Use SQLAlchemy ORMs to Access Parquet Data in Python

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Parquet Python Connector

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

The CData Python Connector for Parquet enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Parquet data.

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

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

Connecting to Parquet Data

Connecting to Parquet 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 Parquet file(s) by setting the URI connection property to the location of the Parquet file.

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

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Parquet Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Parquet 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("parquet:///?URI=C:/folder/table.parquet")

Declare a Mapping Class for Parquet 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 Parquet 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("parquet:///?URI=C:/folder/table.parquet") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(SampleTable_1).filter_by(Column2="SAMPLE_VALUE"): 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( == "SAMPLE_VALUE")): 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

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