Azure Blockchain Workbench
Azure Blockchain Workbench is a packaged solution that allows users to spin up end-to-end blockchain applications integrated with a number of Azure services with minimal effort on their part. The users provide Workbench with an Ethereum smart contract and associated metadata, and Workbench spins up the requisite Azure infrastructure. Users can authenticate and sign transactions using their AAD identities, submit transactions into the blockchain and consume events and transactions from the blockchain through the event grid as well as through a SQL database with no additional development effort on their part.
Ethereum Transaction Submitter is the component within Workbench responsible for submitting transactions into the blockchain. Blockchains are distributed systems where user-submitted transactions are eventually included (or mined) into the blockchain. The node through which the transaction is submitted is typically not the one that mines it and can, in fact, even die before gossiping about the transaction to the rest of the network. This requires periodic resubmissions of transactions if they’re not mined. Blockchains also exhibit forks where history can be rewound. In that case, transactions that were successfully mined previously need to be submitted again. Writing a high-throughput service component that can reliably submit transactions into the blockchain is thus a harder problem than it first appears.
Solution and Coyote’s key advantages
An initial version of the Ethereum Transaction Submitter was facing a number of reliability issues and missed corner cases, so the team decided to write the service component using Coyote. The use of Coyote helped the team in a number of ways.
Coyote safety conditions force the user to think about what must be true in all states (aka invariants) as opposed to the ways in which the system can fail. This helped the team gain clarity in their design.
Coyote helped find and fix a liveness condition that would have been near impossible to detect without Coyote’s testing methodology. Blockchains can occasionally fork, which was causing an initial version of the service component to permanently stall. Without a means of reproducing forks in a controlled way, such issues are hard to debug. The Azure team implemented a mock for the blockchain network using Coyote and were then able to systematically introduce a fork during testing. Coyote was able to detect the liveness bug, a fix was implemented, after which Coyote tests passed. As a result, the team had a much higher confidence in deploying their fix.
Developing the system using Coyote’s state machine programming model led to a highly concurrent and performant implementation.
Coyote allowed the team to concisely state the various success and failure conditions in the individual mocks, leaving the job of exploration to the tester. This allowed the team to effectively have a very large number of (traditional) test cases without having to write them all by hand.
Coyote’s deterministic repro of bugs allowed the team to debug and understand the bugs much more easily than before.