Kubernetes vs Hadoop YARN

By | June 14, 2022
Kubernetes vs Hadoop YARN

Both Kubernetes and Hadoop YARN allow us to manage clusters of nodes and monitor them. The question is, which one to go for? In this comprehensive guide, we will walk you through each and every aspect of cluster management with both Kubernetes and Hadoop YARN so that you will be able to decide which one will be able to fulfill your requirements.

But before that, we must understand some important concepts, like cluster management, task manager and nodes so that we will be able to compare YARN with Kubernetes in-depth.

Some Basic Terminology

You’ll need a way to handle huge workloads within a management layer when you break down units of work into small batches. Across all workloads, this layer would allow you to share resources, schedule activities, and treat multiple operating processes as a single coherent, scalable, and well-behaved solution.

This is how transactional structures have operated in the past. CICS, Tuxedo, and Java containers, like J2EE servers, as well as other commercial clustering technologies, are a few examples.

Compute clusters are a type of clustered computing environment made up of servers (nodes) that have been pooled together to accommodate the workloads and tasks operating within the cluster.

You can combine processes to create tasks and many tasks combined together will perform a job. To do this, you’ll need to use a cluster management system, which usually consists of a resource manager that keeps track of resources, such as computational power, memory, and storage.

If an executing task requires a resource, it must receive it from the resource manager. If you have a well-managed resource pool and easy access to it, this will ensure that you can monitor the platform’s effectiveness and scale the entire thing, either remotely or physically.

The task manager is mainly accountable for the execution of activities or tasks and performs state control. It is another aspect of cluster managers that you need to be familiar with. Schedulers in cluster managers maintain connections among the tasks that combine to create jobs and delegate tasks to nodes. The scheduler is a crucial aspect of a cluster manager.

Kubernetes vs Hadoop YARN

The nodes in the clusters can be either several physical machines, virtual machines, containers, or a combination of them. There are many cluster/container/node managers or orchestration tools, such as Kubernetes, AWS ECS, Docker Swarm, and Hadoop YARN.

In this article, we will detail Hadoop YARN and Kubernetes, so that you can choose the one suitable for you. To make the comparison easier, we will detail each of the 2 technologies, one by one. So, let’s start!

Introduction to Hadoop YARN

Hadoop is a widely used open-source platform in the big data industry. It has become a must-have for any data scientist due to its potential reach, versatility, and functionality. In basic terms, Hadoop is a collection of resources that allows you to store large amounts of data in a distributed and easily accessible manner. It allows you to process data in parallel.

Hadoop is a big platform with a lot of features. It has a number of components that aid in the storage and processing of data. However, it is largely split into 2 parts. They are HDFS and YARN, which stand for Hadoop Distributed File System and Yet Another Resource Negotiator, respectively.

The former is used to store data, while the latter is used to process it. Starting with Hadoop is easy, but mastering it requires a great deal of effort and time.

Hadoop allows you to store data in a number of clusters. The information can be in any format. You can use it for free since it is an open-source software. Apart from that, Hadoop comes with a slew of big data applications to help you get things done quicker.

YARN is a technology for task scheduling and resource control that is one of Hadoop’s core components. Interactive searches, streaming results, and real-time apps are all supported. It also serves a wider variety of technologies.

Yarn is a system that integrates a central resource manager with various containers. It can dynamically integrate services for multiple purposes and the operations are well-monitored.

Despite its mastery of data manipulation and computations, Hadoop 1.x had certain flaws, such as batch processing delays and scalability problems, since it relied on MapReduce to process large datasets.

Hadoop then embraced a wider range of computing techniques and now has a wider range of implementations thanks to YARN. Flow data analysis and dynamic querying now coexist with MapReduce batch jobs in Hadoop YARN clusters. The YARN module runs non-MapReduce apps as well, resolving the limitations of Hadoop 1.x.

YARN Architecture

YARN also does task scheduling in addition to the management of resources. It is based on the principle of using different functions to accommodate parallel processing. The main components of YARN operations i.e. YARN architecture are listed below:

  • Resource Manager

YARN’s master daemon is Resource Manager. It is in charge of handling a number of other applications as well as the global allocation of resources, such as storage, memory, and CPU. It is mostly used to schedule jobs.

There are 2 parts of Resource Manager. The first is the scheduler; responsible for allocating resources to active applications. It just deals with job scheduling and, therefore, does not do any application tracking or reporting.

The Application Manager, on the other hand, is in charge of the cluster’s running applications. It also controls activities, such as initiating Application Master, tracking and monitoring.

  • Node Manager

YARN’s slave daemon is Node Manager. The following are its duties:

  • Keep track of the container’s resource use and report it to the Resource Manager.
  • Responsible for monitoring the node’s health on which YARN is operating.
  • Looks after each node in the cluster while still handling the workflow and user jobs on each node.
  • Ensures that the Resource Manager’s records are up to date.

In addition to the aforementioned tasks, if the Resource Manager issues a command to do something, it can even disable or kill the container.

  • Application Master

Any job that is submitted to the system can be considered as an application, and each of them has its own Master. They have certain responsibilities as an Application Master. It manages faults and coordinates the execution of a program in the cluster.

The application master confers the Resource Manager for exchanging the resources. It collaborates with the Node Manager to execute and track the activities of other components. Heartbeats are sent to the Resource Manager at frequent intervals to check on their health and to update records based on the resource requirements.

  • Container

On a single node, a container can be considered as a collection of physical resources (CPU, storage unit(s), memory, and so on). It allows an application to use a certain number of resources on a given host.

Container-launch manages YARN containers in particular. A map of variables, dependencies, authentication tokens, payloads, and the build command are all included in this record.

Features of YARN

Let’s see into a few important features of YARN:

  1. Supports many engines and meets the need for a real-time machine that knows where data is and how to manage its movement within the context for distributing compute jobs to operate on the right data at the right place.
  2. Guarantees that the compute jobs are independent of one another. Each computes job runs on its own node and does not share the resources it has been assigned. Each job is in charge of its own set of tasks.
  3. Dynamically allocates resources to clusters and optimizes the management and allocation process.
  4. It’s fault-tolerant ability allows failed tasks or jobs to be rescheduled without any implications.
  5. Encourages scalability by appropriately assigning resources and determining where the new nodes are to be set up in the cluster.
  6. Highly compatible with version 1 of MapReduce and the migration of tasks and jobs is easier.

Introduction to Kubernetes

Kubernetes (also referred to as “k8s”) is open-source, free container management and orchestration framework that automates many of the manual processes associated with installing, handling, and scaling containerized systems.

In other terms, you can group together hosts, which are running Linux containers, and Kubernetes makes managing those clusters easy and effective. Kubernetes clusters can encompass on-premises or cloud-based hosts.

As a result, Kubernetes is an excellent platform that can be used for hosting cloud-native servers and applications that need accelerated scaling, such as Apache Kafka-based real-time data streaming.

Engineers at Google created Kubernetes. Google was one of the first organizations to accept Linux container technologies, and it has openly claimed that everything at Google is run using containers. (Google’s cloud services are built on this technology.)

The main benefit of implementing Kubernetes in your system, particularly, if you’re optimizing app development for the cloud, is that it provides a framework for scheduling and running containers on physical or virtual machine clusters.

It also helps you in thoroughly implementing containers and you can rely on a container-based architecture in production environments. And, Kubernetes is all about automating operating processes in containers.

Using Kubernetes, developers can build cloud-native apps as a runtime base. A Kubernetes creator uses patterns to build container-based software and services.

Kubernetes allows you to orchestrate containers through various hosts and determines the best use of hardware resources to run your business applications. To run stateful applications, you can monitor and automate device installations and updates, as well as mount and add storage.

You’ll be able to dynamically scale containerized systems and their services. You will handle resources declaratively, ensuring that deployed apps still work the way you wanted them to. In addition to auto-placement, restart, replication, and scaling, you can conduct a health check and self-heal your applications.

Kubernetes Architecture

Language unique to Kubernetes, as with other technologies, can be an obstacle to entry. To help you better understand Kubernetes, let’s break down some of the more general terms:

  1. Control Plane – It includes a collection of processes that can be used to control the nodes in Kubernetes clusters. It’s the origin point of all the activities.
  2. Nodes – These can be physical or virtual machines or servers that perform the tasks.
  3. Pods – These are groups of containers that make up a node that shares IP addresses, hostnames, and many other resources. The main aim is to abstract the storage and network from the container. This is what helps you to move containers across clusters.
  4. Replication Controller – This determines the number of replicas of pods that ideally should be running inside a cluster.
  5. Service – It directs the get requests to the right pod irrespective of its position in the cluster.
  6. Kubelet – It’s responsible for going through the container manifests and ensuring that they are running actively. It’s a service that runs on the nodes.
  7. Kubectl – It is a command-line interface that is used to work with resources, pods, and deployments.

A cluster is a Kubernetes deployment that is up and running. A Kubernetes cluster is divided into two parts; the control plane and the computing machines, also called nodes. Each node is a separate environment that can be either physical or virtual. Pods, which consist of multiple containers, run on each node.

The control plane is in charge of keeping the cluster in the desired state, such as determining which programs are running and which container images they are using. The applications and workloads run on compute machines.

Kubernetes is a container orchestration system that operates on top of an operating system (for example, RHEL) and connects with pods of containers running on nodes.

The Kubernetes control plane receives instructions from a system administrator and relays them to the compute machines. This handoff uses a variety of utilities to determine which node is ideally fit for the task at hand. It then transfers services to the node’s pods to complete the requested job.

The way you handle containers from an infrastructure standpoint hasn’t changed much. The container management is actually at a higher-level, allowing you to have greater control without having to micromanage each specific container or node.

Your responsibilities include setting up Kubernetes and identifying nodes, pods, and the containers that reside inside them. Kubernetes is in charge of container orchestration.

It’s completely up to you where you run Kubernetes. This can happen on bare metal servers, clouds, virtual machines, etc. One of Kubernetes’ main benefits is that it can run on a variety of infrastructures.

Features of Kubernetes

Let’s discuss a few features of Kubernetes:

  1. Automates repeated tasks, such as determining the right cluster, node, or pod for the task.
  2. Interact with several clusters and manage them simultaneously.
  3. Get additional benefits apart from container orchestration, such as managing the storage, network configuration, and security.
  4. Automatically performs health-checks and thus, it’s self-monitoring.
  5. Apart from scaling vertically, you can also perform horizontal scaling by increasing the number of nodes.
  6. Performs storage orchestration by deciding the storage mounts and choice.
  7. Able to roll back from failures easily.
  8. Performs container balancing which determines in which pod or cluster should a container be best-placed.
  9. It’s platform-independent and thus, can be run anywhere.

Wrapping Up!

Hadoop YARN has totally changed the game when it comes to deploying and processing clustered applications on a network of commodity servers. It overcomes MapReduce’s weakness. When compared to MapReduce, it is more compact, scalable, and effective. Companies are switching from MRV1 to YARN, and there’s no excuse why they shouldn’t.

On the other hand, Kubernetes allows you to perform container management and lets you manage and monitor multiple clusters of nodes containing more than one container very easily. It is the perfect tool and can also be used to manage security, network, and storage.

Hadoop YARN and Kubernetes although have some similarities between them, but, indeed, one cannot completely replace the other. Based on your requirements, it’s upon you to decide which one to opt for.

We certainly hope that this article gives you an overview of both of them as well as their architecture and features, which will help you determine the right tool that will cater perfectly to your requirements.

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