Hadoop Comprehensive Tutorial
Introduction to Hadoop
Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Hadoop Architecture
The Hadoop framework consists of the following modules:
- Hadoop Common: The common utilities that support the other Hadoop modules.
- Hadoop Distributed File System (HDFS): A distributed file system that provides high-throughput access to application data.
- Hadoop YARN: A framework for job scheduling and cluster resource management.
- Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.
Setting Up Hadoop
Follow these steps to set up Hadoop on a single node (pseudo-distributed mode):
Step 1: Download Hadoop
Step 2: Extract the tar file
Step 3: Move to the Hadoop directory
HDFS Commands
Here are some basic HDFS commands:
Create a directory:
List files in a directory:
Upload a file to HDFS:
Download a file from HDFS:
Running a MapReduce Job
To run a MapReduce job, you need to write a Mapper and a Reducer. Here is a simple example:
Mapper Code (WordCountMapper.java):
Reducer Code (WordCountReducer.java):
Compile and Run the Job:
Hadoop and Elasticsearch Integration
Elasticsearch can be integrated with Hadoop to allow for the storage and analysis of large data sets. You can use the elasticsearch-hadoop
connector for this purpose.
Step 1: Download the Elasticsearch-Hadoop connector:
Step 2: Extract the zip file:
Step 3: Add the connector to your Hadoop classpath:
Step 4: Configure the Elasticsearch output in your job:
Conclusion
Hadoop is a powerful tool for managing and processing large data sets. By integrating it with Elasticsearch, you can take advantage of both Hadoop's processing capabilities and Elasticsearch's powerful search and analytics features.