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Python Connector Libraries for Presto Data Connectivity. Integrate Presto with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

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How to use SQLAlchemy ORM to access Presto Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Presto data.

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

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

Connecting to Presto Data

Connecting to Presto 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.

Set the Server and Port connection properties to connect, in addition to any authentication properties that may be required.

To enable TLS/SSL, set UseSSL to true.

Authenticating with LDAP

In order to authenticate with LDAP, set the following connection properties:

  • AuthScheme: Set this to LDAP.
  • User: The username being authenticated with in LDAP.
  • Password: The password associated with the User you are authenticating against LDAP with.

Authenticating with Kerberos

In order to authenticate with KERBEROS, set the following connection properties:

  • AuthScheme: Set this to KERBEROS.
  • KerberosKDC: The Kerberos Key Distribution Center (KDC) service used to authenticate the user.
  • KerberosRealm: The Kerberos Realm used to authenticate the user with.
  • KerberosSPN: The Service Principal Name for the Kerberos Domain Controller.
  • KerberosKeytabFile: The Keytab file containing your pairs of Kerberos principals and encrypted keys.
  • User: The user who is authenticating to Kerberos.
  • Password: The password used to authenticate to Kerberos.

Follow the procedure below to install SQLAlchemy and start accessing Presto 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 Presto Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Presto 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("presto:///?Server=127.0.0.1&Port=8080")

Declare a Mapping Class for Presto 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 Customer 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 Customer(base): __tablename__ = "Customer" FirstName = Column(String,primary_key=True) LastName = Column(String) ...

Query Presto 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("presto:///?Server=127.0.0.1&Port=8080") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Customer).filter_by(Id="123456789"): print("FirstName: ", instance.FirstName) print("LastName: ", instance.LastName) 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

Customer_table = Customer.metadata.tables["Customer"] for instance in session.execute(Customer_table.select().where(Customer_table.c.Id == "123456789")): print("FirstName: ", instance.FirstName) print("LastName: ", instance.LastName) print("---------")

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

Insert Presto Data

To insert Presto 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 Presto.

new_rec = Customer(FirstName="placeholder", Id="123456789") session.add(new_rec) session.commit()

Update Presto Data

To update Presto 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 Presto.

updated_rec = session.query(Customer).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Id = "123456789" session.commit()

Delete Presto Data

To delete Presto 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(Customer).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 Presto to start building Python apps and scripts with connectivity to Presto data. Reach out to our Support Team if you have any questions.