How to Build an ETL App for SingleStore Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for SingleStore and the petl framework, you can build SingleStore-connected applications and pipelines for extracting, transforming, and loading SingleStore data. This article shows how to connect to SingleStore with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData SingleStore Connector, follow the procedure below to install the other required modules and start accessing SingleStore through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for SingleStore Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.singlestore as mod

You can now connect with a connection string. Use the connect function for the CData SingleStore Connector to create a connection for working with SingleStore data.

cnxn = mod.connect("User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=3306;")

Create a SQL Statement to Query SingleStore

Use SQL to create a statement for querying SingleStore. In this article, we read data from the Orders entity.

sql = "SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'"

Extract, Transform, and Load the SingleStore Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the SingleStore data. In this example, we extract SingleStore data, sort the data by the ShipCity column, and load the data into a CSV file.

Loading SingleStore Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ShipCity')

etl.tocsv(table2,'orders_data.csv')

In the following example, we add new rows to the Orders table.

Adding New Rows to SingleStore

table1 = [ ['ShipName','ShipCity'], ['NewShipName1','NewShipCity1'], ['NewShipName2','NewShipCity2'], ['NewShipName3','NewShipCity3'] ]

etl.appenddb(table1, cnxn, 'Orders')

With the CData Python Connector for SingleStore, you can work with SingleStore data just like you would with any database, including direct access to data in ETL packages like petl.

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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Full Source Code

import petl as etl
import pandas as pd
import cdata.singlestore as mod

cnxn = mod.connect("User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=3306;")

sql = "SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ShipCity')

etl.tocsv(table2,'orders_data.csv')

table3 = [ ['ShipName','ShipCity'], ['NewShipName1','NewShipCity1'], ['NewShipName2','NewShipCity2'], ['NewShipName3','NewShipCity3'] ]

etl.appenddb(table3, cnxn, 'Orders')