def test_upcoming_courses_endpoint(client, kwargs, is_upcoming):
"""Test upcoming courses endpoint"""
course = CourseFactory.create(runs=None)
LearningResourceRunFactory.create(content_object=course, **kwargs)
# this should be filtered out
CourseFactory.create(runs=None)
resp = client.get(reverse("courses-list") + "upcoming/")
assert resp.data.get("count") == (1 if is_upcoming else 0)
if is_upcoming:
assert resp.data.get("results")[0]["id"] == course.id
def test_es_run_serializer(has_full_name):
"""
Test that ESRunSerializer correctly serializes a run object
"""
learning_resource_run = (
LearningResourceRunFactory.create()
if has_full_name
else LearningResourceRunFactory.create(instructors__full_name=None)
)
serialized = ESRunSerializer(learning_resource_run).data
assert_json_equal(
serialized,
{
"id": learning_resource_run.id,
"run_id": learning_resource_run.run_id,
"short_description": learning_resource_run.short_description,
"full_description": learning_resource_run.full_description,
"language": learning_resource_run.language,
"semester": learning_resource_run.semester,
"year": int(learning_resource_run.year),
"level": learning_resource_run.level,
"start_date": learning_resource_run.start_date.strftime(ISOFORMAT),
"end_date": learning_resource_run.end_date.strftime(ISOFORMAT),
"enrollment_start": learning_resource_run.enrollment_start.strftime(
ISOFORMAT
),
"enrollment_end": learning_resource_run.enrollment_end.strftime(ISOFORMAT),
"best_start_date": learning_resource_run.best_start_date,
"best_end_date": learning_resource_run.best_end_date,
"title": learning_resource_run.title,
"image_src": learning_resource_run.image_src,
"instructors": [
(
instructor.full_name
if has_full_name
else " ".join([instructor.first_name, instructor.last_name])
)
for instructor in learning_resource_run.instructors.all()
],
"prices": [
ESCoursePriceSerializer(price).data
for price in learning_resource_run.prices.all()
],
"published": True,
"availability": learning_resource_run.availability,
"offered_by": list(
learning_resource_run.offered_by.values_list("name", flat=True)
),
},
)
def test_load_content_file_error(mocker):
""" Test that an exception in load_content_file is logged """
learning_resource_run = LearningResourceRunFactory.create()
mock_log = mocker.patch("course_catalog.etl.loaders.log.exception")
load_content_file(learning_resource_run, {"uid": "badfile", "bad": "data"})
mock_log.assert_called_once_with("ERROR syncing course file %s for run %d",
"badfile", learning_resource_run.id)
def test_load_run(run_exists):
"""Test that load_run loads the course run"""
course = CourseFactory.create(runs=None)
learning_resource_run = (LearningResourceRunFactory.create(
content_object=course) if run_exists else
LearningResourceRunFactory.build())
props = model_to_dict(
LearningResourceRunFactory.build(
run_id=learning_resource_run.run_id,
platform=learning_resource_run.platform))
del props["content_type"]
del props["object_id"]
del props["id"]
assert LearningResourceRun.objects.count() == (1 if run_exists else 0)
result = load_run(course, props)
assert LearningResourceRun.objects.count() == 1
assert result.content_object == course
# assert we got a course run back
assert isinstance(result, LearningResourceRun)
for key, value in props.items():
assert getattr(result,
key) == value, f"Property {key} should equal {value}"
def test_sync_xpro_course_files(mock_xpro_learning_bucket, mocker):
"""sync xpro courses from a tarball stored in S3"""
mock_xpro_learning_bucket.bucket.put_object(
Key="path/to/exported_courses_123.tar.gz",
Body=open("test_json/exported_courses_12345.tar.gz", "rb").read(),
ACL="public-read",
)
mock_load_content_files = mocker.patch(
"course_catalog.api.load_content_files", autospec=True, return_value=[]
)
course_content_type = ContentType.objects.get_for_model(Course)
run = LearningResourceRunFactory.create(
platform=PlatformType.xpro.value,
run_id="content-devops-0001",
content_type=course_content_type,
)
course_id = run.object_id
fake_data = '{"key": "data"}'
mock_log = mocker.patch("course_catalog.api.log.exception")
mock_transform = mocker.patch(
"course_catalog.api.transform_content_files_xpro", return_value=fake_data
)
sync_xpro_course_files(ids=[course_id])
assert mock_transform.call_count == 1
assert mock_transform.call_args[0][0].endswith("content-devops-0001.tar.gz") is True
mock_load_content_files.assert_called_once_with(run, fake_data)
mock_log.assert_not_called()
def test_sync_xpro_course_files_no_courses(mock_xpro_learning_bucket, mocker):
"""If there are no matching runs for the given courses, it should be skipped"""
mock_xpro_learning_bucket.bucket.put_object(
Key="path/to/exported_courses_123.tar.gz",
Body=open("test_json/exported_courses_12345.tar.gz", "rb").read(),
ACL="public-read",
)
mock_load_content_files = mocker.patch(
"course_catalog.api.load_content_files", autospec=True, return_value=[]
)
course_content_type = ContentType.objects.get_for_model(Course)
LearningResourceRunFactory.create(
platform=PlatformType.xpro.value,
run_id="content-devops-0001",
content_type=course_content_type,
)
sync_xpro_course_files(ids=[])
mock_load_content_files.assert_not_called()
def test_load_instructors(instructor_exists):
"""Test that load_instructors creates and/or assigns instructors to the course run"""
instructors = (CourseInstructorFactory.create_batch(3) if instructor_exists
else CourseInstructorFactory.build_batch(3))
run = LearningResourceRunFactory.create(no_instructors=True)
assert run.instructors.count() == 0
load_instructors(run, [{
"full_name": instructor.full_name
} for instructor in instructors])
assert run.instructors.count() == len(instructors)
def test_bulk_index_content_files(mocked_es, mocker, settings, errors,
indexing_func_name, doc): # pylint: disable=too-many-arguments
"""
index functions for content files should call bulk with correct arguments
"""
settings.ELASTICSEARCH_INDEXING_CHUNK_SIZE = 3
course = CourseFactory.create()
run = LearningResourceRunFactory.create(content_object=course)
content_files = ContentFileFactory.create_batch(5, run=run)
mock_get_aliases = mocker.patch("search.indexing_api.get_active_aliases",
autospec=True,
return_value=["a", "b"])
bulk_mock = mocker.patch("search.indexing_api.bulk",
autospec=True,
return_value=(0, errors))
mocker.patch(
f"search.indexing_api.serialize_content_file_for_bulk",
autospec=True,
return_value=doc,
)
mocker.patch(
f"search.indexing_api.serialize_content_file_for_bulk_deletion",
autospec=True,
return_value=doc,
)
index_func = getattr(indexing_api, indexing_func_name)
if errors:
with pytest.raises(ReindexException):
index_func(run.id)
else:
index_func(run.id)
for alias in mock_get_aliases.return_value:
for chunk in chunks(
[doc for _ in content_files],
chunk_size=settings.ELASTICSEARCH_INDEXING_CHUNK_SIZE,
):
bulk_mock.assert_any_call(
mocked_es.conn,
chunk,
index=alias,
doc_type=GLOBAL_DOC_TYPE,
chunk_size=settings.ELASTICSEARCH_INDEXING_CHUNK_SIZE,
routing=gen_course_id(course.platform, course.course_id),
)
def test_load_content_files(mocker, is_published):
"""Test that load_content_files calls the expected functions"""
course_run = LearningResourceRunFactory.create(published=is_published)
content_data = [{"a": "b"}, {"a": "c"}]
mock_load_content_file = mocker.patch(
"course_catalog.etl.loaders.load_content_file", autospec=True)
mock_bulk_index = mocker.patch(
"course_catalog.etl.loaders.search_task_helpers.index_run_content_files",
autospec=True,
)
mock_bulk_delete = mocker.patch(
"course_catalog.etl.loaders.search_task_helpers.delete_run_content_files",
autospec=True,
)
load_content_files(course_run, content_data)
assert mock_load_content_file.call_count == len(content_data)
assert mock_bulk_index.call_count == (1 if is_published else 0)
assert mock_bulk_delete.call_count == (0 if is_published else 1)
def test_load_prices(prices_exist):
"""Test that load_prices creates and/or assigns prices to the parent object"""
prices = (CoursePriceFactory.create_batch(3)
if prices_exist else CoursePriceFactory.build_batch(3))
course_run = LearningResourceRunFactory.create(no_prices=True)
assert course_run.prices.count() == 0
load_prices(
course_run,
[{
"price": price.price,
"mode": price.mode,
"upgrade_deadline": price.upgrade_deadline,
} for price in prices],
)
assert course_run.prices.count() == len(prices)
def test_load_content_file():
"""Test that load_content_file saves a ContentFile object"""
learning_resource_run = LearningResourceRunFactory.create()
props = model_to_dict(
ContentFileFactory.build(run_id=learning_resource_run.id))
props.pop("run")
props.pop("id")
result = load_content_file(learning_resource_run, props)
assert ContentFile.objects.count() == 1
assert result.run == learning_resource_run
# assert we got a content file back
assert isinstance(result, ContentFile)
for key, value in props.items():
assert getattr(result,
key) == value, f"Property {key} should equal {value}"
def test_get_most_relevant_run():
"""Verify that most_relevant_run returns the correct run"""
most_relevant_run = LearningResourceRunFactory.create(
availability=AvailabilityType.archived.value,
best_start_date=datetime(2019, 10, 1, tzinfo=pytz.utc),
run_id="1",
)
LearningResourceRunFactory.create(
availability=AvailabilityType.archived.value,
best_start_date=datetime(2018, 10, 1, tzinfo=pytz.utc),
run_id="2",
)
assert (get_most_relevant_run(
LearningResourceRun.objects.filter(
run_id__in=["1", "2"])) == most_relevant_run)
most_relevant_run = LearningResourceRunFactory.create(
availability=AvailabilityType.upcoming.value,
best_start_date=datetime(2017, 10, 1, tzinfo=pytz.utc),
run_id="3",
)
LearningResourceRunFactory.create(
availability=AvailabilityType.upcoming.value,
best_start_date=datetime(2020, 10, 1, tzinfo=pytz.utc),
run_id="4",
)
assert (get_most_relevant_run(
LearningResourceRun.objects.filter(
run_id__in=["1", "2", "3", "4"])) == most_relevant_run)
most_relevant_run = LearningResourceRunFactory.create(
availability=AvailabilityType.current.value, run_id="5")
assert (get_most_relevant_run(
LearningResourceRun.objects.filter(
run_id__in=["1", "2", "3", "4", "5"])) == most_relevant_run)