fix test

joblibspark/backend.py

@@ -37,10 +37,11 @@
 from py4j.clientserver import ClientServer
 
 import pyspark
-from pyspark.sql import SparkSession
 from pyspark import cloudpickle
 from pyspark.util import VersionUtils
 
+from .utils import create_resource_profile, get_spark_session
+
 
 def register():
     """
@@ -76,10 +77,7 @@ def __init__(self,
         super(SparkDistributedBackend, self).__init__(**backend_args)
         self._pool = None
         self._n_jobs = None
-        self._spark = SparkSession \
-            .builder \
-            .appName("JoblibSparkBackend") \
-            .getOrCreate()
+        self._spark = get_spark_session()
         self._spark_context = self._spark.sparkContext
         self._job_group = "joblib-spark-job-group-" + str(uuid.uuid4())
         self._spark_pinned_threads_enabled = isinstance(
@@ -120,15 +118,9 @@ def _create_resource_profile(self,
         if num_cpus_worker_node is not None and num_gpus_worker_node is not None:
             if self._support_stage_scheduling:
                 self.using_stage_scheduling = True
-                #pylint: disable=import-outside-toplevel
-                from pyspark.resource.profile import ResourceProfileBuilder
-                from pyspark.resource.requests import TaskResourceRequests
-                #pylint: enable=import-outside-toplevel
-
-                task_res_req = TaskResourceRequests().cpus(num_cpus_worker_node)
-                if num_gpus_worker_node > 0:
-                    task_res_req = task_res_req.resource("gpu", num_gpus_worker_node)
-                resource_profile = ResourceProfileBuilder().require(task_res_req).build
+
+                resource_profile = create_resource_profile(num_cpus_worker_node,
+                                                           num_gpus_worker_node)
 
         return resource_profile
 

joblibspark/utils.py

@@ -0,0 +1,51 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+"""
+The utils functions for joblib spark backend.
+"""
+
+
+# pylint: disable=import-outside-toplevel
+def get_spark_session():
+    """
+    Get the spark session from the active session or create a new one.
+    """
+    from pyspark.sql import SparkSession
+
+    spark_session = SparkSession.getActiveSession()
+    if spark_session is None:
+        spark_session = SparkSession \
+            .builder \
+            .appName("JoblibSparkBackend") \
+            .getOrCreate()
+    return spark_session
+
+
+def create_resource_profile(num_cpus_worker_node, num_gpus_worker_node):
+    """
+    Create a resource profile for the task.
+    :param num_cpus_worker_node: Number of cpus for the Spark worker node.
+    :param num_gpus_worker_node: Number of gpus for the Spark worker node.
+    :return: Spark ResourceProfile
+    """
+    from pyspark.resource.profile import ResourceProfileBuilder
+    from pyspark.resource.requests import TaskResourceRequests
+
+    task_res_req = TaskResourceRequests().cpus(num_cpus_worker_node)
+    if num_gpus_worker_node > 0:
+        task_res_req = task_res_req.resource("gpu", num_gpus_worker_node)
+    return ResourceProfileBuilder().require(task_res_req).build