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

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

Connecting to CouchDB Data

Connecting to CouchDB 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:

• Url: The Url of your instance. For example: http://localhost:5984
• User The Apache CouchDB user account used to authenticate.
• Password The Apache CouchDB password associated with the authenticating user.

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

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

cnxn = mod.connect("Url=http://localhost:5984;User=abc123;Password=abcdef;")

Create a SQL Statement to Query CouchDB

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

sql = "SELECT MovieRuntime, MovieRating FROM Movies WHERE MovieRating = 'R'"

Extract, Transform, and Load the CouchDB Data

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

Loading CouchDB Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to CouchDB

table1 = [ ['MovieRuntime','MovieRating'], ['NewMovieRuntime1','NewMovieRating1'], ['NewMovieRuntime2','NewMovieRating2'], ['NewMovieRuntime3','NewMovieRating3'] ]

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

With the CData Python Connector for CouchDB, you can work with CouchDB 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 CouchDB to start building Python apps and scripts with connectivity to CouchDB 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.apachecouchdb as mod

cnxn = mod.connect("Url=http://localhost:5984;User=abc123;Password=abcdef;")

sql = "SELECT MovieRuntime, MovieRating FROM Movies WHERE MovieRating = 'R'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['MovieRuntime','MovieRating'], ['NewMovieRuntime1','NewMovieRating1'], ['NewMovieRuntime2','NewMovieRating2'], ['NewMovieRuntime3','NewMovieRating3'] ]

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