Add security group parameters for EMR

README.md

@@ -205,7 +205,7 @@ session.spark.create_glue_table(dataframe=dataframe,
 
 ```py3
 session = awswrangler.Session(spark_session=spark)
-dfs = session.spark.flatten(df=df_nested)
+dfs = session.spark.flatten(dataframe=df_nested)
 for name, df_flat in dfs:
     print(name)
     df_flat.show()

awswrangler/emr.py

@@ -40,6 +40,18 @@ def _build_cluster_args(**pars):
         if pars["key_pair_name"] is not None:
             args["Instances"]["Ec2KeyName"] = pars["key_pair_name"]
 
+        # Security groups
+        if pars["security_group_master"] is not None:
+            args["Instances"]["EmrManagedMasterSecurityGroup"] = pars["security_group_master"]
+        if pars["security_groups_master_additional"] is not None:
+            args["Instances"]["AdditionalMasterSecurityGroups"] = pars["security_groups_master_additional"]
+        if pars["security_group_slave"] is not None:
+            args["Instances"]["EmrManagedSlaveSecurityGroup"] = pars["security_group_slave"]
+        if pars["security_groups_slave_additional"] is not None:
+            args["Instances"]["AdditionalSlaveSecurityGroups"] = pars["security_groups_slave_additional"]
+        if pars["security_group_service_access"] is not None:
+            args["Instances"]["ServiceAccessSecurityGroup"] = pars["security_group_service_access"]
+
         # Configurations
         if pars["python3"] or pars["spark_glue_catalog"] or pars["hive_glue_catalog"] or pars["presto_glue_catalog"]:
             args["Configurations"]: List = []
@@ -265,7 +277,12 @@ def create_cluster(self,
                        debugging: bool = True,
                        applications: Optional[List[str]] = None,
                        visible_to_all_users: bool = True,
-                       key_pair_name: Optional[str] = None):
+                       key_pair_name: Optional[str] = None,
+                       security_group_master: Optional[str] = None,
+                       security_groups_master_additional: Optional[List[str]] = None,
+                       security_group_slave: Optional[str] = None,
+                       security_groups_slave_additional: Optional[List[str]] = None,
+                       security_group_service_access: Optional[str] = None):
         """
         Create a EMR cluster with instance fleets configuration
         https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-instance-fleet.html
@@ -305,6 +322,11 @@ def create_cluster(self,
         :param applications: List of applications (e.g ["Hadoop", "Spark", "Ganglia", "Hive"])
         :param visible_to_all_users: True or False
         :param key_pair_name: Key pair name (string)
+        :param security_group_master: The identifier of the Amazon EC2 security group for the master node.
+        :param security_groups_master_additional: A list of additional Amazon EC2 security group IDs for the master node.
+        :param security_group_slave: The identifier of the Amazon EC2 security group for the core and task nodes.
+        :param security_groups_slave_additional: A list of additional Amazon EC2 security group IDs for the core and task nodes.
+        :param security_group_service_access: The identifier of the Amazon EC2 security group for the Amazon EMR service to access clusters in VPC private subnets.
         :return: Cluster ID (string)
         """
         args = EMR._build_cluster_args(**locals())

awswrangler/spark.py

@@ -294,28 +294,31 @@ def _build_name(name: str, expr: str) -> str:
         return f"{name}_{suffix}".replace(".", "_")
 
     @staticmethod
-    def flatten(df: sql.DataFrame, explode_outer: bool = True, explode_pos: bool = True,
+    def flatten(dataframe: sql.DataFrame, explode_outer: bool = True, explode_pos: bool = True,
                 name: str = "root") -> Dict[str, sql.DataFrame]:
         """
         Convert a complex nested DataFrame in one (or many) flat DataFrames
         If a columns is a struct it is flatten directly.
         If a columns is an array or map, then child DataFrames are created in different granularities.
-        :param df: Spark DataFrame
+        :param dataframe: Spark DataFrame
         :param explode_outer: Should we preserve the null values on arrays?
         :param explode_pos: Create columns with the index of the ex-array
         :param name: The name of the root Dataframe
         :return: A dictionary with the names as Keys and the DataFrames as Values
         """
-        cols_exprs: List[Tuple[str, str, str]] = Spark._flatten_struct_dataframe(df=df,
+        cols_exprs: List[Tuple[str, str, str]] = Spark._flatten_struct_dataframe(df=dataframe,
                                                                                  explode_outer=explode_outer,
                                                                                  explode_pos=explode_pos)
         exprs_arr: List[str] = [x[2] for x in cols_exprs if Spark._is_array_or_map(x[1])]
         exprs: List[str] = [x[2] for x in cols_exprs if not Spark._is_array_or_map(x[1])]
-        dfs: Dict[str, sql.DataFrame] = {name: df.selectExpr(exprs)}
+        dfs: Dict[str, sql.DataFrame] = {name: dataframe.selectExpr(exprs)}
         exprs = [x[2] for x in cols_exprs if not Spark._is_array_or_map(x[1]) and not x[0].endswith("_pos")]
         for expr in exprs_arr:
-            df_arr = df.selectExpr(exprs + [expr])
+            df_arr = dataframe.selectExpr(exprs + [expr])
             name_new: str = Spark._build_name(name=name, expr=expr)
-            dfs_new = Spark.flatten(df=df_arr, explode_outer=explode_outer, explode_pos=explode_pos, name=name_new)
+            dfs_new = Spark.flatten(dataframe=df_arr,
+                                    explode_outer=explode_outer,
+                                    explode_pos=explode_pos,
+                                    name=name_new)
             dfs = {**dfs, **dfs_new}
         return dfs

docs/source/examples.rst

@@ -163,7 +163,7 @@ Flatten nested PySpark DataFrame
 .. code-block:: python
 
     session = awswrangler.Session(spark_session=spark)
-    dfs = session.spark.flatten(df=df_nested)
+    dfs = session.spark.flatten(dataframe=df_nested)
     for name, df_flat in dfs:
         print(name)
         df_flat.show()

testing/test_awswrangler/test_spark.py

@@ -190,7 +190,7 @@ def test_flatten_simple_struct(session):
     ])
     df = session.spark_session.createDataFrame(data=pdf, schema=schema)
     df.printSchema()
-    dfs = session.spark.flatten(df=df)
+    dfs = session.spark.flatten(dataframe=df)
     assert len(dfs) == 1
     dfs["root"].printSchema()
     dtypes = str(dfs["root"].dtypes)
@@ -261,7 +261,7 @@ def test_flatten_complex_struct(session):
     ])
     df = session.spark_session.createDataFrame(data=pdf, schema=schema)
     df.printSchema()
-    dfs = session.spark.flatten(df=df)
+    dfs = session.spark.flatten(dataframe=df)
     assert len(dfs) == 1
     dfs["root"].printSchema()
     dtypes = str(dfs["root"].dtypes)
@@ -294,7 +294,7 @@ def test_flatten_simple_map(session):
     ])
     df = session.spark_session.createDataFrame(data=pdf, schema=schema)
     df.printSchema()
-    dfs = session.spark.flatten(df=df)
+    dfs = session.spark.flatten(dataframe=df)
     assert len(dfs) == 2
 
     # root
@@ -329,7 +329,7 @@ def test_flatten_simple_array(session):
     ])
     df = session.spark_session.createDataFrame(data=pdf, schema=schema)
     df.printSchema()
-    dfs = session.spark.flatten(df=df)
+    dfs = session.spark.flatten(dataframe=df)
     assert len(dfs) == 2
 
     # root