What is MapReduce interview questions?
Hadoop MapReduce Interview Questions and Answers: Objective
- What is Hadoop MapReduce?
- What is the need of MapReduce?
- What is Mapper in Hadoop MapReduce?
- In MapReduce, ideally how many mappers should be configured on a slave?
- How to set the number of mappers to be created in MapReduce?
How do you explain MapReduce?
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 two types of tasks in MapReduce?
MapReduce is a processing technique and a program model for distributed computing based on java. The MapReduce algorithm contains two important tasks, namely Map and Reduce.
What are the key features of MapReduce system?
Features of MapReduce
- Scalability. Apache Hadoop is a highly scalable framework.
- Flexibility. MapReduce programming enables companies to access new sources of data.
- Security and Authentication.
- Cost-effective solution.
- Fast.
- Simple model of programming.
- Parallel Programming.
- Availability and resilient nature.
How MapReduce jobs can be optimized?
Below are some MapReduce job optimization techniques that would help you in optimizing MapReduce job performance.
- Proper configuration of your cluster.
- LZO compression usage.
- Proper tuning of the number of MapReduce tasks.
- Combiner between Mapper and Reducer.
- Usage of most appropriate and compact writable type for data.
What are supported programming languages for MapReduce question?
Yes, MapReduce can be written in many programming languages Java, R, C++, scripting languages (Python, PHP).
What is MapReduce Geeksforgeeks?
MapReduce is a programming model used for efficient processing in parallel over large data-sets in a distributed manner. The data is first split and then combined to produce the final result. The libraries for MapReduce is written in so many programming languages with various different-different optimizations.
What is MapReduce paradigm?
The MapReduce paradigm was created in 2003 to enable processing of large data sets in a massively parallel manner. The goal of the MapReduce model is to simplify the approach to transformation and analysis of large datasets, as well as to allow developers to focus on algorithms instead of data management.
What is MapReduce used for?
MapReduce serves two essential functions: it filters and parcels out work to various nodes within the cluster or map, a function sometimes referred to as the mapper, and it organizes and reduces the results from each node into a cohesive answer to a query, referred to as the reducer.
What are MapReduce jobs?
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
Why is MapReduce useful?
MapReduce’s biggest advantage is that data processing is easy to scale over multiple computer nodes. The primitive processing of the data is called mappers and reducers under the MapReduce model. It is sometimes nontrivial to break down an application for data processing into mappers and reducers.
What are the benefits of MapReduce?
The advantages of MapReduce programming are,
- Scalability. Hadoop is a platform that is highly scalable.
- Cost-effective solution.
- Flexibility.
- Fast.
- Security and Authentication.
- Parallel processing.
- Availability and resilient nature.
- Simple model of programming.