This blog is dedicated to introducing Apache Hadoop YARN and its various concepts, but before we get into learning what Hadoop YARN is, we must get acquainted with Apache Hadoop first, especially if we are new to Apache family. This way, it will be easy for us to understand Hadoop YARN better. So, click HERE to get a quick introduction to Apache Hadoop.
Coming back to YARN, let’s check out what this blog has to offer:
What is Hadoop YARN?
YARN is one of the core components of the open-source Apache Hadoop distributed processing frameworks which helps in job scheduling of various applications and resource management in the cluster. YARN was initially called ‘MapReduce 2’ since it took the original MapReduce to another level by giving new and better approaches for decoupling MapReduce resource management for scheduling capabilities from the data processing unit.
YARN is being extensively used for writing applications by Hadoop Developers. It lets them create applications, work with huge amounts of data, and manipulate them in an efficient manner. YARN is much more effective and versatile than Hadoop MapReduce, and this is exactly what is required in a world inundated with big data. However, it will remain the most sought-after tool until the perennial search—for a tool that works well in the challenging environment of Big Data Hadoop—comes up with a new befitting tool.
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YARN vs. MapReduce
In Hadoop 1.0, the batch processing framework MapReduce was closely paired with HDFS (Hadoop Distributed File System). With the addition of YARN to these two components, giving birth to Hadoop 2.0, came a lot of differences in the ways in which Hadoop worked. Let’s go through these differences.
|Type of processing
||Real-time, batch, and interactive processing with multiple engines
||Silo and batch processing with a single engine
|Cluster resource optimization
||Excellent due to central resource management
||Average due to fixed Map and Reduce slots
||MapReduce and non-MapReduce applications
||Only MapReduce applications
|Managing cluster resource
||Done by YARN
||Done by JobTracker
||With YARN, Hadoop supports multiple namespaces
||Only one namespace could be supported, i.e., HDFS
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In spite of being thoroughly proficient at data processing and computations, Hadoop had some shortcomings like delays in batch processing, scalability issues, etc. as it relied on MapReduce for processing big datasets. With YARN, Hadoop is now able to support a variety of processing approaches and has a larger array of applications. Hadoop YARN clusters are now able to run stream data processing and interactive querying side by side with MapReduce batch jobs. YARN framework runs even the non-MapReduce applications, thus overcoming the shortcomings of Hadoop 1.0.
Advantages of YARN
The architecture of YARN ensures that the Hadoop cluster can be enhanced in the following ways:
YARN lets you access various proprietary and open-source engines for deploying Hadoop as a standard for real-time, interactive, and batch processing tasks that are able to access the same dataset and parse it.
YARN lets you use the Hadoop cluster in a dynamic way, rather than in a static manner by which MapReduce applications were using it, and this is a better and optimized way of utilizing the cluster.
YARN gives the power of scalability to the Hadoop cluster. YARN ResourceManager (RM) service is the central controlling authority for resource management and it makes allocation decisions.
YARN tool is highly compatible with the existing Hadoop MapReduce applications, and thus those projects that are working with MapReduce in Hadoop 1.0 can easily move on to Hadoop 2.0 with YARN without any difficulty, ensuring complete compatibility.
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Architecture of Hadoop YARN
As it is obvious by now, YARN is used as a system for managing distributed applications. The YARN architecture has a central ResourceManager that is used for arbitrating all the available cluster resources and NodeManagers that take instructions from the ResourceManager and are assigned with the task of managing the resource available on a single node.
YARN ResourceManager of Hadoop 2.0 is fundamentally an application scheduler that is used for scheduling jobs. Mesos scheduler, on the other hand, is a general-purpose scheduler for a data center. The job of YARN scheduler is allocating the available resources in the system, along with the other competing applications. It helps manage the cluster utilization so that all resources are occupied at all times.
One of the key features of Hadoop 2.0 YARN is the availability of the Application Master. It is used for working with NodeManagers and can negotiate the resources with the ResourceManager. It extensively monitors resource consumption, various containers, and the progress of the process.
Application Master adds more to the glory of Hadoop YARN in the following ways:
- Application Master makes the YARN ecosystem much more open, thanks to the application-specific code framework that lets you generalize the system so that various frameworks can now be supported including Graph Processing, MapReduce, and MPI, among others.
- Application Master provides enough functionality while taking care of all the complexities. This allows the application framework authors to have the right amount of power and flexibility.
- Application Master is not a privileged service, but it is more of a user-code.
- Every application has an Application Master instance allocated to it. Thus, it is possible to implement the Application Master for managing a set of applications. However, it is also possible to work with bigger services that are managed by their own applications like HBase in YARN.
What does Apache Hadoop YARN do?
YARN is a very important aspect of the enterprise Hadoop setup that is used for the resource management process. It is a central platform for consistent operations, data governance, security, and other aspects of the Hadoop cluster. YARN can extend the Hadoop ecosystem to newer technologies used in the data centers. It is a consistent platform that is used for writing data access applications that run in Hadoop.
How does Apache Hadoop YARN work?
YARN separates HDFS and MapReduce and this makes the Hadoop environment more suitable for applications that can’t wait for the batch processing jobs to finish. So, no more batch processing delays with YARN! This architecture lets you process data with multiple processing engines using real-time streaming, interactive SQL, batch processing, handling of data stored in a single platform, and working with analytics in a completely different manner. YARN can be considered as the basis of the next generation of the Hadoop ecosystem, ensuring that the forward-thinking organizations are realizing the modern data architecture.
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YARN is an exclusive Hadoop feature that has enhanced the whole application processing speed by making scheduling and resource allocation easier and much efficient.
If you want to learn more about Hadoop YARN and Hadoop Distributed File System, you can watch this informative Hadoop YARN Video by Intellipaat!
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