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How to use SQLAlchemy ORM to access XML Data in Python


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

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

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

Connecting to XML Data

Connecting to XML 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.

See the Getting Started chapter in the data provider documentation to authenticate to your data source: The data provider models XML APIs as bidirectional database tables and XML files as read-only views (local files, files stored on popular cloud services, and FTP servers). The major authentication schemes are supported, including HTTP Basic, Digest, NTLM, OAuth, and FTP. See the Getting Started chapter in the data provider documentation for authentication guides.

After setting the URI and providing any authentication values, set DataModel to more closely match the data representation to the structure of your data.

The DataModel property is the controlling property over how your data is represented into tables and toggles the following basic configurations.

  • Document (default): Model a top-level, document view of your XML data. The data provider returns nested elements as aggregates of data.
  • FlattenedDocuments: Implicitly join nested documents and their parents into a single table.
  • Relational: Return individual, related tables from hierarchical data. The tables contain a primary key and a foreign key that links to the parent document.

See the Modeling XML Data chapter for more information on configuring the relational representation. You will also find the sample data used in the following examples. The data includes entries for people, the cars they own, and various maintenance services performed on those cars.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with XML 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("xml:///?URI=C:/people.xml&DataModel=Relational")

Declare a Mapping Class for XML 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 people 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 people(base): __tablename__ = "people" [ personal.name.first ] = Column(String,primary_key=True) [ personal.name.last ] = Column(String) ...

Query XML 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("xml:///?URI=C:/people.xml&DataModel=Relational") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(people).filter_by([ personal.name.last ]="Roberts"): print("[ personal.name.first ]: ", instance.[ personal.name.first ]) print("[ personal.name.last ]: ", instance.[ personal.name.last ]) 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

people_table = people.metadata.tables["people"] for instance in session.execute(people_table.select().where(people_table.c.[ personal.name.last ] == "Roberts")): print("[ personal.name.first ]: ", instance.[ personal.name.first ]) print("[ personal.name.last ]: ", instance.[ personal.name.last ]) print("---------")

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

Insert XML Data

To insert XML 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 XML.

new_rec = people([ personal.name.first ]="placeholder", [ personal.name.last ]="Roberts") session.add(new_rec) session.commit()

Update XML Data

To update XML 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 XML.

updated_rec = session.query(people).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.[ personal.name.last ] = "Roberts" session.commit()

Delete XML Data

To delete XML 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(people).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

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