How to Build an ETL App for CockroachDB 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 CockroachDB and the petl framework, you can build CockroachDB-connected applications and pipelines for extracting, transforming, and loading CockroachDB data. This article shows how to connect to CockroachDB with the CData Python Connector and use petl and pandas to extract, transform, and load CockroachDB data.

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

Connecting to CockroachDB Data

Connecting to CockroachDB 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.

Set the following to connect to CockroachDB.

• Server: The host name or IP address of the server.
• Port: The port number of the CockroachDB server. If not specified, the default port is 26257.
• Database: The name of the Cockroach database. If not specified, you connect to the user's default database.
• User: The Cockroach DB user account used to authenticate.
• Password: The password used to authenticate the user.

After installing the CData CockroachDB Connector, follow the procedure below to install the other required modules and start accessing CockroachDB 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 CockroachDB 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.cockroachdb as mod

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

cnxn = mod.connect("User=root;Password=root;Database=system;Server=localhost;Port=26257")

Create a SQL Statement to Query CockroachDB

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

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

Extract, Transform, and Load the CockroachDB Data

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

Loading CockroachDB 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 CockroachDB

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

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

With the CData Python Connector for CockroachDB, you can work with CockroachDB 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 CockroachDB to start building Python apps and scripts with connectivity to CockroachDB data. Reach out to our Support Team if you have any questions.

Full Source Code

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

cnxn = mod.connect("User=root;Password=root;Database=system;Server=localhost;Port=26257")

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')