MapReduce Formats and Types in Java: In-Depth-Guide
MapReduce can be termed as an easy-to-understand model for data processing. This includes the inputs & outputs for its Map & Reduce functions which serve as the prime-value pairs. In this guide, we will take an in-depth look at the model for MapReduce in detail.
We will pay special attention to the way in which data of various formats starting from the simple texts all the way to the structured forms of binary objects shall be used in context with the model.
What is MapReduce?
MapReduce is the form of data processing methodology that is used for processing data in a parallel mannerism with distributed structure. This tool was developed back in 2004 based upon the paper with title “MapReduce: Simplified Data Processing on Large Clusters” which was published by the popular search engine, Google.
It is the paradigm that houses two phases, named The Reducer Phase and The Mapper Phase. In this variant of Mapper, input is catered in the version of the key-value pairs. The output for the Mapper can be fed to Reducer in the form of input. Moreover, the Reducer also takes the input as key-value format along with the reducer output for final output.
Steps for MapReduce
- The map acquires data in a variant of returns and pairs in the list of the pairs < key, value > . In this particular case, the keys won’t be unique.
- The use of the Map output requires shuffling and sorting of its Hadoop architecture. This shuffle and sorting process acts upon the list of the associated values enlisting the unique key which is < key, list (values) >.
- Finally, an output for the shuffle and sort process is sent to reduce phase. This reducer tends to perform the defined function depending upon the overall values for the unique keys as well as the final output deemed as < key, value > shall be displayed/stored.
MapReduce Types
The MapReduce functions used in Java application development services context come in the given form:
Map: (K1, V1) list (K2, V2)
Reduce: (K2, list (V2)) list (K3, V3)
The general protocol for the input key of the map along with value types tends to be different when compared to the map’s output types. Not just that, the input protocol for the reduce needs to be same as the output for the map. However, the Reduce for output types needs to be different.
The interface for Java mirrors this particular form as:
public interface Mapper < K1, V1, K2, V2 > extends JobConfigurable, Closeable
{
void map(K1 key, V1 value, OutputCollector < K2, V2 > output, Reporter reporter)
throws IOException;
}
public interface Reducer < K2, V2, K3, V3 > extends JobConfigurable, Closeable
{
void reduce(K2 key, Iterator < V2 > values,
OutputCollector < K3, V3 > output, Reporter reporter) throws IOException;
}
Sort & Shuffle
The Sort & Shuffle method occurs over the Mapper output, and it is done before the reducer. Upon completion of this Mapper task, the results can be sorted with use of key which is partitioned given the presence of multiple reducers. Finally, it is written onto the disk. With use of the input obtained from every Mapper ( < K2, V2 > ), all relevant values are collected for every unique K2 key. Later, this output received from the Shuffle phase written as < K2, list (V2) > is then sent as the input for the reducer phase.
Usecase for MapReduce
- The MapReduce is used for multiple applications such as documentation of clustering, weblink reversal of graph, and distributed sorting.
- It is also used for the search terrain implemented by a distributed pattern.
- It can also be used for Machine learning techniques.
- Initially, it was worked upon by Google for the regeneration of Google’s Index in the WWW or World Wide Web.
- MapReduce is also used for multiple variants of computing environments like multi-core, multi-cluster, as well as the mobile environment.
MapReduce Input Formats
Hadoop development company needs to accept as well as process multiple format variants from the text files all the way to the databases. A section of the input is known as input split, which is processed in the form of single map. Every single split is again divided into the relevant records which are written in the chart for processing of the key-value pairs.
In the database context, this split essentially means reading the range of the tuples from the SQL table. The input split for Java API is noted as follows:
public interface InputSplit extends Writable
{
long getLength() throws IOException;
String[ ] getLocations() throws IOException;
}
In essence, the InputSplit tends to represent the set of data that needs to be processed with use of the Mapper. Following this, it returns length in the bytes form. It has a reference to input data. Moreover, it presents the byte-oriented Mapper view for the input. It is processed by the RecordReader and presented as the record-oriented view. Most cases do not require dealing with the InputSplit in a direct manner as they are mostly created with use of the InputFormat. This leads to creation of input splits & division of the same in accordance with the records.
public interface InputFormat < K, V >
{
InputSplit[ ] getSplits(JobConf job, int numSplits) throws
IOException;
RecordReader < K, V > getRecordReader(InputSplit split,
JobConf job, throws IOException;
}
Output Formats
The classes for Output Format are mostly similar to the corresponding classes for input format & they mostly tend to function in a reverse direction.
An excellent example of the output format is the TextOutputFormat which serves as the default for output format and writes records in the form of plain text-type files. Let the key-values be of n variants; it helps transform them into the format of the string by invoking toString() methodology. Here, the key-value of the character is wholly separated from the regular tab character. This is true, even though it can easily be customized via manipulation of the separator in the text property output format.
For the binary output, you will come across the SequenceFileOutputFormat that helps write the sequence of the binary output onto a file. These binary outputs can be particularly useful for output that becomes the input for future MapReduce jobs.
Output Formats meant for the relational databases & for the HBase can be handled with DBOutputFormat. Next, it sends reduced output onto the SQL table. To understand this in a better way, here is an example. The HBase’s TableOutputFormat tends to enable MapReduce programs to function on data that is stored in HBase table. It is used for jotting down the written outputs for the table meant for HBase.
Conclusion
In short, the MapReduce formats and types come with a line of intricate details that help it function in a segregated manner with the use of Java APIs which become more evident when one starts defining the bases of the programming. Now, this particular program paradigm is functional but only in combination with use of techniques of MapReduce.
They are subjected to the parallel execution for datasets based on an extensive variant of machines that are opted in distributed architecture. When using the MapReduce, one needs to keep in mind that it works better when paired with the small account of larger files as opposed to accounting for large chunks of more secondary data. One apparent technique with MapReduce to avoid the small files talks about merging small files with the large ones by use of SequenceFile & values of the file contents.