The DAO soft-fork try was tough. Not solely did it end up that we underestimated the unintended 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 despite the fact that averted on the final occasion, the quick approaching hard-fork deadline seemed 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 foundation causes of the problem, focusing solely on the technical points and applicable measures to forestall recurrence.
Technical options scale and persist; blaming folks doesn’t. ~ Nick
From the postmortem, one fascinating discovery from the angle of this weblog put up was made. The soft-fork code inside [go-ethereum](https://github.com/ethereum/go-ethereum) appeared stable from all views: a) it was completely coated by unit assessments 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 might have probably brought on 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 assessments can not 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 assessments that might cowl the intricate interaction of a number of nodes in a concurrent networked state of affairs. With out such a instrument, 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 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 assessments are high-quality 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 setting, is all however easy.
Hive is supposed to function an simply expandable take a look at harness the place anybody can add assessments (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 assessments towards all potential purchasers. As such, the harness is supposed to do black field testing the place no shopper particular inside particulars/state may be examined and/or inspected, fairly 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 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 clients are docker pictures in hive, builders of the purchasers can assemble the absolute best setting 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 operating in their very own Linux methods.
Equally, as test suites validating Ethereum purchasers are docker pictures, the author of the assessments can use any programing setting he’s most accustomed to. Hive will guarantee a shopper is operating when it begins the tester, which may then validate if the actual shopper conforms to some desired conduct.
Lastly, network simulations are but once more outlined by docker pictures, however in comparison with easy assessments, simulators not solely execute code towards a operating shopper, however can truly 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 assist 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 characteristic. Hive can present validation past what’s possible from a mean developer’s perspective: operating intensive assessments that may require complicated 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 assessments, we would have liked 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 inconceivable 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:
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The first to test that miners operating the total Ethash DAGs generate right 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 accurately splits into two when the fork block arrives, additionally sustaining the break up afterwards.
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The third to test that given an already forked community, newly becoming a member of nodes can sync, quick sync and lightweight sync to the chain of their selection.
The fascinating query although is: did hive truly 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 improvement by constantly offering suggestions on how modifications affected community conduct.
There was some criticism of the go-ethereum crew for taking their time on the hard-fork implementation. Hopefully folks 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 no less than one EVM benchmark instrument 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 assorted scattered assessments underneath one common shopper validator that has minimal dependencies, may be prolonged by anybody, and may run as a part of the every day CI workflow of shopper builders.
We welcome anybody to make a contribution to the mission, be that including new purchasers to validate, validators to check with, or simulators to search out fascinating 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 fascinating scale.
