Table of Contents
Can a project have multiple design patterns?
1) Can I have multiple design patterns in the application? Yes.
What are the basic MapReduce patterns?
This article discusses four primary MapReduce design patterns:
- Input-Map-Reduce-Output.
- Input-Map-Output.
- Input-Multiple Maps-Reduce-Output 4. Input-Map-Combiner-Reduce-Output.
What is the output of 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 does every mapper output in MapReduce?
The Mapper processes the input, which are, the (key, value) pairs and provides an output, which are also (key, value) pairs. The output from the Mapper is called the intermediate output. The Mapper may use or completely ignore the input key. For example, a standard pattern is to read a file one line at a time.
Which pattern prevents one from creating more than one instance of a variable?
singleton pattern
Which pattern prevents one from creating more than one instance of a variable? Explanation: In singleton pattern, the class itself is made responsible for keeping track of its instance. Thus it ensures that no more than one instance is created.
What is MapReduce system design?
MapReduce is a batch processing programming paradigm that enables massive scalability across a large number of servers in a Hadoop cluster. It was published in 2004 and was called “the algorithm that makes Google so massively scalable.”
What are MapReduce output formats?
MapReduce default Hadoop reducer Output Format is TextOutputFormat, which writes (key, value) pairs on individual lines of text files and its keys and values can be of any type since TextOutputFormat turns them to string by calling toString() on them.
Which of the following is used to provide multiple outputs to Hadoop?
MultipleOutputs class provide facility to write Hadoop map/reducer output to more than one folders. Basically, we can use MultipleOutputs when we want to write outputs other than map reduce job default output and write map reduce job output to different files provided by a user.
Can you get multiple key-value pairs from the map phase?
Yes, you can do this.
How do you know which design pattern to use?
To use design patterns effectively you need to know the context in which each one works best. This context is : Participants — Classes involved. Quality attributes — usability, modifiability, reliability, performance.
Which design pattern suggest multiple classes through which request is passed?
Chain of responsibility pattern
Which design pattern suggests multiple classes through which request is passed and multiple but only relevant classes carry out operations on the request? Explanation: Chain of responsibility pattern creates a chain of receiver objects for a particular request.
Which design pattern ensures that only one object of particular class gets created?
Singleton Design Pattern
The Singleton Design Pattern is a Creational pattern, whose objective is to create only one instance of a class and to provide only one global access point to that object.
What is MapReduce and explain its inputs and outputs of MapReduce?
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.
How MapReduce works explain with example?
MapReduce is a programming framework that allows us to perform distributed and parallel processing on large data sets in a distributed environment. MapReduce consists of two distinct tasks – Map and Reduce. As the name MapReduce suggests, the reducer phase takes place after the mapper phase has been completed.
What is MapReduce and explain its inputs and outputs?
What is MapReduce design pattern?
MapReduce Design Pattern • MapReduce is a framework – Fit your solution into the framework of map and reduce – Can be challenging in some situations • Need to take the algorithm and break it into filter/aggregate steps – Filter becomes part of the map function – Aggregate becomes part of the reduce function
What is the input-multiple maps-reduce-output design pattern?
In the Input-Multiple Maps-Reduce-Output design pattern, our input is taken from two files, each of which has a different schema. (Note that if two or more files have the same schema, then there is no need for two mappers. We can simply write the same logic in one mapper class and provide multiple input files.)
What is the map phase and reduce phase pattern?
In this pattern, a task is divided into two phases: map phase and reduce phase. During the map phase, the source collection is mapped to an intermediate collection and during the following reduce phase, the intermediate collection is grouped by some criterion and each group is reduced to some aggregate result.
Which design pattern is used to change the format of data?
However, if we only want to change the format of the data, then the Input-Map-Output pattern is used: In the Input-Multiple Maps-Reduce-Output design pattern, our input is taken from two files, each of which has a different schema. (Note that if two or more files have the same schema, then there is no need for two mappers.