Apache Hadoop is an open-source/free, software framework and distributed data processing system based on Java. It allows Big Data analytics processing jobs to break down into small jobs. These tasks are executed in parallel by using an algorithm (Such as the MapReduce algorithm). And the same is distributed across a Hadoop cluster.
A Hadoop cluster is a collection of computer systems that join together to execute parallel processing on big data sets. These are different from other computer clusters. Hadoop clusters are built particularly to store, manage, and analyze large amounts of data. This data may be structured and unstructured within a distributed computing ecosystem. Moreover, Hadoop ecosystems are different from other computer clusters as they include unique structure and architecture. Hadoop clusters also include a master and slave nodes network in combination. This uses high availability and low-cost commodity hardware within it.
Moreover, to work with distributed systems we need software. The software should coordinate and manage different processors and devices within the distribution ecosystem. With the increase in the scale of giant entities like Google, they began to build new software. This latest version is built to run on all the distributed systems.
Hadoop Cluster Architecture
Typically Hadoop cluster includes master-slave architecture. It consists of a network of master and worker nodes that orchestrate and perform different tasks across the HDFS. In Hadoop distributed file system the master nodes generally use high-quality hardware. This includes a NameNode, Resource manager, and JobTracker, with each running on an individual machine. The worker nodes include virtual machines (VM), running both DataNode and TaskTracker services on commodity hardware. They actually perform the work of storing and processing the various tasks under the supervision of the master nodes. The final part of the HDFS system is the Client Nodes. These are accountable for loading the data and getting the results.
The following are the different parts of Hadoop cluster architecture in detail.
Master nodes
These are responsible for data storage within HDFS and supervising key operations like running parallel calculations on the data with MapReduce.
MapReduce
This system is built by Google based on Java where the actual data from the HDFS store gets processed significantly. MapReduce helps to make the big data processing job into a simpler task by breaking it into smaller tasks. This is also responsible for analyzing huge datasets in parallel before minimizing it to find the outcome. Within the Hadoop ecosystem, Hadoop MapReduce acts as a framework based on YARN architecture. Moreover, YARN based Hadoop architecture supports distributed parallel processing of big data sets. And MapReduce offers the framework for easy to write applications on thousands of nodes. It also considers the fault and failure management to minimize risk.
There is a fundamental processing principle behind MapReduce working. That is, the “Map” job sends a query for processing to different nodes within a Hadoop cluster. And the “Reduce” job gathers all the outcomes to output it into one value.
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Worker nodes
These nodes include most of the virtual machines (VM) within a Hadoop cluster. They execute the job of data storage and running computations across clusters. Moreover, each worker node here runs the DataNode and TaskTracker services within it. They are useful to get the instructions from the master nodes for further processing.
Client nodes
The client nodes are responsible for data loading into the cluster. These nodes first submit MapReduce jobs defining how data needs to be processed. Later, they fetch the results once the processing completes.
Modules of Hadoop
The following are the various modules within Hadoop that support the system very well.
HDFS:
Hadoop Distributed File System or HDFS within Big data helps to store multiple files and retrieve them at high speed. HDFS was developed on the basis of the GFS paper published by Google Inc. This defines that the files will be separated into blocks and stored within nodes over the distributed structure.
Yarn:
YARN or Yet Another Resource Negotiator is useful in job planning and managing the cluster. It helps to enhance the efficiency of the system in processing data.
Map Reduce:
As we discussed earlier that Map Reduce is a framework with Java program. It is a framework that helps Java programs to do the parallel processing on data with key-value pairs. The Map job gets input data and modifies it into a data set to be computed within the Key value pair. The result of the Map job is taken by the Reduce task and then the outcome of the reducer produces the desired result.
Hadoop Common:
These are Java libraries useful to start Hadoop and are also useful by other modules. It is a set of common utilities that support various modules of Hadoop.
Advantages of a Hadoop Cluster
There are various advantages of the Hadoop cluster that provide systematic Big Data distribution and processing. They are;
Scalability and Robustness
Hadoop clusters boost the processing speed of various big data analytics tasks. They provide the ability to break-down huge computational jobs into smaller tasks to easily run in a parallel, distribution mode. Thus, the clusters in Hadoop have good robustness in the processing of the task.
Moreover, these clusters are easily and highly scalable. They can quickly add nodes to enhance throughput, and manage processing speed while facing huge data blocks. This quality proves that clusters within Hadoop are highly scalable in nature.
Data Disks Failure, Heartbeats, and Replication
The major objective of Hadoop is to store data reliably and securely even in the event of failures. There are different types of failure occurs such as NameNode failure, DataNode failure, and network partition. DataNode occasionally sends a heartbeat signal to the NameNode. Within Network partition failure, a group of DataNodes gets disconnects with the NameNode.
Therefore, NameNode does not get any heartbeat signals from the DataNodes. It refers that these DataNodes are dead. Moreover, Namenode also doesn’t forward any type of input/output request to them. The replication factor of the blocks stored within these Nodes falls below their particular value. As a result, NameNode starts the reproduction of these blocks. In this way, NameNode retrieves data from the failure.
Cost-effective:
Traditional data storage blocks include many limitations and the major limitation was the Storage issue. Hadoop Clusters overcome this problem drastically by its distributed data storage topology. The shortage of storage is managed by just adding additional storage units or blocks to the machine. The usage of low cost, highly-available commodity hardware makes clusters in Hadoop quite easy and inexpensive to set up and use.
Flexibility
The clusters within Hadoop are highly flexible to use. They make it possible to combine and leverage data from different source systems and data types. In addition to this, Hadoop is also useful for a wide range of purposes. Such as processing logs, recommendation systems, data warehousing, and fraud detection, etc.
Resilient to failure
A key benefit of using Hadoop within Big Data is its fault tolerance or saving from failure. When data is sent to a single node, that data is duplicated to other nodes within the cluster. This means that in the event of failure, the cluster saves the copy of data to use.
Moreover, it is possible to deploy a Hadoop cluster by installing a single-node, for estimation purposes.
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Types of Hadoop clusters
There are two different types of clusters available within Hadoop. These are the following types;
Single Node Hadoop Cluster:
The Single Node Hadoop Cluster denotes that there is only a single node within the cluster. This means all our Hadoop daemons, such as NameNode, DataNode, Secondary NameNode, Resource Manager, and the Node Manager will run on the same system or the same device. It means that all the processes will be managed by a single JVM (or Java Virtual Machine) Process Instance.
Multiple Node Hadoop Cluster:
In multi-node Hadoop clusters suggests that it includes many nodes. In this type of cluster set up all of the cluster nodes stores within multiple nodes in the same cluster. Typically, in a multi-node Hadoop cluster setup, we try to use the higher processing nodes for Master. They are the Name node and Resource Manager. Moreover, we also use the low-cost system for the slave structure nodes like Node Manager and Data Node.
How to Build a Cluster in Hadoop
Building a cluster within Hadoop is an important job. Finally, the performance of our machine will depend on the configuration of our cluster. In the below points, we will discuss several parameters that should be taken into consideration while setting-up a Hadoop cluster.
To choose the right hardware one should consider the below points must;
- Understand the workload types that the cluster mainly deals with the capacity of data that the cluster needs to manage. And the type of processing needs like CPU bound, I/O bound, etc.
- Data storage methods such as data reducing technique in case used.
- The data retention policy like how often we need to clean data.
Sizing the Hadoop Cluster
For ascertaining the size of the Hadoop cluster we need to look at how much data left in our hand. We should also inspect daily data production. Based on these factors we can determine the need for a number of systems and their configuration. Moreover, there should be a balance between execution and the cost of hardware agreed.
Configuring Hadoop Cluster
For determining the configuration of clusters in Hadoop, run typical Hadoop tasks on the default config to get the baseline. We can inspect log files task history to check whether a job takes more time than supposed. If so then we need to alter the configuration. After this repeat, the same process again to fine-tune the Hadoop cluster configuration. In this way, it meets the business needs faster. The performance of the cluster greatly depends on the resources assigned to the daemons or nodes. The Hadoop cluster assigns one core CPU for small to medium data size for each DataNode. And for big data sets, it assigns two CPU cores to the HDFS nodes.
Hadoop Cluster Management
When users deploy the Hadoop cluster within production it is apparent that it would scale along all proportions. These are volume, velocity, and variety. Several features that it includes to become production-ready are– robust, whole time availability, performance, and handling. Moreover, Hadoop cluster management is a major aspect of the big data initiative.
A good and effective cluster management tool should include the below features:-
- It should provide multi work-load management, security, performance optimization, health inspection, etc. Also, it should provide, job planning, policy handling, back- up, and retrieval across one or more nodes.
- Deploy Name Node availability with load balancing, auto-failover, and hot standby features.
- Applying policy-based controls that stop any application from restraining more resources than others.
- Handling the deployment of a number of layers of software over Hadoop clusters by executing regression testing. This is to ensure that any tasks or data won’t crash or encounter any obstacles in daily operations.
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Features and limitations of HDFS
The Hadoop Distributed File System or HDFS has some basic features and limitations. These are;
Features:
- Allocated data storage.
- Blocks or chunks minimize seek time.
- The data is highly available as the same block exists at different data nodes.
- Even if different data nodes are down we can still perform our jobs. This makes it highly reliable
- Includes high fault tolerance and quick response.
Limitations:
However, HDFS offers many features but there are some key areas where it doesn’t work well. They are;
- Low latency data access: Apps that need low-latency access to data like within the range of milliseconds won’t work well with HDFS. Due to HDFS is built keeping in mind that users need high-throughput of data even at the low latency.
- Small file issue: Including lots of small files will result in lots of soliciting. And lots of mechanisms from one data node to another one to recover each small file. This whole process makes the data access pattern inefficient to work.
Final Words
Thus, the usage of the Hadoop cluster and the architecture supports the distribution processing of Big Data efficiently. This makes it to scale higher in the technical aspects. Hadoop cluster includes a network topology that effects its performance well while increasing the cluster. So, one should take care of high availability and the failures to handle. Moreover, it offers good support to Big Data. Get more insights on Hadoop and Big Data through Big Data Online Training.