While using py.test, I have some tests that run fine with SQLite but hang silently when I switch to Postgresql. How would I go about debugging something like that? Is there a "verbose" mode I can run my tests in, or set a breakpoint ? More generally, what is the standard plan of attack when pytest stalls silently? I've tried using the pytest-timeout, and ran the test with $ py.test --timeout=300, but the tests still hang with no activity on the screen whatsoever
I ran into the same SQLite/Postgres problem with Flask and SQLAlchemy, similar to Gordon Fierce. However, my solution was different. Postgres is strict about table locks and connections, so explicitly closing the session connection on teardown solved the problem for me.
My working code:
#pytest.yield_fixture(scope='function')
def db(app):
# app is an instance of a flask app, _db a SQLAlchemy DB
_db.app = app
with app.app_context():
_db.create_all()
yield _db
# Explicitly close DB connection
_db.session.close()
_db.drop_all()
Reference: SQLAlchemy
To answer the question "How would I go about debugging something like that?"
Run with py.test -m trace --trace to get trace of python calls.
One option (useful for any stuck unix binary) is to attach to process using strace -p <PID>. See what system call it might be stuck on or loop of system calls. e.g. stuck calling gettimeofday
For more verbose py.test output install pytest-sugar. pip install pytest-sugar And run test with pytest.py --verbose . . .
https://pypi.python.org/pypi/pytest-sugar
I had a similar problem with pytest and Postgresql while testing a Flask app that used SQLAlchemy. It seems pytest has a hard time running a teardown using its request.addfinalizer method with Postgresql.
Previously I had:
#pytest.fixture
def db(app, request):
def teardown():
_db.drop_all()
_db.app = app
_db.create_all()
request.addfinalizer(teardown)
return _db
( _db is an instance of SQLAlchemy I import from extensions.py )
But if I drop the database every time the database fixture is called:
#pytest.fixture
def db(app, request):
_db.app = app
_db.drop_all()
_db.create_all()
return _db
Then pytest won't hang after your first test.
Not knowing what is breaking in the code, the best way is to isolate the test that is failing and set a breakpoint in it to have a look. Note: I use pudb instead of pdb, because it's really the best way to debug python if you are not using an IDE.
For example, you can the following in your test file:
import pudb
...
def test_create_product(session):
pudb.set_trace()
# Create the Product instance
# Create a Price instance
# Add the Product instance to the session.
...
Then run it with
py.test -s --capture=no test_my_stuff.py
Now you'll be able to see exactly where the script locks up, and examine the stack and the database at this particular moment of execution. Otherwise it's like looking for a needle in a haystack.
I just ran into this problem for quite some time (though I wasn't using SQLite). The test suite ran fine locally, but failed in CircleCI (Docker).
My problem was ultimately that:
An object's underlying implementation used threading
The object's __del__ normally would end the threads
My test suite wasn't calling __del__ as it should have
I figured I'd add how I figured this out. Other answers suggest these:
Found usage of pytest-timeout didn't help, the test hung after completion
Invoked via pytest --timeout 5
Versions: pytest==6.2.2, pytest-timeout==1.4.2
Running pytest -m trace --trace or pytest --verbose yielded no useful information either
I ended up having to comment literally everything out, including:
All conftest.py code and test code
Slowly uncommented/re-commented regions and identified the root cause
Ultimate solution: using a factory fixture to add a finalizer to call __del__
In my case the Flask application did not check if __name__ == '__main__': so it executed app.start() when that was not my intention.
You can read many more details here.
In my case diff worked very slow on comparing 4 MB data when assert failed.
with open(path, 'rb') as f:
assert f.read() == data
Fixed by:
with open(path, 'rb') as f:
eq = f.read() == data
assert eq
Related
So I have an Airflow task that is currently a BashOperator() where its bash_command is just "python pppp.py". There is a "__main __" that the only thing it does is calls mycommand(). I tried to switch it to a #task() where I "import pppp" and then call "pppp.mycommand()". My command connects to an internal messaging system. The BashOperator() works but the task() fails to connect. I even kubectl into the pod, and I can start up a python shell, do "import pppp" and call "pppp.mycommand()" and it works. I have confirmed they both ways are using the same image, have all of the same environment variables set to the same value, and such. Obviously there is a difference between the 2 ways. Can anyone think of what is different? THERE IS NO CODE TO SHARE since the issue is with an internal message system that you wouldnt be able to access. I guess I am interested in happens before airflow executes the my_task()
#task(**args)
def my_task():
import pppp
pppp.mycommand()
I am working on script where I will be dealing with huge amount of data to process via python.
I have written a script using asyncio in python3.8 on windows box which is working perfectly fine but when I execute the same script on unix on python3.8 its completing the execution but not terminating the program at the end. Seems like its not release resources/lock.
When I debug further, found that on windows the asyncio uses ProactorEventLoop whereas on Unix it uses _UnixSelectorEventLoop, But not sure if this affect by any means.
I cant share the full script but it follows below structure:
import asyncio
async def myCoroutine():
print("My Coroutine")
try:
loop = asyncio.get_event_loop()
loop.run_until_complete(myCoroutine())
print("Execution Completed")
finally:
print("Closing the loop")
loop.close()
print("loop Closed")
Output:
Execution Completed
Closing the loop
loop closed
But program is not terminating.
Is anyone faced the similar issue before? Any inputs?
Thanks in Advance!!
I am currently doing several simulations in R that each take quite a long time to execute and the time it takes for each to finish varies from case to case. To use the time in between more efficiently, I wondered if it would be possible to set up something (like a e-mail notification system or similar) that would notify me as soon a a chunk of simulation is completed.
Does somebody here have any experience with setting up something similar or does someone know a resource that could teach me to implement a notification system via R?
I recently saw an R package for this kind of thing: pushoverr. However didn't use it myself - so not tested how it works. But seems like it might be useful in your case.
I assume you run the time consuming simulations on a server, correct? If these run own you own PC, your PC will be slow as hell anyway and I would not see something beneficial in sending a mail to myself.
For long calculations: Run them on a virtual machine, I use the following workflow for my own calculations.
Write your R script. Important: Write a .txt file when the calculation file in the end. The shell script will search in a loop for the file to exist.
Copy that code an save it as Python script. I tried one day to get MailR running a Linux and it did not work. This code worked on the first try.
#!/usr/bin/env python3
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email import encoders
email_user = 'youownmail#gmail.com'
email_password = 'password'
email_send = 'theothersmail.com'
subject = 'yourreport'
msg = MIMEMultipart()
msg['From'] = email_user
msg['To'] = email_send
msg['Subject'] = subject
body = 'Calculation is done'
msg.attach(MIMEText(body,'plain'))
part = MIMEBase('application','octet-stream')
part.set_payload((attachment).read())
encoders.encode_base64(part)
msg.attach(part)
text = msg.as_string()
server = smtplib.SMTP('smtp.gmail.com',587)
server.starttls()
server.login(email_user,email_password)
server.sendmail(email_user,email_send,text)
server.quit()
Make sure you are allowed to run the script.
sudo chmod 777 /path/script.R sudo chmod 777 /path/script.py
Run both your script.R and script.py inside a script.sh file. It looks the the following:
R < /path/script.R --no-save
while [ ! -f /tmp/finished.txt ]
do
sleep 2
done
python path/script.py
This may sound a bit overwhelming if you are not familiar with these technologies, but think this is a pretty much automated workflow, which relieves your own resources and can be used "in production". (I use this workflow to send me my own stock reports).
I'm really just looking to pick the community's brain for some leads in figuring out what is going on with the issue I'm having.
I'm writing a MR job with RHadoop (rmr2, v3.0.0) and things are great -- IO with HDFS, mapping, reducing. No problems. Life is great.
I'm trying to schedule the job with Apache Oozie, and am running into some issues:
Error in mr(map = map, reduce = reduce, combine = combine, vectorized.reduce, :
hadoop streaming failed with error code 1
I've read the rmr2 debugging guide, but nothing is really getting to the stderr because the job fails before anything even gets scheduled.
In my head, everything points to a difference in environments. However, Oozie is running the job as the same user that I'm able to run everything with via cli, and all of the R environment variables (fetched with Sys.getenv()) are the same, excepting there's some additional class path stuff set with Oozie.
I can post more of the OS or Hadoop versions and config details, but sleuthing some version-specific bugs seems like a bit of a red herring as everything runs fine at the command line.
Anybody have any thoughts what might be some helpful next steps in hunting this beast down?
UPDATE:
I overwrote the system function in the base package to log the user, the host name of the node, and the command being executed before the internal call to system. So before any system call is actually executed, I get something like the following in the stderr:
user#host.name
/usr/bin/hadoop jar /usr/lib/hadoop-mapreduce/hadoop-streaming-2.2.0.2.0.6.0-102.jar ...
When ran with Oozie, the command printed in the stderr fails with an exit status of 1. When I run the command on user#host.name, it runs successfully. So essentially the EXACT same command with the SAME user on the SAME node fails with Oozie, but runs successfully from cli.
Getting this error soon after running riak start despite a config file that should be working correctly.
Turns out that this is a limit of Riak's error messaging: you will get the above message if you try to do a riak-admin test on your setup before the configuration has finished loading.
I encountered the same problem while starting new Riak clusters over and over again during automated testing. My solution was, in my test fixture setup, to execute code that keeps trying to put an object into a Riak bucket and then eventually succeeding.
Granted, my solution here is an Erlang snippet but it generally solves this problem in lieu of any Riak-supplied admin/wait functions. But since I've used a number of different Riak versions this technique here seems to work for all of them.
wait_for_riak() ->
{ok, C} = riak:local_client(),
io:format("Waiting for Raik..."),
wait_for_riak(C),
io:format("and had a successful put.~n").
wait_for_riak(C) ->
Strawman = riak_object:new(<<"test">>, <<"strawman">>, []),
case C:put(Strawman, 1) of
ok ->
ok;
_Error ->
receive after 1000 -> ok end,
wait_for_riak(C)
end.
adding sleep 4 like so:
brew install riak
riak start
sleep 4
riak-admin test
should help