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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_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