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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Connect to Cloud SQL using the Cloud SQL Python Connector
This notebook will be demonstrating how to connect and query data from a Cloud SQL database in an easy and efficient way all from within a jupyter style notebook! Let's have some fun!
📒 Using this interactive notebook
Click the run icons ▶️  of each section within this notebook.
💡 Alternatively, you can run the currently selected cell with Ctrl + Enter (or ⌘ + Enter on a Mac).
⚠️ To avoid any errors, wait for each section to finish in their order before clicking the next “run” icon.
This sample must be connected to a Google Cloud project, but nothing else is needed other than your Google Cloud project.
You can use an existing project. Alternatively, you can create a new Cloud project with cloud credits for free.
🐍  Cloud SQL Python Connector
To connect and access our Cloud SQL database instance(s) we will leverage the Cloud SQL Python Connector.
The Cloud SQL Python Connector is a library that can be used alongside a database driver to allow users to easily connect to a Cloud SQL database without having to manually allowlist IP or manage SSL certificates. 🥳 🎉 🤩
♥️ Benefits of Using a Connector
Using a Cloud SQL connector provides the following benefits
Step1:  🔗 Connect Your Google Cloud Project
Time to connect your Google Cloud Project to this notebook so that you can leverage Google Cloud from within Colab. 🏅 😀
Step2:  ☁ Configure Your Google Cloud Project
Configure the following in your Google Cloud Project.
IAM principal (user, service account, etc.) with the
Cloud SQL Client role. 
🚨 The user logged into this notebook will be used as the IAM principal and will be granted the Cloud SQL Client role.
Step3:  Enable the Cloud SQL Admin API within your project.
Step4:  ☁️ Setting up Cloud SQL
A MySQL Cloud SQL instance is required for the following stages of this notebook.
💽 Create a MySQL Instance
Running the below cell will verify the existence of a Cloud SQL instance or create a new one if one does not exist.
⏳ - Creating a Cloud SQL instance may take a few minutes.
Step5:  🎬 Create a Movies Database
A movies database will be used in later steps when connecting to and querying a Cloud SQL database.
To create a movies database within your Cloud SQL instance run the below command
Step6:  🥷  Create Batman Database User
To create the batman database user that is used throughout the notebook, run the following gcloud command.
Step7:  <img src='https
Step8:  🐬 Connect to a MySQL Instance
We are now ready to connect to a MySQL instance using the Cloud SQL Python Connector! 🐍 ⭐ ☁
Let's set some parameters that are needed to connect properly to a Cloud SQL instance
Step9:  ✅ Basic Usage
To connect to Cloud SQL using the connector, inititalize a Connector object and call its connect method with the proper input parameters.
The connect method takes in the parameters we previously defined, as well as a few additional parameters such as
Step10:  To use this connector with SQLAlchemy, we use the creator argument for sqlalchemy.create_engine
Now that we have established a connection pool, let's write a query! 🎉 📝
Step11:  You have successfully been able to connect to a Cloud SQL instance from this notebook and make a query. YOU DID IT! 🕺 🎊 💃
<img src=https
Step12:  🪪  IAM Database Authentication
Manual IAM database authentication is supported for MySQL Cloud SQL instances. 
💡 This allows an IAM user to establish an authenticated connection to a MySQL database using an OAuth2 token as their password and enabling the enable_iam_auth parameter in the connector's connect method.
🚨 If you are using a pre-existing Cloud SQL instance within this notebook you may need to configure Cloud SQL instance to allow IAM authentication by setting the cloudsql_iam_authentication database flag to On. 
(Cloud SQL instances created within this notebook already have it enabled)
IAM principals wanting to use IAM authentication to connect to a Cloud SQL instance require the Cloud SQL Instance User and Cloud SQL Client IAM role.
Let's add the Cloud SQL Instance User role to the IAM account logged into this notebook. (Client role previously granted)
Step13:  Now the current IAM user can be added to the Cloud SQL instance as an IAM database user.
Step14:  Finally, let's update our getconn function to connect to our Cloud SQL instance with IAM database authentication enabled.
⚠️ The below sample is a limited example as it logs in to the Cloud SQL instance and outputs the current time. By default new IAM database users have no permissions on a Cloud SQL instance. To connect to specific tables and perform more complex queries, permissions must be granted at the database level. (Grant Database Privileges to the IAM user)
Step15:  Sucess! You were able to connect to Cloud SQL as an IAM authenticated user using the Cloud SQL Python Connector! 🍾 👏 🏆
<img src="https
Step16:  ✍ Appendix
Additional information provided for connecting to a Cloud SQL instance using private IP connections.
🔒 Using Private IP Connections
By default the connector connects to the Cloud SQL instance database using a Public IP address.
Private IP connections are also supported by the connector and can be easily enabled through the ip_type parameter in the connector's connect method.
⚠️ To connect via Private IP, the Cloud SQL instance being connected to must have a Private IP address configured within a VPC Network. (How to Configure Private IP)
🚫 The below cell is a working sample but will not work within this notebook as the notebook is not within your VPC Network! The cell should be copied into an environment (Cloud Run, Cloud Functions, App Engine etc.) that has access to the VPC Network.
Connecting Cloud Run to a VPC Network
Let's update our getconn function to connect to our Cloud SQL instance with Private IP.
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Python Code:
from google.colab import auth
auth.authenticate_user()
Explanation: Connect to Cloud SQL using the Cloud SQL Python Connector
This notebook will be demonstrating how to connect and query data from a Cloud SQL database in an easy and efficient way all from within a jupyter style notebook! Let's have some fun!
📒 Using this interactive notebook
Click the run icons ▶️  of each section within this notebook.
💡 Alternatively, you can run the currently selected cell with Ctrl + Enter (or ⌘ + Enter on a Mac).
⚠️ To avoid any errors, wait for each section to finish in their order before clicking the next “run” icon.
This sample must be connected to a Google Cloud project, but nothing else is needed other than your Google Cloud project.
You can use an existing project. Alternatively, you can create a new Cloud project with cloud credits for free.
🐍  Cloud SQL Python Connector
To connect and access our Cloud SQL database instance(s) we will leverage the Cloud SQL Python Connector.
The Cloud SQL Python Connector is a library that can be used alongside a database driver to allow users to easily connect to a Cloud SQL database without having to manually allowlist IP or manage SSL certificates. 🥳 🎉 🤩
♥️ Benefits of Using a Connector
Using a Cloud SQL connector provides the following benefits:
🔑 IAM Authorization: uses IAM permissions to control who/what can connect to your Cloud SQL instances.
🔒 Improved Security: uses robust, updated TLS 1.3 encryption and identity verification between the client connector and the server-side proxy, independent of the database protocol.
👍 Convenience: removes the requirement to use and distribute SSL certificates, as well as manage firewalls or source/destination IP addresses.
🪪 IAM DB Authentication (optional): provides support for Cloud SQL’s automatic IAM DB AuthN feature.
📱 Supported Dialects/Drivers
Google Cloud SQL and the Python Connector currently support the following dialects of SQL: MySQL, PostgreSQL, and SQL Server.
Depending on which dialect you are using for your relational database(s) the Python Connector will utilize a different database driver.
SUPPORTED DRIVERS:
pymysql (MySQL) 🐬
pg8000 (PostgreSQL) 🐘
pytds (SQL Server) 🗄
Therefore, depending on the dialect of your database you will need to switch to the corresponding notebook!
📗 MySQL Notebook (this notebook)
📘 PostgreSQL Notebook
📕 SQL Server Notebook
🚧 Getting Started
This notebook requires the following steps to be completed in order to successfully make Cloud SQL connections with the Cloud SQL Python Connector.
🔐 Authenticate to Google Cloud within Colab
Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.
End of explanation
#@markdown Please fill in the value below with your GCP project ID and then run the cell.
# Please fill in these values.
project_id = "" #@param {type:"string"}
# Quick input validations.
assert project_id, "⚠️ Please provide a Google Cloud project ID"
# Configure gcloud.
!gcloud config set project {project_id}
Explanation: 🔗 Connect Your Google Cloud Project
Time to connect your Google Cloud Project to this notebook so that you can leverage Google Cloud from within Colab. 🏅 😀
End of explanation
# grant Cloud SQL Client role to authenticated user
current_user = !gcloud auth list --filter=status:ACTIVE --format="value(account)"
!gcloud projects add-iam-policy-binding {project_id} \
  --member=user:{current_user[0]} \
  --role="roles/cloudsql.client"
Explanation: ☁ Configure Your Google Cloud Project
Configure the following in your Google Cloud Project.
IAM principal (user, service account, etc.) with the
Cloud SQL Client role. 
🚨 The user logged into this notebook will be used as the IAM principal and will be granted the Cloud SQL Client role.
End of explanation
# enable Cloud SQL Admin API
!gcloud services enable sqladmin.googleapis.com
Explanation: Enable the Cloud SQL Admin API within your project.
End of explanation
#@markdown Please fill in the both the Google Cloud region and name for your Cloud SQL instance. Once filled in, run the cell.
# Please fill in these values.
region = "us-central1" #@param {type:"string"}
instance_name = "" #@param {type:"string"}
# Quick input validations.
assert region, "⚠️ Please provide a Google Cloud region"
assert instance_name, "⚠️ Please provide the name of your instance"
# check if Cloud SQL instance exists in the provided region
database_version = !gcloud sql instances describe {instance_name} --format="value(databaseVersion)"
if database_version[0].startswith("MYSQL"):
  print("Found existing MySQL Cloud SQL Instance!")
else:
  print("Creating new Cloud SQL instance...")
  password = input("Please provide a password to be used for database 'root' user: ")
  !gcloud sql instances create {instance_name} --database-version=MYSQL_8_0 \
    --region={region} --cpu=1 --memory=4GB --root-password={password} \
    --database-flags=cloudsql_iam_authentication=On
Explanation: ☁️ Setting up Cloud SQL
A MySQL Cloud SQL instance is required for the following stages of this notebook.
💽 Create a MySQL Instance
Running the below cell will verify the existence of a Cloud SQL instance or create a new one if one does not exist.
⏳ - Creating a Cloud SQL instance may take a few minutes.
End of explanation
!gcloud sql databases create movies --instance={instance_name}
Explanation: 🎬 Create a Movies Database
A movies database will be used in later steps when connecting to and querying a Cloud SQL database.
To create a movies database within your Cloud SQL instance run the below command:
End of explanation
!gcloud sql users create batman \
  --instance={instance_name} \
  --password="robin"
Explanation: 🥷  Create Batman Database User
To create the batman database user that is used throughout the notebook, run the following gcloud command.
End of explanation
# install dependencies
import sys
!{sys.executable} -m pip install cloud-sql-python-connector["pymysql"] SQLAlchemy
Explanation: <img src='https://i.pinimg.com/originals/12/64/dd/1264dd5ff31fbc65c5edbb5e1a71830e.gif' class="center"/>
🐍 Python Connector Usage
Let's now connect to Cloud SQL using the Python Connector! 🚀 ⭐ 🐍
🎟 Configuring Credentials
The Cloud SQL Python Connector uses Application Default Credentials (ADC) strategy for resolving credentials. 
💡 Using the Python Connector in Cloud Run, App Engine, or Cloud Functions will automatically use the service account deployed with each service, allowing this step to be skipped. ✅ 
Please see the google.auth package documentation for more information on how these credentials are sourced.
This means setting default credentials was previously done for you when you ran:
```python
from google.colab import auth
auth.authenticate_user()
```
💻 Install Code Dependencies
It is recommended to use the Connector alongside a library that can create connection pools, such as SQLAlchemy. 
This will allow for connections to remain open and be reused, reducing connection overhead and the number of connections needed
Let's pip install the Cloud SQL Python Connector as well as SQLAlchemy, using the below command.
End of explanation
# initialize parameters
INSTANCE_CONNECTION_NAME = f"{project_id}:{region}:{instance_name}" # i.e demo-project:us-central1:demo-instance
print(f"Your instance connection name is: {INSTANCE_CONNECTION_NAME}")
DB_USER = "batman"
DB_PASS = "robin"
DB_NAME = "movies"
Explanation: 🐬 Connect to a MySQL Instance
We are now ready to connect to a MySQL instance using the Cloud SQL Python Connector! 🐍 ⭐ ☁
Let's set some parameters that are needed to connect properly to a Cloud SQL instance:
*   INSTANCE_CONNECTION_NAME : The connection name to your Cloud SQL Instance, takes the form PROJECT_ID:REGION:INSTANCE_NAME.
*   DB_USER : The user in which the connector will use to connect to the database.
*   DB_PASS : The password of the DB_USER.
*   DB_NAME : The name of the database on the Cloud SQL instance to connect to.
End of explanation
from google.cloud.sql.connector import Connector
import sqlalchemy
# initialize Connector object
connector = Connector()
# function to return the database connection object
def getconn():
    conn = connector.connect(
        INSTANCE_CONNECTION_NAME,
        "pymysql",
        user=DB_USER,
        password=DB_PASS,
        db=DB_NAME
    )
    return conn
# create connection pool with 'creator' argument to our connection object function
pool = sqlalchemy.create_engine(
    "mysql+pymysql://",
    creator=getconn,
)
Explanation: ✅ Basic Usage
To connect to Cloud SQL using the connector, inititalize a Connector object and call its connect method with the proper input parameters.
The connect method takes in the parameters we previously defined, as well as a few additional parameters such as: 
*   driver: The name of the database driver to connect with.
*   ip_type (optional): The IP type (public or private)  used to connect. IP types can be either IPTypes.PUBLIC or IPTypes.PRIVATE. (Example)
*   enable_iam_auth: (optional) Boolean enabling IAM based authentication. (Example)
Let's show an example! 🤘 🙌
End of explanation
# connect to connection pool
with pool.connect() as db_conn:
  # create ratings table in our movies database
  db_conn.execute(
      "CREATE TABLE IF NOT EXISTS ratings "
      "( id SERIAL NOT NULL, title VARCHAR(255) NOT NULL, "
      "genre VARCHAR(255) NOT NULL, rating FLOAT NOT NULL, "
      "PRIMARY KEY (id));"
  )
  # insert data into our ratings table
  insert_stmt = sqlalchemy.text(
      "INSERT INTO ratings (title, genre, rating) VALUES (:title, :genre, :rating)",
  )
  # insert entries into table
  db_conn.execute(insert_stmt, title="Batman Begins", genre="Action", rating=8.5)
  db_conn.execute(insert_stmt, title="Star Wars: Return of the Jedi", genre="Action", rating=9.1)
  db_conn.execute(insert_stmt, title="The Breakfast Club", genre="Drama", rating=8.3)
  # query and fetch ratings table
  results = db_conn.execute("SELECT * FROM ratings").fetchall()
  # show results
  for row in results:
    print(row)
Explanation: To use this connector with SQLAlchemy, we use the creator argument for sqlalchemy.create_engine
Now that we have established a connection pool, let's write a query! 🎉 📝
End of explanation
# cleanup connector object
connector.close()
Explanation: You have successfully been able to connect to a Cloud SQL instance from this notebook and make a query. YOU DID IT! 🕺 🎊 💃
<img src=https://media.giphy.com/media/MtHGs1yo4FFKrIs55L/giphy.gif />
To close the Connector object's background resources, call it's close() method at the end of your code as follows:
End of explanation
# add Cloud SQL Instance User role to current logged in IAM user
!gcloud projects add-iam-policy-binding {project_id} \
  --member=user:{current_user[0]} \
  --role="roles/cloudsql.instanceUser"
Explanation: 🪪  IAM Database Authentication
Manual IAM database authentication is supported for MySQL Cloud SQL instances. 
💡 This allows an IAM user to establish an authenticated connection to a MySQL database using an OAuth2 token as their password and enabling the enable_iam_auth parameter in the connector's connect method.
🚨 If you are using a pre-existing Cloud SQL instance within this notebook you may need to configure Cloud SQL instance to allow IAM authentication by setting the cloudsql_iam_authentication database flag to On. 
(Cloud SQL instances created within this notebook already have it enabled)
IAM principals wanting to use IAM authentication to connect to a Cloud SQL instance require the Cloud SQL Instance User and Cloud SQL Client IAM role.
Let's add the Cloud SQL Instance User role to the IAM account logged into this notebook. (Client role previously granted)
End of explanation
# add current logged in IAM user to database
!gcloud sql users create {current_user[0]} \
  --instance={instance_name} \
  --type=cloud_iam_user
Explanation: Now the current IAM user can be added to the Cloud SQL instance as an IAM database user.
End of explanation
from google.cloud.sql.connector import Connector
import google.auth
from google.auth.transport.requests import Request
import sqlalchemy
# IAM database user parameter (IAM user's email before the "@" sign, mysql truncates usernames)
# ex. IAM user with email "demo-user@test.com" would have database user name "demo-user"
IAM_USER = current_user[0].split("@")[0]
# get application default credentials of IAM user (current logged in user)
credentials, project = google.auth.default()
# refresh credentials if expired
if not credentials.valid:
      request = Request()
      credentials.refresh(request)
# initialize connector
connector = Connector()
# getconn now using IAM user and OAuth 2 token with IAM Auth enabled
def getconn():
    conn = connector.connect(
      INSTANCE_CONNECTION_NAME,
      "pymysql",
      user=IAM_USER,
      password=credentials.token,
      db="", # log in to instance but don't connect to specific database
      enable_iam_auth=True
    )
    return conn
# create connection pool
pool = sqlalchemy.create_engine(
    "mysql+pymysql://",
    creator=getconn,
)
# connect to connection pool
with pool.connect() as db_conn:
    # get current datetime from database
    results = db_conn.execute("SELECT NOW()").fetchone()
    # output time
    print("Current time: ", results[0])
# cleanup connector
connector.close()
Explanation: Finally, let's update our getconn function to connect to our Cloud SQL instance with IAM database authentication enabled.
⚠️ The below sample is a limited example as it logs in to the Cloud SQL instance and outputs the current time. By default new IAM database users have no permissions on a Cloud SQL instance. To connect to specific tables and perform more complex queries, permissions must be granted at the database level. (Grant Database Privileges to the IAM user)
End of explanation
# delete Cloud SQL instance
!gcloud sql instances delete {instance_name}
Explanation: Sucess! You were able to connect to Cloud SQL as an IAM authenticated user using the Cloud SQL Python Connector! 🍾 👏 🏆
<img src="https://media.giphy.com/media/YTbZzCkRQCEJa/giphy.gif" />
🗑 Clean Up Notebook Resources
Make sure to delete your Cloud SQL instance when your are finished with this notebook to avoid further costs. 💸 💰
End of explanation
from google.cloud.sql.connector import Connector, IPTypes
import sqlalchemy
# initialize connector
connector = Connector()
# getconn now set to private IP
def getconn():
    conn = connector.connect(
      INSTANCE_CONNECTION_NAME, # <PROJECT-ID>:<REGION>:<INSTANCE-NAME>
      "pymysql",
      user=DB_USER,
      password=DB_PASS,
      db=DB_NAME,
      ip_type=IPTypes.PRIVATE
    )
    return conn
# create connection pool
pool = sqlalchemy.create_engine(
    "mysql+pymysql://",
    creator=getconn,
)
# connect to connection pool
with pool.connect() as db_conn:
    # query database and fetch results
    results = db_conn.execute("SELECT * FROM ratings").fetchall()
    # show results
    for row in results:
        print(row)
# cleanup connector
connector.close()
Explanation: ✍ Appendix
Additional information provided for connecting to a Cloud SQL instance using private IP connections.
🔒 Using Private IP Connections
By default the connector connects to the Cloud SQL instance database using a Public IP address.
Private IP connections are also supported by the connector and can be easily enabled through the ip_type parameter in the connector's connect method.
⚠️ To connect via Private IP, the Cloud SQL instance being connected to must have a Private IP address configured within a VPC Network. (How to Configure Private IP)
🚫 The below cell is a working sample but will not work within this notebook as the notebook is not within your VPC Network! The cell should be copied into an environment (Cloud Run, Cloud Functions, App Engine etc.) that has access to the VPC Network.
Connecting Cloud Run to a VPC Network
Let's update our getconn function to connect to our Cloud SQL instance with Private IP.
End of explanation
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