Use SQLAlchemy ORMs to Access FTP Data in Python

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FTP Python Connector

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

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

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

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

Connecting to FTP Data

Connecting to FTP 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 to FTP or SFTP servers, specify at least RemoteHost and FileProtocol. Specify the port with RemotePort.

Set User and Password to perform Basic authentication. Set SSHAuthMode to use SSH authentication. See the Getting Started section of the data provider help documentation for more information on authenticating via SSH.

Set SSLMode and SSLServerCert to secure connections with SSL.

The data provider lists the tables based on the available folders in your FTP server. 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 folders to list as views.
  • FileRetrievalDepth: Set this to retrieve and list files recursively from the root table.

Stored Procedures are available to download files, upload files, and send protocol commands. See the Data Model chapter of the FTP data provider documentation for more information.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with FTP 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("ftp:///?RemoteHost=MyFTPServer")

Declare a Mapping Class for FTP 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 FTP 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("ftp:///?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( == "/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 FTP Data

To insert FTP 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 FTP.

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

Update FTP Data

To update FTP 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 FTP.

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

Delete FTP Data

To delete FTP 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()

Free Trial & More Information

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