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Merge pull request #849 from microbiomedata/725-core-functionality-of…
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…-data_objectsstudystudy_id-endpoint-is-not-being-tested

Add tests targeting core functionality of `/data_objects/study/{study_id}` endpoint
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@sujaypatil96
sujaypatil96 authored Dec 19, 2024
2 parents 410856f + d98ddfa commit 78f160f
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Showing 2 changed files with 198 additions and 12 deletions.
4 changes: 3 additions & 1 deletion Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,8 @@ test-dbinit:
docker compose --file docker-compose.test.yml \
exec mongo /bin/bash -c "/mongorestore-nmdc-testdb.sh"

# Tip: If you append a file path to this "recipe", pytest will run only the tests defined in that file.
# For example, append `tests/test_api/test_endpoints.py` to have pytest only run the endpoint tests.
test-run:
docker compose --file docker-compose.test.yml run test

Expand Down Expand Up @@ -101,7 +103,7 @@ mongorestore-nmdc-db:
mkdir -p /tmp/remote-mongodump/nmdc
# Optionally, manually update MONGO_REMOTE_DUMP_DIR env var:
# ```bash
# export MONGO_REMOTE_DUMP_DIR=$(ssh -i ~/.ssh/nersc -q ${NERSC_USERNAME}@dtn01.nersc.gov 'bash -s ' < get_latest_nmdc_prod_dump_dir.sh 2>/dev/null)
# export MONGO_REMOTE_DUMP_DIR=$(ssh -i ~/.ssh/nersc -q ${NERSC_USERNAME}@dtn01.nersc.gov 'bash -s ' < util/get_latest_nmdc_prod_dump_dir.sh 2>/dev/null)
# ```
# Rsync the remote dump directory items of interest:
rsync -av --exclude='_*' --exclude='fs\.*' \
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206 changes: 195 additions & 11 deletions tests/test_api/test_endpoints.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,23 +26,26 @@
mongo_resource,
RuntimeApiUserClient,
)
from nmdc_runtime.util import REPO_ROOT_DIR, ensure_unique_id_indexes
from nmdc_runtime.util import REPO_ROOT_DIR, ensure_unique_id_indexes, validate_json


def ensure_schema_collections_and_alldocs():
# Return if `alldocs` collection has already been materialized.
def ensure_schema_collections_and_alldocs(force_refresh_of_alldocs: bool = False):
r"""
This function can be used to ensure properties of schema-described collections and the "alldocs" collection.
:param bool force_refresh_of_alldocs: Whether you want to force a refresh of the "alldocs" collection,
regardless of whether it is empty or not. By default, this function
will only refresh the "alldocs" collection if it is empty.
"""
mdb = get_mongo_db()
if mdb.alldocs.estimated_document_count() > 0:
ensure_unique_id_indexes(mdb)
# Return if `alldocs` collection has already been materialized, and caller does not want to force a refresh of it.
if mdb.alldocs.estimated_document_count() > 0 and not force_refresh_of_alldocs:
print(
"ensure_schema_collections_and_alldocs: `alldocs` collection already materialized"
)
return

# FIXME: Seed the database with documents that would be included in an `alldocs` collection,
# such that the `/data_objects/study/{study_id}` endpoint (which uses that collection)
# would return some data. Currently, we are practically _not testing_ that endpoint.

ensure_unique_id_indexes(mdb)
print("materializing alldocs...")
materialize_alldocs(
build_op_context(
Expand Down Expand Up @@ -438,8 +441,6 @@ def test_find_data_objects_for_nonexistent_study(api_site_client):
Note: The `api_site_client` fixture's `request` method will raise an exception if the server responds with
an unsuccessful status code.
TODO: Add tests focused on the situation where the `Study` _does_ exist.
"""
ensure_schema_collections_and_alldocs()
with pytest.raises(requests.exceptions.HTTPError):
Expand All @@ -449,6 +450,189 @@ def test_find_data_objects_for_nonexistent_study(api_site_client):
)


def test_find_data_objects_for_study_having_none(api_site_client):
# Seed the test database with a study having no associated data objects.
mdb = get_mongo_db()
study_id = "nmdc:sty-00-beeeeeef"
study_dict = {
"id": study_id,
"type": "nmdc:Study",
"study_category": "research_study",
}
assert validate_json({"study_set": [study_dict]}, mdb)["result"] != "errors"

mdb.get_collection(name="study_set").replace_one(
{"id": study_id}, study_dict, upsert=True
)

# Update the `alldocs` collection, which is a cache used by the endpoint under test.
ensure_schema_collections_and_alldocs(force_refresh_of_alldocs=True)

# Confirm the endpoint responds with no data objects.
response = api_site_client.request("GET", f"/data_objects/study/{study_id}")
assert response.status_code == 200
data_objects_by_biosample = response.json()
assert len(data_objects_by_biosample) == 0

# Clean up: Delete the documents we created within this test, from the database.
mdb.get_collection(name="study_set").delete_one({"id": study_id})
mdb.get_collection(name="alldocs").delete_many({})


def test_find_data_objects_for_study_having_one(api_site_client):
# Seed the test database with a study having one associated data object.
mdb = get_mongo_db()
study_id = "nmdc:sty-11-r2h77870"
study_dict = {
"id": study_id,
"type": "nmdc:Study",
"study_category": "research_study",
}
fakes = set()
assert validate_json({"study_set": [study_dict]}, mdb)["result"] != "errors"
if mdb.get_collection(name="study_set").find_one({"id": study_id}) is None:
mdb.get_collection(name="study_set").insert_one(study_dict)
fakes.add("study")
biosample_id = "nmdc:bsm-11-6zd5nb38"
biosample_dict = {
"id": biosample_id,
"env_broad_scale": {
"has_raw_value": "ENVO_00000446",
"term": {
"id": "ENVO:00000446",
"name": "terrestrial biome",
"type": "nmdc:OntologyClass",
},
"type": "nmdc:ControlledIdentifiedTermValue",
},
"env_local_scale": {
"has_raw_value": "ENVO_00005801",
"term": {
"id": "ENVO:00005801",
"name": "rhizosphere",
"type": "nmdc:OntologyClass",
},
"type": "nmdc:ControlledIdentifiedTermValue",
},
"env_medium": {
"has_raw_value": "ENVO_00001998",
"term": {
"id": "ENVO:00001998",
"name": "soil",
"type": "nmdc:OntologyClass",
},
"type": "nmdc:ControlledIdentifiedTermValue",
},
"type": "nmdc:Biosample",
"associated_studies": [study_id],
}
assert validate_json({"biosample_set": [biosample_dict]}, mdb)["result"] != "errors"
if mdb.get_collection(name="biosample_set").find_one({"id": biosample_id}) is None:
mdb.get_collection(name="biosample_set").insert_one(biosample_dict)
fakes.add("biosample")

data_generation_id = "nmdc:omprc-11-nmtj1g51"
data_generation_dict = {
"id": data_generation_id,
"has_input": [biosample_id],
"type": "nmdc:NucleotideSequencing",
"analyte_category": "metagenome",
"associated_studies": [study_id],
}
assert (
validate_json({"data_generation_set": [data_generation_dict]}, mdb)["result"]
!= "errors"
)
if (
mdb.get_collection(name="data_generation_set").find_one(
{"id": data_generation_id}
)
is None
):
mdb.get_collection(name="data_generation_set").insert_one(data_generation_dict)
fakes.add("data_generation")

data_object_id = "nmdc:dobj-11-cpv4y420"
data_object_dict = {
"id": data_object_id,
"name": "Raw sequencer read data",
"description": "Metagenome Raw Reads for nmdc:omprc-11-nmtj1g51",
"type": "nmdc:DataObject",
}
assert (
validate_json({"data_object_set": [data_object_dict]}, mdb)["result"]
!= "errors"
)
if (
mdb.get_collection(name="data_object_set").find_one({"id": data_object_id})
is None
):
mdb.get_collection(name="data_object_set").insert_one(data_object_dict)
fakes.add("data_object")

workflow_execution_id = "nmdc:wfmsa-11-fqq66x60.1"
workflow_execution_dict = {
"id": workflow_execution_id,
"started_at_time": "2023-03-24T02:02:59.479107+00:00",
"ended_at_time": "2023-03-24T02:02:59.479129+00:00",
"was_informed_by": data_generation_id,
"execution_resource": "JGI",
"git_url": "https://github.com/microbiomedata/RawSequencingData",
"has_input": [biosample_id],
"has_output": [data_object_id],
"type": "nmdc:MetagenomeSequencing",
}
assert (
validate_json({"workflow_execution_set": [workflow_execution_dict]}, mdb)[
"result"
]
!= "errors"
)
if (
mdb.get_collection(name="workflow_execution_set").find_one(
{"id": workflow_execution_id}
)
is None
):
mdb.get_collection(name="workflow_execution_set").insert_one(
workflow_execution_dict
)
fakes.add("workflow_execution")

# Update the `alldocs` collection, which is a cache used by the endpoint under test.
ensure_schema_collections_and_alldocs(force_refresh_of_alldocs=True)

# Confirm the endpoint responds with the data object we inserted above.
response = api_site_client.request("GET", f"/data_objects/study/{study_id}")
assert response.status_code == 200
data_objects_by_biosample = response.json()
assert any(
biosample_data_objects["biosample_id"] == biosample_id
and any(
do["id"] == data_object_id for do in biosample_data_objects["data_objects"]
)
for biosample_data_objects in data_objects_by_biosample
)

# Clean up: Delete the documents we created within this test, from the database.
if "study" in fakes:
mdb.get_collection(name="study_set").delete_one({"id": study_id})
if "biosample" in fakes:
mdb.get_collection(name="biosample_set").delete_one({"id": biosample_id})
if "data_generation":
mdb.get_collection(name="data_generation_set").delete_one(
{"id": data_generation_id}
)
if "data_object" in fakes:
mdb.get_collection(name="data_object_set").delete_one({"id": data_object_id})
if "workflow_execution" in fakes:
mdb.get_collection(name="workflow_execution_set").delete_one(
{"id": workflow_execution_id}
)

mdb.get_collection(name="alldocs").delete_many({})


def test_find_planned_processes(api_site_client):
mdb = get_mongo_db()
database_dict = json.loads(
Expand Down

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