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Use SQLAlchemy ORMs to Access FedEx Data in Python

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

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

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

Connecting to FedEx Data

Connecting to FedEx 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.

There are five pieces of information needed in order to authenticate its actions with the FedEx service. This information is below.

  • Server: This controls the URL where the requests should be sent. Common testing options for this are: "https://gatewaybeta.fedex.com:443/xml", "https://wsbeta.fedex.com:443/xml", "https://gatewaybeta.fedex.com:443/web-service", and "https://wsbeta.fedex.com:443/web-service"
  • DeveloperKey: This is the identifier part of the authentication key for the sender's identity. This value will be provided to you by FedEx after registration.
  • Password: This is the secret part of the authentication key for the sender's identity. This value will be provided to you by FedEx after registration.
  • AccountNumber: This valid 9-digit FedEx account number is used for logging into the FedEx server.
  • MeterNumber: This value is used for submitting requests to FedEx. This value will be provided to you by FedEx after registration.
  • PrintLabelLocation: This property is required if one intends to use the GenerateLabels or GenerateReturnLabels stored procedures. This should be set to the folder location where generated labels should be stored.

The Cache Database

Many of the useful tasks available from FedEx require a lot of data. To ensure this data is easy to input and recall later, utilizes a cache database to make these requests. You must set the cache connection properties:

  • CacheProvider: The specific database you are using to cache with. For example, org.sqlite.JDBC.
  • CacheConnection: The connection string to be passed to the cache provider. For example, jdbc:sqlite:C:\users\username\documents\fedexcache.db

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

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

engine = create_engine("fedex///?Server='https://gatewaybeta.fedex.com:443/xml'&DeveloperKey='alsdkfjpqoewiru'&Password='zxczxqqtyiuowkdlkn'&AccountNumber='110371337'&MeterNumber='240134349'&
PrintLabelLocation='C:\users\username\documents\mylabels'&CacheProvider='org.sqlite.JDBC'&CacheConnection='jdbc:sqlite:C:\users\username\documents\fedexcache.db'")

Declare a Mapping Class for FedEx 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 Senders 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 Senders(base):
	__tablename__ = "Senders"
	FirstName = Column(String,primary_key=True)
	Phone = Column(String)
	...

Query FedEx 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("fedex///?Server='https://gatewaybeta.fedex.com:443/xml'&DeveloperKey='alsdkfjpqoewiru'&Password='zxczxqqtyiuowkdlkn'&AccountNumber='110371337'&MeterNumber='240134349'&
PrintLabelLocation='C:\users\username\documents\mylabels'&CacheProvider='org.sqlite.JDBC'&CacheConnection='jdbc:sqlite:C:\users\username\documents\fedexcache.db'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Senders).filter_by(SenderID="ab26f704-5edf-4a9f-9e4c-25"):
	print("FirstName: ", instance.FirstName)
	print("Phone: ", instance.Phone)
	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

Senders_table = Senders.metadata.tables["Senders"]
for instance in session.execute(Senders_table.select().where(Senders_table.c.SenderID == "ab26f704-5edf-4a9f-9e4c-25")):
	print("FirstName: ", instance.FirstName)
	print("Phone: ", instance.Phone)
	print("---------")

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

Insert FedEx Data

To insert FedEx 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 FedEx.

new_rec = Senders(FirstName="placeholder", SenderID="ab26f704-5edf-4a9f-9e4c-25")
session.add(new_rec)
session.commit()

Update FedEx Data

To update FedEx 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 FedEx.

updated_rec = session.query(Senders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.SenderID = "ab26f704-5edf-4a9f-9e4c-25"
session.commit()

Delete FedEx Data

To delete FedEx 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 recoreds (rows).

deleted_rec = session.query(Senders).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 FedEx Python Connector to start building Python apps and scripts with connectivity to FedEx data. Reach out to our Support Team if you have any questions.