Is it possible to provide multiple inputs to Hadoop? If yes, explain.

Yes, it is possible to provide multiple inputs to Hadoop. In Hadoop, the MapReduce programming model allows the processing of large datasets by breaking them into smaller chunks and processing them in parallel across a distributed cluster.

When you submit a MapReduce job, you can specify multiple input paths, and Hadoop will process data from all of these paths in parallel. Each input path can represent a different set of data or directories containing data that needs to be processed.

Here’s a brief explanation of how it works:

1. InputFormat and Multiple Input Paths:
   • Hadoop uses InputFormat to read data from the input sources. InputFormat is responsible for dividing the input data into splits that are processed by individual map tasks.
   • Multiple input paths can be specified in a Hadoop job. These paths can represent files or directories on the Hadoop Distributed File System (HDFS) or other supported file systems.
2. TextInputFormat Example:
   • For instance, if you’re using TextInputFormat, you can specify multiple input paths when configuring your job:
   FileInputFormat.addInputPaths(job, "inputPath1, inputPath2");


   This allows the MapReduce job to process data from both inputPath1 and inputPath2 in parallel.

3. Map Task for Each Input Split:
   • When the job is executed, Hadoop creates a map task for each input split, and these map tasks run in parallel across the cluster.
4. Parallel Processing:
   • Each map task processes a portion of the input data, and the results are then combined to produce the final output.

In summary, by specifying multiple input paths when configuring a Hadoop job, you enable the processing of multiple datasets or portions of data in parallel, which is a key feature of Hadoop’s distributed processing capabilities.