Bulk load Pandas DataFrames into SQL databases using Jaydebeapi

Loading Pandas DataFrames into SQL databases of all names is a common task between all developers working on building data pipelines for their environments or trying to automate ETL jobs generally.
And for that, Pandas DataFrame class has the built-in method pandas.DataFrame.to_sql that allows to do so very quickly, for SQLite and all the databases supported by SQLAlchemy library, and when it comes to those who doesn’t have a good support by it ( in my case was IBM DB2 ), developers find themselves forced to think twice for some work around in order to get the job done.
Jaydebeapi introduces himself as a good alternative, and it’s particularly seen thus by all developers coming from a Java background and having some familiarities of working with JDBC API to access the database.
Let’s start first by creating the database connection. for that reason I will be creating a simple function that takes in params all the informations required and it will give a connection to DB2 as a return.

def get_conn_to_db( user: str,
                    password: str,
                    host: str,
                    port: str,
                    db_name: str,
                    driver_name: str ): 

    """ Return a connection to DB2 database  """

    login          = {'user': user, 'password': password} 
    drivers_path   = [path_to/driver_file.jar]     
    connection_url = 'jdbc:db2://'+host+':'+port+'/'+database 
    connection     = jaydebeapi.connect(driver_name, connection_url, login,jars= drivers_path)     
    return connection

And then let’s move on to build the bulk_load function that’s going to be charged to load our Pandas DataFrame into DB2 in a chunked way.

def bulk_load(df: pandas.DataFrame, conn, schema_name: str, table: str, chunksize: int) -> []:

    cursor = connection.cursor()    
    sql_exceptions = []
    row_nbr = 0
    df_length = df.shape[0]
    schema_table = f"{schema_name}.{table}"
    # You should make sure that the columns names doesn't 
    # contain any SQL key word
    cols_names_list = df.columns.values.tolist()
    cols_names = f"({ ",".join(cols_names_list) })"

    while row_nbr < df_length:       
        # Determine insert statement boundaries (size)
        beginrow = row_nbr
        endrow = df_length if (row_nbr+chunksize) > df_length 
                           else row_nbr + chunksize 

        # Extract the chunk
        tuples = [tuple(x) for x in df.values[beginrow : endrow]]            
        values_params = '('+",".join('?' for i in cols_names)+')'       
        sql = f"INSERT INTO {schema_table} {cols_names} VALUES {values_params}"

        try:
            cursor.executemany(sql, tuples)
            connection.commit()
        except Exception as e: 
            sql_exceptions.append((beginrow,endrow, e))

        row_nbr = endrow

    cursor.close()
    connection.close()
    return sql_exceptions

Now Let’s see how we can apply those functions on our main task

import numpy as np
import pandas as pd
import jaydebeapi


db_settings = {    
    'host': 'host-adress',
    'port': '50000',
    'user':'username',
    'password':"password",
    'driver_name':'com.ibm.db2.jcc.DB2Driver',
    'db_name':'bludb'
} 

data = np.random.choice(['foo',3,'bar'],size=(100000,3))
df = pd.DataFrame(data, columns=['random_1', 'random_2', 'random_3'])

with get_conn_to_db(**db_settings) as conn:
     bulk_load(df, conn, 'RANDOM_SCHEMA_NAME', 'RANDOM_TABLE_NAME',  1000)