feat: add kind field which is used to distinguish between response …

…types (#60)

feat: add `potentially_thresholded_requests_per_hour` field to `PropertyQuota`

google/analytics/data_v1beta/types/analytics_data_api.py

@@ -220,6 +220,11 @@ class RunReportResponse(proto.Message):
         property_quota (google.analytics.data_v1beta.types.PropertyQuota):
             This Analytics Property's quota state
             including this request.
+        kind (str):
+            Identifies what kind of resource this message is. This
+            ``kind`` is always the fixed string
+            "analyticsData#runReport". Useful to distinguish between
+            response types in JSON.
     """
 
     dimension_headers = proto.RepeatedField(
@@ -244,6 +249,8 @@ class RunReportResponse(proto.Message):
 
     property_quota = proto.Field(proto.MESSAGE, number=9, message=data.PropertyQuota,)
 
+    kind = proto.Field(proto.STRING, number=10)
+
 
 class RunPivotReportRequest(proto.Message):
     r"""The request to generate a pivot report.
@@ -405,6 +412,11 @@ class RunPivotReportResponse(proto.Message):
         property_quota (google.analytics.data_v1beta.types.PropertyQuota):
             This Analytics Property's quota state
             including this request.
+        kind (str):
+            Identifies what kind of resource this message is. This
+            ``kind`` is always the fixed string
+            "analyticsData#runPivotReport". Useful to distinguish
+            between response types in JSON.
     """
 
     pivot_headers = proto.RepeatedField(
@@ -427,6 +439,8 @@ class RunPivotReportResponse(proto.Message):
 
     property_quota = proto.Field(proto.MESSAGE, number=7, message=data.PropertyQuota,)
 
+    kind = proto.Field(proto.STRING, number=8)
+
 
 class BatchRunReportsRequest(proto.Message):
     r"""The batch request containing multiple report requests.
@@ -460,10 +474,17 @@ class BatchRunReportsResponse(proto.Message):
         reports (Sequence[google.analytics.data_v1beta.types.RunReportResponse]):
             Individual responses. Each response has a
             separate report request.
+        kind (str):
+            Identifies what kind of resource this message is. This
+            ``kind`` is always the fixed string
+            "analyticsData#batchRunReports". Useful to distinguish
+            between response types in JSON.
     """
 
     reports = proto.RepeatedField(proto.MESSAGE, number=1, message="RunReportResponse",)
 
+    kind = proto.Field(proto.STRING, number=2)
+
 
 class BatchRunPivotReportsRequest(proto.Message):
     r"""The batch request containing multiple pivot report requests.
@@ -499,12 +520,19 @@ class BatchRunPivotReportsResponse(proto.Message):
         pivot_reports (Sequence[google.analytics.data_v1beta.types.RunPivotReportResponse]):
             Individual responses. Each response has a
             separate pivot report request.
+        kind (str):
+            Identifies what kind of resource this message is. This
+            ``kind`` is always the fixed string
+            "analyticsData#batchRunPivotReports". Useful to distinguish
+            between response types in JSON.
     """
 
     pivot_reports = proto.RepeatedField(
         proto.MESSAGE, number=1, message="RunPivotReportResponse",
     )
 
+    kind = proto.Field(proto.STRING, number=2)
+
 
 class GetMetadataRequest(proto.Message):
     r"""Request for a property's dimension and metric metadata.
@@ -634,6 +662,11 @@ class RunRealtimeReportResponse(proto.Message):
         property_quota (google.analytics.data_v1beta.types.PropertyQuota):
             This Analytics Property's Realtime quota
             state including this request.
+        kind (str):
+            Identifies what kind of resource this message is. This
+            ``kind`` is always the fixed string
+            "analyticsData#runRealtimeReport". Useful to distinguish
+            between response types in JSON.
     """
 
     dimension_headers = proto.RepeatedField(
@@ -656,5 +689,7 @@ class RunRealtimeReportResponse(proto.Message):
 
     property_quota = proto.Field(proto.MESSAGE, number=8, message=data.PropertyQuota,)
 
+    kind = proto.Field(proto.STRING, number=9)
+
 
 __all__ = tuple(sorted(__protobuf__.manifest))

google/analytics/data_v1beta/types/data.py

@@ -562,9 +562,8 @@ class Pivot(proto.Message):
             is counted as row 0.
         limit (int):
             The number of unique combinations of dimension values to
-            return in this pivot. If unspecified, up to 10,000 unique
-            combinations of dimension values are returned. ``limit``
-            must be positive.
+            return in this pivot. The ``limit`` parameter is required. A
+            ``limit`` of 10,000 is common for single pivot requests.
 
             The product of the ``limit`` for each ``pivot`` in a
             ``RunPivotReportRequest`` must not exceed 100,000. For
@@ -955,6 +954,13 @@ class PropertyQuota(proto.Message):
             per hour; Analytics 360 Properties and cloud
             project pairs can have up to 50 server errors
             per hour.
+        potentially_thresholded_requests_per_hour (google.analytics.data_v1beta.types.QuotaStatus):
+            Analytics Properties can send up to 120
+            requests with potentially thresholded dimensions
+            per hour. In a batch request, each report
+            request is individually counted for this quota
+            if the request contains potentially thresholded
+            dimensions.
     """
 
     tokens_per_day = proto.Field(proto.MESSAGE, number=1, message="QuotaStatus",)
@@ -967,6 +973,10 @@ class PropertyQuota(proto.Message):
         proto.MESSAGE, number=4, message="QuotaStatus",
     )
 
+    potentially_thresholded_requests_per_hour = proto.Field(
+        proto.MESSAGE, number=5, message="QuotaStatus",
+    )
+
 
 class QuotaStatus(proto.Message):
     r"""Current state for a particular quota group.

synth.metadata

@@ -4,15 +4,15 @@
       "git": {
         "name": ".",
         "remote": "https://github.com/googleapis/python-analytics-data.git",
-        "sha": "ad51cf28f6c3e306780ca48eb26299b4158068ad"
+        "sha": "1c1d58f1ee88e50ce92c40b0ec58c19cec631297"
       }
     },
     {
       "git": {
         "name": "googleapis",
         "remote": "https://github.com/googleapis/googleapis.git",
-        "sha": "149a3a84c29c9b8189576c7442ccb6dcf6a8f95b",
-        "internalRef": "364411656"
+        "sha": "95dd24960cf9f794ef583e59ad9f1fabe1c4a924",
+        "internalRef": "365882072"
       }
     },
     {

tests/unit/gapic/data_v1beta/test_beta_analytics_data.py

@@ -471,7 +471,9 @@ def test_run_report(
     # Mock the actual call within the gRPC stub, and fake the request.
     with mock.patch.object(type(client.transport.run_report), "__call__") as call:
         # Designate an appropriate return value for the call.
-        call.return_value = analytics_data_api.RunReportResponse(row_count=992,)
+        call.return_value = analytics_data_api.RunReportResponse(
+            row_count=992, kind="kind_value",
+        )
 
         response = client.run_report(request)
 
@@ -487,6 +489,8 @@ def test_run_report(
 
     assert response.row_count == 992
 
+    assert response.kind == "kind_value"
+
 
 def test_run_report_from_dict():
     test_run_report(request_type=dict)
@@ -524,7 +528,7 @@ async def test_run_report_async(
     with mock.patch.object(type(client.transport.run_report), "__call__") as call:
         # Designate an appropriate return value for the call.
         call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
-            analytics_data_api.RunReportResponse(row_count=992,)
+            analytics_data_api.RunReportResponse(row_count=992, kind="kind_value",)
         )
 
         response = await client.run_report(request)
@@ -540,6 +544,8 @@ async def test_run_report_async(
 
     assert response.row_count == 992
 
+    assert response.kind == "kind_value"
+
 
 @pytest.mark.asyncio
 async def test_run_report_async_from_dict():
@@ -613,7 +619,9 @@ def test_run_pivot_report(
     # Mock the actual call within the gRPC stub, and fake the request.
     with mock.patch.object(type(client.transport.run_pivot_report), "__call__") as call:
         # Designate an appropriate return value for the call.
-        call.return_value = analytics_data_api.RunPivotReportResponse()
+        call.return_value = analytics_data_api.RunPivotReportResponse(
+            kind="kind_value",
+        )
 
         response = client.run_pivot_report(request)
 
@@ -627,6 +635,8 @@ def test_run_pivot_report(
 
     assert isinstance(response, analytics_data_api.RunPivotReportResponse)
 
+    assert response.kind == "kind_value"
+
 
 def test_run_pivot_report_from_dict():
     test_run_pivot_report(request_type=dict)
@@ -665,7 +675,7 @@ async def test_run_pivot_report_async(
     with mock.patch.object(type(client.transport.run_pivot_report), "__call__") as call:
         # Designate an appropriate return value for the call.
         call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
-            analytics_data_api.RunPivotReportResponse()
+            analytics_data_api.RunPivotReportResponse(kind="kind_value",)
         )
 
         response = await client.run_pivot_report(request)
@@ -679,6 +689,8 @@ async def test_run_pivot_report_async(
     # Establish that the response is the type that we expect.
     assert isinstance(response, analytics_data_api.RunPivotReportResponse)
 
+    assert response.kind == "kind_value"
+
 
 @pytest.mark.asyncio
 async def test_run_pivot_report_async_from_dict():
@@ -754,7 +766,9 @@ def test_batch_run_reports(
         type(client.transport.batch_run_reports), "__call__"
     ) as call:
         # Designate an appropriate return value for the call.
-        call.return_value = analytics_data_api.BatchRunReportsResponse()
+        call.return_value = analytics_data_api.BatchRunReportsResponse(
+            kind="kind_value",
+        )
 
         response = client.batch_run_reports(request)
 
@@ -768,6 +782,8 @@ def test_batch_run_reports(
 
     assert isinstance(response, analytics_data_api.BatchRunReportsResponse)
 
+    assert response.kind == "kind_value"
+
 
 def test_batch_run_reports_from_dict():
     test_batch_run_reports(request_type=dict)
@@ -810,7 +826,7 @@ async def test_batch_run_reports_async(
     ) as call:
         # Designate an appropriate return value for the call.
         call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
-            analytics_data_api.BatchRunReportsResponse()
+            analytics_data_api.BatchRunReportsResponse(kind="kind_value",)
         )
 
         response = await client.batch_run_reports(request)
@@ -824,6 +840,8 @@ async def test_batch_run_reports_async(
     # Establish that the response is the type that we expect.
     assert isinstance(response, analytics_data_api.BatchRunReportsResponse)
 
+    assert response.kind == "kind_value"
+
 
 @pytest.mark.asyncio
 async def test_batch_run_reports_async_from_dict():
@@ -903,7 +921,9 @@ def test_batch_run_pivot_reports(
         type(client.transport.batch_run_pivot_reports), "__call__"
     ) as call:
         # Designate an appropriate return value for the call.
-        call.return_value = analytics_data_api.BatchRunPivotReportsResponse()
+        call.return_value = analytics_data_api.BatchRunPivotReportsResponse(
+            kind="kind_value",
+        )
 
         response = client.batch_run_pivot_reports(request)
 
@@ -917,6 +937,8 @@ def test_batch_run_pivot_reports(
 
     assert isinstance(response, analytics_data_api.BatchRunPivotReportsResponse)
 
+    assert response.kind == "kind_value"
+
 
 def test_batch_run_pivot_reports_from_dict():
     test_batch_run_pivot_reports(request_type=dict)
@@ -959,7 +981,7 @@ async def test_batch_run_pivot_reports_async(
     ) as call:
         # Designate an appropriate return value for the call.
         call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
-            analytics_data_api.BatchRunPivotReportsResponse()
+            analytics_data_api.BatchRunPivotReportsResponse(kind="kind_value",)
         )
 
         response = await client.batch_run_pivot_reports(request)
@@ -973,6 +995,8 @@ async def test_batch_run_pivot_reports_async(
     # Establish that the response is the type that we expect.
     assert isinstance(response, analytics_data_api.BatchRunPivotReportsResponse)
 
+    assert response.kind == "kind_value"
+
 
 @pytest.mark.asyncio
 async def test_batch_run_pivot_reports_async_from_dict():
@@ -1265,7 +1289,9 @@ def test_run_realtime_report(
         type(client.transport.run_realtime_report), "__call__"
     ) as call:
         # Designate an appropriate return value for the call.
-        call.return_value = analytics_data_api.RunRealtimeReportResponse(row_count=992,)
+        call.return_value = analytics_data_api.RunRealtimeReportResponse(
+            row_count=992, kind="kind_value",
+        )
 
         response = client.run_realtime_report(request)
 
@@ -1281,6 +1307,8 @@ def test_run_realtime_report(
 
     assert response.row_count == 992
 
+    assert response.kind == "kind_value"
+
 
 def test_run_realtime_report_from_dict():
     test_run_realtime_report(request_type=dict)
@@ -1323,7 +1351,9 @@ async def test_run_realtime_report_async(
     ) as call:
         # Designate an appropriate return value for the call.
         call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
-            analytics_data_api.RunRealtimeReportResponse(row_count=992,)
+            analytics_data_api.RunRealtimeReportResponse(
+                row_count=992, kind="kind_value",
+            )
         )
 
         response = await client.run_realtime_report(request)
@@ -1339,6 +1369,8 @@ async def test_run_realtime_report_async(
 
     assert response.row_count == 992
 
+    assert response.kind == "kind_value"
+
 
 @pytest.mark.asyncio
 async def test_run_realtime_report_async_from_dict():