Ready to get started?

Download a free trial of the EnterpriseDB Connector to get started:

Learn more:

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

How to use SQLAlchemy ORM to access EnterpriseDB Data in Python

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

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

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

Connecting to EnterpriseDB Data

Connecting to EnterpriseDB 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.

The following connection properties are required in order to connect to data.

Server: The host name or IP of the server hosting the EnterpriseDB database.
Port: The port of the server hosting the EnterpriseDB database.

You can also optionally set the following:

Database: The default database to connect to when connecting to the EnterpriseDB Server. If this is not set, the user's default database will be used.

Connect Using Standard Authentication

To authenticate using standard authentication, set the following:

User: The user which will be used to authenticate with the EnterpriseDB server.
Password: The password which will be used to authenticate with the EnterpriseDB server.

Connect Using SSL Authentication

You can leverage SSL authentication to connect to EnterpriseDB data via a secure session. Configure the following connection properties to connect to data:

SSLClientCert: Set this to the name of the certificate store for the client certificate. Used in the case of 2-way SSL, where truststore and keystore are kept on both the client and server machines.
SSLClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
SSLClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
SSLClientCertType: The certificate type of the client store.
SSLServerCert: The certificate to be accepted from the server.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with EnterpriseDB 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("enterprisedb:///?User=postgres&Password=admin&Database=postgres&Server=127.0.0.1&Port=5444")

Declare a Mapping Class for EnterpriseDB 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 Orders 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 Orders(base):
	__tablename__ = "Orders"
	ShipName = Column(String,primary_key=True)
	ShipCity = Column(String)
	...

Query EnterpriseDB 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("enterprisedb:///?User=postgres&Password=admin&Database=postgres&Server=127.0.0.1&Port=5444")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Orders).filter_by(ShipCountry="USA"):
	print("ShipName: ", instance.ShipName)
	print("ShipCity: ", instance.ShipCity)
	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


Orders_table = Orders.metadata.tables["Orders"]
for instance in session.execute(Orders_table.select().where(Orders_table.c.ShipCountry == "USA")):
	print("ShipName: ", instance.ShipName)
	print("ShipCity: ", instance.ShipCity)
	print("---------")

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

Insert EnterpriseDB Data

To insert EnterpriseDB 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 EnterpriseDB.


new_rec = Orders(ShipName="placeholder", ShipCountry="USA")
session.add(new_rec)
session.commit()

Update EnterpriseDB Data

To update EnterpriseDB 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 EnterpriseDB.


updated_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.ShipCountry = "USA"
session.commit()

Delete EnterpriseDB Data

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

CData Software is a leading provider of data access and connectivity solutions. Our standards-based connectors streamline data access and insulate customers from the complexities of integrating with on-premise or cloud databases, SaaS, APIs, NoSQL, and Big Data.

Connect With Us

Get Started

Data Connectors

ETL/ ELT Solutions

Cloud & API Connectivity

OEM & Custom Drivers