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 a knowledge race into the rushed implementation that was a ticking time bomb. It was not ideally suited, and though 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 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 problem, focusing solely on the technical points and applicable measures to stop recurrence.
Technical options scale and persist; blaming folks doesn’t. ~ Nick
From the postmortem, one fascinating discovery from the angle of this weblog submit was made. The soft-fork code inside [go-ethereum](https://github.com/ethereum/go-ethereum) appeared strong from all views: a) it was completely lined 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 non-public community… But nonetheless, a deadly information race remained, which might have doubtlessly 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 assessments 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 assessments that will cowl the intricate interaction of a number of nodes in a concurrent networked situation. With out such a software, manually checking the assorted edge circumstances is unwieldy; and with out doing these checks constantly as a part of the event workflow, uncommon errors would turn into unimaginable to find in time.
And thus, hive was born…
What’s hive?
Ethereum grew massive to the purpose the place testing implementations turned an enormous burden. Unit assessments are advantageous for checking numerous implementation quirks, however validating {that a} consumer conforms to some baseline high quality, or validating that shoppers can play properly collectively in a multi consumer surroundings, 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 shoppers. As such, the harness is supposed to do black field testing the place no consumer particular inside particulars/state might be examined and/or inspected, relatively emphasis could be placed on adherence to official specs or behaviors below 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 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. It is a very highly effective abstraction…
Since Ethereum clients are docker photos in hive, builders of the shoppers can assemble the absolute best surroundings for his or her shoppers to run in (dependency, tooling and configuration sensible). Hive will spin up as many cases as wanted, all of them operating in their very own Linux programs.
Equally, as test suites validating Ethereum shoppers are docker photos, the author of the assessments can use any programing surroundings he’s most conversant in. Hive will guarantee a consumer is operating when it begins the tester, which might then validate if the actual consumer conforms to some desired habits.
Lastly, network simulations are but once more outlined by docker photos, however in comparison with easy assessments, simulators not solely execute code towards a operating consumer, 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 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 median developer’s perspective: operating in depth assessments that may require advanced 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 assessments, 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 unimaginable to foretell what adversarial results operating two competing chains in a single community may need, particularly from the minority’s perspective.
As such we have applied three particular community simulations in hive:
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The first to examine 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 blended pro-fork and no-fork nodes/miners appropriately splits into two when the fork block arrives, additionally sustaining the cut up afterwards.
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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 alternative.
The fascinating query although is: did hive really catch any errors, or did is simply act as an additional affirmation that every thing’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 habits.
There was some criticism of the go-ethereum staff 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 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=take a look at), 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 assorted scattered assessments below one common consumer validator that has minimal dependencies, might be prolonged by anybody, and may run as a part of the every day CI workflow of consumer builders.
We welcome anybody to make a contribution to the venture, be that including new shoppers 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.
