Learn more
 

Ready to get started?

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

 Download Now

Learn more:

MarkLogic Icon MarkLogic Python Connector

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

In this article

Related articles

How to use SQLAlchemy ORM to access MarkLogic Data in Python


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

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

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

Connecting to MarkLogic Data

Connecting to MarkLogic 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 User, Password, and Server to the credentials for the MarkLogic account and the address of the server you want to connect to. You should also specify the REST API Port if you want to use a specific instance of a REST Server.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with MarkLogic 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("marklogic:///?User='myusername'&Password='mypassword'&Server='http://marklogic'")

Declare a Mapping Class for MarkLogic 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" Name = Column(String,primary_key=True) TotalDue = Column(String) ...

Query MarkLogic 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("marklogic:///?User='myusername'&Password='mypassword'&Server='http://marklogic'") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Customer).filter_by(Id="1"): print("Name: ", instance.Name) print("TotalDue: ", instance.TotalDue) 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 == "1")): print("Name: ", instance.Name) print("TotalDue: ", instance.TotalDue) print("---------")

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

Insert MarkLogic Data

To insert MarkLogic 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 MarkLogic.

new_rec = Customer(Name="placeholder", Id="1") session.add(new_rec) session.commit()

Update MarkLogic Data

To update MarkLogic 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 MarkLogic.

updated_rec = session.query(Customer).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Id = "1" session.commit()

Delete MarkLogic Data

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