The “nrunner” and “legacy 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, legacy (no longer 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
nrunner and are not used in the legacy architecture.
Motivation¶
There are a number of reasons for introducing a different architecture and implementation. Some of them are related to limitations found in the legacy 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 limitations of the Avocado legacy 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
NRunner and Legacy Runner components of Avocado¶
Whenever we mention the current architecture or implementation, we are talking about the nrunner. It includes:
avocado list
commandavocado run
commandavocado.core.resolver
module to resolve testsavocado.core.spawners
modules to spawn tasks
Whenever we talk about legacy runner, we are talking about:
avocado list --loader
commandavocado run --test-runner=runner
commandavocado.core.loader
module to find tests
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 mytests.py othertests.sh
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 legacy architecture, and
the nrunner architecture. In the legacy 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 --loader | -> | avocado.core.loader |
| avocado run --test-runner=runner | | |
+----------------------------------+ +---------------------+
|
+--------------------------------------------------+
|
v
+--------------------------------------+
| Test Factory 1 |
+--------------------------------------+
| Class: TestFoo |
| Parameters: |
| - modulePath: /path/to/module.py |
| - methodName: test_foo |
| ... |
+--------------------------------------+
+--------------------------------------+
| Test Factory 2 |
+--------------------------------------+
| Class: TestBar |
| Parameters: |
| - modulePath: /path/to/module.py |
| - methodName: test_bar |
| ... |
+--------------------------------------+
...
Because the nrunner is now the default implementation, to distinguish
between implementations and select the legacy implementation you must
use: avocado list --loader
and avocado
run--test-runner=runner
.
On the nrunner architecture, a different terminology and
foundation is used. Each one of the test references given to list
or run
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 | run | -> | avocado.core.resolver | ---+
+--------------------+ +-----------------------+ |
|
+---------------------------------------------------------+
|
v
+--------------------------------------+
| ReferenceResolution #1 |
+--------------------------------------+
| Reference: /bin/true |
| Result: SUCCESS |
| +----------------------------------+ |
| | Resolution #1 (Runnable): | |
| | - kind: exec-test | |
| | - uri: /bin/true | |
| +----------------------------------+ |
+--------------------------------------+
+--------------------------------------+
| ReferenceResolution #2 |
+--------------------------------------+
| Reference: test.py |
| Result: SUCCESS |
| +----------------------------------+ |
| | Resolution #1 (Runnable): | |
| | - kind: python-unittest | |
| | - uri: test.py:Test.test_1 | |
| +----------------------------------+ |
| +----------------------------------+ |
| | Resolution #2 (Runnable): | |
| | - kind: python-unittest | |
| | - uri: test.py:Test.test_2 | |
| +----------------------------------+ |
+--------------------------------------+
...
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 legacy 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 legacy
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 legacy 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 legacy architecture runs
tests roughly following these steps:
- Create one (and only one) queue to communicate with the test processes
- For each test factory found by the loader:
- Unpack the test factory into a test class and its parameters, that is,
test_class, parameters = test_factory
- Instantiate a new process for the test
- Within the new process, instantiate the Python class, that is,
test = test_class(**parameters)
- Give the test access to queue, that is
test.set_runner_queue(queue)
- 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:
- one unique queue makes communicating with multiple tests at the same time hard
- test factories contain a Python class (code) that will be instantiated in the new process
- to instantiate Python classes in other systems would require serializing them, which is error prone (AKA pickling nightmares)
- 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 nrunner architecture. In
the nrunner 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:
- Creates a status server that binds to a TCP port and waits for status messages from any number of clients
- Creates the chosen
Spawner
, withProcessSpawner
being the default - For each
avocado.core.nrunner.Runnable
found by the resolver, turns it into aavocado.core.nrunner.Task
, which means giving it the following extra information:
- The status server(s) that it should report to
- An unique identification, so that its messages to the status server can be uniquely identified
- For each resulting
avocado.core.nrunner.Task
in the previous step:
- Asks the spawner to spawn it
- 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
- 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.
Concepts¶
Runnable¶
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 https://example.org:8080
.
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">>
Runner¶
A Runner, within the context of the nrunner 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.
Tip
for a very basic example of the interface expected, refer to
selftests/functional/test_nrunner_interface.py
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''}
Tip
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).
Task¶
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 won’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.
Recipe¶
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.
Runners¶
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-test” runner with:
avocado-runner runnable-run-recipe examples/nrunner/recipes/runnables/exec_test_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
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | #!/usr/bin/env python3
from avocado.core import nrunner
from avocado.core.runners.utils.messages import FinishedMessage, StartedMessage
class FooRunner(nrunner.BaseRunner):
def run(self):
yield StartedMessage.get()
yield FinishedMessage.get('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():
nrunner.main(RunnerApp)
if __name__ == '__main__':
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.
The messages are standard Python dictionaries with a specific structure.
You can create it by yourself based on the table Supported message types,
or you can use helper methods in avocado.core.runners.utils.messages
which will generate them for you.
Supported message types¶
-
class
avocado.core.messages.
StartMessageHandler
¶ Handler for started message.
It will create the test base directories and triggers the ‘start_test’ event.
This have to be triggered when the runner starts the test.
Parameters: - status – ‘started’
- time (float) – start time of the test
example: {‘status’: ‘started’, ‘time’: 16444.819830573}
-
class
avocado.core.messages.
FinishMessageHandler
¶ Handler for finished message.
It will report the test status and triggers the ‘end_test’ event.
This is triggered when the runner ends the test.
Parameters: - status – ‘finished’
- result (avocado.core.teststatus.STATUSES) – test result
- time (float) – end time of the test
- fail_reason (string) – Optional parameter for brief specification, of the failed result.
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.
-
class
avocado.core.messages.
LogMessageHandler
¶ Handler for log message.
It will save the log to the debug.log file in the task directory.
Parameters: - status – ‘running’
- type – ‘log’
- log (string) – log message
- time (float) – Time stamp of the message
- example: {‘status’: ‘running’, ‘type’: ‘log’, ‘log’: ‘log message’,
- ‘time’: 18405.55351474}
-
class
avocado.core.messages.
StdoutMessageHandler
¶ Handler for stdout message.
It will save the stdout to the stdout and debug file in the task directory.
Parameters: - example: {‘status’: ‘running’, ‘type’: ‘stdout’, ‘log’: ‘stdout message’,
- ‘time’: 18405.55351474}
-
class
avocado.core.messages.
StderrMessageHandler
¶ Handler for stderr message.
It will save the stderr to the stderr and debug file in the task directory.
Parameters: - example: {‘status’: ‘running’, ‘type’: ‘stderr’, ‘log’: ‘stderr message’,
- ‘time’: 18405.55351474}
-
class
avocado.core.messages.
WhiteboardMessageHandler
¶ Handler for whiteboard message.
It will save the stderr to the whiteboard file in the task directory.
Parameters: - example: {‘status’: ‘running’, ‘type’: ‘whiteboard’,
- ‘log’: ‘whiteboard message’, ‘time’: 18405.55351474}
-
class
avocado.core.messages.
FileMessageHandler
¶ Handler for file message.
In task directory will save log into the runner specific file. When the file doesn’t exist, the file will be created. If the file exist, the message data will be appended at the end.
Parameters: - example: {‘status’: ‘running’, ‘type’: ‘file’, ‘path’:’foo/runner.log’,
- ‘log’: ‘this will be saved inside file’, ‘time’: 18405.55351474}