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The DAO soft-fork try was troublesome. Not solely did it end up that we underestimated the negative 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 very best, and despite the fact that 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 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 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 checks with a 3:1 test-to-code ratio; b) it was completely reviewed by six basis builders; and c) it was even manually stay examined on a non-public community… But nonetheless, a deadly knowledge race remained, which might have doubtlessly 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 situations held true, there was solely a slight probability for the bug to floor. Unit checks can not catch it, code reviewers might or might not catch it, and handbook testing catching it might be unlikely. Our conclusion was that the event groups wanted extra instruments to carry out reproducible checks that may cowl the intricate interaction of a number of nodes in a concurrent networked state of affairs. With out such a device, 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 change into inconceivable 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 positive 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 atmosphere, 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 snug with, and hive ought to concurrently be capable 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 inside particulars/state will be examined and/or inspected, somewhat emphasis could be placed on adherence to official specs or behaviors beneath 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. It is a very highly effective abstraction…
Since Ethereum clients are docker photos in hive, builders of the purchasers can assemble the very best atmosphere for his or her purchasers to run in (dependency, tooling and configuration smart). 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 purchasers are docker photos, the author of the checks can use any programing atmosphere 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 conduct.
Lastly, network simulations are but once more outlined by docker photos, however in comparison with easy checks, simulators not solely execute code in opposition to a operating 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 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 characteristic. Hive can present validation past what’s possible from a median developer’s perspective: operating 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 stage: one supporting and one opposing the fork. This was important because it’s inconceivable to foretell what hostile results operating 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:
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The first to verify that miners operating 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 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 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 fascinating query although is: did hive really catch any errors, or did is simply act as an additional affirmation that every little 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 growth by repeatedly offering suggestions on how modifications affected community conduct.
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 vital 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 not less than one EVM benchmark device 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 combination all the varied scattered checks beneath one common consumer validator that has minimal dependencies, will be prolonged by anybody, and might run as a part of the each 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 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.
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