How to use SQLAlchemy ORM to access Amazon Athena Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Amazon Athena and the SQLAlchemy toolkit, you can build Amazon Athena-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Amazon Athena data to query, update, delete, and insert Amazon Athena data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Amazon Athena data in Python. When you issue complex SQL queries from Amazon Athena, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Amazon Athena and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Getting Started

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Connecting to Amazon Athena Data

Connecting to Amazon Athena data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Authenticating to Amazon Athena

To authorize Amazon Athena requests, provide the credentials for an administrator account or for an IAM user with custom permissions: Set to the access key Id. Set to the secret access key.

Note: Though you can connect as the AWS account administrator, it is recommended to use IAM user credentials to access AWS services.

Obtaining the Access Key

To obtain the credentials for an IAM user, follow the steps below:

1. Sign into the IAM console.
2. In the navigation pane, select Users.
3. To create or manage the access keys for a user, select the user and then select the Security Credentials tab.

To obtain the credentials for your AWS root account, follow the steps below:

1. Sign into the AWS Management console with the credentials for your root account.
2. Select your account name or number and select My Security Credentials in the menu that is displayed.
3. Click Continue to Security Credentials and expand the Access Keys section to manage or create root account access keys.

Authenticating from an EC2 Instance

If you are using the CData Data Provider for Amazon Athena 2018 from an EC2 Instance and have an IAM Role assigned to the instance, you can use the IAM Role to authenticate. To do so, set to true and leave and empty. The CData Data Provider for Amazon Athena 2018 will automatically obtain your IAM Role credentials and authenticate with them.

Authenticating as an AWS Role

In many situations it may be preferable to use an IAM role for authentication instead of the direct security credentials of an AWS root user. An AWS role may be used instead by specifying the . This will cause the CData Data Provider for Amazon Athena 2018 to attempt to retrieve credentials for the specified role. If you are connecting to AWS (instead of already being connected such as on an EC2 instance), you must additionally specify the and of an IAM user to assume the role for. Roles may not be used when specifying the and of an AWS root user.

Authenticating with MFA

For users and roles that require Multi-factor Authentication, specify the and connection properties. This will cause the CData Data Provider for Amazon Athena 2018 to submit the MFA credentials in a request to retrieve temporary authentication credentials. Note that the duration of the temporary credentials may be controlled via the (default 3600 seconds).

Connecting to Amazon Athena

In addition to the and properties, specify , and . Set to the region where your Amazon Athena data is hosted. Set to a folder in S3 where you would like to store the results of queries.

If is not set in the connection, the data provider connects to the default database set in Amazon Athena.

Follow the procedure below to install SQLAlchemy and start accessing Amazon Athena through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy
pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Model Amazon Athena Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Amazon Athena data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("amazonathena:///?AWSAccessKey='a123'&AWSSecretKey='s123'&AWSRegion='IRELAND'&Database='sampledb'&S3StagingDirectory='s3://bucket/staging/'")

Declare a Mapping Class for Amazon Athena Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Customers table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class Customers(base):
	__tablename__ = "Customers"
	Name = Column(String,primary_key=True)
	TotalDue = Column(String)
	...

Query Amazon Athena Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("amazonathena:///?AWSAccessKey='a123'&AWSSecretKey='s123'&AWSRegion='IRELAND'&Database='sampledb'&S3StagingDirectory='s3://bucket/staging/'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Customers).filter_by(CustomerId="12345"):
	print("Name: ", instance.Name)
	print("TotalDue: ", instance.TotalDue)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Customers_table = Customers.metadata.tables["Customers"]
for instance in session.execute(Customers_table.select().where(Customers_table.c.CustomerId == "12345")):
	print("Name: ", instance.Name)
	print("TotalDue: ", instance.TotalDue)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert Amazon Athena Data

To insert Amazon Athena data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Amazon Athena.

new_rec = Customers(Name="placeholder", CustomerId="12345")
session.add(new_rec)
session.commit()

Update Amazon Athena Data

To update Amazon Athena data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Amazon Athena.

updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.CustomerId = "12345"
session.commit()

Delete Amazon Athena Data

To delete Amazon Athena data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Amazon Athena to start building Python apps and scripts with connectivity to Amazon Athena data. Reach out to our Support Team if you have any questions.