Tag: MySQL

We will show how to access relational database using Google CoLab. We will use both python module and PySpark for accessing MySQL database and writing data to it.

See other articles related to BigQuery and Google Colab

Prerequisites

• Install python relevant packages
  • !pip3 install pyspark mysql-connector-python
• upload jar files for PySpark
  • gcs-connector-hadoop3-latest.jar
  • spark-bigquery-latest_2.12.jar
  • mariadb-java-client-2.4.4.jar

For example, you will only need mysql-connector-python for accessing MySQL database.

Connect to MySQL database

Assuming you have access to MySQL server. You may get free MySQL instance from: (https://www.db4free.net/ or https://pantheon.io/register)

# importing required libraries
import mysql.connector
  
dataBase = mysql.connector.connect(
  host ="ipaddress or hostname",
  port = 11111,
  user ="pantheon",
  passwd ="password",
  database = "pantheon"
) 
# preparing a cursor object
cursorObject = dataBase.cursor()
  
query = "SELECT * FROM wp_users"
cursorObject.execute(query)
   
myresult = cursorObject.fetchall()
   
for x in myresult:
  print(x)
 
# disconnecting from server
dataBase.close()

Accessing MySQL using PySpark

We need to setup a SparkContext with option – spark.jars. I am also using other options to use BigQuery and Cloud Storage.

Upload your jar files in a folder. Provide jar file location when setting spark.jars option. For more than one jar files, please separate each jar file name with comma.

• mysql-connector-java-8.0.19.jar or mariadb-java-client-2.4.4.jar (for MariaDB)
• gcs-connector-hadoop3-latest.jar
• spark-bigquery-latest_2.12.jar

import os
sparkjarsdir = "/content/companieshouse-data/jars"
sparkjars = [os.path.join(sparkjarsdir, x) for x in os.listdir(sparkjarsdir)]

from pyspark.sql import SparkSession
spark = SparkSession.builder \
  .appName('BigQuery')\
  .config("spark.hadoop.fs.gs.impl", "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem") \
  .config("spark.hadoop.google.cloud.auth.service.account.enable", "true") \
  .config("spark.hadoop.google.cloud.auth.service.account.json.keyfile", GOOGLE_APPLICATION_CREDENTIALS) \
  .config("spark.jars", ",".join(sparkjars))\
  .enableHiveSupport()\
  .getOrCreate()

Read data from a table

# jdbc:mariadb will return column name for field value
# query or table - using query
query = f"""select display_name, user_email from {table}
"""
driver = "org.mariadb.jdbc.Driver"
url = f"jdbc:mysql://{database_host}:{database_port}/{database_name}"
# create dataframe
df = (spark.read
  .format("jdbc")
  .option("driver", driver)
  .option("url", url)
  .option("query", query)
  .option("user", user)
  .option("password", password)
  .load()
  )

Save dataframe to database

# write to MySQL database - for adding new rows use - mode("append")
(df.write
  .format("jdbc")
  .option("driver", driver)
  .option("url", url)
  .option("dbtable", "aa_user")
  .option("user", user)
  .option("password", password)
  .mode("overwrite")
  .save()
)