On 04/15/2016 03:05 AM, Lukáš Doktor wrote:
Hello again,
There were couple of changes and the new Job API RFC, which might sound
similar to this RFC, but it covers different parts. Let's update the
multi-test RFC and fix the terminology, which might had been a bit
misleading.
Changes:
v2: Rewritten from scratch
v2: Added examples for the demonstration to avoid confusion
v2: Removed the mht format (which was there to demonstrate manual
execution)
v2: Added 2 solutions for multi-tests
v2: Described ways to support synchronization
v3: Renamed to multi-stream as it befits the purpose
v3: Improved introduction
v3: Workers are renamed to streams
v3: Added example which uses library, instead of new test
v3: Multi-test renamed to nested tests
v3: Added section regarding Job API RFC
v3: Better description of the Synchronization section
v3: Improved conclusion
v3: Removed the "Internal API" section (it was a transition between
no support and "nested test API", not a "real" solution)
v3: Using per-test granularity in nested tests (requires plugins
refactor from Job API, but allows greater flexibility)
The problem
===========
Allow tests to have some if its block of code run in separate stream(s).
We'll discuss the range of "block of code" further in the text.
I believe it's also important to define what "stream" means. The reason
is that it's used both as an abstraction, and as a more concrete
component in the code examples that follow.
One example could be a user, who wants to run netperf on 2 machines,
which requires following manual steps:
machine1: netserver -D
machine1: # Wait till netserver is initialized
machine2: netperf -H $machine1 -l 60
machine2: # Wait till it finishes and report the results
machine1: # stop the netserver and report possible failures
the test would have to contain the code for both, machine1 and machine2
and it executes them in two separate streams, which might or not be
executed on the same machine.
I can understand what you mean here just fine, but it's rather confusing
to say "machine1 and machine2" and at the same time "migh or not be
executed on the same machine".
This brings us back to the stream concept. I see the streams as the
running, isolated, execution of "code blocks". This execution may be on
the same machine or not.
With those statements in mind, I'd ask you to give your formal
definition and vision of the the stream concept.
You can see that each stream is valid even without the other, so
additional requirement would be to allow easy share of those block of
codes among other tests. Splitting the problem in two could also
sometimes help in analyzing the failures.
Here you say that a stream is isolated from each other. This matches my
understanding of streams as "running, isolated execution of code blocks".
But "help in analyzing failures" should not be a core part or reason for
this architecture. It can be a bonus point. Still, let's try to focus
on the very core components on the architecture and drop the discussion
about the lesser important aspects.
Some other examples might be:
1. A simple stress routine being executed in parallel (the same or
different hosts)
2. Several code blocks being combined into a complex scenario(s)
3. Running the same test along with stress test in background
For demonstrating purposes this RFC uses a very simple example fitting
in the category (1). It downloads the main page from "example.org"
location using "wget" (almost) concurrently from several machines.
Standard python libraries
-------------------------
One can run pieces of python code directly using python's
multiprocessing library, without any need for the avocado-framework
support. But there is quite a lot of cons:
+ no need for framework API
- lots of boilerplate code in each test
- each solution would be unique and therefor hard to analyze the logs
- no decent way of sharing the code with other tests
IMHO you can drop the reasons on why *not* to use lower level or just
different code. If, during research we came to find some other external
project/framework/library, we should just have used it and documented
it. Since this is not the case, let's just avoid getting distracted on
this RFC.
Yes, it's possible to share the code by writing libraries, but that does
not scale as other solutions...
This is a justification of why we should do it the Avocado way. Again,
if there were better non-Avocado ways, we shouldn't be discussing
anything else than those other possible solutions. I'm being repetitive
here because I really believe we should focus on our proposed
architecture key points.
Example (simplified):
from avocado.core.remoter import Remote
from threading import Thread
...
class Wget(Thread):
def __init__(self, machine, url):
self.remoter = Remote(machine)
self.url = url
self.status = None
def run(self):
ret = self.remoter.run("wget %s" % self.target,
ignore_status=True)
self.status = ret.exit_status
...
threads = []
for machine in machines:
threads.append(Wget(machine, "example.org"))
for thread in threads:
thread.start()
for thread in threads:
thread.join()
self.failif(thread.status is 0, ...)
...
Where is the Avocado test here?
This should serve the purpose, but to be able to understand failures,
one would have to add a lot of additional debug information and if one
wanted to re-use the Wget in other tests, he'd have to make it a library
shared with all the tests.
Making debug easy should not the reason to settle on a given
architecture. I understand this is *not* the solution you're proposing,
so, let's focus on the architecture of the proposed solution.
Nested tests API
----------------
Another approach would be to say the "block of code" is the full avocado
test. The main benefits here are, that each avocado test provides
additional debug information in a well established format people are
used to from normal tests, allows one to split the complex problem into
separate parts (including separate development) and easy sharing of an
existing tests (eg. stress test, server setup, ...) and putting them
together like a Lego into complex scenarios.
Here you're proposing that a "block of code" could be an Avocado test
(avocado.Test). Right, this is pretty clear and I should have no
questions about it since I mentioned that on v2.
Now, on an updated RFC version like this, you should focus on why this
is a good idea. It's obviously not a one-size-fits-all solution, but you
should defend why it's an appropriate choice/compromise. Focusing on
the proposed choice will make the sight of the overall architecture clearer.
On the negative side, avocado test is not the smallest piece of code and
it adds quite a bit of overhead. But for simpler code, one can execute
the code directly (threads, remoter) without a framework support.
And THB, one universal way to defend it is as a choice is that, for
lesser common use cases, users can always use their own code, as you put
here.
Example (simplified):
import avocado
class WgetExample(avocado.Test):
def setUp(self):
self.streams = avocado.Streams(self)
for machine in machines:
self.streams.add_stream(machine)
def test(self)
for stream in self.streams:
stream.run_bg("/usr/bin/wget example.org")
self.streams.wait(ignore_errors=False)
where the `avocado.Stream` represents a worker (local or remote) which
allows running avocado tests in it (foreground or background). This
should provide enough flexibility to combine existing tests in complex
tests.
This definition should have been given earlier, and without code to
support it. Then, when it appears on a code example such as this, it
would be trivial to understand your proposal.
Instead of using plugin library for streams, we can develop it as
another test variant (not a new test type, only avocado.Test with some
additional initialization), called `avocado.MultiTest` or
`avocado.NestedTest`:
I miss what you mean by "plugin library for streams". Also, by focusing
on (how) "we can develop it" you miss the architecture, which, by the
lack of complete definitions, is still unclear.
import avocado
class WgetExample(avocado.NestedTest):
# Machines are defined via params adn initialized
# in NestedTest.setUp
def test(self):
for stream in self.streams:
stream.run("/usr/bin/wget example.org")
self.wait(ignore_errors=False)
So `avocado.NestedTest` is introduced, and has default code that sets up
`self.streams`. Then, all of a sudden, a stream becomes the executor of
commands? You had just a few paragraphs earlier defined the proposal
for "blocks of code" was an `avocado.Test`. This makes this example
confusing.
API backed by internal API
~~~~~~~~~~~~~~~~~~~~~~~~~~
_supported by cleber in v2 and I agree now_
This would implement the nested test API using the internal API (from
avocado.core).
+ runs native python
+ easy interaction and development
+ easily extensible by either using internal API (and risk changes) or
by inheriting and extending the features.
- lots of internal API will be involved, thus with almost every change
of internal API we'd have to adjust this code to keep the NestedTest
working
- fabric/paramiko is not thread/parallel process safe and fails badly so
first we'd have to rewrite our remote execution code (use autotest's
worker, or aexpect+ssh)
API backed by cmdline
~~~~~~~~~~~~~~~~~~~~~
_liked by me in v2, hated by others, rejected in v3_
This would implement the nested test API by translating it into "avocado
run" commands.
+ easy to debug as users are used to the "avocado run" syntax and issues
+ allows manual mode where users trigger the "avocado run" manually
+ cmdline args are part of public API so they should stay stable
+ no issues with fabric/paramiko as each process is separate
+ even easier extensible as one just needs to implement the feature for
"avocado run" and then can use it as extra_params in the worker, or send
PR to support it in the stable environment.
- would require additional features to be available on the cmdline like
streamline way of triggering tests
- only features available on the cmdline can be supported (currently not
limiting)
- rely on stdout parsing (but avocado supports machine readable output)
If this is really rejected (including by you), go ahead and drop it from
the proposal.
Synchronization
===============
Some tests do not need any synchronization, users just need to run them.
But some multi-stream tests needs to be precisely synchronized or they
need to exchange data.
For synchronization purposes usually "barriers" are used, where barrier
guards the entry into a section identified by "name" and "number of
clients". All parties asking an entry into the section will be delayed
until the "number of clients" reach the section (or timeout). Then they
are resumed and can entry the section. Any failure while waiting for a
barrier propagates to other waiting parties.
This can be all automated inside the `avocado.Streams`, which could
start listening on a free port and pass this information to the executed
code blocks. In the code blocks one simply imports `Sync` and initialize
it with the address+port and can use it for synchronization (or later
for data exchange).
from avocado.plugins.sync import Sync
# Connect the sync server on address stored in params
# which could be injected by the multi-stream test
# or set manually.
sync = Sync(self, params.get("sync_server", "/plugins/sync_server"))
# wait until 2 tests ask to enter "setup" barrier (60s timeout)
sync.barrier("setup", 2, 60)
As before it can be part of the "NestedTest" test, initialized based on
params without the need for boilerplate code. The result would be the
same, avocado listens on some port and the tests can connect to this
port and asks for a barrier/data exchange, with the support for
re-connection.
For debugging purposes it might be useful to allow starting the sync
server as avocado plugin eg. by `--sync-server ...` (or having another
command just to start listening, eg `avocado syncserver`). With that one
could spawn the multiple processes manually, without the need to run the
main multi-stream test and communicate over this manually started
server, or even just debug the behavior of one existing piece of the
bigger test and fake the other components by sending the messages
manually instead (eg. to see how it handles errors, timing issues,
unexpected situations).
Again, there are several ways to implement this:
Standard multiprocess API
-------------------------
The standard python's multiprocessing library contains over the TCP
synchronization. The only problem is that "barriers" were introduced in
python3 so we'd have to backport it. Additionally it does not fit 100%
to our needs, so we'd have to adjust it a bit (eg. to allow manual
interaction)
Autotest's syncdata
-------------------
Python 2.4 friendly, supports barriers and data synchronization. On the
contrary it's quite hackish and full of shortcuts.
Custom code
-----------
We can inspire by the above and create simple human-readable (easy to
debug or interact with manually) protocol to support barriers and data
exchange via pickling. IMO that would be easier to maintain than
backporting and adjusting of the multiprocessing or fixing the autotest
syncdata. A proof-of-concept can be found here:
https://github.com/avocado-framework/avocado/pull/1019
It modifies the "passtest" to be only executed when it's executed by 2
tests at the same time. The proof-of-concept does not support the
multi-stream tests, so one has to run "avocado run passtest" twice using
the same "--sync-server" (once --sync-server and once --sync).
Having sync support is part of the code concept. Choosing one is
simpler, IMHO. I trust and agree your choice so far. At implementation
time, if limitations become clearer, we can revisit this.
Job API RFC
===========
Recently introduced Job API RFC covers very similar topic as "nested
test", but it's not the same. The Job API is enabling users to modify
the job execution, eventually even write a runner which would suit them
to run groups of tests. On the contrary this RFC covers a way to combine
code-blocks/tests to reuse them into a single test. In a hackish way,
they can supplement each others, but the purpose is different.
I think we should give the message about what a user of the Job API
gets, and what the user of multi-stream test gets. Let's just state
what is the goal of each one. If a user wants to hack their way into a
"Frankenstein" approach, it's not our issue.
One of the most obvious differences is, that a failed "nested" test can
be intentional (eg. reusing the NetPerf test to check if unreachable
machines can talk to each other), while in Job API it's always a failure.
To me the difference is that by using the Job API the user would get to
trigger one or more tests, with Job-level control benefits. With
multi-stream, a given test can have parts of it run on separate
execution streams. Each one is intended for a different scenario. Our
goal should be to make the stated goals nice and easy. If user chooses
to hammer them down to achieve different goals, it's their problem. If
user finds it easy to solve the stated goals with the different tool,
it's our (design) problem.
I hope you see the pattern. They are similar, but on a different layer.
Internally, though, they can share some pieces like execution the
individual tests concurrently with different params/plugins
(locally/remotely). All the needed plugin modifications would also be
useful for both of these RFCs.
Some examples:
User1 wants to run "compile_kernel" test on a machine followed by
"install_compiled_kernel passtest failtest warntest" on "machine1
machine2". They depend on the status of the previous test, but they
don't create a scenario. So the user should use Job API (or execute 3
jobs manually).
User 1 writes a custom job, that optionally runs 3 tests. Looks fine.
User2 wants to create migration test, which starts migration from
machine1 and receives the migration on machine2. It requires cooperation
and together it creates one complex usecase so the user should use
multi-stream test.
Yes, one single test here (migration), some pieces executed as different
streams (on different machines).
Conclusion
==========
Given the reasons I like the idea of "nested tests" using "API backed by
internal API" as it is simple to start with, allows test reuse which
gives us well known test result format and internal API allow greater
flexibility for the future.
The netperf example from introduction would look like this:
Machine1:
class NetServer(avocado.NestedTest):
def setUp(self):
process.run("netserver")
self.barrier("setup", self.params.get("no_clients"))
def test(self):
pass
def tearDown(self):
self.barrier("finished", self.params.get("no_clients"))
process.run("killall netserver")
Machine2:
class NetPerf(avocado.NestedTest):
def setUp(self):
self.barrier("setup", params.get("no_clients"))
def test(self):
process.run("netperf -H %s -l 60"
% params.get("server_ip"))
barrier("finished", params.get("no_clients"))
It's unclear why those are `avocado.NestedTest`, since the previous
example suggested a specialized `avocado.Test` that would have
`self.streams` already setup.
One would be able to run this manually (or from build systems) using:
avocado syncserver &
avocado run NetServer --mux-inject /plugins/sync_server:sync-server
$SYNCSERVER &
avocado run NetPerf --mux-inject /plugins/sync_server:sync-server
$SYNCSERVER &
(where the --mux-inject passes the address of the "syncserver" into test
params)
When the code is stable one would write this multi-stream test (or
multiple variants of them) to do the above automatically:
class MultiNetperf(avocado.NestedTest):
def setUp(self):
self.failif(len(self.streams) < 2)
def test(self):
self.streams[0].run_bg("NetServer",
{"no_clients": len(self.streams)})
for stream in self.streams[1:]:
stream.add_test("NetPerf",
{"no_clients": len(self.workers),
"server_ip": machines[0]})
self.wait(ignore_failures=False)
How would a user specify where a given stream is going to be run?
Executing of the complex example would become:
avocado run MultiNetperf
You can see that the test allows running several NetPerf tests
simultaneously, either locally, or distributed across multiple machines
(or combinations) just by changing parameters. Additionally by adding
features to the nested tests, one can use different NetPerf commands, or
add other tests to be executed together.
The results could look like this:
$ tree $RESULTDIR
└── test-results
└── MultiNetperf
├── job.log
...
├── 1
│ └── job.log
...
└── 2
└── job.log
...
The multiple `job.log` files here makes things confusing... do we have a
single job that ran a single test?
Where the MultiNetperf/job.log contains combined logs of the "master"
test and all the "nested" tests and the sync server.
Directories [12] contain results of the created (possibly even named)
streams. I think they should be in form of standard avocado Job to keep
the well known structure.
To keep the Avocado Job structure, they'd either have to be Avocado
Jobs, or we'd have to fake them... Then, of a sudden, we have things
that look like jobs, but are not jobs. How would users of the Job API
react when then find out that their custom jobs have a single `job.log`
and users of a multi-stream tests have multiple `job.log`s?
I'd not trade the familiarity of the job log format for the structure of
the architecture we've been struggling to define.
My final suggestion: define all the core concepts and let us know how
they all fit. In text form. Then, when we get to code examples, they
should all be obvious. Refrain from implementation details at this point.
--
Cleber Rosa
[ Sr Software Engineer - Virtualization Team - Red Hat ]
[ Avocado Test Framework - avocado-framework.github.io ]
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