The DAO soft-fork try was tough. Not solely did it prove that we underestimated the unintended effects on the consensus protocol (i.e. DoS vulnerability), however we additionally managed to introduce a knowledge race into the rushed implementation that was a ticking time bomb. It was not best, and though averted on the final occasion, the quick approaching hard-fork deadline appeared eerily bleak to say the least. We wanted a brand new technique…
The stepping stone in direction of this was an concept borrowed from Google (courtesy of Nick Johnson): writing up an in depth postmortem of the occasion, aiming to evaluate the basis causes of the difficulty, focusing solely on the technical points and applicable measures to stop recurrence.
Technical options scale and persist; blaming individuals doesn’t. ~ Nick
From the postmortem, one attention-grabbing discovery from the attitude of this weblog put up was made. The soft-fork code inside [go-ethereum](https://github.com/ethereum/go-ethereum) appeared strong from all views: a) it was completely coated by unit checks with a 3:1 test-to-code ratio; b) it was completely reviewed by six basis builders; and c) it was even manually dwell examined on a personal community… But nonetheless, a deadly information race remained, which may have doubtlessly precipitated extreme community disruption.
It transpired that the flaw may solely ever happen in a community consisting of a number of nodes, a number of miners and a number of blocks being minted concurrently. Even when all of these eventualities held true, there was solely a slight likelihood for the bug to floor. Unit checks can’t catch it, code reviewers might or might not catch it, and handbook testing catching it could be unlikely. Our conclusion was that the event groups wanted extra instruments to carry out reproducible checks that might cowl the intricate interaction of a number of nodes in a concurrent networked situation. With out such a instrument, manually checking the varied edge circumstances is unwieldy; and with out doing these checks repeatedly as a part of the event workflow, uncommon errors would develop into inconceivable to find in time.
And thus, hive was born…
What’s hive?
Ethereum grew giant to the purpose the place testing implementations grew to become an enormous burden. Unit checks are fantastic for checking varied implementation quirks, however validating {that a} consumer conforms to some baseline high quality, or validating that purchasers can play properly collectively in a multi consumer setting, is all however easy.
Hive is supposed to function an simply expandable take a look at harness the place anybody can add checks (be these easy validations or community simulations) in any programming language that they’re snug with, and hive ought to concurrently have the ability to run these checks in opposition to all potential purchasers. As such, the harness is supposed to do black field testing the place no consumer particular inner particulars/state could be examined and/or inspected, reasonably emphasis could be placed on adherence to official specs or behaviors below completely different circumstances.
Most significantly, hive was designed from the bottom as much as run as a part of any purchasers’ CI workflow!
How does hive work?
Hive’s physique and soul is [docker](https://www.docker.com/). Each consumer implementation is a docker picture; each validation suite is a docker picture; and each community simulation is a docker picture. Hive itself is an all encompassing docker picture. This can be a very highly effective abstraction…
Since Ethereum clients are docker photographs in hive, builders of the purchasers can assemble the very best setting for his or her purchasers to run in (dependency, tooling and configuration sensible). Hive will spin up as many cases as wanted, all of them working in their very own Linux techniques.
Equally, as test suites validating Ethereum purchasers are docker photographs, the author of the checks can use any programing setting he’s most accustomed to. Hive will guarantee a consumer is working when it begins the tester, which might then validate if the actual consumer conforms to some desired conduct.
Lastly, network simulations are but once more outlined by docker photographs, however in comparison with easy checks, simulators not solely execute code in opposition to a working consumer, however can really begin and terminate purchasers at will. These purchasers run in the identical digital community and might freely (or as dictated by the simulator container) join to one another, forming an on-demand non-public Ethereum community.
How did hive assist the fork?
Hive is neither a substitute for unit testing nor for thorough reviewing. All present employed practices are important to get a clear implementation of any characteristic. Hive can present validation past what’s possible from a median developer’s perspective: working intensive checks that may require complicated execution environments; and checking networking nook circumstances that may take hours to arrange.
Within the case of the DAO hard-fork, past all of the consensus and unit checks, we would have liked to make sure most significantly that nodes partition cleanly into two subsets on the networking degree: one supporting and one opposing the fork. This was important because it’s inconceivable to foretell what hostile results working two competing chains in a single community may need, particularly from the minority’s perspective.
As such we have carried out three particular community simulations in hive:
The first to examine that miners working the complete Ethash DAGs generate appropriate block extra-data fields for each pro-forkers and no-forkers, even when attempting to naively spoof.
The second to confirm {that a} community consisting of blended pro-fork and no-fork nodes/miners accurately splits into two when the fork block arrives, additionally sustaining the cut up afterwards.
The third to examine that given an already forked community, newly becoming a member of nodes can sync, quick sync and light-weight sync to the chain of their selection.
The attention-grabbing query although is: did hive really catch any errors, or did is simply act as an additional affirmation that the whole lot’s all proper? And the reply is, each. Hive caught three fork-unrelated bugs in Geth, however additionally closely aided Geth’s hard-fork growth by repeatedly offering suggestions on how modifications affected community conduct.
There was some criticism of the go-ethereum workforce for taking their time on the hard-fork implementation. Hopefully individuals will now see what we have been as much as, whereas concurrently implementing the fork itself. All in all, I imagine hive turned out to play fairly an vital position within the cleanness of this transition.
What’s hive’s future?
The Ethereum GitHub group options [4 test tools already](https://github.com/ethereum?utf8=%E2percent9Cpercent93&question=take a look at), with at the very least one EVM benchmark instrument cooking in some exterior repository. They aren’t being utilised to their full extent. They’ve a ton of dependencies, generate a ton of junk and are very difficult to make use of.
With hive, we’re aiming to combination all the varied scattered checks below one common consumer validator that has minimal dependencies, could be prolonged by anybody, and might run as a part of the every day CI workflow of consumer builders.
We welcome anybody to contribute to the undertaking, be that including new purchasers to validate, validators to check with, or simulators to search out attention-grabbing networking points. Within the meantime, we’ll attempt to additional polish hive itself, including help for working benchmarks in addition to mixed-client simulations.
With a bit or work, possibly we’ll even have help for working hive within the cloud, permitting it to run community simulations at a way more attention-grabbing scale.
