Revert "trying to mitigate memory usage during csv download"

This reverts commit 48262ea.

Author: Richard Kuo (Onyx)

backend/onyx/connectors/salesforce/connector.py

@@ -253,7 +253,6 @@ def _fetch_from_salesforce(
                     )
 
                     os.remove(csv_path)
-                    gc.collect()
 
             gc.collect()
 

backend/onyx/connectors/salesforce/salesforce_calls.py

@@ -1,4 +1,3 @@
-import gc
 import os
 from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime
@@ -145,13 +144,6 @@ def _bulk_retrieve_from_salesforce(
         proxies=sf_client.proxies,
         session=sf_client.session,
     )
-
-    # NOTE(rkuo): there are signs this download is allocating large
-    # amounts of memory instead of streaming the results to disk.
-    # we're doing a gc.collect to try and mitigate this.
-
-    # see https://github.com/simple-salesforce/simple-salesforce/issues/428 for a
-    # possible solution
     bulk_2_type = SFBulk2Type(
         object_name=sf_type,
         bulk2_url=bulk_2_handler.bulk2_url,
@@ -180,17 +172,14 @@ def _bulk_retrieve_from_salesforce(
             new_file_path = os.path.join(directory, new_filename)
             os.rename(original_file_path, new_file_path)
             all_download_paths.append(new_file_path)
+        logger.info(f"Downloaded {sf_type} to {all_download_paths}")
+        return sf_type, all_download_paths
     except Exception as e:
         logger.error(
             f"Failed to download salesforce csv for object type {sf_type}: {e}"
         )
         logger.warning(f"Exceptioning query for object type {sf_type}: {query}")
         return sf_type, None
-    finally:
-        gc.collect()
-
-    logger.info(f"Downloaded {sf_type} to {all_download_paths}")
-    return sf_type, all_download_paths
 
 
 def fetch_all_csvs_in_parallel(
@@ -240,8 +229,7 @@ def fetch_all_csvs_in_parallel(
             time_filter_for_each_object_type[sf_type] = last_modified_time_filter
 
     # Run the bulk retrieve in parallel
-    # limit to 4 to help with memory usage
-    with ThreadPoolExecutor(max_workers=4) as executor:
+    with ThreadPoolExecutor() as executor:
         results = executor.map(
             lambda object_type: _bulk_retrieve_from_salesforce(
                 sf_client=sf_client,