README.md

hive - Ethereum end-to-end test harness

Ethereum grew large to the point where testing implementations is a huge burden. Unit tests are fine for checking various implementation quirks, but validating that a client conforms to some baseline quality or validating that clients can play nicely together in a multi client environment is all but simple.

This project is meant to serve as an easily expandable test harness where anyone can add tests in any programming language felt comfortable with and it should simultaneously be able to run those tests against all potential clients. As such the harness is meant to do black box testing where no client specific internal details/state can be tested and/or inspected, rather emphasis being put on adherence to official specs or behaviours under different circumstances.

Most importantly, it is essential to be able to run this test suite as part of the CI workflow! To this effect the entire suite is based on docker containers.

Installing the hive validator

The hive project is based on Go. Although there are plans to make running hive possible using only docker, for now you'll need a valid Go (1.6 and upwards) installation available.

You can install hive via:

$ go get github.com/ethereum/hive

Note: For now hive requires running from the repository root as it needs access to quite a number of resource files to build the corrent docker images and containers. This requirement will be removed in the future.

Running on Windows

The following information assumes Docker for Windows (CE) is installed on Windows 10 Pro.

Docker daemon

hive uses the Docker API to connect to the Docker Daemon to dynamically create and run containers. At the time of writing, the daemon is disabled by default. This must be enabled.

To enable the daemon, right click on the Docker Whale in the system tray and press Settings. Under 'General' select "Expose daemon on tcp.... without TLS".

To run hive, use the following command line option --docker-endpoint tcp://localhost:2375 Alternatively, if using VSCode simply run using the supplied launch.json (see below)

Shell container

Currently, the Windows version must be run from the Host. To achieve this run with the --docker-noshell command line option.

Debugging or executing from Visual Studio Code

As described above, golang must be installed on the machine. The golang extension for VSCode is then required, along with Delve and the standard tools recommended by the Golang extension.

When VS Code is configured for general go development, hive may be run simply by launching with F5 with the following launch.json. This launch.json includes example parameters that limit the client to geth as the full client suite may take significant time to build initial docker images.

{
   
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Launch",
            "type": "go",
            "request": "launch",
            "mode": "debug",
            "remotePath": "",
            "port": 2345,
            "host": "127.0.0.1",
            "program": "${workspaceFolder}",
            "env": {},
            "args": [
                
                "--docker-endpoint","tcp://localhost:2375",
                "--docker-noshell",
                "--client","go-ethereum_master" , 
                "--loglevel","6",
                "--smoke"
                
               
            ],
            "showLog": true
        }
    ]
}

Access to the local drive

Docker will need access to the workspace folder. This will either be requested automatically in an Windows notification, or permission can be set in the docker settings in advance.

To set the permissions to access your drive, right click on the Docker Whale in the system tray and press Settings. Under 'Shared Drives' select the drive where the workspace folder is for sharing.

Host access to the docker network

hive requires network access to the docker containers it creates. While this is automatically available on Linux, at the time of writing because of virtualisation there needs to be some further network configuration so that the hive host can connect. The following is dependent on your docker configuration, and there may be other ways to achieve the same result, but a typical setting may be:

'route /P add 172.17.0.0 MASK 255.255.0.0 10.0.75.2'

An administrator level command prompt must be opened and the target IPs of the containers routed to HyperV's IP address for the docker containers.

Validating clients

UPDATE: Unless we hear a desire to keep them, Validators will be deprecated. Please see Simulators for updates.

You can run the full suite of hive validation tests against all the known implementations tagged master by simply running hive from the repository root. It will build a docker image for every known client as well as one for every known validation test.

The end result should be a JSON report, detailing for each client the list of validations failed and those passed. If you wish to explore the reasons of failure, full logs from all clients and testers are pushed into the workspace/logs folder.

$ hive --client=go-ethereum:master --test=.
...
Validation results:
{
  "go-ethereum:master": {
    "fail": [
      "ethereum/rpc-tests"
    ],
    "pass": [
      "smoke/genesis-chain-blocks",
      "smoke/genesis-only",
      "smoke/genesis-chain"
    ]
  }
}

You may request a different set of clients to be validated via the --client regexp flag (e.g. validating all go-ethereum versions would be --client=go-ethereum:). Similarly you may request only a subset of validation tests to be run via the --test regexp flag (e.g. running only the smoke validation tests would be --test=smoke).

Simulating clients

---

hive supports a more advanced form of client testing called simulations, where entire networks of clients are run concurrently under various circumstances and their behavior monitored and checked.

Running network simulations is completely analogous to validations from the user's perspective: you can specify which clients to simulate with the --client regexp flag, and you can specify which simulations to run via the --sim regexp flag. By default simulations aren't being run as they can be quite lengthy.

Simulators now offer a golang client framework, that allows them to call into the Hive Simulator API and create different types of client. The simulator can run tests or other experiments written in Golang against one or more instances of clients. To achieve this, a number of new options are added:

--sim-rootcontext a boolean, which when set tells the compiler to build the docker image with 'simulators' as the root of the context, allowing the simulators\common and simulators\devp2p common code to be included in the simulator.

Sim-rootcontext needs to be set differently depending on the type of simulation being run. For the consensus tests the base simulator image relies on files to be added from a folder local to the image. For developing new simulations, or extending the existing ones, it is recommended to use sim-rootcontext as true.

--debug allows a flag to be set that is passed into the simulator as an environment variable, allowing the simulator to be run as a delve 'headless server. The go simulator can then be remote debugged by attaching to the delve headless server.

--sim-parallelism a flag to indicate how many tests or containers should be run concurrently. This can be implementation specific. In this version it is used to drive the -test.parallel flag in the devp2p simulation.

Similarly to validations, end result of simulations should be a JSON report, detailing for each client the list of simulations failed and those passed. Likewise, if you wish to explore the reasons of failure, full logs from all clients and testers are pushed into the log.txt log file.

$ hive --client=go-ethereum:master --test=NONE --sim=.
...
Simulation results:
{
  "go-ethereum:master": {
    "pass": [
      "dao-hard-fork",
      "smoke/single-node"
    ]
  }
}

Adding new clients

The hive test harness can validate arbitrary implementations of the Ethereum yellow paper.

Being based on docker containers, hive is pretty liberal on pretty much all aspects of a client implementation:

• hive doesn't care what dependencies a client has: language, libraries or otherwise.
• hive doesn't care how the client is built: environment, tooling or otherwise.
• hive doesn't care what garbage a client generates during execution.

As long as a client can run on Linux, and you can package it up into a Docker image, hive can test it!

Creating a client image

Adding a new client implementation to hive entails creating a Dockerfile (and related resources), based on which hive will assemble the docker image to use as the blueprint for testing.

The client definition(s) should reside in the clients folder, each named <project>_<tag> where <project> is the official name of the client (lowercase, no fancy characters), and <tag> is an arbitrary id up to the client maintainers to make the best use of. hive will automatically pick up all clients from this folder.

There are little contraints on the image itself, though a few required caveats are:

• It should be as tiny as possible (play nice with others). Preferably use alpine Linux.
• It should expose the following ports: 8545 (HTTP RPC), 8546 (WS RPC), 30303 (devp2p).
• It should have a single entrypoint (script?) defined, which can initialize and run the client.

For devp2p tests or other simulations that require to know the specific enode of the client instance, the client must provide an enode.sh that echoes the enode of the running instance. This is executed by the Hive host remotely to get the id.

The client has the responsibility of mapping the Hive genesis.json and Hive environment variables to its own local genesis format and command line flags. To assist in this, Hive illustrates a technique in the clients/trinity_master folder using mapper.jq, which is invoked in trinity.sh This technique can be replicated for other clients.

For guidance, check out the reference go-ethereum:master client.

Initializing the client

Since hive does not want to enforce any CLI parameterization scheme on client implementations, it injects all the required configurations into the Linux containers prior to launching the client's entrypoint script. It is then left to this script to interpret all the environmental configs and initialize the client appropriately.

The chain configurations files:

• /genesis.json contains the JSON specification of the Ethereum genesis states
• /chain.rlp contains a batch of RLP encoded blocks to import before startup
• /blocks/ folder with numbered singleton blocks to import before startup
• /keys/ contains account keys that should be imported before startup

Client startup scripts need to ensure that they load the genesis state first, then import a possibly longer blockchain and then import possibly numerous individual blocks. The reason for requiring two different block sources is that specifying a single chain is more optimal, but tests requiring forking chains cannot create a single chain.

Beside the standardized chain configurations, clients can in general be modified behavior-wise in quite a few ways that are mostly supported by all clients, yet are implemented differently in each. As such, each possible behavioral change required by some validator or simulator is characterized by an environment variable, which clients should interpret as best as they can.

The behavioral configuration variables:

• HIVE_BOOTNODE enode URL of the discovery-only node to bootstrap the client
• HIVE_TESTNET whether clients should run with modified starting nonces (2^20)
• HIVE_NODETYPE specifying the sync and pruning algos that should be used
  • If unset, then uninteresting and run in the node's default mode
  • If archive, assumes that all historical state is retained after sync
  • If full, assumes fast sync and consecutive pruning of historical state
  • If light, assumes header only sync and no state maintenance at all
• HIVE_FORK_HOMESTEAD the block number of the Ethereum Homestead transition
• HIVE_FORK_DAO_BLOCK the block number of the DAO hard-fork transition
• HIVE_FORK_DAO_VOTE whether the node supports or opposes the DAO hard-fork
• HIVE_FORK_TANGERINE the block number of the Ethereum TangerineWhistle transition
  • The HF for repricing certain opcodes, EIP 150
• HIVE_FORK_SPURIOUS the block number of the Ethereum Homestead transition
  • The HF for replay protection, state cleaning etc. EIPs 155,160,161.
• HIVE_FORK_METROPOLIS the block number of the Metropolis hardfork
• HIVE_MINER address to credit with mining rewards (if set, start mining)
• HIVE_MINER_EXTRA extra-data field to set for newly minted blocks

Starting the client

After initializing the client blockchain (genesis, chain, blocks), the last task of the entry script is to start up the client itself. The following defaults are required by hive to enable automatic network assembly and firewall enforcement:

• Clients should open their HTTP-RPC endpoint on 0.0.0.0:8545 (mandatory)
• Clients should open their WS-RPC endpoint on 0.0.0.0:8546 (optional)
• Clients should open their IPC-RPC endpoints at /rpc.ipc (optional)

There is no need to handle graceful client termination. Clients will be forcefully aborted upon test suite completion and all related data purged. A new instance will be started for every test.

Smoke testing new clients

To quickly check if a client adheres to the requirements of hive, there is a suite of smoke test validations and simulations that just initialize clients with some pre-configured states and query it from the various RPC endpoints.

$ hive --client=go-ethereum:master --smoke
...
Validation results:
{
  "go-ethereum:master": {
    "pass": [
      "smoke/genesis-chain",
      "smoke/genesis-chain-blocks",
      "smoke/genesis-only"
    ]
  }
}
...
Simulation results:
{
  "go-ethereum:master": {
    "smoke/lifecycle": {
      "start": "2017-01-31T09:20:16.975219924Z",
      "end": "2017-01-31T09:20:18.705302536Z",
      "success": true
    }
  }
}

Note: All smoke tests must pass for a client to be included into hive.

Adding new validators

Validators are hive testers whose sole purpose is to check that a client implementation conforms to some standardized specifications (e.g. RPC API conformance, Mist compatibility, consensus tests, etc). They are not meant to check a client's behavior in complex network environment, rather that given a single client instance, it seems to function correctly from the users perspective.

Similar to client implementations inside hive, validators themselves are also based on docker images and containers:

• hive doesn't care what dependencies a validator has: language, libraries or otherwise.
• hive doesn't care how the validator is built: environment, tooling or otherwise.
• hive doesn't care what garbage a validator generates during execution.

As long as a validator can run on Linux, and you can package it up into a Docker image, hive can use it to test every client implementation with it!

Creating a validator image

Adding a new client validator to hive entails creating a Dockerfile (and related resources), based on which hive will assemble the docker image to use as the blueprint for validation.

The validator definition(s) should reside in the validators folder, each nested as <group>/<test>, where <group> is a higher level collection of similar tests (e.g. mist, consensus), and <test> is an individual validator. Contributors are free to define new groups as long as it makes sense from a cross-client perspective. A few existing ones are:

• ethereum contains ports of old test frameworks from the Ethereum repositories
• issues contains interesting corner cases from clients that may affect others too
• mist contains API conformance tests to validate if a client can be a Mist backend
• smoke contains general smoke tests to insta-check if a client image is correct

There are little contraints on the image itself, though a few required caveats are:

• It should be as tiny as possible (play nice with others). Preferably use alpine Linux.
• It should have a single entrypoint (script?) defined, which can initialize and run the test.

For guidance, check out the genesis-only smoke test.

Defining the validator

Since hive does not want to enforce any CLI parameterization scheme on client implementations, it injects all the required configurations into the Linux containers prior to launching the client's entrypoint script. It is then left to this script to interpret all the environmental configs and initialize the client appropriately.

What this means from a validator's perspective is, that validators themselves must define these initial chain configurations files and client behavioral modifier environment variables that will be loaded into the client images.

To prevent duplicating the list of config files and env vars that need to be implemented to cross over from validators to clients, we'll refer the reader to the readme's "Initializing the client" section which lists all of them, also detailing what each means.

In short, a validator must create all of the above linked chain configuration files and define some subset of behavioral environmental variables in the validator's docker image. This is important as the validator is always started after the client, so all information needs to be already ready for client initialization prior to running the validator entrypoint.

Trick: If you don't want to initialize a tested client with a starting chain, only the genesis file, you can specify RUN touch chain.rlp && mkdir /blocks in the validator Dockerfile, which will create an empy initial chain and empty set of blocks.

Executing the validation

After hive creates and initializes a client implementation based on the docker image of the chosen validator, it will extract the IP address of the running client and boot up the validator with the client's IP address injected as the HIVE_CLIENT_IP environmental variable.

From this point onward the validator may execute arbitrary code against the running clients:

• HTTP RPC endpoint exposed at HIVE_CLIENT_IP:8545
• WebSocket RPC (if supported) at HIVE_CLIENT_IP:8546
• devp2p TCP and UDP endpoints at HIVE_CLIENT_IP:30303

The validation will be considered successful if and only if the exit code of the entrypoint script is zero! Any output that the validator generates will be saved to an appropriate log file in the hive workspace folder and also echoed out to the console on --loglevel=6.

Note: There is no constraint on how much a validation may run, but please be considerate.

Adding new simulators

Simulators are hive testers whose purpose is to check that client implementations conform to some desired behavior when running in both multi-node network environments, as well as for scenarios where participant nodes perform live mining using the full ethash DAG. The goal of a simulator is to try and test a node in an almost-live way: don't take testing shortcuts like fake PoW, smaller DAGs, less secure key encryption schemas.

Similar to all other entities inside hive, simulators too are based on docker images and containers:

• hive doesn't care what dependencies a simulator has: language, libraries or otherwise.
• hive doesn't care how the simulator is built: environment, tooling or otherwise.
• hive doesn't care what garbage a simulator generates during execution.

As long as a simulator can run on Linux, and you can package it up into a Docker image, hive can use it to test every client implementation with it!

Creating a simulator image

Adding a new client simulator to hive entails creating a Dockerfile (and related resources), based on which hive will assemble the docker image to use as the blueprint for simulation.

The simulator definition(s) should reside in the simulators folder, each nested as <group>/<sim>, where <group> is a higher level collection of similar tests (e.g. dao-hard-fork), and <sim> is an individual simulator. Contributors are free to define new groups as long as it makes sense from a cross-client perspective. A few existing ones are:

• dao-hard-fork contains network simulations/tests with regard to the DAO hard-fork
• smoke contains general smoke tests to insta-check if a client image is correct

There are little contraints on the image itself, though a few required caveats are:

• It should be as tiny as possible (play nice with others). Preferably use alpine Linux.
• It should have a single entrypoint (script?) defined, which can initialize and run the test.

For guidance, check out the lifecycle smoke test.

Defining the simulator

Defining a simulator is exactly the same as defining the docker image of a validator with regard to every aspect of hive (chain configs, behavioral envvars), As such, we refer the user to the readme's "Defining the validator" section. Apart from what the entrypoint script is allowed to do, validator and simulator images are equivalent.

Executing the simulation

As detailed in the readme's "Executing the validation" section, during validation hive starts a client node first, and then the validator itself. This is not true in the case of simulations however. Since it is impossible to define arbitrary networking scenarios with simple configuration files, hive will instead boot up only the simulator instance, and will provide it with the necessary mechanisms to create any scenario it wants.

To this effect, hive exposes a RESTful HTTP API that all simulators can use to direct how hive should create and organize the simulated network. This API is exposed at the HTTP endpoint set in the HIVE_SIMULATOR environmental variable. The currently available topology endpoints are:

• /nodes with method POST boots up a new client instance, returning its unique ID
  • Simulators may override any chain init files via URL and form parameters (see below)
  • Simulators may override any behavioral envvars directly via URL and form parameters
• /nodes/$ID with method GET retrieves the IP address of an existing client instance
  • The client's exposed services can be reached via ports 8545, 8546 and 30303
• /nodes/$ID with method DELETE instantly terminates an existing client instance
• /log with method POST sends a logging message from the simulator to the main process.

Overriding environmental variables that change client behaviors via HTTP parameters is easy to do in any HTTP client utility and/or library, but uploading files needed for chain initializations is much more complex, especially if multiple files are needed. As long as all clients run with the same set of init files this is not an issue (they can be placed in the default locations). However if instances need to run with different initial chain setups, a simulator needs to be able to specify these per client. To avoid file uploads, hive solves this by defining a set of API variables that allow a simulator to specify the source paths to use for specific init files which will be extracted from the live container:

• HIVE_INIT_GENESIS path to the genesis file to seed the client with (default = "/genesis.json")
• HIVE_INIT_CHAIN path to an initial blockchain to seed the client with (default = "/chain.rlp")
• HIVE_INIT_BLOCKS path to a folder of blocks to import after seeding (default = "/blocks/")
• HIVE_INIT_KEYS path to a folder of account keys to import after init (default = "/keys/")

Note: It is up to simulators to wire the clients together. The simplest way to do this is to start a bootnode inside the simulator and specify it for new clients via the documented HIVE_BOOTNODE environment variable. This is required to make simulators fully self contained, also enabling much more complex networking scenarios not doable with forced fixed topologies.

The simulation will be considered successful if and only if the exit code of the entrypoint script is zero! Any output that the simulator generates will be saved to an appropriate log file in the hive workspace folder and also echoed out to the console on --loglevel=6.

Reporting sub-results

It may happen that the setup/teardown cost of a simulation be large enough to warrant validating not just one, but perhaps multiple invariants in the same test. Although the results for these subtests could be reported in the log file, retrieving them would become unwieldy. As such, hive exposes a special HTTP endpoint on its RESTful API that can add sub-results to a single simulation. The endpoint resides at /subresults and has the following parameters:

• name: textual name to report for the subtest
• success: boolean flag (true or false) representing whether the subtest failed
• error: textual details for the reason behind the subtest failing
• details: structured JSON object containing arbitrary extra infos to surface

For example, doing a call to this endpoint with curl:

$ curl -sf -v -X POST -F 'details={"Preconditions failed": ["nonce 1 != 2", "balance 0"]}' \
  "$HIVE_SIMULATOR/subresults?name=Demo%20error&success=false&error=Something%20went%20wrong..."

will result a subresults field

"subresults": [
  {
    "name": "Demo error",
    "success": false,
    "error": "Something went wrong...",
    "details": {
      "Preconditions failed": [
        "nonce 1 != 2",
        "balance 0"
      ]
    }
  }
]

Closing notes

• There is no constraint on how much time a simulation may run, but please be considerate.
• The simulator doesn't have to terminate nodes itself, upon exit all resources are reclaimed.

Continuous integration

As mentioned in the beginning of this document, one of the major goals of hive is to support running the validators, simulators and benchmarks as part of an Ethereum client's CI workflow. This is needed to ensure a baseline quality for implementations, particularly because developers notoriously hate the idea of having to do a manual testing step, heaven forbid having to install dependencies of "that" language.

Providing detailed configuration description for multiple CI services is out of scope of this project, but we did put together a document about configuring it for circleci, leaving it up to the user to port it to other platforms. The single most important feature a CI service must support for hive is docker containers, specifically allowing multiple ones concurrently.

Integration via circleci

Since hive is quite an unorthodox test harness, circleci has no chance of automatically inferring any details on how it should be used. As such all configurations need to be manually specified via a circle.yml YAML file placed into the repository root. You'll need to add three important sections to this.

The first part is easy, we request a machine that has a docker service running:

machine:
  services:
    - docker

Now comes the juicy part. Although we could just simply install hive and run it as the main test step, the circleci service offers an interesting concept called dependencies. Users can define and install various libraries, tools, etc. that will be persisted between test runs. This is a very powerful feature as it allows caching all those non-changing dependencies between CI runs and only download and rebuild those that have changed.

Since hive is both based on docker containers that may take a non-insignificant time to build, as well as the simulations needing a full ethash DAG which can take at least 5 minutes to build, we'd like to cache as much as possible and only rebuild what changed.

To this effect we will define two special folders we'd like to include in the circleci caches. One to collect all the docker images built by hive; and another to store the ethash DAG:

dependencies:
  cache_directories:
    - "~/.ethash" # Cache the ethash DAG generated by hive for consecutive builds
    - "~/.docker" # Cache all docker images manually to avoid lengthy rebuilds

Of course we also need to tell circleci how to populate these caches and how to reload them into the live test environment. To do that we'll add an additional sub-section to the top dependencies section called override, which will do a couple things:

• Import all the docker images from the cache into the locally running service
• Install hive and import all the ethash DAGs into the local workspace
• Dry run hive, to rebuild all docker images, but not run any tests
• Export all new docker images and ethash DAGs into the cache

  override:
    # Restore all previously cached docker images
    - mkdir -p ~/.docker
    - for img in `ls ~/.docker`; do docker load -i ~/.docker/$img; done

    # Pull in and hive, restore cached ethash DAGs and do a dry run
    - go get -u github.com/karalabe/hive
    - (cd ~/.go_workspace/src/github.com/karalabe/hive && mkdir -p workspace/ethash/ ~/.ethash)
    - (cd ~/.go_workspace/src/github.com/karalabe/hive && cp -r ~/.ethash/. workspace/ethash/)
    - (cd ~/.go_workspace/src/github.com/karalabe/hive && hive --docker-noshell --client=NONE --test=. --sim=. --loglevel=6)

    # Cache all the docker images and the ethash DAGs
    - for img in `docker images | grep -v "^<none>" | tail -n +2 | awk '{print $1}'`; do docker save $img > ~/.docker/`echo $img | tr '/' ':'`.tar; done
    - cp -r ~/.go_workspace/src/github.com/karalabe/hive/workspace/ethash/. ~/.ethash

Although it may look a bit overwhelming, all the above code does is it loads the cache contents, updates it with a freshly installed version of hive, and then pushes everything new back into the cache for next time.

With hive installed and all optimisations and caches out of the way, the remaining step is to run the actual continuous integration: build your project and invoke hive to test it. The first part is implementation dependent, but for example go-ethereum has a simple make geth command for building the client.

With the client built, we'll need to start hive from its repository root, specify the client image we want to use (e.g. --client=go-ethereum:local), override any files in the image (i.e. inject our freshly built binary --override=$HOME/geth) and run the whole suite (--test=. --sim=.).

Note, as circleci seems unable to handle multiple docker containers embedded in one another, we'll need to specify the --docker-noshell flag to omit hive's outer shell container. This is fine as we don't care about any junk generated at this point, circleci will just discard it after the test.

test:
  override:
    # Build Geth and move into a known folder
    - make geth
    - cp ./build/bin/geth $HOME/geth

    # Run hive and move all generated logs into the public artifacts folder
    - (cd ~/.go_workspace/src/github.com/karalabe/hive && hive --docker-noshell --client=go-ethereum:local --override=$HOME/geth --test=. --sim=.)
    - cp -r ~/.go_workspace/src/github.com/karalabe/hive/workspace/logs/* $CIRCLE_ARTIFACTS

If all went well, you should see hive assembled in the circleci dashboard (first run can take a bit of time to cache all the tester images and ethash DAG) and hive should be happily crunching through its defined tests with your fresh client binary.

If you get stuck, you can always take a look at the current live circle.yml file being used by the go-ethereum client.

Trophies

If you find a bug in your client implementation due to this project, please be so kind as to add it here to the trophy list. It could help prove that hive is indeed a useful tool for validating Ethereum client implementations.

• go-ethereum
  • Genesis chain config couldn't handle present but empty settings: #2790
  • Data race between remote block import and local block mining: #2793
  • Downloader didn't penalize incompatible forks hashly enough: #2801

Contributions

This project takes a different approach to code contributions than your usual FOSS project with well ingrained maintainers and relatively few external contributors. It is an experiment, whether it will work out or not is for the future to decide.

We follow the Collective Code Construction Contract (C4), code contribution model, as expanded and explained in The ZeroMQ Process. The core idea being that any patch that successfully solves an issue (bug/feature) and doesn't break any existing code/contracts must be optimistically merged by maintainers. Followup patches may be used to for additional polishes – and patches may even be outright reverted if they turn out to have a negative impact – but no change must be rejected based on personal values.

Please consult the two C4 documents for details:

• Collective Code Construction Contract (C4)
• The ZeroMQ Process

License

The hive project is licensed under the GNU General Public License v3.0, also included in our repository in the COPYING file.