Added local_folder param to get_result_df (#394) (#400)

* added local_folder param to get_result_df (#394)

* replaced temp_dir with local_folder to fix undefined variable error

* added docstring and changed local_folder to absolute path

* changed laucher too launcher

feathr_project/feathr/utils/job_utils.py

@@ -7,28 +7,35 @@
 import tempfile
 
 
-def get_result_df(client: FeathrClient, format: str = None, res_url: str = None) -> pd.DataFrame:
+def get_result_df(client: FeathrClient, format: str = None, res_url: str = None, local_folder: str = None) -> pd.DataFrame:
     """Download the job result dataset from cloud as a Pandas dataframe.
 
     format: format override, could be "parquet", "delta", etc.
     res_url: output URL to download files. Note that this will not block the job so you need to make sure the job is finished and result URL contains actual data.
+    local_folder: optional parameter to specify the absolute download path. if the user does not provide this, function will create a temporary directory and delete it after reading the dataframe.
     """
     res_url: str = res_url or client.get_job_result_uri(block=True, timeout_sec=1200)
     format: str = format or client.get_job_tags().get(OUTPUT_FORMAT, "")
-    tmp_dir = tempfile.TemporaryDirectory()
-    client.feathr_spark_launcher.download_result(result_path=res_url, local_folder=tmp_dir.name)
+    # if local_folder params is not provided then create a temporary folder
+    if local_folder is not None:
+        local_dir_path = local_folder
+    else:
+        tmp_dir = tempfile.TemporaryDirectory()
+        local_dir_path = tmp_dir.name
+
+    client.feathr_spark_launcher.download_result(result_path=res_url, local_folder=local_dir_path)
     dataframe_list = []
     # by default the result are in avro format
     if format:
         # helper function for only parquet and avro
         if format.casefold()=="parquet":
-            files =  glob.glob(os.path.join(tmp_dir.name, '*.parquet'))
+            files =  glob.glob(os.path.join(local_dir_path, '*.parquet'))
             from pyarrow.parquet import ParquetDataset
             ds = ParquetDataset(files)
             result_df = ds.read().to_pandas()
         elif format.casefold()=="delta":
             from deltalake import DeltaTable
-            delta = DeltaTable(tmp_dir.name)
+            delta = DeltaTable(local_dir_path)
             if not client.spark_runtime == 'azure_synapse':
                 # don't detect for synapse result with Delta as there's a problem with underlying system
                 # Issues are trached here: https://github.com/delta-io/delta-rs/issues/582
@@ -38,14 +45,15 @@ def get_result_df(client: FeathrClient, format: str = None, res_url: str = None)
                 result_df = pd.DataFrame()
         elif format.casefold()=="avro":
             import pandavro as pdx
-            for file in glob.glob(os.path.join(tmp_dir.name, '*.avro')):
+            for file in glob.glob(os.path.join(local_dir_path, '*.avro')):
                 dataframe_list.append(pdx.read_avro(file))
             result_df = pd.concat(dataframe_list, axis=0)
     else:
         # by default use avro
         import pandavro as pdx
-        for file in glob.glob(os.path.join(tmp_dir.name, '*.avro')):
+        for file in glob.glob(os.path.join(local_dir_path, '*.avro')):
             dataframe_list.append(pdx.read_avro(file))
         result_df = pd.concat(dataframe_list, axis=0)
-    tmp_dir.cleanup()
+    if local_folder is None:
+        tmp_dir.cleanup()
     return result_df