New KB articles published for the week ending 25th May,2019
June 13, 2019
New KB articles published for the week ending 1st June,2019
June 13, 2019
How to use the BigQuery Chainlink oracle
In this section we’ll describe how useful applications can be built using Google Cloud and Chainlink.
Use case 1: Prediction marketplaces
Participants in prediction marketplaces allocate capital to speculate on future events in general. One area of intense interest is which smart contract platform will predominate because, being networks ecosystems, their value will follow a power law (i.e. winner-take-all) distribution. There are many differing opinions about which platform will succeed, as well as how success can be quantified.
By using the crypto public datasets, it’s possible for even complex predictions like the recent $500,000 bet about Ethereum’s future state to be settled successfully on-chain. We’ve also documented how the variety, volume, recency, and frequency of Dapp utilization can be measured by retrieving 1-, 7-, and 30-day activity for a specific Dapp.
These metrics are known as daily-, weekly-, and monthly-active users and are frequently used by web analytics and mobile app analytics professionals to assess website and app and success.
Use case 2: Hedging against blockchain platform risk
The decentralized finance movement is rapidly gaining adoption due to its successful reinvention of the existing financial system in blockchain environments which, on a technical basis, are more trustworthy and transparent than current systems.
Financial contracts like futures and options were originally developed to enable enterprises to reduce/hedge their risk related to resources critical to their operation. Similarly, data about on-chain activity such as average gas prices, can be used to create simple financial instruments that provide payouts to their holders in cases where gas prices rise too high. Other qualities of a blockchain network, e.g. block times and/or miner centralization, create risks that Dapp developers want to protect themselves against. By bringing high quality data from the crypto public datasets to financial smart contracts, Dapp developers’ risk exposure can be reduced. The net result is more innovation and accelerated blockchain adoption.
We’ve documented how an Ethereum smart contract can interact with the BigQuery oracle to retrieve gas price data at a particular point in time. We’ve also implemented a stub of a smart contract option showing how the oracle can be used to implement a collateralized contract on future gas prices, a critical input for a Dapp to function.
Use Case 3: Enabling commit/reveals across Ethereum using submarine sends
One of the commonly mentioned limitations in Ethereum itself is a lack of transaction privacy, creating the ability for adversaries to take advantage of on-chain data leakage to exploit users of commonly used smart contracts. This can take the form of front-running transactions involving distributed exchange (DEx) addresses. As described in To Sink Frontrunners, Send in the Submarines, the problem of front-running plagues all current DExs and slows down the Decentralized Finance movement’s progress, as exchanges are a key component of many DeFi products/applications.
By using the submarine sends approach, smart contract users can increase the privacy of their transactions, successfully avoiding adversaries that want to front-run them, making DExs more immediately useful. Though this approach is uniquely useful in stopping malicious behavior like front-running, it also has its own limitations, if done without an oracle.
Implementing submarine sends without an oracle produces blockchain bloat. Specifically, the Ethereum virtual machine allows a contract to see at maximum 256 blocks upstream in the chain, or approximately one hour. This maximum scope limits the practical usefulness of submarine sends because it creates unnecessary denormalization when rebroadcasting of data is required. In contrast, by implementing submarine sends with an oracle, bloat is eliminated because operating scope is increased to include all historical chain data.
Conclusion
We’ve demonstrated how to use Chainlink services to provide data from the BigQuery crypto public datasets on-chain.This technique can be used to reduce inefficiencies (submarine sends use case) and in some cases add entirely new capabilities (hedging use case) to Ethereum smart contracts, enabling new on-chain business models to emerge (prediction markets use case).
The essence of our approach is to trade a small amount of latency and transaction overhead for a potentially large amount of economic utility. As a concrete example, ordinary submarine sends require on-chain storage that scales O(n) with blocks added to the blockchain, but can be reduced to O(1) if the calling contract waits an extra two blocks to call the BigQuery oracle.
We anticipate that this interoperability technique will lead developers to create hybrid applications that take the best of what smart contract platforms and cloud platforms have to offer. We’re particularly interested in bringing Google Cloud Platform’s ML services (e.g. AutoML and Inference APIs).
By allowing reference to on-chain data that is out of scope, we improve the operational efficiency of the smart contract platform. In the case of submarine sends, storage consumption that scales O(n) with block height is reduced to O(1), at the trade-off cost of additional transactional latency to interact with an oracle contract.
