feat: multi column pivot for druid connector (#374)

Explain what this PR does.

Implemented pivot with multiple columns in druid connector

---------

Signed-off-by: Nandita Koppisetty <[email protected]>

numalogic/connectors/druid/_druid.py

@@ -195,12 +195,18 @@ def fetch(
         if group_by:
             df = df.groupby(by=group_by).sum().reset_index()
 
-        if pivot and pivot.columns:
-            df = df.pivot(
-                index=pivot.index,
-                columns=pivot.columns,
-                values=pivot.value,
-            )
+        # TODO: performance review
+        if pivot:
+            pivoted_frames = []
+            for column in pivot.columns:
+                _df = df.pivot(
+                    index=pivot.index,
+                    columns=[column],
+                    values=pivot.value,
+                )
+                pivoted_frames.append(_df)
+
+            df = pd.concat(pivoted_frames, axis=1, join="outer")
             df.columns = df.columns.map("{0[1]}".format)
             df.reset_index(inplace=True)
 
@@ -273,7 +279,7 @@ def chunked_fetch(
                     datasource=datasource,
                     dimensions=dimensions,
                     filter_pairs=filter_pairs,
-                    static_filter=static_filter,
+                    static_filters=static_filter,
                     granularity=granularity,
                     hours=min(chunked_hours, hours - hours_elapsed),
                     delay=delay,
@@ -295,12 +301,17 @@ def chunked_fetch(
         if group_by:
             df = df.groupby(by=group_by).sum().reset_index()
 
-        if pivot and pivot.columns:
-            df = df.pivot(
-                index=pivot.index,
-                columns=pivot.columns,
-                values=pivot.value,
-            )
+        if pivot:
+            pivoted_frames = []
+            for column in pivot.columns:
+                _df = df.pivot(
+                    index=pivot.index,
+                    columns=[column],
+                    values=pivot.value,
+                )
+                pivoted_frames.append(_df)
+
+            df = pd.concat(pivoted_frames, axis=1, join="outer")
             df.columns = df.columns.map("{0[1]}".format)
             df.reset_index(inplace=True)
 

tests/connectors/test_druid.py

@@ -96,6 +96,44 @@ def group_by(*_, **__):
     mocker.patch.object(PyDruid, "groupby", side_effect=group_by)
 
 
+@pytest.fixture
+def mock_group_by_multi_column(mocker):
+    """Creates a Mock for PyDruid's groupby method for doubles sketch."""
+
+    def group_by(*_, **__):
+        """Mock group by response for doubles sketch from druid."""
+        result = [
+            {
+                "event": {
+                    "service_alias": "identity.authn.signin",
+                    "env": "prod",
+                    "status": 200,
+                    "http_status": "2xx",
+                    "count": 20
+                },
+                "timestamp": "2023-09-06T07:50:00.000Z",
+                "version": "v1",
+            },
+            {
+                "event": {
+                    "service_alias": "identity.authn.signin",
+                    "env": "prod",
+                    "status": 500,
+                    "http_status": "5xx",
+                    "count": 10
+
+                },
+                "timestamp": "2023-09-06T07:53:00.000Z",
+                "version": "v1",
+            },
+        ]
+        query = pydruid.query.Query(query_dict={}, query_type="groupBy")
+        query.parse(json.dumps(result))
+        return query
+
+    mocker.patch.object(PyDruid, "groupby", side_effect=group_by)
+
+
 def test_fetch(setup, mock_group_by):
     start, end, fetcher = setup
     _out = fetcher.fetch(
@@ -251,3 +289,23 @@ def test_chunked_fetch_err(get_args):
             **get_args,
             chunked_hours=0,
         )
+
+
+def test_multi_column_pivot(setup, mock_group_by_multi_column):
+    start, end, fetcher = setup
+    _out = fetcher.fetch(
+        filter_keys=["service_alias"],
+        filter_values=["identity.authn.signin"],
+        dimensions=["http_status", "status"],
+        datasource="ip-apigw-telegraf-druid",
+        aggregations={"count": aggregators.doublesum("count")},
+        group_by=["timestamp", "http_status", "status"],
+        hours=2,
+        pivot=Pivot(
+            index="timestamp",
+            columns=["http_status", "status"],
+            value=["count"],
+        ),
+    )
+    print(_out)
+    assert (2, 5) == _out.shape