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Use SQLAlchemy ORMs to Access HPCC Systems Data in Python

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

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

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

Connecting to HPCC Systems Data

Connecting to HPCC Systems 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.

To connect, set the following connection properties: Set URL to the machine name or IP address of the server and the port the server is running on, for example, https://server:port. The User and Password are required to authenticate to the HPCC Systems cluster specified in the URL. Note that LDAP authentication is not currently supported by our ODBC driver.

Set Version to the WsSQL Web server version. Note that if you have not already done so, you will need to install the WsSQL service on the HPCC Systems server. The WsSQL Web service is used to interact with the underlying HPCC Systems platform.

Set Cluster to the target cluster.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with HPCC Systems data.

engine = create_engine("hpcc///?URL=http://127.0.0.1:8510&User=test&password=xA123456&Version=1&Cluster=hthor")

Declare a Mapping Class for HPCC Systems 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 hpcc::test::orders 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 hpcc::test::orders(base):
	__tablename__ = "hpcc::test::orders"
	CustomerName = Column(String,primary_key=True)
	Price = Column(String)
	...

Query HPCC Systems 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("hpcc///?URL=http://127.0.0.1:8510&User=test&password=xA123456&Version=1&Cluster=hthor")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(hpcc::test::orders).filter_by(ShipCity="New York"):
	print("CustomerName: ", instance.CustomerName)
	print("Price: ", instance.Price)
	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

hpcc::test::orders_table = hpcc::test::orders.metadata.tables["hpcc::test::orders"]
for instance in session.execute(hpcc::test::orders_table.select().where(hpcc::test::orders_table.c.ShipCity == "New York")):
	print("CustomerName: ", instance.CustomerName)
	print("Price: ", instance.Price)
	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 HPCC Systems Python Connector to start building Python apps and scripts with connectivity to HPCC Systems data. Reach out to our Support Team if you have any questions.