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Source code for galaxy_test.api.test_jobs
import datetime
import json
import os
import time
from operator import itemgetter
import requests
from galaxy_test.api.test_tools import TestsTools
from galaxy_test.base.api_asserts import assert_status_code_is_ok
from galaxy_test.base.populators import (
DatasetCollectionPopulator,
DatasetPopulator,
skip_without_tool,
uses_test_history,
wait_on,
wait_on_state,
)
from ._framework import ApiTestCase
[docs]class JobsApiTestCase(ApiTestCase, TestsTools):
[docs] def setUp(self):
super().setUp()
self.dataset_populator = DatasetPopulator(self.galaxy_interactor)
self.dataset_collection_populator = DatasetCollectionPopulator(self.galaxy_interactor)
[docs] @uses_test_history(require_new=True)
def test_index(self, history_id):
# Create HDA to ensure at least one job exists...
self.__history_with_new_dataset(history_id)
jobs = self.__jobs_index()
assert "upload1" in map(itemgetter("tool_id"), jobs)
[docs] @uses_test_history(require_new=True)
def test_system_details_admin_only(self, history_id):
self.__history_with_new_dataset(history_id)
jobs = self.__jobs_index(admin=False)
job = jobs[0]
self._assert_not_has_keys(job, "external_id")
jobs = self.__jobs_index(admin=True)
job = jobs[0]
self._assert_has_keys(job, "command_line", "external_id")
[docs] @uses_test_history(require_new=True)
def test_index_state_filter(self, history_id):
# Initial number of ok jobs
original_count = len(self.__uploads_with_state("ok"))
# Run through dataset upload to ensure num uplaods at least greater
# by 1.
self.__history_with_ok_dataset(history_id)
# Verify number of ok jobs is actually greater.
count_increased = False
for _ in range(10):
new_count = len(self.__uploads_with_state("ok"))
if original_count < new_count:
count_increased = True
break
time.sleep(.1)
if not count_increased:
template = "Jobs in ok state did not increase (was %d, now %d)"
message = template % (original_count, new_count)
raise AssertionError(message)
[docs] @uses_test_history(require_new=True)
def test_index_date_filter(self, history_id):
self.__history_with_new_dataset(history_id)
two_weeks_ago = (datetime.datetime.utcnow() - datetime.timedelta(7)).isoformat()
last_week = (datetime.datetime.utcnow() - datetime.timedelta(7)).isoformat()
next_week = (datetime.datetime.utcnow() + datetime.timedelta(7)).isoformat()
today = datetime.datetime.utcnow().isoformat()
tomorrow = (datetime.datetime.utcnow() + datetime.timedelta(1)).isoformat()
jobs = self.__jobs_index(data={"date_range_min": today[0:10], "date_range_max": tomorrow[0:10]})
assert len(jobs) > 0
today_job_id = jobs[0]["id"]
jobs = self.__jobs_index(data={"date_range_min": two_weeks_ago, "date_range_max": last_week})
assert today_job_id not in map(itemgetter("id"), jobs)
jobs = self.__jobs_index(data={"date_range_min": last_week, "date_range_max": next_week})
assert today_job_id in map(itemgetter("id"), jobs)
[docs] @uses_test_history(require_new=True)
def test_index_history(self, history_id):
self.__history_with_new_dataset(history_id)
jobs = self.__jobs_index(data={"history_id": history_id})
assert len(jobs) > 0
with self.dataset_populator.test_history() as other_history_id:
jobs = self.__jobs_index(data={"history_id": other_history_id})
assert len(jobs) == 0
[docs] @uses_test_history(require_new=True)
def test_index_multiple_states_filter(self, history_id):
# Initial number of ok jobs
original_count = len(self.__uploads_with_state("ok", "new"))
# Run through dataset upload to ensure num uplaods at least greater
# by 1.
self.__history_with_ok_dataset(history_id)
# Verify number of ok jobs is actually greater.
new_count = len(self.__uploads_with_state("new", "ok"))
assert original_count < new_count, new_count
[docs] @uses_test_history(require_new=True)
def test_show(self, history_id):
# Create HDA to ensure at least one job exists...
self.__history_with_new_dataset(history_id)
jobs_response = self._get("jobs")
first_job = jobs_response.json()[0]
self._assert_has_key(first_job, 'id', 'state', 'exit_code', 'update_time', 'create_time')
job_id = first_job["id"]
show_jobs_response = self._get("jobs/%s" % job_id)
self._assert_status_code_is(show_jobs_response, 200)
job_details = show_jobs_response.json()
self._assert_has_key(job_details, 'id', 'state', 'exit_code', 'update_time', 'create_time')
show_jobs_response = self._get("jobs/%s" % job_id, {"full": True})
self._assert_status_code_is(show_jobs_response, 200)
job_details = show_jobs_response.json()
self._assert_has_key(job_details, 'id', 'state', 'exit_code', 'update_time', 'create_time', 'stdout', 'stderr', 'job_messages')
[docs] @uses_test_history(require_new=True)
def test_show_security(self, history_id):
self.__history_with_new_dataset(history_id)
jobs_response = self._get("jobs", data={"history_id": history_id})
job = jobs_response.json()[0]
job_id = job["id"]
job_lock_response = self._get("job_lock", admin=True)
job_lock_response.raise_for_status()
assert not job_lock_response.json()["active"]
show_jobs_response = self._get("jobs/%s" % job_id, admin=False)
self._assert_not_has_keys(show_jobs_response.json(), "external_id")
# TODO: Re-activate test case when API accepts privacy settings
# with self._different_user():
# show_jobs_response = self._get( "jobs/%s" % job_id, admin=False )
# self._assert_status_code_is( show_jobs_response, 200 )
show_jobs_response = self._get("jobs/%s" % job_id, admin=True)
self._assert_has_keys(show_jobs_response.json(), "command_line", "external_id")
def _run_detect_errors(self, history_id, inputs):
payload = self.dataset_populator.run_tool_payload(
tool_id='detect_errors_aggressive',
inputs=inputs,
history_id=history_id,
)
return self._post("tools", data=payload).json()
[docs] @skip_without_tool("detect_errors_aggressive")
def test_unhide_on_error(self):
with self.dataset_populator.test_history() as history_id:
inputs = {'error_bool': 'true'}
run_response = self._run_detect_errors(history_id=history_id, inputs=inputs)
job_id = run_response['jobs'][0]["id"]
self.dataset_populator.wait_for_job(job_id)
job = self.dataset_populator.get_job_details(job_id).json()
assert job['state'] == 'error'
dataset = self.dataset_populator.get_history_dataset_details(history_id=history_id,
dataset_id=run_response['outputs'][0]['id'],
assert_ok=False)
assert dataset['visible']
[docs] @skip_without_tool("detect_errors_aggressive")
def test_no_unhide_on_error_if_mapped_over(self):
with self.dataset_populator.test_history() as history_id:
hdca1 = self.dataset_collection_populator.create_list_in_history(history_id, contents=[("sample1-1", "1 2 3")]).json()
inputs = {
'error_bool': 'true',
'dataset': {
'batch': True,
'values': [{'src': 'hdca', 'id': hdca1['id']}],
}
}
run_response = self._run_detect_errors(history_id=history_id, inputs=inputs)
job_id = run_response['jobs'][0]["id"]
self.dataset_populator.wait_for_job(job_id)
job = self.dataset_populator.get_job_details(job_id).json()
assert job['state'] == 'error'
dataset = self.dataset_populator.get_history_dataset_details(history_id=history_id,
dataset_id=run_response['outputs'][0]['id'],
assert_ok=False)
assert not dataset['visible']
[docs] @skip_without_tool('empty_output')
def test_common_problems(self):
with self.dataset_populator.test_history() as history_id:
empty_run_response = self.dataset_populator.run_tool(
tool_id='empty_output',
inputs={},
history_id=history_id,
)
empty_hda = empty_run_response["outputs"][0]
cat_empty_twice_run_response = self.dataset_populator.run_tool(
tool_id='cat1',
inputs={
'input1': {'src': 'hda', 'id': empty_hda['id']},
'queries_0|input2': {'src': 'hda', 'id': empty_hda['id']}
},
history_id=history_id,
)
empty_output_job = empty_run_response["jobs"][0]
cat_empty_job = cat_empty_twice_run_response["jobs"][0]
empty_output_common_problems_response = self._get('jobs/%s/common_problems' % empty_output_job["id"]).json()
cat_empty_common_problems_response = self._get('jobs/%s/common_problems' % cat_empty_job["id"]).json()
self._assert_has_keys(empty_output_common_problems_response, "has_empty_inputs", "has_duplicate_inputs")
self._assert_has_keys(cat_empty_common_problems_response, "has_empty_inputs", "has_duplicate_inputs")
assert not empty_output_common_problems_response["has_empty_inputs"]
assert cat_empty_common_problems_response["has_empty_inputs"]
assert not empty_output_common_problems_response["has_duplicate_inputs"]
assert cat_empty_common_problems_response["has_duplicate_inputs"]
[docs] @skip_without_tool('detect_errors_aggressive')
def test_report_error(self):
with self.dataset_populator.test_history() as history_id:
payload = self.dataset_populator.run_tool_payload(
tool_id='detect_errors_aggressive',
inputs={'error_bool': 'true'},
history_id=history_id,
)
run_response = self._post("tools", data=payload).json()
job_id = run_response['jobs'][0]["id"]
self.dataset_populator.wait_for_job(job_id)
dataset_id = run_response['outputs'][0]['id']
response = self._post('jobs/%s/error' % job_id,
data={'dataset_id': dataset_id})
assert response.status_code == 200, response.text
[docs] @skip_without_tool('detect_errors_aggressive')
def test_report_error_anon(self):
# Need to get a cookie and use that for anonymous tool runs
cookies = requests.get(self.url).cookies
payload = json.dumps({"tool_id": "detect_errors_aggressive",
"inputs": {"error_bool": "true"}})
run_response = requests.post("%s/tools" % self.galaxy_interactor.api_url, data=payload, cookies=cookies).json()
job_id = run_response['jobs'][0]["id"]
dataset_id = run_response['outputs'][0]['id']
response = requests.post(f'{self.galaxy_interactor.api_url}/jobs/{job_id}/error',
data={'email': 'someone@domain.com', 'dataset_id': dataset_id},
cookies=cookies)
assert response.status_code == 200, response.text
[docs] @uses_test_history(require_new=True)
def test_deleting_output_keep_running_until_all_deleted(self, history_id):
job_state, outputs = self._setup_running_two_output_job(history_id, 120)
self._hack_to_skip_test_if_state_ok(job_state)
# Delete one of the two outputs and make sure the job is still running.
self._raw_update_history_item(history_id, outputs[0]["id"], {"deleted": True})
self._hack_to_skip_test_if_state_ok(job_state)
time.sleep(1)
self._hack_to_skip_test_if_state_ok(job_state)
state = job_state().json()["state"]
assert state == "running", state
# Delete the second output and make sure the job is cancelled.
self._raw_update_history_item(history_id, outputs[1]["id"], {"deleted": True})
final_state = wait_on_state(job_state, assert_ok=False, timeout=15)
assert final_state in ["deleting", "deleted"], final_state
[docs] @uses_test_history(require_new=True)
def test_purging_output_keep_running_until_all_purged(self, history_id):
job_state, outputs = self._setup_running_two_output_job(history_id, 120)
# Pretty much right away after the job is running, these paths should be populated -
# if they are grab them and make sure they are deleted at the end of the job.
dataset_1 = self._get_history_item_as_admin(history_id, outputs[0]["id"])
dataset_2 = self._get_history_item_as_admin(history_id, outputs[1]["id"])
if "file_name" in dataset_1:
output_dataset_paths = [dataset_1["file_name"], dataset_2["file_name"]]
# This may or may not exist depending on if the test is local or not.
output_dataset_paths_exist = os.path.exists(output_dataset_paths[0])
else:
output_dataset_paths = []
output_dataset_paths_exist = False
self._hack_to_skip_test_if_state_ok(job_state)
current_state = job_state().json()["state"]
assert current_state == "running", current_state
# Purge one of the two outputs and make sure the job is still running.
self._raw_update_history_item(history_id, outputs[0]["id"], {"purged": True})
time.sleep(1)
self._hack_to_skip_test_if_state_ok(job_state)
current_state = job_state().json()["state"]
assert current_state == "running", current_state
# Purge the second output and make sure the job is cancelled.
self._raw_update_history_item(history_id, outputs[1]["id"], {"purged": True})
final_state = wait_on_state(job_state, assert_ok=False, timeout=15)
assert final_state in ["deleting", "deleted"], final_state
def paths_deleted():
if not os.path.exists(output_dataset_paths[0]) and not os.path.exists(output_dataset_paths[1]):
return True
if output_dataset_paths_exist:
wait_on(paths_deleted, "path deletion")
[docs] @uses_test_history(require_new=True)
def test_purging_output_cleaned_after_ok_run(self, history_id):
job_state, outputs = self._setup_running_two_output_job(history_id, 10)
# Pretty much right away after the job is running, these paths should be populated -
# if they are grab them and make sure they are deleted at the end of the job.
dataset_1 = self._get_history_item_as_admin(history_id, outputs[0]["id"])
dataset_2 = self._get_history_item_as_admin(history_id, outputs[1]["id"])
if "file_name" in dataset_1:
output_dataset_paths = [dataset_1["file_name"], dataset_2["file_name"]]
# This may or may not exist depending on if the test is local or not.
output_dataset_paths_exist = os.path.exists(output_dataset_paths[0])
else:
output_dataset_paths = []
output_dataset_paths_exist = False
if not output_dataset_paths_exist:
# Given this Galaxy configuration - there is nothing more to be tested here.
# Consider throwing a skip instead.
return
# Purge one of the two outputs and wait for the job to complete.
self._raw_update_history_item(history_id, outputs[0]["id"], {"purged": True})
wait_on_state(job_state, assert_ok=True)
if output_dataset_paths_exist:
time.sleep(.5)
# Make sure the non-purged dataset is on disk and the purged one is not.
assert os.path.exists(output_dataset_paths[1])
assert not os.path.exists(output_dataset_paths[0])
def _hack_to_skip_test_if_state_ok(self, job_state):
from nose.plugins.skip import SkipTest
if job_state().json()["state"] == "ok":
message = "Job state switch from running to ok too quickly - the rest of the test requires the job to be in a running state. Skipping test."
raise SkipTest(message)
def _setup_running_two_output_job(self, history_id, sleep_time):
payload = self.dataset_populator.run_tool_payload(
tool_id='create_2',
inputs=dict(
sleep_time=sleep_time,
),
history_id=history_id,
)
run_response = self._post("tools", data=payload).json()
outputs = run_response["outputs"]
jobs = run_response["jobs"]
assert len(outputs) == 2
assert len(jobs) == 1
def job_state():
jobs_response = self._get("jobs/%s" % jobs[0]["id"])
return jobs_response
# Give job some time to get up and running.
time.sleep(2)
running_state = wait_on_state(job_state, skip_states=["queued", "new"], assert_ok=False, timeout=15)
assert running_state == "running", running_state
def job_state():
jobs_response = self._get("jobs/%s" % jobs[0]["id"])
return jobs_response
return job_state, outputs
def _raw_update_history_item(self, history_id, item_id, data):
update_url = self._api_url(f"histories/{history_id}/contents/{item_id}", use_key=True)
update_response = requests.put(update_url, json=data)
assert_status_code_is_ok(update_response)
return update_response
[docs] @skip_without_tool("cat_data_and_sleep")
@uses_test_history(require_new=True)
def test_resume_job(self, history_id):
hda1 = self.dataset_populator.new_dataset(history_id, content="samp1\t10.0\nsamp2\t20.0\n")
hda2 = self.dataset_populator.new_dataset(history_id, content="samp1\t30.0\nsamp2\t40.0\n")
# Submit first job
payload = self.dataset_populator.run_tool_payload(
tool_id='cat_data_and_sleep',
inputs={
'sleep_time': 15,
'input1': {'src': 'hda', 'id': hda2['id']},
'queries_0|input2': {'src': 'hda', 'id': hda2['id']}
},
history_id=history_id,
)
run_response = self._post("tools", data=payload).json()
output = run_response["outputs"][0]
# Submit second job that waits on job1
payload = self.dataset_populator.run_tool_payload(
tool_id='cat1',
inputs={
'input1': {'src': 'hda', 'id': hda1['id']},
'queries_0|input2': {'src': 'hda', 'id': output['id']}
},
history_id=history_id,
)
run_response = self._post("tools", data=payload).json()
job_id = run_response['jobs'][0]['id']
output = run_response["outputs"][0]
# Delete second jobs input while second job is waiting for first job
delete_response = self._delete("histories/{}/contents/{}".format(history_id, hda1['id']))
self._assert_status_code_is(delete_response, 200)
self.dataset_populator.wait_for_history_jobs(history_id, assert_ok=False)
dataset_details = self._get("histories/{}/contents/{}".format(history_id, output['id'])).json()
assert dataset_details['state'] == 'paused'
# Undelete input dataset
undelete_response = self._put("histories/{}/contents/{}".format(history_id, hda1['id']),
data=json.dumps({'deleted': False}))
self._assert_status_code_is(undelete_response, 200)
resume_response = self._put("jobs/%s/resume" % job_id)
self._assert_status_code_is(resume_response, 200)
self.dataset_populator.wait_for_history_jobs(history_id, assert_ok=True)
dataset_details = self._get("histories/{}/contents/{}".format(history_id, output['id'])).json()
assert dataset_details['state'] == 'ok'
def _get_history_item_as_admin(self, history_id, item_id):
response = self._get(f"histories/{history_id}/contents/{item_id}?view=detailed", admin=True)
assert_status_code_is_ok(response)
return response.json()
[docs] @uses_test_history(require_new=True)
def test_search(self, history_id):
dataset_id = self.__history_with_ok_dataset(history_id)
# We first copy the datasets, so that the update time is lower than the job creation time
new_history_id = self.dataset_populator.new_history()
copy_payload = {"content": dataset_id, "source": "hda", "type": "dataset"}
copy_response = self._post("histories/%s/contents" % new_history_id, data=copy_payload)
self._assert_status_code_is(copy_response, 200)
inputs = json.dumps({
'input1': {'src': 'hda', 'id': dataset_id}
})
self._job_search(tool_id='cat1', history_id=history_id, inputs=inputs)
# We test that a job can be found even if the dataset has been copied to another history
new_dataset_id = copy_response.json()['id']
copied_inputs = json.dumps({
'input1': {'src': 'hda', 'id': new_dataset_id}
})
search_payload = self._search_payload(history_id=history_id, tool_id='cat1', inputs=copied_inputs)
self._search(search_payload, expected_search_count=1)
# Now we delete the original input HDA that was used -- we should still be able to find the job
delete_respone = self._delete(f"histories/{history_id}/contents/{dataset_id}")
self._assert_status_code_is(delete_respone, 200)
self._search(search_payload, expected_search_count=1)
# Now we also delete the copy -- we shouldn't find a job
delete_respone = self._delete(f"histories/{new_history_id}/contents/{new_dataset_id}")
self._assert_status_code_is(delete_respone, 200)
self._search(search_payload, expected_search_count=0)
[docs] @uses_test_history(require_new=True)
def test_search_handle_identifiers(self, history_id):
# Test that input name and element identifier of a jobs' output must match for a job to be returned.
dataset_id = self.__history_with_ok_dataset(history_id)
inputs = json.dumps({
'input1': {'src': 'hda', 'id': dataset_id}
})
self._job_search(tool_id='identifier_single', history_id=history_id, inputs=inputs)
dataset_details = self._get(f"histories/{history_id}/contents/{dataset_id}").json()
dataset_details['name'] = 'Renamed Test Dataset'
dataset_update_response = self._put(f"histories/{history_id}/contents/{dataset_id}", data=dict(name='Renamed Test Dataset'))
self._assert_status_code_is(dataset_update_response, 200)
assert dataset_update_response.json()['name'] == 'Renamed Test Dataset'
search_payload = self._search_payload(history_id=history_id, tool_id='identifier_single', inputs=inputs)
self._search(search_payload, expected_search_count=0)
[docs] @uses_test_history(require_new=True)
def test_search_delete_outputs(self, history_id):
dataset_id = self.__history_with_ok_dataset(history_id)
inputs = json.dumps({
'input1': {'src': 'hda', 'id': dataset_id}
})
tool_response = self._job_search(tool_id='cat1', history_id=history_id, inputs=inputs)
output_id = tool_response.json()['outputs'][0]['id']
delete_respone = self._delete(f"histories/{history_id}/contents/{output_id}")
self._assert_status_code_is(delete_respone, 200)
search_payload = self._search_payload(history_id=history_id, tool_id='cat1', inputs=inputs)
self._search(search_payload, expected_search_count=0)
[docs] @uses_test_history(require_new=True)
def test_search_with_hdca_list_input(self, history_id):
list_id_a = self.__history_with_ok_collection(collection_type='list', history_id=history_id)
list_id_b = self.__history_with_ok_collection(collection_type='list', history_id=history_id)
inputs = json.dumps({
'f1': {'src': 'hdca', 'id': list_id_a},
'f2': {'src': 'hdca', 'id': list_id_b},
})
tool_response = self._job_search(tool_id='multi_data_param', history_id=history_id, inputs=inputs)
# We switch the inputs, this should not return a match
inputs_switched = json.dumps({
'f2': {'src': 'hdca', 'id': list_id_a},
'f1': {'src': 'hdca', 'id': list_id_b},
})
search_payload = self._search_payload(history_id=history_id, tool_id='multi_data_param', inputs=inputs_switched)
self._search(search_payload, expected_search_count=0)
# We delete the ouput (this is a HDA, as multi_data_param reduces collections)
# and use the correct input job definition, the job should not be found
output_id = tool_response.json()['outputs'][0]['id']
delete_respone = self._delete(f"histories/{history_id}/contents/{output_id}")
self._assert_status_code_is(delete_respone, 200)
search_payload = self._search_payload(history_id=history_id, tool_id='multi_data_param', inputs=inputs)
self._search(search_payload, expected_search_count=0)
[docs] @uses_test_history(require_new=True)
def test_search_delete_hdca_output(self, history_id):
list_id_a = self.__history_with_ok_collection(collection_type='list', history_id=history_id)
inputs = json.dumps({
'input1': {'src': 'hdca', 'id': list_id_a},
})
tool_response = self._job_search(tool_id='collection_creates_list', history_id=history_id, inputs=inputs)
output_id = tool_response.json()['outputs'][0]['id']
# We delete a single tool output, no job should be returned
delete_respone = self._delete(f"histories/{history_id}/contents/{output_id}")
self._assert_status_code_is(delete_respone, 200)
search_payload = self._search_payload(history_id=history_id, tool_id='collection_creates_list', inputs=inputs)
self._search(search_payload, expected_search_count=0)
tool_response = self._job_search(tool_id='collection_creates_list', history_id=history_id, inputs=inputs)
output_collection_id = tool_response.json()['output_collections'][0]['id']
# We delete a collection output, no job should be returned
delete_respone = self._delete(f"histories/{history_id}/contents/dataset_collections/{output_collection_id}")
self._assert_status_code_is(delete_respone, 200)
search_payload = self._search_payload(history_id=history_id, tool_id='collection_creates_list', inputs=inputs)
self._search(search_payload, expected_search_count=0)
[docs] @uses_test_history(require_new=True)
def test_search_with_hdca_pair_input(self, history_id):
list_id_a = self.__history_with_ok_collection(collection_type='pair', history_id=history_id)
inputs = json.dumps({
'f1': {'src': 'hdca', 'id': list_id_a},
'f2': {'src': 'hdca', 'id': list_id_a},
})
self._job_search(tool_id='multi_data_param', history_id=history_id, inputs=inputs)
# We test that a job can be found even if the collection has been copied to another history
new_history_id = self.dataset_populator.new_history()
copy_payload = {"content": list_id_a, "source": "hdca", "type": "dataset_collection"}
copy_response = self._post("histories/%s/contents" % new_history_id, data=copy_payload)
self._assert_status_code_is(copy_response, 200)
new_list_a = copy_response.json()['id']
copied_inputs = json.dumps({
'f1': {'src': 'hdca', 'id': new_list_a},
'f2': {'src': 'hdca', 'id': new_list_a},
})
search_payload = self._search_payload(history_id=new_history_id, tool_id='multi_data_param', inputs=copied_inputs)
self._search(search_payload, expected_search_count=1)
# Now we delete the original input HDCA that was used -- we should still be able to find the job
delete_respone = self._delete(f"histories/{history_id}/contents/dataset_collections/{list_id_a}")
self._assert_status_code_is(delete_respone, 200)
self._search(search_payload, expected_search_count=1)
# Now we also delete the copy -- we shouldn't find a job
delete_respone = self._delete(f"histories/{history_id}/contents/dataset_collections/{new_list_a}")
self._assert_status_code_is(delete_respone, 200)
self._search(search_payload, expected_search_count=0)
[docs] @uses_test_history(require_new=True)
def test_search_with_hdca_list_pair_input(self, history_id):
list_id_a = self.__history_with_ok_collection(collection_type='list:pair', history_id=history_id)
inputs = json.dumps({
'f1': {'src': 'hdca', 'id': list_id_a},
'f2': {'src': 'hdca', 'id': list_id_a},
})
self._job_search(tool_id='multi_data_param', history_id=history_id, inputs=inputs)
[docs] @uses_test_history(require_new=True)
def test_search_with_hdca_list_pair_collection_mapped_over_pair_input(self, history_id):
list_id_a = self.__history_with_ok_collection(collection_type='list:pair', history_id=history_id)
inputs = json.dumps({
'f1': {'batch': True, 'values': [{'src': 'hdca', 'id': list_id_a, 'map_over_type': 'paired'}]},
})
self._job_search(tool_id='collection_paired_test', history_id=history_id, inputs=inputs)
def _get_simple_rerun_params(self, history_id, private=False):
list_id_a = self.__history_with_ok_collection(collection_type='list:pair', history_id=history_id)
inputs = {'f1': {'batch': True, 'values': [{'src': 'hdca', 'id': list_id_a, 'map_over_type': 'paired'}]}}
run_response = self._run(
history_id=history_id,
tool_id="collection_paired_test",
inputs=inputs,
wait_for_job=True,
assert_ok=True,
)
rerun_params = self._get("jobs/%s/build_for_rerun" % run_response['jobs'][0]['id']).json()
# Since we call rerun on the first (and only) job we should get the expanded input
# which is a dataset collection element (and not the list:pair hdca that was used as input to the original
# job).
assert rerun_params['state_inputs']['f1']['values'][0]['src'] == 'dce'
if private:
hdca = self.dataset_populator.get_history_collection_details(history_id=history_id, content_id=list_id_a)
for element in hdca['elements'][0]['object']['elements']:
self.dataset_populator.make_private(history_id, element['object']['id'])
return rerun_params
[docs] @skip_without_tool("collection_paired_test")
@uses_test_history(require_new=False)
def test_job_build_for_rerun(self, history_id):
rerun_params = self._get_simple_rerun_params(history_id)
self._run(
history_id=history_id,
tool_id="collection_paired_test",
inputs=rerun_params['state_inputs'],
wait_for_job=True,
assert_ok=True,
)
[docs] @skip_without_tool("collection_paired_test")
@uses_test_history(require_new=False)
def test_dce_submission_security(self, history_id):
rerun_params = self._get_simple_rerun_params(history_id, private=True)
with self._different_user():
other_history_id = self.dataset_populator.new_history()
response = self._run(
history_id=other_history_id,
tool_id="collection_paired_test",
inputs=rerun_params['state_inputs'],
wait_for_job=False,
assert_ok=False,
)
assert response.status_code == 403
[docs] @skip_without_tool("identifier_collection")
@uses_test_history(require_new=False)
def test_job_build_for_rerun_list_list(self, history_id):
list_id_a = self.__history_with_ok_collection(collection_type='list', history_id=history_id)
list_id_b = self.__history_with_ok_collection(collection_type='list', history_id=history_id)
list_list = self.dataset_collection_populator.create_nested_collection(
history_id=history_id,
collection_type='list:list',
name='list list collection',
collection=[list_id_a, list_id_b]).json()
list_list_id = list_list['id']
first_element = list_list['elements'][0]
assert first_element['element_type'] == 'dataset_collection'
assert first_element['element_identifier'] == 'test0'
assert first_element['model_class'] == 'DatasetCollectionElement'
inputs = {'input1': {'batch': True, 'values': [{'src': 'hdca', 'id': list_list_id, 'map_over_type': 'list'}]}}
run_response = self._run(
history_id=history_id,
tool_id="identifier_collection",
inputs=inputs,
wait_for_job=True,
assert_ok=True,
)
assert len(run_response['jobs']) == 2
rerun_params = self._get("jobs/%s/build_for_rerun" % run_response['jobs'][0]['id']).json()
# Since we call rerun on the first (and only) job we should get the expanded input
# which is a dataset collection element (and not the list:list hdca that was used as input to the original
# job).
assert rerun_params['state_inputs']['input1']['values'][0]['src'] == 'dce'
rerun_response = self._run(
history_id=history_id,
tool_id="identifier_collection",
inputs=rerun_params['state_inputs'],
wait_for_job=True,
assert_ok=True,
)
assert len(rerun_response['jobs']) == 1
rerun_content = self.dataset_populator.get_history_dataset_content(history_id=history_id, dataset=rerun_response['outputs'][0])
run_content = self.dataset_populator.get_history_dataset_content(history_id=history_id, dataset=run_response['outputs'][0])
assert rerun_content == run_content
def _job_search(self, tool_id, history_id, inputs):
search_payload = self._search_payload(history_id=history_id, tool_id=tool_id, inputs=inputs)
empty_search_response = self._post("jobs/search", data=search_payload)
self._assert_status_code_is(empty_search_response, 200)
self.assertEqual(len(empty_search_response.json()), 0)
tool_response = self._post("tools", data=search_payload)
self.dataset_populator.wait_for_tool_run(history_id, run_response=tool_response)
self._search(search_payload, expected_search_count=1)
return tool_response
def _search_payload(self, history_id, tool_id, inputs, state='ok'):
search_payload = dict(
tool_id=tool_id,
inputs=inputs,
history_id=history_id,
state=state
)
return search_payload
def _search(self, payload, expected_search_count=1):
# in case job and history aren't updated at exactly the same
# time give time to wait
for _ in range(5):
search_count = self._search_count(payload)
if search_count == expected_search_count:
break
time.sleep(1)
assert search_count == expected_search_count, "expected to find %d jobs, got %d jobs" % (expected_search_count, search_count)
return search_count
def _search_count(self, search_payload):
search_response = self._post("jobs/search", data=search_payload)
self._assert_status_code_is(search_response, 200)
search_json = search_response.json()
return len(search_json)
def __uploads_with_state(self, *states):
jobs_response = self._get("jobs", data=dict(state=states))
self._assert_status_code_is(jobs_response, 200)
jobs = jobs_response.json()
assert not [j for j in jobs if not j['state'] in states]
return [j for j in jobs if j['tool_id'] == 'upload1']
def __history_with_new_dataset(self, history_id):
dataset_id = self.dataset_populator.new_dataset(history_id)["id"]
return dataset_id
def __history_with_ok_dataset(self, history_id):
dataset_id = self.dataset_populator.new_dataset(history_id, wait=True)["id"]
return dataset_id
def __history_with_ok_collection(self, collection_type='list', history_id=None):
if not history_id:
history_id = self.dataset_populator.new_history()
if collection_type == 'list':
fetch_response = self.dataset_collection_populator.create_list_in_history(history_id, direct_upload=True).json()
elif collection_type == 'pair':
fetch_response = self.dataset_collection_populator.create_pair_in_history(history_id, direct_upload=True).json()
elif collection_type == 'list:pair':
fetch_response = self.dataset_collection_populator.create_list_of_pairs_in_history(history_id).json()
self.dataset_collection_populator.wait_for_fetched_collection(fetch_response)
return fetch_response["outputs"][0]['id']
def __jobs_index(self, **kwds):
jobs_response = self._get("jobs", **kwds)
self._assert_status_code_is(jobs_response, 200)
jobs = jobs_response.json()
assert isinstance(jobs, list)
return jobs