Data Processor

Unlocking the Full Potential of On-Chain Data with Verifiable Compute

HDP (short for Herodotus Data Processor) is a coprocessor that focuses on accessing extensive sets of on-chain data and running computations over them in a trustless manner.

HDP provides a powerful data-proving pipeline for computations over massive on-chain data. By verifying on-chain storage proofs on a zkVM, it securely enables the delegation of intensive computations involving authenticated on-chain data to fully off-chain logic.

Getting started by following the guide!

What HDP Enables

With HDP, you can unlock the following features:

• Access historical on-chain data:

HDP mainly uses the idea of storage proofs. By providing any historical snapshot of a block's root (such as a storage root or account root) and verifying a Merkle-Patricia proof against the root, you can retrieve verified historical data.

• Access on-chain data across multiple blockchains:

Using storage proofs and by supplying the valid root, you can access data from multiple chains on any chain via native messages or commitments.

• Run custom computations on accessible data:

We provide basic aggregate functions to compute the accessed on-chain data, and we also support custom computations by allowing you to write arbitrary programs.

Triggering HDP with a Single Request

Users can utilize the extensive features that HDP provides via a single request—either on-chain or off-chain—to trigger the full pipeline and access the computed results on-chain.

The process is straightforward:

• Specify which function to run

• Define the data to process

• Provide the context of the computation

This request is then processed, either synchronously or asynchronously, by the HDP operator. The result is delivered on-chain, allowing smart contracts to query it.

HDP Enables Powerful Use Cases

HDP enables cutting-edge use cases that were previously impossible to perform in a fully sound way due to computing complexity or data size constraints.

• Calculating Time-Weighted Average Price (TWAP):

TWAP smooths out short-term price fluctuations by averaging prices over a specified period. This results in a more stable and representative value of an asset pair, which is particularly valuable for financial applications like options pricing.

• Verifying Average Balance:

To prove an account maintained an average balance of 1 ETH over 1,000 blocks, you would set up a data lake to fetch the account balances for these blocks. Using the avg function, you can compute the average balance, verifying the account's balance consistency over time.

• Identifying Balance Fluctuations:

To count how often an account's average balance drops below 50 ETH, you could use the count_if function. This helps in assessing the frequency of significant balance reductions, providing insights into account activity.

• Ensuring Compliance with OFAC Sanctions:

If you want to restrict access to your dApp to addresses that have never sent funds to a sanctioned address, HDP enables this capability efficiently in a trustless way due to its ability to analyze historical on-chain data.

• Predicting a User's Balance:

HDP can perform sophisticated computations, such as linear regression. Given a set of historical ETH balances for an address, you can predict future balances using linear regression within HDP.