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

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How to use SQLAlchemy ORM to access Avro Data in Python


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

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

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

Connecting to Avro Data

Connecting to Avro 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.

Connect to your local Avro file(s) by setting the URI connection property to the location of the Avro file.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Avro 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("avro:///?URI=C:/folder/table.avroInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Avro 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 SampleTable_1 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 SampleTable_1(base): __tablename__ = "SampleTable_1" Id = Column(String,primary_key=True) Column1 = Column(String) ...

Query Avro 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("avro:///?URI=C:/folder/table.avroInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(SampleTable_1).filter_by(Column2="value_2"): print("Id: ", instance.Id) print("Column1: ", instance.Column1) 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

SampleTable_1_table = SampleTable_1.metadata.tables["SampleTable_1"] for instance in session.execute(SampleTable_1_table.select().where(SampleTable_1_table.c.Column2 == "value_2")): print("Id: ", instance.Id) print("Column1: ", instance.Column1) print("---------")

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

Free Trial & More Information

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