First, you need to have Python installed on your system. Then, install the following Python libraries if you haven’t already:

‍

pip install pandas pyarrow pymysql

‍

pandas is used for data manipulation, pyarrow is for reading Parquet files, and pymysql is a MySQL client library that allows you to interact with MySQL databases.

Import the required libraries and read your Parquet file into a pandas DataFrame:

‍

import pandas as pd

# Replace 'your_data.parquet' with the path to your Parquet file

parquet_file = 'your_data.parquet'

df = pd.read_parquet(parquet_file)

Make sure you have MySQL installed and running on your system.

1. Log in to MySQL:

mysql -u username -p

2. Create a new database (if necessary):

CREATE DATABASE your_database;

3. Select the database:

USE your_database;

4. Create a table that matches the schema of the DataFrame:

CREATE TABLE your_table (

    column1 datatype,

    column2 datatype,

    ...

);

‍

Replace column1, column2, ..., and datatype with the appropriate column names and data types that correspond to your Parquet file’s schema.

Now, you can insert the data from the DataFrame into the MySQL table. Use pymysql to connect to the MySQL database and pandas to insert the data:

‍

import pymysql

# Database connection parameters

db_params = {

    'host': 'localhost',

    'user': 'your_username',

    'password': 'your_password',

    'database': 'your_database'

}

# Establish a database connection

connection = pymysql.connect(db_params)

# Function to insert data in chunks

def insert_data(df, table_name, conn):

    # Define the base insert query

    cols = ','.join(list(df.columns))

    placeholders = ','.join(['%s'] * len(df.columns))

    base_query = f"INSERT INTO {table_name} ({cols}) VALUES ({placeholders})"

    # Execute the insert query in chunks

    for chunk in df:

        with conn.cursor() as cursor:

            for row in chunk.itertuples(index=False, name=None):

                cursor.execute(base_query, row)

        conn.commit()

# Optional: Convert DataFrame to chunks if it is too large to fit in memory

chunk_size = 1000  # Define the chunk size

df_chunks = (chunk for chunk in df)

# Insert data

insert_data(df_chunks, 'your_table', connection)

# Close the database connection

connection.close()

‍

Make sure to replace 'your_username', 'your_password', 'your_database', and 'your_table' with your actual MySQL credentials, database name, and table name.

After running the script, log in to your MySQL database and verify that the data has been transferred correctly:

‍

mysql -u username -p

USE your_database;

SELECT * FROM your_table LIMIT 10;

‍

This will display the first 10 rows of the table to confirm that the data has been inserted.

‍

Additional Notes:

• The code provided above assumes that you can load the entire DataFrame into memory. If your Parquet file is too large, you should read and insert the data in chunks.
• Adjust the chunk_size according to your system’s memory capacity.
• The data types in the MySQL table must be compatible with the data types in the DataFrame.
• Error handling is not included in the code above. In a production environment, you should add try-except blocks and logging to handle and record any issues that may occur during the data transfer process.
• Always back up your MySQL database before performing bulk insert operations to prevent data loss in case of an error.