How to use SQLAlchemy ORM to access Reckon Data in Python

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

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

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

Connecting to Reckon Data

Connecting to Reckon 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.

When you are connecting to a local Reckon instance, you do not need to set any connection properties.

Requests to Reckon are made through the Remote Connector. The Remote Connector runs on the same machine as Reckon and accepts connections through a lightweight, embedded Web server. The server supports SSL/TLS, enabling users to connect securely from remote machines.

The first time you connect to your company file, you will need to authorize the Remote Connector with Reckon. See the "Getting Started" chapter of the help documentation for a guide.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Reckon 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("reckon:///?User=RCUser&Password=RCUserPassword&URL=http://remotehost:8166")

Declare a Mapping Class for Reckon 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 Customers 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 Customers(base):
	__tablename__ = "Customers"
	Name = Column(String,primary_key=True)
	CustomerBalance = Column(String)
	...

Query Reckon 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("reckon:///?User=RCUser&Password=RCUserPassword&URL=http://remotehost:8166")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Customers).filter_by(Type="Commercial"):
	print("Name: ", instance.Name)
	print("CustomerBalance: ", instance.CustomerBalance)
	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


Customers_table = Customers.metadata.tables["Customers"]
for instance in session.execute(Customers_table.select().where(Customers_table.c.Type == "Commercial")):
	print("Name: ", instance.Name)
	print("CustomerBalance: ", instance.CustomerBalance)
	print("---------")

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

Insert Reckon Data

To insert Reckon 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 Reckon.


new_rec = Customers(Name="placeholder", Type="Commercial")
session.add(new_rec)
session.commit()

Update Reckon Data

To update Reckon 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 Reckon.


updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Type = "Commercial"
session.commit()

Delete Reckon Data

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

Ready to get started?

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

Download Now

Learn more:

Reckon Python Connector

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