Ethereum transcends the conventional boundaries of cryptocurrency and has over the years evolved into an expansive ecosystem designed to support decentralized applications (dApps) and smart contracts.
The Ethereum blockchain stands out thanks to its complex data structures, transaction records and programmable logic in the form of smart contracts. This intricate setup paves the way for many applications, from finance to healthcare, going beyond traditional industry limitations.
The Ethereum Virtual Machine (EVM) is what enables Ethereum's smart contract functionality, making Ethereum a programmable blockchain. However, data extraction from EVM is often complex because transactions and contract executions are encoded in bytecode, which is not human-readable.
In March 2024, Dencun was launched to bring a new age of quicker, cheaper transactions. Ethereum has always struggled to increase the speed of its base layer in response to demand; hence, Dencun focuses on scaling via Layer 2.
How can you extract data from the Ethereum blockchain? And what are the key methods for accelerating this complex process? Keep reading to explore industry best practices for this challenging area of blockchain.
Let’s start at the beginning.
Unlike Bitcoin and its relatively simple Bitcoin Script, the Ethereum blockchain provides more than just money transfers through end-to-end transactions. Ethereum's smart contracts allow for much more complexity and are primarily based on a Turing-complete language, meaning they can run any computation given enough resources. Running Turing-complete code is Ethereum's fundamental differentiator, that’s why the most interesting data is found in the smart contract interactions.
To run code, Ethereum provides a virtual machine known as the Ethereum Virtual Machine (EVM). EVM abstracts the underlying machine, allowing smart contracts to operate on any computer with an Ethereum node running. A smart contract is just a program or piece of code written in a programming language and compiled for use with the Ethereum virtual machine.
Smart contracts create logs by firing events whenever a function is invoked by an external account (transaction) or another smart contract (internal transaction). Events can be broadly defined as asynchronous triggers with data. They’re asynchronous because the log isn’t written until the initiating transaction has been mined into a block.
The primary use case for events is to give smart contract return values to a user interface. Logs can also be used as a cost-efficient storage option.
To understand how the function is invoked, consider the contents of a transaction's (optional) data field. It might be any data, but it is usually a function call to a smart contract.
The transaction is directed at the smart contract and includes its address in the "to" column.
To determine which function the transaction is calling within the smart contract, the contract's functions must be understood before creating a hash table. The first 32 bits in the transaction data field correspond to the function's hash (function selector). This is followed by 256 bits for each parameter of the function. In essence, this implies that data fields in function calls are encoded. Decoding them is required before they can be used and interpreted.
Interacting with smart contracts involves decoding and understanding the data structured by Ethereum's Solidity programming language. It’s vital for reading from and writing to the blockchain. However, transforming this raw data into actionable insights is the real challenge.
Once you know the source code and have access to its ABI (Application Binary Interface), decoding the contract is quite easy. That is why contracts are verified on websites such as Etherscan or sourcify.dev. You can generate the contract ABI and that is enough to interact with it, for example, with the help of libraries such as ethers.
Ethereum's blockchain is a sequential ledger composed of blocks containing transactions and smart contract executions. These are linked using cryptographic hashes, which are secure but not straightforward to interpret without specialized tools.
Ethereum data, such as addresses and transaction values, is typically represented in hexadecimal format, making direct interpretation challenging without conversion tools. The data is sequential and sluggish.
The most obvious challenge is that almost everything is made of hexadecimal hashes rather than explicit text labels. For account addresses, this may be seen as a feature that allows for pseudonymity. However, to interact with smart contracts, you need to transform the data into a human-readable format.
Another consideration is the serialized nature of the data. Only a few use cases such as token analytics or DeFi tracking allow you to read the response from the blockchain in a single query. Typically, you'll have to traverse the chain with many queries for simple operations like showing an account's transaction history.
Finally, the interface for querying the data must be investigated. Before you begin, you must set up an archive node (such as Geth or Parity) to ensure all past data is available. As of March 2024, the size is around 1 TB, and it must be stored on SSD hard drives to function successfully. Synchronizing the Ethereum Mainnet would take many days or even weeks. Once the node operates, you can only query it using the JSON-RPC API. This is quite slow, especially given the large number of calls required due to the serial structure of the data.
Sharding represents a significant evolution in blockchain technology to enhance scalability and speed. By partitioning the blockchain into smaller, manageable pieces (shards), Ethereum aims to increase transaction throughput, paving the way for broader adoption. This development is especially pertinent as Ethereum grows and attracts diverse applications.
Initially proposed as part of Ethereum 2.0, sharding aims to split the blockchain into smaller, manageable pieces (shards) to improve scalability and transaction speed. However, traditional sharding presents technical and security challenges.
Ethereum's new approach, which focuses on data availability and rollups, is Danksharding. It divides data into "blobs" rather than partitioning the entire blockchain, aiming for a more scalable and secure solution.
Proto-Danksharding is a transitional step towards Danksharding, introducing data blobs for temporary storage, which helps reduce costs for Layer 2 solutions like rollups and increase network throughput.
There are a few approaches teams can use to accelerate data extraction from Ethereum:
To monitor smart contract development on a test net or the Ethereum Mainnet, you need to set up a node and create an index (database). There are essentially two ways to do so: by indexing the entire blockchain or by limiting the amount of data drained from the node and into the index. The choice of strategy depends on the balance of resource consumption and flexibility limits.
Another important factor in querying your blockchain index is the volume and type of data that you want to retrieve. The query language and database system are largely linked, so you must evaluate them simultaneously.
You need to be aware of the growing complexity of architectural issues and the distribution of such a database index across many teams/departments or even corporate organizations. This would call for authorization features and more.
Here’s a quick overview of the Extract, Transform, and Load (ETL) method for moving the Ethereum content into the database index.
First, extract the data relevant to you from the node, such as all information about certain smart contracts or the whole blockchain history beginning at a given point in time. The issue of how quickly you want to retrieve incoming blocks is of particular significance.
While it is certainly advantageous to keep up to date rapidly, you may encounter chain reorganizations on occasion. This happens when a client node discovers a new difficulty-longest well-formed blockchain that excludes one or more blocks that the client had previously assumed were part of the longest blockchain. These omitted blocks become orphans, and the data stored inside them must be cleansed from or at least marked in the index.
During the transformation process, you should probably make the data human-readable. Examples include labeling Ethereum accounts of known provenance (exchange wallets, smart contract names, etc.) and retrieving smart contract thanks to Application Binary Interfaces (ABI).
To match the raw data to the clear text labels, you'll need to use some sort of mapping. This might also imply obtaining previous pricing information for ERC-20 tokens and integrating the timestamp with the correct block height. That would be required to quantify value transfer transactions in fiat currencies, an example of enhancing blockchain data with external sources.
This data must then be placed into a database and indexed to ensure optimal query performance. Depending on the technology used, this procedure may take time and include several processes. You can now read from a database index considerably faster and begin looking into the blockchain data.
While the Ethereum Virtual Machine (EVM) has been foundational to Ethereum’s architecture, the future beckons with the possibility of incorporating a broader range of programming languages and technologies. Ethereum's exploration into environments like WebAssembly (Wasm) could revolutionize smart contract development by enabling compilation from multiple languages, not limited to Rust. This adaptability allows developers the flexibility to employ languages they are already proficient in, moving beyond the necessity of learning Solidity.
The integration of languages with robust safety and performance features, such as Rust, certainly presents exciting prospects due to its famed memory safety. However, the broader perspective includes leveraging any advanced technology that enhances performance, security, and developer accessibility. As Ethereum evolves with developments like sharding, the ease and efficiency of handling blockchain data are set to improve significantly, paving the way for a more robust and versatile platform.
These advancements in Ethereum’s technological framework could significantly widen its appeal and utility, fostering a more inclusive and innovative blockchain community.
Ethereum offers a complex but profoundly impactful framework for developing and deploying decentralized applications. Understanding its components, from the EVM to data extraction methods, is crucial for leveraging its full potential. As Ethereum continues to evolve, notably with sharding and potential new programming paradigms, its influence across industries is bound to grow.
