What are the configuration parameters in a MapReduce program?

What are the configuration parameters in a MapReduce program?

The MapReduce programmers need to specify the following configuration parameters to perform the map and reduce jobs: The output location of the job in HDFS. The input location of the job in HDFS….The four parameter for mappers are:

  • IntWritable(intermediate output)
  • Text(input)
  • Text(intermediate output)
  • LongWritable(input)

How do I configure MapReduce?

The following steps are used to verify the Hadoop installation.

  1. Step 1 − Name Node Setup.
  2. Step 2 − Verifying Hadoop dfs.
  3. Step 3 − Verifying Yarn Script.
  4. Step 4 − Accessing Hadoop on Browser.
  5. Step 5 − Verify all Applications of a Cluster.

What are the various configuration parameters required to run a MapReduce job?

The main configuration parameters which users need to specify in “MapReduce” framework are:

  • Job’s input locations in the distributed file system.
  • Job’s output location in the distributed file system.
  • Input format of data.
  • Output format of data.
  • Class containing the map function.
  • Class containing the reduce function.

What are the properties of Hadoop?

Let’s discuss the key features which make Hadoop more reliable to use, an industry favorite, and the most powerful Big Data tool.

  1. Open Source:
  2. Highly Scalable Cluster:
  3. Fault Tolerance is Available:
  4. High Availability is Provided:
  5. Cost-Effective:
  6. Hadoop Provide Flexibility:
  7. Easy to Use:
  8. Hadoop uses Data Locality:

What is MapReduce and how it works?

MapReduce facilitates concurrent processing by splitting petabytes of data into smaller chunks, and processing them in parallel on Hadoop commodity servers. In the end, it aggregates all the data from multiple servers to return a consolidated output back to the application.

What are properties and limitations of Hadoop?

Hadoop can efficiently perform over a small number of files of large size. Hadoop stores the file in the form of file blocks which are from 128MB in size(by default) to 256MB. Hadoop fails when it needs to access the small size file in a large amount.

What are the three phases in MapReduce?

MapReduce program executes in three stages, namely map stage, shuffle stage, and reduce stage. Map stage − The map or mapper’s job is to process the input data.

What are the advantages and disadvantages of MapReduce?

The primitive processing of the data is called mappers and reducers under the MapReduce model….Advantages of MapReduce

  • Scalability.
  • Flexibility.
  • Security and Authentication.
  • Cost-effective Solution.
  • Fast.
  • Simple Model of Programming.
  • Parallel Processing.
  • Availability and Resilient Nature.