Hadoop has emerged as a pivotal technology for processing and analysing vast amounts of data efficiently. While Hadoop is traditionally associated with Java, Python has gained popularity as a versatile language for working with it due to its simplicity and flexibility. In this article, we’ll explore how to use it in Python, step by step, to harness its capabilities for handling large-scale data processing tasks.

In the era of big data, the ability to efficiently process and analyze vast amounts of information is paramount. It has emerged as a cornerstone technology in this domain, offering a scalable and distributed framework for handling large-scale data processing tasks. While traditionally associated with Java, the integration of Python with it has opened up new avenues for developers, allowing them to leverage Python’s simplicity and versatility for working with Hadoop.

This article aims to provide a comprehensive guide on how to use it in Python, offering a step-by-step approach for beginners and seasoned developers alike. By harnessing the power of Python’s extensive libraries for data manipulation and analysis, users can tap into it’s distributed computing capabilities to tackle complex data processing challenges.

The first section of this article will focus on connecting to the Hadoop Distributed File System (HDFS) using Python, highlighting the necessary steps to establish a connection and perform basic file operations. Subsequently, the article will delve into running Python scripts in it, demonstrating how Hadoop Streaming can be utilised to execute Python code for MapReduce tasks.

Can I use Python for Hadoop?

Yes, you can use Python with it. While Java is the primary language for it, Python has gained traction as an alternative due to its ease of use and extensive libraries for data analysis and manipulation. Using Python with it allows developers to leverage the power of  it’s distributed computing framework while writing code in a language they are comfortable with.

How to connect to HDFS using Python?

Before diving into the details of using it in Python, let’s first understand how to connect to the Hadoop Distributed File System (HDFS), which is it’s primary storage system.

Step 1: Install Required Libraries

To interact with HDFS from Python, you’ll need to install the pyarrow library, which provides an interface for working with it’s file systems.

pip install pyarrow

Step 2: Import Required Modules

Once “pyarrow” is installed, import the necessary modules in your Python script:

import pyarrow.fs as fs

Step 3: Connect to HDFS
Now, you can establish a connection to HDFS using the HadoopFileSystem class provided by pyarrow:


hdfs = fs.HadoopFileSystem(host=’your-hadoop-host’, port=8020, user=’your-username’)

Replace ‘your-hadoop-host’ with the hostname of your Hadoop cluster, and ‘your-username’ with your username.

Step 4: Interact with HDFS

Once connected, you can perform various operations on HDFS such as reading files, writing files, creating directories, etc. For example, to list the contents of a directory in HDFS:


file_list = hdfs.ls(‘/path/to/directory’)


How do I run a Python file in Hadoop?

Running Python code in it typically involves using Hadoop Streaming, which allows you to use any executable as a mapper or reducer. Here’s a step-by-step process to run a Python file in it using Hadoop Streaming:

Step 1: Prepare Your Python Script

Write your Python script, making sure it reads input from stdin and writes output to stdout. Here’s a simple example of a Python script that counts the occurrences of words:


#!/usr/bin/env python

import sys

# Initialise a dictionary to store word counts

word_counts = {}

# Read input from stdin and count words

for line in sys.stdin:

    for word in line.strip().split():

        word_counts[word] = word_counts.get(word, 0) + 1

# Output word counts to stdout

for word, count in word_counts.items():


Save this script as word_count.py on your local machine.

Step 2: Upload Input Data to HDFS

Upload your input data to HDFS using the hdfs command-line tool or any other method.


hdfs dfs -put input.txt /path/to/input.txt

Step 3: Run Hadoop Streaming Job

Run a Hadoop Streaming job, specifying your Python script as the mapper and reducer (if needed), and providing input and output paths:


hadoop jar hadoop-streaming.jar \

    -input /path/to/input.txt \

    -output /path/to/output \

    -mapper word_count.py \

    -reducer aggregate \

    -file word_count.py

Is Hadoop similar to Python?

Hadoop and Python serve different purposes in the realm of data processing. It is a distributed computing framework designed for processing and analysing large datasets across clusters of computers. Python, on the other hand, is a high-level programming language known for its simplicity and versatility, commonly used for data analysis, machine learning, and web development.

While Python can be used to interact with it and write MapReduce jobs, they are fundamentally different technologies. Python provides a convenient interface for working with it, but it does not replace Hadoop itself.


Q: Can I use Python for data processing tasks in it?
A: Yes, Python can be used for data processing tasks in it, either by writing MapReduce jobs using it Streaming or using higher-level libraries like PySpark.

Q: What are the advantages of using Python with it?
A: Using Python with it offers several advantages, including its simplicity, extensive libraries for data analysis, and a large community of developers.

Q: Is Python the only language that can be used with Hadoop?
A: No, while Python is commonly used with it, you can also use other languages such as Java, Scala, or R for developing applications and running jobs in it.

In conclusion, Python provides a convenient and powerful way to interact with it for data processing tasks. By following the step-by-step process outlined in this article, you can leverage the capabilities of it while writing code in Python, opening up a world of possibilities for handling large-scale data processing tasks efficiently.


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