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MapReduce has two phases. The first phase being the map phase and the second phase being the reduce phase. The map phase includes the counting of words or sorting out data. The second phase that is reduced as the name suggests includes reducing the data and aggregating them. Hence, the data first gets divided for analysis.
The data used in MapReduce work is generally massive and huge data. The file or the data consists of two factors, one is the key and the other is value. If five files are taken as an example, the data of some of the files must be repetitive. In this way, the MapReduce framework sorts the data in a particular format and sends it to the reducer for the aggregate calculation.
Following are the main components of MapReduce:
Partitioner works with the hash function which helps in controlling the partitioning of the various output data of MapReduce. This process also helps in providing the input data to the reducer. The total number of partitioners is actually equal to the total number of reducers.
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Shuffling is part of the first phase of the MapReduce framework. The mapper and reduces are the two main components of this programming model. The process of transferring the output data from the mapper to providing input data to the reducer is known as shuffling. The data which is transferred through the shuffling process is already sorted for the reducing phase. Hence, that data acts as the input to the reducers.
Chain Mapper is basically dividing the single mapping task across different mapper classes. Hence, the single data and information get sorted in this way through a series of chain operations. The data can be out through any number of mappers. For example, the data gets sorted by the first mapper and then this output becomes the input for the second mapper where it gets sorted again. This continues till it reaches the last mapper whose output becomes the input for the reducer.
Identity Mapper is a default mapper. Here, there are no specifically defined mapper calls and hence the data is just put through one single class of mapper and gets sorted in this way.
The first one consists of Input data and the latter one consists of intermediate output data.
The first one consists of Intermediate Output data and the latter one consists of Final Output Data.
The mapper class consists of mapper nodes. The mapper output values are stored in these mapper nodes. They are basically kept in the local file system.
The user has to most definitely specify the following types of parameters:
Different reducers can't communicate with each other. They work in isolation.
The text input format is just the default format for text files or the input data. The files are broken within the text input format. The line of the text refers to the value and the key is referred to the position. These two are the main components of data files.
Input Format is a type of MapReduce programming feature. This feature helps in specifying the different job requirements. It has the following function:
Both Input Split and HDFS Block divide the data into various files. However, the first one splits the data in a logical manner while the latter one makes a physical division of the data. Input Split controls the split size, the number of mappers but the HDFS Block is fixed that is 64 MB for 1 GB data.
Splitting such a huge data worth of input I'd possible in the form of a single split is only possible by using the Class NLine Input Format.
All the local tasks of reducing the local data files are done with the help of a combiner. This mainly works on the Map Output. Just like a reducer, it also produces the output for the reducer's input. Combiner has other uses too like it is often used for the job of network optimization especially when the outputs increase in numbers by the map generator. Combiner also varies from the reducer in many ways like for example, a reducer is limited but however, a combiner has limitations like the input data or the output data, and the values must be similar to the output data of the mapper. A combiner can also work on the commutative function like for example; it can operate on subsets of the values and keys of the data. A combiner can get its input from only one single mapper whereas; a reducer gets its input from several numbers of mappers.
The efficiency of MapReduce is increased by using a combiner. It helps in aggregating the data locally and hence helps in reducing the huge bulk of data while transferring them to the reducers. The combiner uses the reducer code when the function is commutative.
A job tracker is the best way to track the submitted output data. It has the following functions -
The signal which is used in HDFS is known as the heartbeat. This signal is mainly passed between two types of nodes namely data nodes and name nodes. This occurs between the job tracker and the task tracker. It is considered to have a poor heartbeat if the signal doesn't work properly and if some issues arise with the two nodes or the trackers.
Sharing files and data is mainly done by the distributed cache. This is considered to be an important feature as it allows the distribution of files across all the nodes is it data nodes or name nodes.
The following are the consequences of the failure of a data node -
Speculative execution is a type of feature that allows the launch of several tasks on different kinds of nodes. Sometimes, even some multiple copies as also made by the speculative execution. Generally, duplicate copies of the task are actually created using the feature if one task takes a long time to get completed.
The interaction of the mapper with the rest of the system is done with the help of Context Object. It also consists of data configuration.
Sequence file input format is a type of feature of MapReduce which allows the data to be transferred from the various mapping classes to the reducing class.
The files in the MapReduce job can be searched with the help of wildcards.
The Input type in MapReduce is Text.
The output file can be renamed with the help of the implementation of the various format output classes.
The system in which the execution of the business logic is performed is known as the compute node. On the other hand, the storage node is the system in which the entire file system containing numerous kinds of data is stored which is later used for processing.
Stragglers are referred to the process of MapReduce during which the task takes a long time to get completed.
Identity Mapper refers to the default mapper class and identity reducer refers to the default reducer class. When the number of the mapper is not defined during the work process, it is known as the identity mapper. When the reducer class is undefined, it is known as an identity reducer. Hence, this class transfers the key values to the output directory.
PIG is basically the data flow language that manages the data flow from one source to another. It also manages the data storage system and also helps in compressing them. Pig rearranges the steps for faster and better processing. The output data of the MapReduce job is basically managed by PIG. Some functions of MapReduce processing are also added in the processing of PIG. The functions include grouping, ordering, and counting data.
MapReduce is basically the framework for writing code for the developers. This is a data processing paradigm that separated the application of two types of developers, one who writes it and another who scales it.
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