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

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK 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 Connect AI and use petl to extract, transform, and load SingleStore data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to SingleStore in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "SingleStore" from the Add Connection panel

3. Enter the necessary authentication properties to connect to SingleStore.

   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.
   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for SingleStore Data in Python

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

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

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. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, SingleStore1).

sql = (
    "SELECT ShipName, ShipCity "
    "FROM [SingleStore1].[SingleStore].[Orders] "
    "WHERE ShipCountry = 'USA'"
)

Extract, Transform, and Load the SingleStore Data

With a connection and query in hand, 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.

table1 = etl.fromdb(conn, sql)

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

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

Load New Rows Back into SingleStore

When SingleStore supports writes, load rows back with a batch INSERT. The SDK's executemany takes @name placeholders and a list of parameter dictionaries, one per row.

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [SingleStore1].[SingleStore].[Orders] (ShipName, ShipCity) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")

conn.close()

Note: Even for writable sources, a read-only PAT or connection permission will reject write operations.

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

More Information and Free Trial

Now you can pipe live SingleStore data through petl using the CData Connect AI Python SDK. For more information on connecting to SingleStore (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live SingleStore data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT ShipName, ShipCity "
    "FROM [SingleStore1].[SingleStore].[Orders] "
    "WHERE ShipCountry = 'USA'"
)

table1 = etl.fromdb(conn, sql)

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

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

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [SingleStore1].[SingleStore].[Orders] (ShipName, ShipCity) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")
conn.close()