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Update tests/unit/test_job_retry.py
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tests/unit/test_job_retry.py

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@@ -695,66 +695,3 @@ def test_retry_load_job_result(result_retry_param, PROJECT, DS_ID):
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# The number of api_request calls should still be 3
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assert conn.api_request.call_count == 3
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# @pytest.mark.parametrize(
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# "result_retry",
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# [
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# pytest.param(
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# {},
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# id="default retry use case",
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# ),
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# pytest.param(
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# {
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# "retry": google.cloud.bigquery.retry.DEFAULT_RETRY.with_timeout(
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# timeout=10.0
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# )
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# },
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# id="custom retry object use case",
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# ),
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# ],
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# )
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# def test_retry_load_job_result(result_retry, PROJECT, DS_ID):
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# from google.cloud.bigquery.dataset import DatasetReference
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# from google.cloud.bigquery.job.load import LoadJob
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# import google.cloud.bigquery.retry
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# client = make_client()
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# conn = client._connection = make_connection(
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# dict(
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# status=dict(state="RUNNING"),
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# jobReference={"jobId": "id_1"},
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# ),
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# google.api_core.exceptions.ServiceUnavailable("retry me"),
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# dict(
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# status=dict(state="DONE"),
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# jobReference={"jobId": "id_1"},
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# statistics={"load": {"outputRows": 1}},
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# ),
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# )
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# table_ref = DatasetReference(project=PROJECT, dataset_id=DS_ID).table("new_table")
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# job = LoadJob("id_1", source_uris=None, destination=table_ref, client=client)
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# result = job.result(**result_retry)
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# print(f"Test Case ID: {result_retry}")
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# for call in conn.api_request.mock_calls:
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# name, args, kwargs = call
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# print(f" Call to api_request with kwargs: {kwargs}")
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# if result_retry:
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# custom_retry_used = False
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# for name, args, kwargs in conn.api_request.mock_calls:
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# if 'retry' in kwargs:
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# # Check if the retry object passed has the custom timeout
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# if kwargs['retry']._timeout == 10.0:
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# custom_retry_used = True
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# assert kwargs['retry']._deadline == 10.0 # Deadline should also be updated
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# # Optional: check other properties if needed
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# assert custom_retry_used, "Custom retry object with timeout 10.0 was not used"
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# assert job.state == "DONE"
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# assert result.output_rows == 1
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# # We made all the calls we expected to.
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# assert conn.api_request.call_count == 3

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