Use SQLAlchemy ORMs to Access SFTP Data in Python

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SFTP Python Connector

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



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

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

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

Connecting to SFTP Data

Connecting to SFTP 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.

SFTP can be used to transfer files to and from SFTP servers using the SFTP Protocol. To connect, specify the RemoteHost;. service uses the User and Password and public key authentication (SSHClientCert). Choose an SSHAuthMode and specify connection values based on your selection.

Set the following connection properties to control the relational view of the file system:

  • RemotePath: Set this to the current working directory.
  • TableDepth: Set this to control the depth of subfolders to report as views.
  • FileRetrievalDepth: Set this to retrieve files recursively and list them in the Root table.
Stored Procedures are available to download files, upload files, and send protocol commands. See gdatamodel for more on using SQL to interact with the server.

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

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

engine = create_engine("sftp:///?RemoteHost=MyFTPServer")

Declare a Mapping Class for SFTP 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 MyDirectory 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 MyDirectory(base):
	__tablename__ = "MyDirectory"
	Filesize = Column(String,primary_key=True)
	Filename = Column(String)
	...

Query SFTP 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("sftp:///?RemoteHost=MyFTPServer")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(MyDirectory).filter_by(FilePath="/documents/doc.txt"):
	print("Filesize: ", instance.Filesize)
	print("Filename: ", instance.Filename)
	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

MyDirectory_table = MyDirectory.metadata.tables["MyDirectory"]
for instance in session.execute(MyDirectory_table.select().where(MyDirectory_table.c.FilePath == "/documents/doc.txt")):
	print("Filesize: ", instance.Filesize)
	print("Filename: ", instance.Filename)
	print("---------")

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

Insert SFTP Data

To insert SFTP 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 SFTP.

new_rec = MyDirectory(Filesize="placeholder", FilePath="/documents/doc.txt")
session.add(new_rec)
session.commit()

Update SFTP Data

To update SFTP 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 SFTP.

updated_rec = session.query(MyDirectory).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.FilePath = "/documents/doc.txt"
session.commit()

Delete SFTP Data

To delete SFTP 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(MyDirectory).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

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