[Avocado-devel] RFC: Avocado Job API
Lukáš Doktor
ldoktor at redhat.com
Tue Apr 12 09:22:40 UTC 2016
Dne 12.4.2016 v 02:31 Ademar Reis napsal(a):
> 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)
>
Exactly, for example run on several machines, or run in parallel.
>>
>> 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.
Currently the mapping is:
config -> args -> job.args
but we discussed that this is wrong, as some functions use `job.args`,
some rely on `config`. So we need to rework this and either create yet
another abstract entity which contains all, or update the `config`
values and use that one everywhere, or accept only related params for
each level (job accepts, test accepts, ...)
From this RFC I understand (Cleber) wants to use `config` inside
plugins/job/test, so the existing argument parser would have to be
modified to map arguments not to processed-arguments, but rather to
update the config values. Do I understand it correctly, Cleber?
>
> 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
Could be (after we finish with everything else). I had a different idea
in my head, please see the other email. (sorry I did not wanted to
provide my feedback before reading other responses)
>
> 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.
That's what the env.config.set() does, right? It prepares the env
(parsed args) and it's instantiated during `run_test`.
>
>>
>> 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
+1
>
> 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):
I think multiplexer is not the best example here as it's not a plugin.
It needs to be extracted and the API should be (finally) defined.
The other plugins define the interface either by abstract class. Still
we have 3 ways of invoking the plugins:
1. Stevedore: registers all plugins and skips the execution based on
job.environment variable (subcommands, arguments, pre/post job plugin)
2. Proxy: We have a proxy and we manually add plugins to be used there
(TestResultProxy, TestLoaders, ...)
3. We set one plugin to handle the execution - TestRunner
The (1) can be tweaked to actually invoke only plugins enabled by
job.environment variable by default (currently it's always executed).
The (2) is currently hardcoded inside job (loaders use config) and
allows multiple instances of the same plugin and I like that.
And the (3) is handled by arguments.
As I mentioned earlier, we should identify types of plugin and the
test-related should probably be either passed to the test by the user,
or be instantiated by the test based on the env variables.
Anyway back to the multiplexer:
>
> - 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`)
It's iterative
>
> - 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).
Not dictionaries (that'd be way to strict). It allows `AvocadoParams`
object. `AvocadoParams` is "a better dictionary" and is not related to
multiplexer and is the API (currently inside `avocado.core`).
>
> 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).
There are still missing pieces, but some are already there.
>
> 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"))
Btw this raised another concern in my head. Until now I thought
`job.run_test` runs one job, but it's true that the resolver can return
several tests (eg. resolve("virtio_console")). So shouldn't the API be:
tests = resolver(...)
and then:
for test in tests:
job.run_test(test)
alternatively it'd have to be `job.run_tests(tests)`, which would return
list of results including multiplexed test results. I could live with
`run_tests` but I don't like to trigger all variants of the test. That
would make:
passtest.1 failtest.1 passtest2 failtest2
harder to define. IMO params belongs to test, not all combinations. (see
the modified example below)
>
> print('JOB STATUS: %s' % job.status)
> exit()
Please take a look at my plugins description in the other email. I think
`multiplexer` (and other variants generating) plugins are a bit
different and should be handled separately and even allow multiple
instances of it and maybe it should be like that with other plugins too:
job = Job()
job.add(avocado.plugins.sysinfo.SysInfo())
# Uses the defaults
job.add(avocaod.plugins.json.JsonResults(filename="myfile"))
# Uses defaults, but overrides the "filename"
resolver.cleanup() # remove previously defined resolvers
resolver.add(my.plugin())
test = resolver("passtest")
test_gdb = resolver("passtest")
test_gdb.add(avocado.plugins.gdb.Gdb())
mux1 = avocado.plugins.multiplexer.Multiplexer()
# Get's the value from "--multiplex", or config value []
mux2 = avocado.plugins.multiplexer.Multiplexer("MyFile.yaml")
for variant in mux1:
test.params = variant
test_gdb.params = variant
job.run_test(test)
job.run_test(test_gdb)
for variant in mux2:
test.params = variant
job.run_test(test)
Eventually we could simplify things and allow some assignments directly
on call, for example `job.run_test(test, params=None, ...)`.
Note: By default avocado would load all plugins enabled in config.
>
>>
>> * 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
>
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