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How to use SQLAlchemy ORM to access SingleStore Data in Python


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

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

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

Connecting to SingleStore Data

Connecting to SingleStore 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.

The following connection properties are required in order to connect to data.

  • Server: The host name or IP of the server hosting the SingleStore database.
  • Port: The port of the server hosting the SingleStore database.
  • Database (Optional): The default database to connect to when connecting to the SingleStore Server. If this is not set, tables from all databases will be returned.

Connect Using Standard Authentication

To authenticate using standard authentication, set the following:

  • User: The user which will be used to authenticate with the SingleStore server.
  • Password: The password which will be used to authenticate with the SingleStore server.

Connect Using Integrated Security

As an alternative to providing the standard username and password, you can set IntegratedSecurity to True to authenticate trusted users to the server via Windows Authentication.

Connect Using SSL Authentication

You can leverage SSL authentication to connect to SingleStore data via a secure session. Configure the following connection properties to connect to data:

  • SSLClientCert: Set this to the name of the certificate store for the client certificate. Used in the case of 2-way SSL, where truststore and keystore are kept on both the client and server machines.
  • SSLClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
  • SSLClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
  • SSLClientCertType: The certificate type of the client store.
  • SSLServerCert: The certificate to be accepted from the server.

Connect Using SSH Authentication

Using SSH, you can securely login to a remote machine. To access SingleStore data via SSH, configure the following connection properties:

  • SSHClientCert: Set this to the name of the certificate store for the client certificate.
  • SSHClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
  • SSHClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
  • SSHClientCertType: The certificate type of the client store.
  • SSHPassword: The password that you use to authenticate with the SSH server.
  • SSHPort: The port used for SSH operations.
  • SSHServer: The SSH authentication server you are trying to authenticate against.
  • SSHServerFingerPrint: The SSH Server fingerprint used for verification of the host you are connecting to.
  • SSHUser: Set this to the username that you use to authenticate with the SSH server.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with SingleStore 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("singlestore:///?User=myUser&Password=myPassword&Database=NorthWind&Server=myServer&Port=3306")

Declare a Mapping Class for SingleStore 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 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 Orders(base): __tablename__ = "Orders" ShipName = Column(String,primary_key=True) ShipCity = Column(String) ...

Query SingleStore 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("singlestore:///?User=myUser&Password=myPassword&Database=NorthWind&Server=myServer&Port=3306") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Orders).filter_by(ShipCountry="USA"): print("ShipName: ", instance.ShipName) print("ShipCity: ", instance.ShipCity) 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

Orders_table = Orders.metadata.tables["Orders"] for instance in session.execute(Orders_table.select().where(Orders_table.c.ShipCountry == "USA")): print("ShipName: ", instance.ShipName) print("ShipCity: ", instance.ShipCity) print("---------")

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

Insert SingleStore Data

To insert SingleStore 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 SingleStore.

new_rec = Orders(ShipName="placeholder", ShipCountry="USA") session.add(new_rec) session.commit()

Update SingleStore Data

To update SingleStore 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 SingleStore.

updated_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.ShipCountry = "USA" session.commit()

Delete SingleStore Data

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

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