Hadoop Batch Processing Simplified 101

Today’s data volumes are frequently too large for a single server – or node – to handle. As a result, it was necessary to create code that could execute on several nodes. People created various frameworks to simplify our lives while writing distributed systems since there is a limitless array of challenges. 

In today’s Hadoop batch processing ecosystem, developers analyze terabytes and petabytes of data. Many projects are counting on this breakthrough to speed up processing. All these depend on Hadoop batch processing and stream processing. This article looks at Hadoop batch processing, particularly in the Hadoop ecosystem. 

Table of Contents

• What is Hadoop Batch processing?
• What is MapReduce?
• How is MapReduce Executed?
  • Map Phase
  • Reduce Phase
• Conclusion

What is Hadoop Batch processing?

Hadoop Batch processing refers to processing data blocks that one keeps for an extended time. For instance, imagine processing a big financial firm’s transactions for a single week’s data comprising millions of daily records. The firm may save the data as a file, record, or another type of data. 

 Hadoop batch processing is an automated activity that regularly performs calculations and executes the processing code for a batch of inputs. The task will often read batch data from a database and save the output in the same or another database. With the help of Hadoop batch processing, the firm will process this file at the end of the day for different analyses the company wishes to do.

Another example of a Hadoop batch processing operation is scanning all of an online shop’s sale records for a single day and aggregating them into statistics (number of users per country, the average spent amount, etc.). It’s possible that doing this daily will provide insight into client trends.

What is MapReduce?

Google presented MapReduce as a programming approach in a white paper in 2004. Most large data Hadoop batch processing systems use it as a core building piece. Hadoop batch processing uses MapReduce programming for extensive data analysis. Within a distributed cluster of machines, it generally follows the divide-and-conquer strategy:

MapReduce must be a distributed paradigm that executes its code across numerous nodes to compute enormous volumes of data. By adding more computers, the computation can process more significant quantities of data – this is known as horizontal scaling. On the other hand, vertical scaling refers to improving the performance of a single machine.

How is MapReduce Executed?

MapReduce seeks to eliminate shuffling (moving) data from one node to another by spreading the computation, eventually on the same node where the data is stored, to shorten the time of our distributed calculation. 

To conduct a MapReduce job, the user must implement two functions, map and reduce, and the MapReduce framework distributes those implemented functions to nodes that store the data. The data remains on the same node, but the code transfers via the network. Because the code is considerably smaller than the data, this is great.

Each node performs (executes) the supplied tasks on the data it possesses (shuffling data) to reduce network traffic. A mapping process runs in a distributed/parallel method for each data piece. Each process performs the same transformation or filtering function on the partial input data, which generates a portion of the output data.

One or more operations are in charge of receiving the mappers’ output and running the same aggregation or joining function, known as reduction, in each process. Hadoop batch processing calculates the number of times the reduced process takes place.

MapReduce’s compute efficiency comes at the expense of its expressivity. When constructing a MapReduce job, we must adhere to the map and reduce functions’ familiar interface (return and input data structure). 

The map phase creates key-value data pairs from the input data (partitions), which are subsequently aggregated by key and used by the reduction job in the reduce phase. The user may program everything except the interface of the functionalities.

Map Phase

Hadoop Batch processing was the first open-source implementation of MapReduce, among its many other capabilities. Hadoop Batch Processing also contains HDFS, which is a distributed file system. A directory in HDFS (Hadoop distributed file system) is a standard input to a MapReduce job in Hadoop. 

A partition divides each guide into smaller pieces to enhance parallelization, and each division can be handled individually by a mapping job (the process that executes the map function). Although the user is unaware of this, it is crucial to know it since the number of partitions might affect execution performance.

The map task (mapper) is called just once for each input partition, and its role is to extract key-value pairs from it. The mapper may create any number of key-value pairs from a single input. The only thing required from the user is to define the code within the mapper.

The mapping function determines the key based on the application logic; it might be a constant value or a tag describing the type of altered data. When the transformation or filtering operation ends on the input line, it generates another key-value pair. 

As you might expect, the value is the converted or filtered value. It is crucial to determine the output key accurately since it delivers reducer lists of key-value pairs with the same key.

Reduce Phase 

The MapReduce framework gathers all of the key-value pairs created by the mappers, organizes them by key, and runs the reduction function. The framework sorts all of the grouped data that enter the reducers. 

The reducer can provide output files that can be used as input for another MapReduce job, allowing many MapReduce processes to be chained together to create a more complicated data processing pipeline. To establish a distributed word counting pipeline, call the reducer on all values with the same key (word). 

The mapper produced key-value pairs with the word as the key and the number 1 as the value, which occurs because the user must provide the number of reducers, which in our instance is three. When a reducer completes its action, it must move on to the next group, which is not processed.

Each reducer receives a list of key-value pairs generated by the mappers, with all pairs having the same key. Other reducers iterate the list to perform a particular aggregate or joining operation, returning another key-value pair. 

The value relates to the computed total, whereas the application logic determines the key. Hadoop batch processing system determines the number of reducers based on numerous criteria and variables.

A good rule of thumb for calculating the appropriate number of reducers using Hadoop Batch Processing is to strive for reducers that operate for around five minutes and create a big output block. Too many reducers in Hadoop Batch Processing may result in a large number of inefficiently-sized files.

Conclusion

In this article, you learned about Hadoop, its features, Map Reduce and Hadoop batch Processing. You also read how MapReduce is executed with the help of Hadoop Batch Processing. Hadoop batch processing with the Map-reduce function limits the user to follow a logic format. The logic focuses on key-value pairs using the Map function and then summarizes using Reduce function. The system converts the user’s code into one or more MapReduce tasks, eliminating the need for the user to write the actual map and reduce routines.

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