Azure · rakshith91 · Aug 20, 2021 · Aug 16, 2021 · Aug 16, 2021 · Aug 17, 2021
@@ -4,6 +4,8 @@
 
 ### Features Added
 
+- Added additional `display_description` attribute to the `Metric` type.
+
 ### Breaking Changes
 
 - Rename `batch_query` to `query_batch`.
@@ -16,6 +18,8 @@
 - `metric_namespace_name` is renamed to `fully_qualified_namespace`
 - `is_dimension_required` is renamed to `dimension_required`
 - `time_grain` is renamed to `granularity`
+- `interval`  and `time_grain` are renamed to `granularity`
+- `orderby` is renamed to `order_by`
 - `LogsQueryResult` now returns `datetime` objects for a time values.
 
 ### Bugs Fixed

@@ -289,11 +289,11 @@ for metric in response.metrics:
 
 ### Handle metrics response
 
-The metrics query API returns a `MetricsResult` object. The `MetricsResult` object contains properties such as a list of `Metric`-typed objects, `interval`, `namespace`, and `timespan`. The `Metric` objects list can be accessed using the `metrics` param. Each `Metric` object in this list contains a list of `TimeSeriesElement` objects. Each `TimeSeriesElement` contains `data` and `metadata_values` properties. In visual form, the object hierarchy of the response resembles the following structure:
+The metrics query API returns a `MetricsResult` object. The `MetricsResult` object contains properties such as a list of `Metric`-typed objects, `granularity`, `namespace`, and `timespan`. The `Metric` objects list can be accessed using the `metrics` param. Each `Metric` object in this list contains a list of `TimeSeriesElement` objects. Each `TimeSeriesElement` contains `data` and `metadata_values` properties. In visual form, the object hierarchy of the response resembles the following structure:
 
 ```
 MetricsResult
-|---interval
+|---granularity
 |---timespan
 |---cost
 |---namespace

@@ -68,19 +68,19 @@ def query(self, resource_uri, metric_names, **kwargs):
          a timedelta and a start datetime, or a start datetime/end datetime.
         :paramtype timespan: ~datetime.timedelta or tuple[~datetime.datetime, ~datetime.timedelta]
          or tuple[~datetime.datetime, ~datetime.datetime]
-        :keyword interval: The interval (i.e. timegrain) of the query.
-        :paramtype interval: ~datetime.timedelta
+        :keyword granularity: The granularity (i.e. timegrain) of the query.
+        :paramtype granularity: ~datetime.timedelta
         :keyword aggregations: The list of aggregation types to retrieve. Use `azure.monitor.query.AggregationType`
          enum to get each aggregation type.
         :paramtype aggregations: list[str]
         :keyword max_results: The maximum number of records to retrieve.
          Valid only if $filter is specified.
          Defaults to 10.
         :paramtype max_results: int
-        :keyword orderby: The aggregation to use for sorting results and the direction of the sort.
+        :keyword order_by: The aggregation to use for sorting results and the direction of the sort.
          Only one order can be specified.
          Examples: sum asc.
-        :paramtype orderby: str
+        :paramtype order_by: str
         :keyword filter: The **$filter** is used to reduce the set of metric data
          returned.:code:`<br>`Example::code:`<br>`Metric contains metadata A, B and C.:code:`<br>`-
          Return all time series of C where A = a1 and B = b1 or b2:code:`<br>`\ **$filter=A eq ‘a1’ and
@@ -117,6 +117,8 @@ def query(self, resource_uri, metric_names, **kwargs):
         kwargs.setdefault("metricnames", ",".join(metric_names))
         kwargs.setdefault("timespan", timespan)
         kwargs.setdefault("top", kwargs.pop("max_results", None))
+        kwargs.setdefault("interval", kwargs.pop("granularity", None))
+        kwargs.setdefault("orderby", kwargs.pop("order_by", None))
         generated = self._metrics_op.list(resource_uri, connection_verify=False, **kwargs)
         return MetricsResult._from_generated(generated) # pylint: disable=protected-access
 

@@ -115,10 +115,10 @@ class MetricsResult(object):
      two datetimes concatenated, separated by '/'. This may be adjusted in the future and returned
      back from what was originally requested.
     :vartype timespan: str
-    :ivar interval: The interval (window size) for which the metric data was returned in. This
+    :ivar granularity: The granularity (window size) for which the metric data was returned in. This
      may be adjusted in the future and returned back from what was originally requested. This is
      not present if a metadata request was made.
-    :vartype interval: ~datetime.timedelta
+    :vartype granularity: ~datetime.timedelta
     :ivar namespace: The namespace of the metrics that has been queried.
     :vartype namespace: str
     :ivar resource_region: The region of the resource that has been queried for metrics.
@@ -130,7 +130,7 @@ def __init__(self, **kwargs):
         # type: (Any) -> None
         self.cost = kwargs.get("cost", None)
         self.timespan = kwargs["timespan"]
-        self.interval = kwargs.get("interval", None)
+        self.granularity = kwargs.get("granularity", None)
         self.namespace = kwargs.get("namespace", None)
         self.resource_region = kwargs.get("resource_region", None)
         self.metrics = kwargs["metrics"]
@@ -142,7 +142,7 @@ def _from_generated(cls, generated):
         return cls(
             cost=generated.cost,
             timespan=generated.timespan,
-            interval=generated.interval,
+            granularity=generated.interval,
             namespace=generated.namespace,
             resource_region=generated.resourceregion,
             metrics=[Metric._from_generated(m) for m in generated.value] # pylint: disable=protected-access
@@ -459,6 +459,8 @@ class Metric(object):
     :vartype unit: str
     :ivar timeseries: Required. The time series returned when a data query is performed.
     :vartype timeseries: list[~monitor_query_client.models.TimeSeriesElement]
+    :ivar display_description: Detailed description of this metric.
+    :vartype display_description: str
     """
     def __init__(
         self,
@@ -470,6 +472,7 @@ def __init__(
         self.name = kwargs['name']
         self.unit = kwargs['unit']
         self.timeseries = kwargs['timeseries']
+        self.display_description = kwargs['display_description']
 
     @classmethod
     def _from_generated(cls, generated):
@@ -482,7 +485,8 @@ def _from_generated(cls, generated):
             unit=generated.unit,
             timeseries=[
                 TimeSeriesElement._from_generated(t) for t in generated.timeseries # pylint: disable=protected-access
-                ]
+                ],
+            display_description=generated.display_description,
         )
 
 

@@ -62,19 +62,19 @@ async def query(
          a timedelta and a start datetime, or a start datetime/end datetime.
         :paramtype timespan: ~datetime.timedelta or tuple[~datetime.datetime, ~datetime.timedelta]
          or tuple[~datetime.datetime, ~datetime.datetime]
-        :keyword interval: The interval (i.e. timegrain) of the query.
-        :paramtype interval: ~datetime.timedelta
+        :keyword granularity: The interval (i.e. timegrain) of the query.
+        :paramtype granularity: ~datetime.timedelta
         :keyword aggregations: The list of aggregation types to retrieve. Use `azure.monitor.query.AggregationType`
          enum to get each aggregation type.
         :paramtype aggregations: list[str]
         :keyword max_results: The maximum number of records to retrieve.
          Valid only if $filter is specified.
          Defaults to 10.
         :paramtype max_results: int
-        :keyword orderby: The aggregation to use for sorting results and the direction of the sort.
+        :keyword order_by: The aggregation to use for sorting results and the direction of the sort.
          Only one order can be specified.
          Examples: sum asc.
-        :paramtype orderby: str
+        :paramtype order_by: str
         :keyword filter: The **$filter** is used to reduce the set of metric data
          returned.:code:`<br>`Example::code:`<br>`Metric contains metadata A, B and C.:code:`<br>`-
          Return all time series of C where A = a1 and B = b1 or b2:code:`<br>`\ **$filter=A eq ‘a1’ and
@@ -98,6 +98,8 @@ async def query(
         kwargs.setdefault("metricnames", ",".join(metric_names))
         kwargs.setdefault("timespan", timespan)
         kwargs.setdefault("top", kwargs.pop("max_results", None))
+        kwargs.setdefault("interval", kwargs.pop("granularity", None))
+        kwargs.setdefault("orderby", kwargs.pop("order_by", None))
         aggregations = kwargs.pop("aggregations", None)
         if aggregations:
             kwargs.setdefault("aggregation", ",".join(aggregations))

@@ -20,18 +20,26 @@
 query = """AppRequests |
 summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId"""
 
+query = """
+AppRequests
+| where TimeGenerated > ago(1h)
+| fork
+    ( summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId )
+"""
+
 # returns LogsQueryResult 
 response = client.query(os.environ['LOG_WORKSPACE_ID'], query, timespan=timedelta(days=1))
 
 if not response.tables:
     print("No results for the query")
 
-try:
-    table = response.tables[0]
-    df = pd.DataFrame(table.rows, columns=[col.name for col in table.columns])
-    print(df)
-except TypeError:
-    print(response.error)
+for table in response.tables:
+    try:
+        print ([col.name for col in table.columns])
+        df = pd.DataFrame(table.rows, columns=[col.name for col in table.columns])
+        print(df)
+    except TypeError:
+        print(response.error)
 # [END send_logs_query]
 """
     TimeGenerated                                        _ResourceId          avgRequestDuration

@@ -13,5 +13,5 @@
 response = client.list_metric_namespaces(metrics_uri)
 
 for item in response:
-    print(item.metric_namespace_name)
+    print(item.fully_qualified_namespace)
     print(item.type)
@@ -19,9 +19,8 @@
 metrics_uri = os.environ['METRICS_RESOURCE_URI']
 response = client.query(
     metrics_uri,
-    metric_names=["MatchedEventCount"],
-    start_time=datetime(2021, 6, 21),
-    duration=timedelta(days=1),
+    metric_names=["MatchedEventCount", "DeliverySuccesssCount"],
+    timespan=timedelta(days=1),
     aggregations=[AggregationType.COUNT]
     )
 

@@ -14,6 +14,7 @@ def _credential():
     return credential
 
 @pytest.mark.live_test_only
+@pytest.mark.asyncio
 async def test_metrics_auth():
     credential = _credential()
     client = MetricsQueryClient(credential)
@@ -26,6 +27,21 @@ async def test_metrics_auth():
     assert response
     assert response.metrics
 
+@pytest.mark.live_test_only
+@pytest.mark.asyncio
+async def test_metrics_granularity():
+    credential = _credential()
+    client = MetricsQueryClient(credential)
+    response = await client.query(
+        os.environ['METRICS_RESOURCE_URI'],
+        metric_names=["MatchedEventCount"],
+        timespan=timedelta(days=1),
+        granularity=timedelta(minutes=5),
+        aggregations=[AggregationType.COUNT]
+        )
+    assert response
+    assert response.granularity == timedelta(minutes=5)
+
 @pytest.mark.live_test_only
 async def test_metrics_namespaces():
     client = MetricsQueryClient(_credential())

@@ -25,6 +25,20 @@ def test_metrics_auth():
     assert response
     assert response.metrics
 
+@pytest.mark.live_test_only
+def test_metrics_granularity():
+    credential = _credential()
+    client = MetricsQueryClient(credential)
+    response = client.query(
+        os.environ['METRICS_RESOURCE_URI'],
+        metric_names=["MatchedEventCount"],
+        timespan=timedelta(days=1),
+        granularity=timedelta(minutes=5),
+        aggregations=[AggregationType.COUNT]
+        )
+    assert response
+    assert response.granularity == timedelta(minutes=5)
+
 @pytest.mark.live_test_only
 def test_metrics_namespaces():
     client = MetricsQueryClient(_credential())