available for use in other parts of the code.
Let’s test our implementation using the testScript file.
The code starts with importing the necessary modules to use in the code.
A new
TransactionListis created and populated with eight random transactions using a loop.A new instance of the Patricia trie data structure is created, and all transactions from the
TransactionListare added to it. The structure of the Patricia trie is displayed.It retrieves and prints the first transaction from the Patricia Trie using its hash.
The first transaction is removed from Patricia Trie, and the result is printed.
We try to retrieve the removed transaction (which should return null).
We try to retrieve a non-existent transaction by its hash (which should return null).
The picture below shows the output of the implementation:
Future Considerations for Merkle Patricia Tries
Evolving Technology and Trends
Interoperability Solutions: As the blockchain landscape diversifies, Merkle Patricia Tries should adapt to support interoperability between different blockchains. This involves developing protocols and standards that enable seamless data sharing and verification across disparate networks, promoting a more connected and efficient blockchain ecosystem.
Layer 2 Scaling: With the growing demand for scalable blockchain solutions, Merkle Patricia Tries can play a critical role in layer 2 scaling solutions. By optimizing the structure and algorithms, they can enhance the performance of off-chain and side-chain solutions like Plasma and Optimistic Rollups, providing faster and more cost-effective transactions while preserving data integrity.
Potential Improvements and Optimizations
Gas Efficiency: Gas costs remain a concern in blockchain transactions. To make Merkle Patricia Tries more accessible and cost-effective, ongoing research should focus on reducing the computational resources required for tree operations. This will make blockchain usage more efficient for both developers and end-users.
State Pruning: Blockchain networks can become unwieldy due to their ever-expanding state. Further research and development are needed to implement state-pruning techniques that allow blockchain nodes to efficiently manage their storage requirements. This optimization will enhance network sustainability and performance.
Exploring New Use Cases
Decentralized Identity (DID): Merkle Patricia Tries has the potential to revolutionize decentralized identity solutions. Research should delve into how they can be utilized to secure and manage DID data, ensuring privacy and security while allowing users to control their digital identities.
Non-Fungible Tokens (NFTs): NFTs have gained significant traction in the blockchain space. Merkle Patricia Tries can be used to maintain NFT ownership and provenance records efficiently. This could lead to improved transparency and trust in the NFT market.
Ongoing Research and Development
Cross-Chain Data Proofs: Enabling the generation of cryptographic proofs for data integrity across different blockchain networks will foster cross-chain compatibility. This research aims to create bridges that allow secure data transfer and validation between different blockchains, enhancing the overall blockchain ecosystem.
Conclusion
While MPTs find applications across various domains, they shine most brightly in blockchain technology. Ethereum, among other blockchain platforms, relies heavily on the Merkle Patricia Trie for secure and efficient data organization, storage, and validation. As we look to the future, the roadmap for Merkle Patricia Tries is promising. Their adaptability to evolving technology trends, potential improvements in gas efficiency and state pruning, exploration of new use cases such as decentralized identity and NFTs, and ongoing research into cross-chain data proofs make them a vital force in shaping the decentralized technology landscape.
