The “nrunner” and “runner” test runner

This section details a test runner called “nrunner”, also known as N(ext) Runner, and the architecture around. It compares it with the older (and default) test runner, simply called “runner”.

At its essence, this new architecture is about making Avocado more capable and flexible, and even though it starts with a major internal paradigm change within the test runner, it will also affect users and test writers.

The avocado.core.nrunner module was initially responsible for most of the N(ext)Runner code, but as development continues, it’s spreading around to other places in the Avocado source tree. Other components with different and seemingly unrelated names, say the “resolvers” or the “spawners”, are also pretty much about the N(ext)Runner and are not used in the current (default) architecture.


There are a number of reasons for introducing a different architecture and implementation. Some of them are related to limitations found in the current implementation, that were found to be too hard to remove without major breakage. Also, missing features that are deemed important would be a better fit wihin a different architecture.

For instance, these are the current limitations of the Avocado test runner:

  • Test execution limited to the same machine, given that the communication between runner and test is a Python queue
  • Test execution is limited to a single test at a time (serial execution)
  • Test processes are not properly isolated and can affect the test runner (including the “UI”)

And these are some features which it’s believed to be more easily implemented under a different architecture and implementation:

  • Remote test execution
  • Different test execution isolation models provided by the test runner (process, container, virtual machine)
  • Distributed execution of tests across a pool of any combination of processes, containers, virtual machines, etc.
  • Parallel execution of tests
  • Optimized runners for a given environment and or test type (for instance, a runner written in RUST to run tests written in RUST in an environment that already has RUST installed but not much else)
  • Notification of execution results to many simultaneous “status servers”
  • Disconnected test execution, so that results can be saved to a device and collected by the runner
  • Simplified and automated deployment of the runner component into execution environments such as containers and virtual machines

Current and N(ext) Runner components of Avocado

Whenever we mention the current architecture or implementation, we are talking about:

Whenever we talk about the N(ext)Runner, we are talking about:

Basic Avocado usage and workflow

Avocado is described as “a set of tools and libraries to help with automated testing”. The most visible aspect of Avocado is its ability to run tests, and display the results. We’re talking about someone doing:

$ avocado run

To be able to complete such a command, Avocado needs to find the tests, and then to execute them. Those two major steps are described next.

Finding tests

The first thing Avocado needs to do, before actually running any tests, is translating the “names” given as arguments to avocado run into actual tests. Even though those names will usually be file names, this is not a requirement. Avocado calls those “names” given as arguments to avocado run “test references”, because they are references that hopefully “point to” tests.

Here we need to make a distincion between the current architecture, and the architecture which the N(ext)Runner introduces. In the current Avocado test runner, this process happens by means of the avocado.core.loader module. The very same mechanism, is used when listing tests. This produces an internal representation of the tests, which we simply call a “factory”:

+--------------------+    +---------------------+
| avocado list | run | -> | avocado.core.loader | ---+
+--------------------+    +---------------------+    |
| Test Factory 1                       |
| Class: TestFoo                       |
| Parameters:                          |
|  - modulePath: /path/to/    |
|  - methodName: test_foo              |
|  ...                                 |

| Test Factory 2                       |
| Class: TestBar                       |
| Parameters:                          |
|  - modulePath: /path/to/    |
|  - methodName: test_bar              |
|  ...                                 |


Because the N(ext)Runner is living side by side with the current architecture, command line options have been introduced to distinguish between them: avocado list --resolver and avocado run --test-runner=nrunner.

On the N(ext)Runner architecture, a different terminology and foundation is used. Each one of the test references given to list --resolver or run --test-runner=runner will be “resolved” into zero or more tests. Being more precise and verbose, resolver plugins will produce avocado.core.resolver.ReferenceResolution, which contain zero or more avocado.core.nrunner.Runnable, which are described in the following section. Overall, the process looks like:

+-------------------------+    +-----------------------+
| avocado list --resolver | -> | avocado.core.resolver | ---+
+-------------------------+    +-----------------------+    |
| ReferenceResolution #1               |
| Reference: /bin/true                 |
| Result: SUCCESS                      |
| +----------------------------------+ |
| | Resolution #1 (Runnable):        | |
| |  - kind: exec-test               | |
| |  - uri: /bin/true                | |
| +----------------------------------+ |

| ReferenceResolution #2               |
| Reference:                   |
| Result: SUCCESS                      |
| +----------------------------------+ |
| | Resolution #1 (Runnable):        | |
| |  - kind: python-unittest         | |
| |  - uri:      | |
| +----------------------------------+ |
| +----------------------------------+ |
| | Resolution #2 (Runnable):        | |
| |  - kind: python-unittest         | |
| |  - uri:      | |
| +----------------------------------+ |


Running Tests

The idea of testing has to do with checking the expected output of a given action. This action, within the realm of software development with automated testing, has to do with the output or outcome of a “code payload” when executed under a given controlled environment.

The current Avocado architecture uses the “Test Factories” described earlier to load and execute such a “code payload”. Each of those test factories contain the name of a Python class to be instantiated, and a number of arguments that will be given to that class initialization.

So the primary “code payload” for every Avocado test in the current architecture will always be Python code that inherits from avocado.core.test.Test. Even when the user wants to run a standalone executable (a SIMPLE test in the current architecture terminology), that still means loading and instantiating (effectively executing) the Python class’ avocado.core.test.SimpleTest code.

Once all the test factories are found by avocado.core.loader, as described in the previous section, the current architecture runs tests roughly following these steps:

  1. Create one (and only one) queue to communicate with the test processes
  2. For each test factory found by the loader:
  1. Unpack the test factory into a test class and its parameters, that is, test_class, parameters = test_factory
  2. Instantiate a new process for the test
  3. Within the new process, instantiate the Python class, that is, test = test_class(**parameters)
  4. Give the test access to queue, that is test.set_runner_queue(queue)
  5. Monitor the queue and the test process until it finishes or needs to be terminated.

Having to describe the “Test factory” as Python classes and its parameters, besides increasing the complexity for new types of tests, severely limits or prevents some of goals for the N(ext)Runner architecture listed earlier. It should be clear that:

  1. one unique queue makes communicating with multiple tests at the same time hard
  2. test factories contain a Python class (code) that will be instantiated in the new process
  3. to instantiate Python classes in other systems would require serializing them, which is error prone (AKA pickling nightmares)
  4. the execution of tests depends on the previous point, so running tests in a local process is tightly coupled and hard coded into the test execution code

Now let’s shift our attention to the N(ext)Runner architecture. In the N(ext)Runner architecture, a avocado.core.nrunner.Runnable describe a “code payload” that will be executed, but they are not executable code themselves. Because they are data and not code, they are easily serialized and transported to different environments. Running the payload described by a Runnable is delegated to another component.

Most often, this component is a standalone executable (see avocado.core.spawners.common.SpawnMethod.STANDALONE_EXECUTABLE) compatible with a specific command line interface. The most important interfaces such scripts must implement are the runnable-run and task-run interfaces.

Once all the Runnable(s) (within the ReferenceResolution(s)) are created by avocado.core.resolver, the avocado run --test-runner=nrunner implementation follows roughly the following steps:

  1. Creates a status server that binds to a TCP port and waits for status messages from any number of clients
  2. Creates the chosen Spawner, with ProcessSpawner being the default
  3. For each avocado.core.nrunner.Runnable found by the resolver, turns it into a avocado.core.nrunner.Task, which means giving it the following extra information:
  1. The status server(s) that it should report to
  2. An unique identification, so that its messages to the status server can be uniquely identified
  1. For each resulting avocado.core.nrunner.Task in the previous step:
  1. Asks the spawner to spawn it
  2. Asks the spawner to check if the task seems to be alive right after spawning it, to give the user early indication of possible crashes
  1. Waits until all tasks have provided a result to the status server

If any of the concepts mentioned here were not clear, please check their full descriptions in the next section.



A runnable is a description of an entity that can be executed and produce some kind of result. It’s a passive entity that can not execute itself and can not produce results itself.

This description of a runnable is abstract on purpose. While the most common use case for a Runnable is to describe how to execute a test, there seems to be no reason to bind that concept to a test. Other Avocado subsystems, such as sysinfo, could very well leverage the same concept to describe say, commands to be executed.

A Runnable’s kind

The most important information about a runnable is the declaration of its kind. A kind should be a globally unique name across the entire Avocado community and users.

When choosing a Runnable kind name, it’s advisable that it should be:

  • Informative
  • Succinct
  • Unique

If a kind is thought to be generally useful to more than one user (where a user may mean a project using Avocado), it’s a good idea to also have a generic name. For instance, if a Runnable is going to describe how to run native tests for the Go programming language, its kind should probably be go.

On the other hand, if a Runnable is going to be used to describe tests that behave in a very peculiar way for a specific project, it’s probably a good idea to map its kind name to the project name. For instance, if one is describing how to run an iotest that is part of the QEMU project, it may be a good idea to name this kind qemu-iotest.

A Runnable’s uri

Besides a kind, each runnable kind may require a different amount of information to be provided so that it can be instantiated.

Based on the accumulated experience so far, it’s expected that a Runnable’s uri is always going to be required. Think of the URI as the one piece of information that can uniquely distinguish the entity (of a given kind) that will be executed.

If, for instance, a given runnable describes the execution of a executable file already present in the system, it may use its path, say /bin/true, as its uri value. If a runnable describes a web service endpoint, its uri value may just as well be its network URI, such as

Runnable examples

Possibly the simplest example for the use of a Runnable is to describe how to run a standalone executable, such as the ones available on your /bin directory.

As stated earlier, a runnable must declare its kind. For standalone executables, a name such as exec fulfills the naming suggestions given earlier.

A Runnable can be created in a number of ways. The first one is through avocado.core.nrunner.Runnable, a very low level (and internal) API. Still, it serves as an example:

>>> from avocado.core import nrunner
>>> runnable = nrunner.Runnable('exec', '/bin/true')
>>> runnable
<Runnable kind="exec" uri="/bin/true" args="()" kwargs="{}" tags="None" requirements="None">

The second way is through a JSON based file, which, for the lack of a better term, we’re calling a (Runnable) “recipe”. The recipe file itself will look like:

{"kind": "exec", "uri": "/bin/true"}

And example the code to create it:

>>> from avocado.core import nrunner
>>> runnable = nrunner.Runnable.from_recipe("/path/to/recipe.json")
>>> runnable
<Runnable kind="exec" uri="/bin/true" args="()" kwargs="{}" tags="None" requirements="None">>

The third way to create a Runnable, is even more internal. Its usage is discouraged, unless you are creating a tool that needs to create Runnables based on the user’s input from the command line:

>>> from avocado.core import nrunner
>>> runnable = nrunner.Runnable.from_args({'kind': 'exec', 'uri': '/bin/true'})
>>> runnable
<Runnable kind="exec" uri="/bin/true" args="()" kwargs="{}" tags="None" requirements="None">>


A Runner, within the context of the N(ext)Runner architecture, is an active entity. It acts on the information that a runnable contains, and quite simply, should be able to run what the Runnable describes.

A Runner will usually be tied to a specific kind of Runnable. That type of relationship (Runner is capable of running kind “foo” and Runnable is of the same kind “foo”) is the expected mechanism that will be employed when selecting a Runner.

A Runner can take different forms, depending on which layer one is interacting with. At the lowest layer, a Runner may be a Python class that inherits from avocado.core.nrunner.BaseRunner, and implements at least a matching constructor method, and a run() method that should yield dictionary(ies) as result(s).

At a different level, a runner can take the form of an executable that follows the avocado-runner-$KIND naming pattern and conforms to a given interface/behavior, including accepting standardized command line arguments and producing standardized output.


for a very basic example of the interface expected, refer to selftests/functional/ on the Avocado source code tree.

Runner output

A Runner should, if possible, produce status information on the progress of the execution of a Runnable. While the Runner is executing what a Runnable describes, should it produce interesting information, the Runner should attempt to forward that along its generated status.

For instance, using the exec Runner example, it’s helpful to start producing status that the process has been created and it’s running as soon as possible, even if no other output has been produced by the executable itself. These can be as simple as a sequence of:

{"status": "started"}
{"status": "running"}
{"status": "running"}

When the process is finished, the Runner may return:

{"status": "finished", "returncode": 0, 'stdout': b'', 'stderr': b''}


Besides the status of finished, and a return code which can be used to determine a success or failure status, a Runner may not be obliged to determine the overall PASS/FAIL outcome. Whoever called the runner may be responsible to determine its overall result, including a PASS/FAIL judgement.

Even though this level of information is expected to be generated by the Runner, whoever is calling a Runner, should be prepared to receive as little information as possible, and act accordingly. That includes receiving no information at all.

For instance, if a Runner fails to produce any information within a given amount of time, it may be considered faulty and be completely discarded. This would probably end up being represented as a TIMED_OUT kind of status on a higher layer (say at the “Job” layer).


A task is one specific instance/occurrence of the execution of a runnable with its respective runner. They should have a unique identifier, although a task by itself wont’t enforce its uniqueness in a process or any other type of collection.

A task is responsible for producing and reporting status updates. This status updates are in a format similar to those received from a runner, but will add more information to them, such as its unique identifier.

A different agreggate structure should be used to keep track of the execution of tasks.


A recipe is the serialization of the runnable information in a file. The format chosen is JSON, and that should allow both quick and easy machine handling and also manual creation of recipes when necessary.


A runner can be capable of running one or many different kinds of runnables. A runner should implement a capabilities command that returns, among other info, a list of runnable kinds that it can (to the best of its knowledge) run. Example:

python3 -m avocado.core.nrunner capabilities
{"runnables": ["noop", "exec", "exec-test", "python-unittest"],
 "commands": ["capabilities", "runnable-run", "runnable-run-recipe",
 "task-run", "task-run-recipe"]}

Runner scripts

The primary runner implementation is a Python module that can be run, as shown before, with the avocado.core.nrunner module name. Additionally it’s also available as the avocado-runner script.

Runner Execution

While the exec runner given as example before will need to create an extra process to actually run the standalone executable given, that is an implementation detail of that specific runner. Other types of runners may be able to run the code the users expects it to run, while still providing feedback about it in the same process.

The runner’s main method (run()) operates like a generator, and yields results which are dictionaries with relevant information about it.

Trying it out - standalone

It’s possible to interact with the runner features by using the command line. This interface is not stable at all, and may be changed or removed in the future.

Runnables from parameters

You can run a “noop” runner with:

avocado-runner runnable-run -k noop

You can run an “exec” runner with:

avocado-runner runnable-run -k exec -u /bin/sleep -a 3.0

You can run an “exec-test” runner with:

avocado-runner runnable-run -k exec-test -u /bin/true

You can run a “python-unittest” runner with:

avocado-runner runnable-run -k python-unittest -u unittest.TestCase

Runnables from recipes

You can run a “noop” recipe with:

avocado-runner runnable-run-recipe examples/nrunner/recipes/runnables/noop.json

You can run an “exec” runner with:

avocado-runner runnable-run-recipe examples/nrunner/recipes/runnables/exec_sleep_3.json

You can run a “python-unittest” runner with:

avocado-runner runnable-run-recipe examples/nrunner/recipes/runnables/python_unittest.json

Writing new runner scripts

Even though you can write runner scripts in any language, if you’re writing a new runner script in Python, you can benefit from the avocado.core.nrunner.BaseRunnerApp class and from the avocado.core.nrunner.BaseRunner class.

The following is a complete example of a script that could be named avocado-runner-foo that could act as a nrunner compatible runner for runnables with kind foo.

#!/usr/bin/env python3

from avocado.core import nrunner

class FooRunner(nrunner.BaseRunner):
    def run(self):
        yield self.prepare_status('started')
        yield self.prepare_status('finished', {'result': 'pass'})

class RunnerApp(nrunner.BaseRunnerApp):
    PROG_NAME = 'avocado-runner-foo'
    PROG_DESCRIPTION = '*EXPERIMENTAL* N(ext) Runner for tests foo'
    RUNNABLE_KINDS_CAPABLE = {'foo': FooRunner}

def main():

if __name__ == '__main__':

Runners messages

When run as part of a job, every runner has to send information about its execution status to the Avocado job. That information is sent by messages which have different types based on the information which they are transmitting.

Avocado understands three main types of messages:

  • started (required)
  • running
  • finished (required)

The started and finished messages are obligatory and every runner has to send those. The running messages can contain different information during runner run-time like logs, warnings, errors .etc and that information will be processed by the avocado core.

Supported message types

Started message

This message has to be sent when the runner starts the test.

param status:‘started’
param time:start time of the test
type time:float
example:{‘status’: ‘started’, ‘time’: 16444.819830573}

Finished message

This message has to be sent when the runner finishes the test.

param status:‘finished’
param result:test result
type result:Lowercase values for the statuses defined in avocado.core.teststatus.STATUSES
param time:end time of the test
type time:float
example:{‘status’: ‘finished’, ‘result’: ‘pass’, ‘time’: 16444.819830573}

Running messages

This message can be used during the run-time and has different properties based on the information which is being transmitted.

Log message

It will save the log to the debug.log file in the task directory.

param status:‘running’
param type:‘log’
param log:log message
type log:string
param time:Time stamp of the message
type time:float
example:{‘status’: ‘running’, ‘type’: ‘log’, ‘log’: ‘log message’, ‘time’: 18405.55351474}
Stdout message

It will save the stdout to the stdout file in the task directory.

param status:‘running’
param type:‘stdout’
param log:stdout message
type log:string
param time:Time stamp of the message
type time:float
example:{‘status’: ‘running’, ‘type’: ‘stdout’, ‘log’: ‘stdout message’, ‘time’: 18405.55351474}
Stderr message

It will save the stderr to the stderr file in the task directory.

param status:‘running’
param type:‘stderr’
param log:stderr message
type log:string
param time:Time stamp of the message
type time:float
example:{‘status’: ‘running’, ‘type’: ‘stderr’, ‘log’: ‘stderr message’, ‘time’: 18405.55351474}
Whiteboard message

It will save the stderr to the whiteboard file in the task directory.

param status:‘running’
param type:‘whiteboard’
param log:whiteboard message
type log:string
param time:Time stamp of the message
type time:float
example:{‘status’: ‘running’, ‘type’: ‘whiteboard’, ‘log’: ‘whiteboard message’, ‘time’: 18405.55351474}