def test_batch_upload(
large_upload_collection: UploadCollection,
fake_session: HexpySession,
caplog: CaptureFixture,
) -> None:
"""Test more than 1000 items are uploaded in batches"""
responses.add(responses.POST,
HexpySession.ROOT + "content/upload",
json={},
status=200)
client = ContentUploadAPI(fake_session)
with caplog.at_level(logging.INFO):
response = client.upload(document_type=123456789,
items=large_upload_collection,
request_usage=True)
assert (caplog.records[0].msg ==
"More than 1000 items found. Uploading in batches of 1000.")
assert response == {
"Batch 0": {},
"Batch 1": {},
"Batch 2": {},
"Batch 3": {}
}
def test_rate_limiting_window(
caplog: CaptureFixture, base_func: Callable[[str], JSONDict]
) -> None:
"""Test sliding window when rate limit not exceeded."""
modified_func = rate_limited(base_func, max_calls=10, period=1)
with caplog.at_level(logging.INFO):
for _ in range(12):
modified_func()
time.sleep(0.2)
assert len(caplog.records) == 0
def test_rate_limiting(
caplog: CaptureFixture, base_func: Callable[[str], JSONDict]
) -> None:
"""Test function rate limiting"""
modified_func = rate_limited(base_func, max_calls=10, period=1)
with caplog.at_level(logging.INFO):
for _ in range(12):
modified_func()
assert caplog.records[0].msg == "Rate Limit Reached. (Sleeping for 6 seconds)"
def test_batch_train(
large_train_collection: TrainCollection,
fake_session: HexpySession,
caplog: CaptureFixture,
) -> None:
"""Test more than 1000 items are trained in batches"""
responses.add(responses.POST,
HexpySession.ROOT + "monitor/train",
json={},
status=200)
client = MonitorAPI(fake_session)
with caplog.at_level(logging.INFO):
response = client.train_monitor(monitor_id=123456789,
items=large_train_collection)
assert (
caplog.records[0].msg ==
"More than 1000 training items found. Uploading in batches of 1000."
)
assert response == {"Batch 0": {}, "Batch 1": {}, "Batch 2": {}}
