Efficiency Meets Scalability: Hadoop Schedulers at Work
Hadoop Schedulers are general-purpose systems because they enable Hadoop, a distributed node set, to do high-performance data processing. A few of the Hadoop schedulers that are included in Hadoop are Hadoop Capacity Scheduler, Hadoop First in First out (FIFO) Scheduler, and Hadoop Fair type Scheduler. These schedulers assist in guaranteeing optimal resource use and access to the unused level of capacity.
On clusters of affordable hardware, Hadoop is an open-source software framework for storing and analysing big datasets. It has a distributed file system (HDFS) and a framework for distributed processing (MapReduce).
In a Hadoop cluster, the scheduling of jobs is an important aspect of its operation. Schedulers are responsible for deciding which tasks to run on which machines, based on various factors such as the availability of resources, data locality, and task dependencies.
There are several types of schedulers available in Hadoop:
FIFO (First In, First Out) Scheduler: This is the default scheduler in Hadoop. It processes jobs in the order they are submitted, without taking into account the resource requirements of the jobs or the available resources on the cluster.
Fair Scheduler: This scheduler aims to allocate resources fairly among all the running jobs. It divides the available resources among the jobs in a way that allows each job to make progress based on the task requirements and resource availability.
Capacity Scheduler: This scheduler is designed for large Hadoop clusters where multiple organizations or groups share the same cluster. It allows administrators to allocate a certain percentage of the cluster’s resources to each organization or group.
YARN (Yet Another Resource Negotiator) Scheduler: YARN is a newer scheduling framework that was introduced in Hadoop 2.0. It decouples the resource management and job scheduling functions, allowing users to choose different scheduling algorithms and resource allocation policies.
It’s worth noting that Hadoop schedulers can now be customized to meet all the specific needs of an organization or use case. For example, you can write custom code to implement your own scheduling algorithms or modify the behavior of the built-in schedulers.