fix: Update BatchPredictionJob.iter_outputs() and BQ docstrings

google/cloud/aiplatform/jobs.py

@@ -406,7 +406,7 @@ def create(
                 https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
             bigquery_source (Optional[str]):
                 BigQuery URI to a table, up to 2000 characters long. For example:
-                `projectId.bqDatasetId.bqTableId`
+                `bq://projectId.bqDatasetId.bqTableId`
             gcs_destination_prefix (Optional[str]):
                 The Google Cloud Storage location of the directory where the
                 output is to be written to. In the given directory a new
@@ -808,23 +808,30 @@ def iter_outputs(
         # BigQuery Destination, return RowIterator
         elif output_info.bigquery_output_dataset:
 
-            # Build a BigQuery Client using the same credentials as JobServiceClient
-            bq_client = bigquery.Client(
-                project=self.project,
-                credentials=self.api_client._transport._credentials,
-            )
-
-            # Format from service is `bq://projectId.bqDatasetId`
+            # Format of `bigquery_output_dataset` from service is `bq://projectId.bqDatasetId`
             bq_dataset = output_info.bigquery_output_dataset
+            bq_table = output_info.bigquery_output_table
+
+            if not bq_table:
+                raise RuntimeError(
+                    "A BigQuery table with predictions was not found, this "
+                    f"might be due to errors. Visit {self._dashboard_uri()} for details."
+                )
 
             if bq_dataset.startswith("bq://"):
                 bq_dataset = bq_dataset[5:]
 
             # # Split project ID and BQ dataset ID
             _, bq_dataset_id = bq_dataset.split(".", 1)
 
+            # Build a BigQuery Client using the same credentials as JobServiceClient
+            bq_client = bigquery.Client(
+                project=self.project,
+                credentials=self.api_client._transport._credentials,
+            )
+
             row_iterator = bq_client.list_rows(
-                table=f"{bq_dataset_id}.predictions", max_results=bq_max_results
+                table=f"{bq_dataset_id}.{bq_table}", max_results=bq_max_results
             )
 
             return row_iterator

google/cloud/aiplatform/models.py

@@ -2038,7 +2038,7 @@ def batch_predict(
                 https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
             bigquery_source: Optional[str] = None
                 BigQuery URI to a table, up to 2000 characters long. For example:
-                `projectId.bqDatasetId.bqTableId`
+                `bq://projectId.bqDatasetId.bqTableId`
             instances_format: str = "jsonl"
                 Required. The format in which instances are given, must be one
                 of "jsonl", "csv", "bigquery", "tf-record", "tf-record-gzip",

tests/unit/aiplatform/test_jobs.py

@@ -56,6 +56,7 @@
 _TEST_ALT_ID = "8834795523125638878"
 _TEST_DISPLAY_NAME = "my_job_1234"
 _TEST_BQ_DATASET_ID = "bqDatasetId"
+_TEST_BQ_TABLE_NAME = "someBqTable"
 _TEST_BQ_JOB_ID = "123459876"
 _TEST_BQ_MAX_RESULTS = 100
 _TEST_GCS_BUCKET_NAME = "my-bucket"
@@ -108,6 +109,9 @@
     gcs_output_directory=_TEST_GCS_BUCKET_NAME
 )
 _TEST_BQ_OUTPUT_INFO = gca_batch_prediction_job.BatchPredictionJob.OutputInfo(
+    bigquery_output_dataset=_TEST_BQ_PATH, bigquery_output_table=_TEST_BQ_TABLE_NAME
+)
+_TEST_BQ_OUTPUT_INFO_INCOMPLETE = gca_batch_prediction_job.BatchPredictionJob.OutputInfo(
     bigquery_output_dataset=_TEST_BQ_PATH
 )
 
@@ -296,6 +300,23 @@ def get_batch_prediction_job_bq_output_mock():
         yield get_batch_prediction_job_mock
 
 
+@pytest.fixture
+def get_batch_prediction_job_incomplete_bq_output_mock():
+    with patch.object(
+        job_service_client.JobServiceClient, "get_batch_prediction_job"
+    ) as get_batch_prediction_job_mock:
+        get_batch_prediction_job_mock.return_value = gca_batch_prediction_job.BatchPredictionJob(
+            name=_TEST_BATCH_PREDICTION_JOB_NAME,
+            display_name=_TEST_DISPLAY_NAME,
+            model=_TEST_MODEL_NAME,
+            input_config=_TEST_GCS_INPUT_CONFIG,
+            output_config=_TEST_BQ_OUTPUT_CONFIG,
+            output_info=_TEST_BQ_OUTPUT_INFO_INCOMPLETE,
+            state=_TEST_JOB_STATE_SUCCESS,
+        )
+        yield get_batch_prediction_job_mock
+
+
 @pytest.fixture
 def get_batch_prediction_job_empty_output_mock():
     with patch.object(
@@ -397,7 +418,22 @@ def test_batch_prediction_iter_dirs_bq(self, bq_list_rows_mock):
         bp.iter_outputs()
 
         bq_list_rows_mock.assert_called_once_with(
-            table=f"{_TEST_BQ_DATASET_ID}.predictions", max_results=_TEST_BQ_MAX_RESULTS
+            table=f"{_TEST_BQ_DATASET_ID}.{_TEST_BQ_TABLE_NAME}",
+            max_results=_TEST_BQ_MAX_RESULTS,
+        )
+
+    @pytest.mark.usefixtures("get_batch_prediction_job_incomplete_bq_output_mock")
+    def test_batch_prediction_iter_dirs_bq_raises_on_empty(self, bq_list_rows_mock):
+        bp = jobs.BatchPredictionJob(
+            batch_prediction_job_name=_TEST_BATCH_PREDICTION_JOB_NAME
+        )
+        with pytest.raises(RuntimeError) as e:
+            bp.iter_outputs()
+        assert e.match(
+            regexp=(
+                "A BigQuery table with predictions was not found,"
+                " this might be due to errors. Visit http"
+            )
         )
 
     @pytest.mark.usefixtures("get_batch_prediction_job_running_bq_output_mock")