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It covers Ethereum as a concept, explains the Ethereum tech stack, and documents advanced topics for more complex applications and use cases. What are the main problems with Ethereum? One common concern about Ethereum is the issue of scalability. Like Bitcoin, Ethereum suffers from the flaw that every transaction needs to be processed by every node in the network. With Bitcoin, the size of the current blockchain rests at about 15 GB, growing by about 1 MB per hour.
What is the storage and transaction Trie in Ethereum? Every ethereum account has its storage trie. Bottom up, we hash the proofs pairwise until we end up with one hash only, which forms the root of the tree. Solution overview The architecture on AWS consists of three main parts: The backend with document storage and logic to create the Merkle trees and store them on blockchain The blockchain node itself connecting to Ethereum mainnet A front-end component that can verify individual proofs with their data on blockchain We receive the documents for storage and proofing through Amazon API Gateway.
The event documents bucket holds all the XML documents. Periodically, the aggregator Lambda function is called to do four things: Take all documents for a specific month from Amazon S3 and hash them. Aggregate the individual hashes into a Merkle tree and send the root hash as a transaction to blockchain. Store the Merkle proof of each individual hash as Amazon S3 metadata with the document.
The front end is a static website based on React. It retrieves the documents for a particular month from Amazon S3 and verifies them against the root hash on blockchain. Implementation The challenging parts of the implementation are in the aggregator Lambda function and the smart contract. Aggregator Lambda function The aggregator function creates the Merkle tree of all documents for a specific month.
Here it is written in Python. The function is not restricted to Python, the same functionality could be implemented in Javascript or another suitable language too. Now we can iterate through the list. It has to match the order in the verification step. The following figure illustrates this Merkle tree. Merkle trees are very useful to prove that a particular data point is part of the data structure.
We can recreate the branch that leads from the data point to the root of the tree, shown as the orange path in the preceding figure. In the tree, we can verify the existence of the orange XML document. We need two additional data points: first, we need the so-called proof blue hashes for an element. The proof contains the hashes to do the pairwise hashing without recreating the entire tree each time. Now we need our second data point, the actual root hash red , which we can retrieve from the blockchain.
If our calculated root matches the one retrieved from the blockchain, we have proven two things: The original document was part of the Merkle tree at its original creation The document existed when the root hash was stored on blockchain Therefore, to allow for verification at a later time, we need to store the Merkle proofs for each document. We do that by adding Amazon S3 metadata to the object in Amazon S3.
That way, the proof can never be separated from the document itself. We can later retrieve the event from blockchain. Additionally, it stores the block number of the last time an event has been emitted. This is useful to traverse all events that the smart contract has ever received. It is not necessary for verification. It then iterates through the proof to recreate the branch. The if Here we assume a tree from sorted pairs. Finally, the function returns whether the recreated root hash matches the provided root.
Conclusion Due to the immutability and transparency of blockchains, they can be a useful tool for notarizing documents. However, storage on blockchain is very expensive and transaction fees are volatile. Therefore, we have to make sure to reduce the number of transactions to a minimum. Merkle trees enable us to compress all digital fingerprints into a single root hash.
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