Here are a few things you can do to improve the performance of the data loading:

• convert the JSON file to CSV and use LOAD DATA to load from a file (sample). It is probably the fastest way to do what you are trying to do.

• use .executemany() instead of .execute():

   datajson = json.load(testFile)
  
   insert_query = """
       INSERT INTO 
           cstore (AccountNo) 
       VALUES (%(AccountNo)s)
   """
   cursor.executemany(insert_query, datajson)
  

• look into disabling/removing existing indexes during the insertion and then re-creating them after the insertion is done

• loading the JSON file is probably not a bottleneck, but you may also look into faster JSON parsers like ujson

• I remember we also had some data-loading-performance-related success with the ultra-fast umysql Python database driver, but it looks like the package has not been maintained for quite a while

Here are a few things you can do to improve the performance of the data loading:

• convert the JSON file to CSV and use LOAD DATA to load from a file (sample). It is probably the fastest way to do what you are trying to do.

• use .executemany() instead of .execute():

   datajson = json.load(testFile)
  
   insert_query = """
       INSERT INTO 
           cstore (AccountNo) 
       VALUES (%(AccountNo)s)
   """
   cursor.executemany(insert_query, datajson)
  

• look into disabling/removing existing indexes during the insertion and then re-creating them after the insertion is done

• make sure you are not doing the data-load "over the internet" and there is no network-latency and bandwidth impact, be "closer" to the database server

• loading the JSON file is probably not a bottleneck, but you may also look into faster JSON parsers like ujson

• I remember we also had some data-loading-performance-related success with the ultra-fast umysql Python database driver, but it looks like the package has not been maintained for quite a while