The DAO soft-fork try was tough. Not solely did it prove that we underestimated the uncomfortable 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 very best, and regardless that averted on the final occasion, the quick approaching hard-fork deadline regarded eerily bleak to say the least. We wanted a brand new technique…
The stepping stone in the direction of this was an thought 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 forestall recurrence.
Technical options scale and persist; blaming individuals doesn’t. ~ Nick
From the postmortem, one attention-grabbing discovery from the angle of this weblog publish 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 checks with a 3:1 test-to-code ratio; b) it was totally reviewed by six basis builders; and c) it was even manually stay examined on a non-public community… But nonetheless, a deadly information race remained, which might have probably induced extreme community disruption.
It transpired that the flaw might 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 not 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 checks that might cowl the intricate interaction of a number of nodes in a concurrent networked state of affairs. With out such a device, manually checking the assorted edge instances is unwieldy; and with out doing these checks constantly as a part of the event workflow, uncommon errors would turn into unattainable 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 checks are effective for checking numerous implementation quirks, however validating {that a} shopper conforms to some baseline high quality, or validating that purchasers can play properly collectively in a multi shopper surroundings, is all however easy.
Hive is supposed to function an simply expandable check harness the place anybody can add checks (be these easy validations or community simulations) in any programming language that they’re comfy with, and hive ought to concurrently be capable of run these checks towards all potential purchasers. As such, the harness is supposed to do black field testing the place no shopper particular inner particulars/state may be examined and/or inspected, relatively emphasis could be placed on adherence to official specs or behaviors beneath totally 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 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. It is a very highly effective abstraction…
Since Ethereum purchasers are docker photographs in hive, builders of the purchasers can assemble the very best surroundings for his or her purchasers to run in (dependency, tooling and configuration sensible). Hive will spin up as many situations as wanted, all of them working in their very own Linux methods.
Equally, as check suites validating Ethereum purchasers are docker photographs, the author of the checks can use any programing surroundings he’s most accustomed to. Hive will guarantee a shopper is working when it begins the tester, which may then validate if the actual shopper conforms to some desired habits.
Lastly, community simulations are but once more outlined by docker photographs, however in comparison with easy checks, simulators not solely execute code towards a working shopper, however can really begin and terminate purchasers at will. These purchasers run in the identical digital community and may freely (or as dictated by the simulator container) join to one another, forming an on-demand personal Ethereum community.
How did hive help the fork?
Hive is neither a alternative 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: working in depth checks 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 checks, we wanted 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 unattainable to foretell what adversarial results working two competing chains in a single community might need, particularly from the minority’s perspective.
As such we have applied three particular community simulations in hive:
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The primary to verify 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.
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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 break up afterwards.
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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 constantly offering suggestions on how adjustments affected community habits.
There was some criticism of the go-ethereum group 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 consider hive turned out to play fairly an essential 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=check), with not less than one EVM benchmark device 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 sophisticated to make use of.
With hive, we’re aiming to combination all the assorted scattered checks beneath one common shopper validator that has minimal dependencies, may be prolonged by anybody, and may run as a part of the day by day CI workflow of shopper builders.
We welcome anybody to contribute to the mission, be that including new purchasers 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 working benchmarks in addition to mixed-client simulations.
With a bit or work, possibly we’ll even have assist for working hive within the cloud, permitting it to run community simulations at a way more attention-grabbing scale.