[Avocado-devel] RFC: Avocado Job API
Ademar Reis
areis at redhat.com
Tue Apr 12 00:31:12 UTC 2016
On Mon, Apr 11, 2016 at 09:09:58AM -0300, Cleber Rosa wrote:
> Note: the same content on this message is available at:
>
> https://github.com/clebergnu/avocado/blob/rfc_job_api/docs/rfcs/job-api.rst
>
> Some users may find it easier to read with a prettier formatting.
>
> Problem statement
> =================
>
> An Avocado job is created by running the command line ``avocado``
> application with the ``run`` command, such as::
>
> $ avocado run passtest.py
>
> But most of Avocado's power is activated by additional command line
> arguments, such as::
>
> $ avocado run passtest.py --vm-domain=vm1
> $ avocado run passtest.py --remote-hostname=machine1
>
> Even though Avocado supports many features, such as running tests
> locally, on a Virtual Machine and on a remote host, only one those can
> be used on a given job.
>
> The observed limitations are:
>
> * Job creation is limited by the expressiveness of command line
> arguments, this causes mutual exclusion of some features
> * Mapping features to a subset of tests or conditions is not possible
> * Once created, and while running, a job can not have its status
> queried and can not be manipulated
>
> Even though Avocado is a young project, its current feature set
> already exceeds its flexibility. Unfortunately, advanced users are
> not always free to mix and match those features at will.
>
> Reviewing and Evaluating Avocado
> ================================
>
> In light of the given problem, let's take a look at what Avocado is,
> both by definition and based on its real world, day to day, usage.
>
> Avocado By Definition
> ---------------------
>
> Avocado is, by definition, "a set of tools and libraries to help with
> automated testing". Here, some points can be made about the two
> components that Avocado are made of:
>
> 1. Libraries are commonly flexible enough and expose the right
> features in a consistent way. Libraries that provide good APIs
> allow users to solve their own problems, not always anticipated by
> the library authors.
>
> 2. The majority of the Avocado library code fall in two categories:
> utility and test APIs. Avocado's core libraries are so far, not
> intended to be consumed by third party code and its use is not
> supported in any way.
>
> 3. Tools (as in command line applications), are commonly a lot less
> flexible than libraries. Even the ones driven by command line
> arguments, configuration files and environment variables fall
> short in flexibility when compared to libraries. That is true even
> when respecting the basic UNIX principles and features that help to
> reuse and combine different tools in a single shell session.
>
> How Avocado is used
> -------------------
>
> The vast majority of the observed Avocado use cases, present and
> future, includes running tests. Given the Avocado architecture and
> its core concepts, this means running a job.
>
> Avocado, with regards to its real world usage, is pretty much a job
> (and test) runner, and there's no escaping that. It's probable that,
> for every one hundredth ``avocado run`` commands, a different
> ``avocado <subcommand>`` is executed.
>
> Proposed solution & RFC goal
> ----------------------------
>
> By now, the title of this document may seem a little less
> misleading. Still, let's attempt to make it even more clear.
>
> Since Avocado is mostly a job runner that needs to be more flexible,
> the most natural approach is to turn more of it into a library. This
> would lead to the creation of a new set of user consumable APIs,
> albeit for a different set of users. Those APIs should allow the
> creation of custom job executions, in ways that the Avocado authors
> have not yet anticipated.
>
> Having settled on this solution to the stated problem, the primary
> goal of this RFC is to propose how such a "Job API" can be
> implemented.
So in theory, given a comprehensive enough API it should be
possible to rewrite the entire "Avocado Test Runner" using the
Job API.
Actually, in the future we could have multiple Test Runners (for
example in contrib/) with different feature sets or approaches at
creating and managing jobs.
(in practice we will approach the problem incrementally, so this
should be a very long term goal)
>
> Analysis of a Job Environment
> =============================
>
> To properly implement a Job API, it's necessary to review what
> influences the creation and execution of a job. Currently, a Job
> execution based on the current command line, is driven by, at least,
> the following factors:
>
> * Configuration state
> * Command line parameters
> * Active plugins
>
> The following subsections examines how these would behave in an API
> based approach to Job execution.
>
> Configuration state
> -------------------
>
> Even though Avocado has a well defined `settings`_ module, it only
> provides support for `getting the value`_ of configuration keys. It
> lacks the ability to set configuration values at run time.
>
> If the configuration state allowed modifications at run time (in a
> well defined and supported way), users could then create many types of
> custom jobs with that "tool" alone.
>
> Command line parameters
> -----------------------
>
> The need for a strong and predictable correlation between application
> builtin defaults, configuration keys and command line parameters is
> also a MUST for the implementation of the Job API.
>
> Users writing a custom job will very often need to set a given
> behavior that may influence different parts of the Job execution.
>
> Not only that, many use cases may be implemented simply by changing
> those defaults in the midst of the job execution.
>
> If users know how to map command line parameters into their
> programmable counterparts, advanced custom jobs will be created much
> more naturally.
So if I understand it correctly, the configuration state
(configuration keys/values) and command line parameters are two
high-level approaches to the same thing, which we could probably
define as the job environment, job configuration, or job state.
So, for example, there could be multiple ways to configure a job
environment:
1. Via configuration file (at least some parameters, as of today)
2. Via command line options available in the Job API (at least
some parameters, as of today)
3. Via fine-grained APIs (future)
In the case of (2), we could have an opaque configuration API
available as part of the Job API which would allow avocado
command line options to be processed at runtime.
For example, by having methods such as:
job.process_args(argv) --> process "argv" and configure the
job environment according to the options provided by user.
job.args_help() and job.args_usage() --> show command line
help and usage messages
In this case all the Job API "knows" is that
job.process_args(sys.argv) was called. Internally it could
implement things such as --multiplexer, --profilers, --wrappers,
--gdb, etc.
Using job.process_args() would change the *job configuration* at
runtime.
>
> Plugins
> -------
>
> Avocado currently relies exclusively on setuptools `entry points`_ to
> define the active plugins. It may be beneficial to add a secondary
> activation and deactivation mechanism, one that is locally
> configurable. This is a rather common pattern, and well supported by
> the underlying stevedore library.
>
> Given that all plugable components of Avocado are updated to adhere to
> the "new plugin" standard, some use cases could be implemented simply
> by enabling/disabling plugins (think of "driver" style plugins). This
> can be exclusively or in addition to setting the plugin's own
> configuration.
>
> Also, depending on the type of plugin, it may be useful to activate,
> deactivate and configure those plugins per job. Thus, as part of the
> Job state, APIs would allow for querying/setting plugins.
The plugin interfaces in Avocado are not very mature yet and I
anticipate many discussions about what plugins are and which kind
of interfaces should be supported during the creation of the Job
API.
My impression is that there are two different levels of APIs in
the context of plugins:
1. The abstract APIs available *to* plugins
2. The specific APIs provided *by* plugins
Avocado should properly expose (1) via the Job API when
necessary, while (2) should be available in a generic plugin
configuration API.
Let me explain with an example using the multiplexer API (this is
hypothetical, because in practice that's not how the current
Avocado implementation works):
- The multiplexer is a plugin which turns a yaml file into a
set of variants (a variant is basically a dictionary). It has
a complex filtering mechanisms to control how the yaml file
is processed and generates variants with combinations from a
tree structure. That's what we're all familiar with in
Avocado (`run --multiplexer` and `avocado multiplexer`)
- The multiplexer plugin should use a "Variant API", which is
way more abstract and generic: it simply provides a set of
variants (dictionaries) identified by a "Variant ID" (please
check the "Test ID" RFC for details on how it relates to
everything else in Avocado).
There could be multiple plugins that use this "Variant API"
to deliver functionality similar to what the multiplexer
does. Which one is being used and how it's configured will
depend on the "job configuration" (or state, whatever you
prefer to call it).
So Avocado should provide the "Variant API" in the "Job API", but
the more complex and plugin-specific operations (like filtering
in this case), should be made available via a generic plugin API.
At runtime, the "Variant API" will behave in ways dictated by how
the job environment is *configured*.
Some pseudo-code examples are provided further down, when you
mention this specific multiplex use-case.
>
> Use cases
> =========
>
> To aid in the design of an API that solves unforeseen needs, let's
> think about a couple of use cases. Most of these use cases are based
> on feedback already received and/or features already requested.
>
> Ordered and conditional test execution
> --------------------------------------
>
> A user wants to create a custom job that only runs a benchmark test on
> a VM if the VM installation test succeeds.
>
> Possible use case fulfillment
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>
> Pseudo code::
>
> #!/usr/bin/env python
> from avocado import Job
> from avocado.resolver import resolve
>
> job = Job()
>
> vm_install = resolve('io-github-autotest-qemu.unattended_install.cdrom.http_ks.default_install.aio_native')
> vm_disk_benchmark = resolve('io-github-autotest-qemu.autotest.bonnie')
>
> if job.run_test(vm_install).result == 'PASS':
> job.run_test(vm_disk_benchmark)
>
> API Requirements
> ~~~~~~~~~~~~~~~~
>
> 1. Job creation API
> 2. Test resolution API
> 3. Single test execution API
>
> Run profilers on a single test
> ------------------------------
>
> A user wants to create a custom job that only runs profilers for the
> very first test. Running the same profilers for all other tests may
> be useless to the user, or maybe consume too much I/O resources that
> would influence the remaining tests.
>
> Possible use case fulfillment
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>
> Avocado, has a configuration key that controls profilers::
>
> [sysinfo.collect]
> ...
> profiler = False
> ...
>
> By exposing the configuration state, the ``profiler`` key of the
> ``sysinfo.collect`` section could be enabled for one test, and
> disabled for all others. Pseudo code::
>
> #!/usr/bin/env python
> from avocado import Job
> from avocado.resolver import resolve
>
> job = Job()
> env = job.environment # property
>
> env.config.set('sysinfo.collect', 'profiler', True)
> job.run_test(resolve('build'))
>
> env.config.set('sysinfo.collect', 'profiler', False)
> job.run_test(resolve('benchmark'))
> job.run_test(resolve('stress'))
> ...
> job.run_test(resolve('netperf'))
>
> API Requirements
> ~~~~~~~~~~~~~~~~
>
> 1. Job creation API
> 2. Test resolution API
> 3. Configuration API
> 4. Single test execution API
>
> Multi-host test execution
> -------------------------
>
> Use case description
> ~~~~~~~~~~~~~~~~~~~~
>
> User needs to run the same test on different platforms. User has
> hosts with the different platforms already setup and remotely
> accessible.
>
> Possible use case fulfillment
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>
> Avocado currently runs all tests in a job with a single runner. The
> `default runner`_ implementation is a local test runner. Other tests
> runners include the `remote runner`_ and the `vm runner`_.
>
> Pseudo code such as the following could implement the (serial, for
> simplicity) test execution in multiple different hosts::
>
> from avocado import Job
> from avocado.plugin_manager import require
> from avocado.resolver import resolve
>
> job = Job()
> print('JOB ID: %s' % job.unique_id)
> print('JOB LOG: %s' % job.log)
>
> runner_plugin = 'avocado.plugins.runner:RemoteTestRunner'
> require(runner_plugin)
>
> env = job.environment # property
> env.config.set('plugin.runner', 'default', runner_plugin)
> env.config.set('plugin.runner.RemoteTestRunner', 'username', 'root')
> env.config.set('plugin.runner.RemoteTestRunner', 'password', '123456')
>
> test = resolve('hardware_validation.py:RHEL.test')
>
> host_list = ['rhel6.x86_64.internal',
> ...
> 'rhel7.ppc64.internal']
>
> for host in host_list:
> env.config.set('plugin.runner.RemoteTestRunner', 'host', host)
> job.run_test(test)
>
> print('JOB STATUS: %s' % job.status)
>
> It's actually quite simple to move from a custom Job execution to a
> custom Job runner, example::
>
> #!/usr/bin/env python
> import sys
> from avocado import Job
> from avocado.plugin_manager import require
> from avocado.resolver import resolve
>
> test = resolve(sys.argv[1])
> host_list = sys.argv[2:]
>
> runner_plugin = 'avocado.plugins.runner:RemoteTestRunner'
> require(runner_plugin)
>
> job = Job()
> print('JOB ID: %s' % job.unique_id)
> print('JOB LOG: %s' % job.log)
> env = job.environment # property
> env.config.set('plugin.runner', 'default', runner_plugin)
> env.config.set('plugin.runner.RemoteTestRunner', 'username', 'root')
> env.config.set('plugin.runner.RemoteTestRunner', 'password', '123456')
>
> for host in host_list:
> env.config.set('plugin.runner.RemoteTestRunner', 'host', host)
> job.run_test(test)
>
> print('JOB STATUS: %s' % job.status)
>
> Which could be run as::
>
> $ multi hardware_validation.py:RHEL.test rhel{6,7}.{x86_64,ppc64}.internal
> JOB ID: 54cacfb42f3fa9566b6307ad540fbe594f4a5fa2
> JOB LOG:
> /home/<user>/avocado/job-results/job-2016-04-07T16.46-54cacfb/job.log
> JOB STATUS: AVOCADO_ALL_OK
>
> API Requirements
> ~~~~~~~~~~~~~~~~
>
> 1. Job creation API
> 2. Test resolution API
> 3. Configuration API
> 4. Plugin Management API
> 5. Single test execution API
>
> Current shortcomings
> ~~~~~~~~~~~~~~~~~~~~
>
> 1. The current Avocado runner implementations do not follow the "new
> style" plugin standard.
>
> 2. There's no concept of job environment
>
> 3. Lack uniform definition of plugin implementation for "driver" style
> plugins.
>
> 4. Lack of automatic ownership of configuration namespace by plugin name.
>
>
> Other use cases
> ===============
>
> The following is a list of other valid use cases which can be
> discussed at a later time:
>
> * Use the multiplexer only for some tests.
The example I promised while discussing how to handle the core
and plugin APIs with the example of the multiplexer:
Using only the Variant API (a core concept):
from avocado import Job
from avocado import resolver
job = Job()
job.process_args(sys.argv)
print('JOB ID: %s' % job.unique_id)
print('JOB LOG: %s' % job.log)
test = resolver("passtest.py")
for v in env.variants():
...
print "%s;%s" % (test.name, v.id)
test.variants.append(v)
job.run_test(test)
job.run_test(resolver("foo.py"))
job.run_test(resolver("bar.py"))
print('JOB STATUS: %s' % job.status)
exit()
Now using the Multiplexer API (from the plugin):
from avocado import Job
from avocado import resolver
from avocado.plugin_manager import require
job = Job()
job.process_args(sys.argv)
print('JOB ID: %s' % job.unique_id)
print('JOB LOG: %s' % job.log)
env = job.environment # property
require('avocado.plugins.runner:Multiplexer')
env.config.set("plugin.runner:Multiplexer", "reset")
env.config.set("plugin.runner:Multiplexer", "yaml-file", "pass.yaml")
env.config.set("plugin.runner:Multiplexer", "filter-out", "hw/")
env.config.set("plugin.runner:Multiplexer", "filter-only", "linux/")
test = resolver("passtest.py")
for v in env.variants():
...
print "%s;%s" % (test.name, v.id)
test.variants.append(v)
job.run(test)
env.config.set("plugin.runner.multiplexer", "reset")
env.config.set("plugin.runner.multiplexer", "yaml-file", "foo.yaml")
test = resolver("foo.py")
for v in env.variants():
...
print "%s;%s" % (test.name, v.id)
test.variants.append(v)
job.run(test)
job.run_test(resolver("bar.py"))
print('JOB STATUS: %s' % job.status)
exit()
>
> * Use the gdb or wrapper feature only for some tests.
>
> * Run Avocado tests and external-runner tests in the same job.
The same applies to these two use-cases: Runtime job
configuration will make gdb or wrappers be used when
job.run(test) is used.
I think the above makes a lot of sense, although the specifics of
how the actual API will look like still needs to be properly
defined. Please let me know if we're headed in the same
direction.
Thanks.
- Ademar
>
> * Run tests in parallel.
>
> * Take actions based on test results (for example, run or skip other
> tests)
>
> * Post-process the logs or test results before the job is done
>
> Development Milestones
> ======================
>
> Since it's clear that Avocado demands many changes to be able to
> completely fulfill all mentioned use cases, it seems like a good idea
> to define milestones. Those milestones are not intended to set the
> pace of development, but to allow for the maximum number of real world
> use cases fulfillment as soon as possible.
>
> Milestone 1
> -----------
>
> Includes the delivery of the following APIs:
>
> * Job creation API
> * Test resolution API
> * Single test execution API
>
> Milestone 2
> -----------
>
> Adds to the previous milestone:
>
> * Configuration API
>
> Milestone 3
> -----------
>
> Adds to the previous milestone:
>
> * Plugin management API
>
> Milestone 4
> -----------
>
> Introduces proper interfaces where previously Configuration and Plugin
> management APIs were being used. For instance, where the following
> pseudo code was being used to set the current test runner::
>
> env = job.environment
> env.config.set('plugin.runner', 'default',
> 'avocado.plugins.runner:RemoteTestRunner')
> env.config.set('plugin.runner.RemoteTestRunner', 'username', 'root')
> env.config.set('plugin.runner.RemoteTestRunner', 'password', '123456')
>
> APIs would be introduced that would allow for the following pseudo
> code::
>
> job.load_runner_by_name('RemoteTestRunner')
> if job.runner.accepts_credentials():
> job.runner.set_credentials(username='root', password='123456')
>
> .. _settings: https://github.com/avocado-framework/avocado/blob/0.34.0/avocado/core/settings.py
> .. _getting the value: https://github.com/avocado-framework/avocado/blob/0.34.0/avocado/core/settings.py#L221
> .. _default runner: https://github.com/avocado-framework/avocado/blob/0.34.0/avocado/core/runner.py#L193
> .. _remote runner: https://github.com/avocado-framework/avocado/blob/0.34.0/avocado/core/remote/runner.py#L37
> .. _vm runner: https://github.com/avocado-framework/avocado/blob/0.34.0/avocado/core/remote/runner.py#L263
> .. _entry points:
> https://pythonhosted.org/setuptools/pkg_resources.html#entry-points
>
> --
> Cleber Rosa
> [ Sr Software Engineer - Virtualization Team - Red Hat ]
> [ Avocado Test Framework - avocado-framework.github.io ]
>
> _______________________________________________
> Avocado-devel mailing list
> Avocado-devel at redhat.com
> https://www.redhat.com/mailman/listinfo/avocado-devel
--
Ademar Reis
Red Hat
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