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How to use SQLAlchemy ORM to access SAP HANA Data in Python


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

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

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

Connecting to SAP HANA Data

Connecting to SAP HANA 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 the Server, Database and Port properties to specify the address of your SAP Hana database to interact with. Set the User and the Password properties to authenticate to the server.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with SAP HANA 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("saphana:///?User=system&Password=mypassword&Server=localhost&Database=systemdb")

Declare a Mapping Class for SAP HANA 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 Buckets 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 Buckets(base): __tablename__ = "Buckets" Name = Column(String,primary_key=True) OwnerId = Column(String) ...

Query SAP HANA 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("saphana:///?User=system&Password=mypassword&Server=localhost&Database=systemdb") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Buckets).filter_by(Name="TestBucket"): print("Name: ", instance.Name) print("OwnerId: ", instance.OwnerId) 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

Buckets_table = Buckets.metadata.tables["Buckets"] for instance in session.execute(Buckets_table.select().where(Buckets_table.c.Name == "TestBucket")): print("Name: ", instance.Name) print("OwnerId: ", instance.OwnerId) print("---------")

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

Insert SAP HANA Data

To insert SAP HANA 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 SAP HANA.

new_rec = Buckets(Name="placeholder", Name="TestBucket") session.add(new_rec) session.commit()

Update SAP HANA Data

To update SAP HANA 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 SAP HANA.

updated_rec = session.query(Buckets).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Name = "TestBucket" session.commit()

Delete SAP HANA Data

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

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