The DAO soft-fork try was troublesome. Not solely did it prove that we underestimated the unwanted side effects on the consensus protocol (i.e. DoS vulnerability), however we additionally managed to introduce an information race into the rushed implementation that was a ticking time bomb. It was not superb, 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 the 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 elements 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 totally lined by unit exams with a 3:1 test-to-code ratio; b) it was totally reviewed by six basis builders; and c) it was even manually dwell examined on a non-public community… But nonetheless, a deadly information race remained, which may have doubtlessly brought on 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 probability for the bug to floor. Unit exams can’t catch it, code reviewers might or might not catch it, and guide testing catching it might be unlikely. Our conclusion was that the event groups wanted extra instruments to carry out reproducible exams that will cowl the intricate interaction of a number of nodes in a concurrent networked state of affairs. With out such a software, manually checking the varied edge instances is unwieldy; and with out doing these checks repeatedly as a part of the event workflow, uncommon errors would develop into not possible to find in time.
And thus, hive was born…
What’s hive?
Ethereum grew massive to the purpose the place testing implementations grew to become an enormous burden. Unit exams are high-quality for checking numerous implementation quirks, however validating {that a} shopper conforms to some baseline high quality, or validating that shoppers can play properly collectively in a multi shopper setting, is all however easy.
Hive is supposed to function an simply expandable check harness the place anybody can add exams (be these easy validations or community simulations) in any programming language that they’re comfy with, and hive ought to concurrently be capable to run these exams in opposition to all potential shoppers. As such, the harness is supposed to do black field testing the place no shopper particular inner particulars/state might be examined and/or inspected, slightly emphasis could be placed on adherence to official specs or behaviors underneath totally different circumstances.
Most significantly, hive was designed from the bottom as much as run as a part of any shoppers’ CI workflow!
How does hive work?
Hive’s physique and soul is [docker](https://www.docker.com/). Each shopper 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 photos in hive, builders of the shoppers can assemble the absolute best setting for his or her shoppers to run in (dependency, tooling and configuration clever). Hive will spin up as many situations as wanted, all of them operating in their very own Linux techniques.
Equally, as test suites validating Ethereum shoppers are docker photos, the author of the exams can use any programing setting he’s most aware of. Hive will guarantee a shopper is operating when it begins the tester, which may then validate if the actual shopper conforms to some desired habits.
Lastly, network simulations are but once more outlined by docker photos, however in comparison with easy exams, simulators not solely execute code in opposition to a operating shopper, however can really begin and terminate shoppers at will. These shoppers run in the identical digital community and may 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 function. Hive can present validation past what’s possible from a mean developer’s perspective: operating intensive exams that may require advanced execution environments; and checking networking nook instances that may take hours to arrange.
Within the case of the DAO hard-fork, past all of the consensus and unit exams, we wanted to make sure most significantly that nodes partition cleanly into two subsets on the networking stage: one supporting and one opposing the fork. This was important because it’s not possible to foretell what antagonistic results operating two competing chains in a single community might need, particularly from the minority’s perspective.
As such we have carried out three particular community simulations in hive:
The first to verify that miners operating the total Ethash DAGs generate right block extra-data fields for each pro-forkers and no-forkers, even when making an attempt to naively spoof.
The second to confirm {that a} community consisting of combined pro-fork and no-fork nodes/miners appropriately splits into two when the fork block arrives, additionally sustaining the cut up afterwards.
The third to verify 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 alternative.
The attention-grabbing query although is: did hive really catch any errors, or did is simply act as an additional affirmation that every part’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 adjustments affected community habits.
There was some criticism of the go-ethereum crew for taking their time on the hard-fork implementation. Hopefully individuals will now see what we had been as much as, whereas concurrently implementing the fork itself. All in all, I consider hive turned out to play fairly an necessary function 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=check), with no less than one EVM benchmark software cooking in some exterior repository. They don’t seem to be being utilised to their full extent. They’ve a ton of dependencies, generate a ton of junk and are very sophisticated to make use of.
With hive, we’re aiming to mixture all the varied scattered exams underneath one common shopper validator that has minimal dependencies, might be prolonged by anybody, and may run as a part of the each day CI workflow of shopper builders.
We welcome anybody to contribute to the challenge, be that including new shoppers to validate, validators to check with, or simulators to seek out attention-grabbing networking points. Within the meantime, we’ll attempt to additional polish hive itself, including assist for operating benchmarks in addition to mixed-client simulations.
With a bit or work, possibly we’ll even have assist for operating hive within the cloud, permitting it to run community simulations at a way more attention-grabbing scale.
