Due to their restricted capacity and speed, databases have been delayed for years while the processing power of application servers is growing. However, Hadoop is integral in bringing a much-needed makeover to the database world since many applications create enormous data that they must process.
In the technological age, Hadoop is running like a storm. It is believed that Hadoop is the latest Big Data. You will receive almost 28 million results when you Google Hadoop. The remarkable cloud expansion is one of the main reasons for the popularity of Hadoop.
Hadoop Developer is currently one of the many famous Hadoop roles. It’s the ultimate data management platform for enterprises of all sizes due to Hadoop’s distributed architecture, excellent scalability, high defect tolerance, vast processing capacity, and quick processing speed. As a result, Hadoop is adopted by major companies and small and medium-sized enterprises. This increased acceptance and demand for Hadoop services created a significant need for skilled Hadoop professionals in the sector. It’s the most crucial moment to register for the Hadoop course and enhance your Big Data career.
Do you want to know how Hadoop works internally, too? Then let’s first explore what Hadoop is and the key components and daemons before discussing the actual topic.
What is Hadoop?
Hadoop is a Java-based, open-source framework used for big data storage and processing. Hadoop, developed by Doug Cutting and Michael J. Cafarella, uses a paradigm of MapReduce to store and retrieve data from its nodes quickly. The data is stored as clusters on cheap commodity servers. The distributed file system allows simultaneous tolerance of malfunction and processing. Apache software foundation manages the framework and is licensed under the Apache License 2.0.
Now, let’s know Hadoop components –
Hadoop distributes large data sets across the commodity servers’ cluster and works simultaneously on numerous machines. The client sends information and programs to Hadoop for processing of any data. HDFS stores the data, MapReduce processes the data, and Yarn splits the tasks.
Let us talk in-depth about how Hadoop works –
- Hadoop Distributed File System (HDFS)
HDFS provides the master-slave topology. It is running on two daemons, namely DataNode and NameNode.
NameNode is a daemon (background process) that runs on the master machine. This central part of an HDFS file system saves all files in the file system’s directory tree. It monitors the file’s position throughout the cluster. Data is not kept in these files. If you want to add/copy/move/delete a file, the client programs will communicate with NameNode. The NameNode answers the client’s request by returning a list of the respective DataNode servers where the data resides.
On the slave nodes, the DataNode daemon is operating. It saves the data in the HadoopFileSystem. Data replicates on numerous DataNodes in the functional file system. A DataNode connects to the NameNode when it is started and continues to search for the data access request from NameNode. When NameNode offers the data location, client applications can speak directly with a DataNode. DataNode instances can talk to one other while replicating the data.
- Replica Placement:
The replica positioning determines the reliability and performance of HDFS. Replica placement optimization makes HDFS different from other distributed systems.
Massive HDFS instances run on multiple racks on a cluster of computers. The communication between nodes on various racks must be carried out through switches. The network bandwidth between the nodes on the same rack is typically higher on separate racks than between the machines.
The main principle of the MapReduce algorithm is to process the data on your distributed cluster in parallel. It then combines it with the intended output or outcome.
Hadoop MapReduce contains several phases:
- The program identifies and reads the “input file” containing the raw data in the first phase.
- The arbitrary format of the file requires data to be converted into something the software can process. It is the job of “InputFormat” and “RR” (RR).
- InputFormat utilizes the InputSplit function to divide the file into smaller parts.
- The RecordReader then converts the raw data for processing map treatment. A list of key-value pairs is produced.
- The results move to “OutputCollector” once the mapper works on those key-value pairs. There is another “reporter” function that tells the user after finishing the mapping task.
- The Reduce function conducts the task in the next step on each key-value pair from the mapper.
- OutputFormat finally organizes the Reducer’s key-value pairs to write it on HDFS.
- Map-Reduce, which is the core of the Hadoop system, processes the data in a robust and resilient manner.
Yarn separates the role of resource management, job programming, and monitoring into specific daemons. ApplicationMaster and ResourceManager are provided.
Two components of the ResourceManger are Scheduler and ApplicationManager.
- The scheduler is a pure programmer, i.e., the running application status is not tracked. It provides resources solely to different competing applications. Furthermore, due to a hardware or application failure, it does not restart the job. Based on a brief notion of a container, the scheduler allocates resources. A container is just a fraction of resources such as CPU, memory, disc, and network.
- Through the ReservationSystem, Yarn enables the concept of Resource Reservation. A user can determine the number of resources to carry out a particular job throughout time and period. The ReservationSystem ensures that the job has the resources till they are completed. It also carries out reservation admission control.
YARN Federation enables several sub-clusters to be integrated into one massive cluster. For one large job together, we can employ multiple independent clusters. Through the Yarn Federation, Yarn can scale beyond a few thousand nodes. They can utilize it for a wide-ranging system.
This post gives you an overview of how Hadoop works when we handle massive data. Understanding Hadoop’s work is vital to start coding for it. The way you think of a code is to be changed. It would help if you now began to assume that parallel processing is feasible. On Hadoop, you may execute various procedures; however, all of these codes need to be converted to a map-reduce function.