Understanding Hadoop and YARN
The growth of Apache Hadoop during the last ten years is a proof of ability of this framework to process large volumes of data and allowing user-access to shared resources. Although like any other emerging technology Hadoop also faces certain challenges one of which is its unpredictable nature. Enterprise using Hadoop are never sure whether the framework would be able to deliver important jobs on time. The ineffective usage of the full capacity of the cluster is another concern in a Hadoop environment. YARN is a resource-management platform responsible for managing computing resources in clusters and using them for scheduling of users' applications. It has the ability of pre-empting jobs which allows making way for other data jobs queued up, waiting for their turn. It allows eliminating jobs which take up cluster resources otherwise needed to schedule high-priority jobs. This is done by statically configuring both capacity scheduler and fair scheduler.
The suitable time of using these tools is when the jobs are getting queued up while waiting for resources. Though they solve this issue but they are unable to resolve real-time contention problems for tasks already in flight. Monitoring actual resource utilization of tasks while they are still running is not done by YARN. This means that if low-priority applications are monopolizing disk I/O or saturating another hardware resource, high-priority applications will have to wait.
These are certain intricacies of working in a Hadoop environment which are understood by skilled Hadoop professionals. Being one of the hottest skills in demand by enterprises, professionals having the knowledge of Hadoop are the most sought after. There are numerous Hadoop Administrator Training in the market which educate you on various aspects of Hadoop technology.
With time companies are advancing in their Hadoop usage and are beginning to run business critical applications in multitenant clusters. This is a scenario where the organizations need to ensure that high-priority jobs do not get run over by jobs which are low on priority. This measure is a prerequisite for providing quality of service (QoS) for Hadoop, but the framework has not addressed it so far.
The suitable time of using these tools is when the jobs are getting queued up while waiting for resources. Though they solve this issue but they are unable to resolve real-time contention problems for tasks already in flight. Monitoring actual resource utilization of tasks while they are still running is not done by YARN. This means that if low-priority applications are monopolizing disk I/O or saturating another hardware resource, high-priority applications will have to wait.
These are certain intricacies of working in a Hadoop environment which are understood by skilled Hadoop professionals. Being one of the hottest skills in demand by enterprises, professionals having the knowledge of Hadoop are the most sought after. There are numerous Hadoop Administrator Training in the market which educate you on various aspects of Hadoop technology.
With time companies are advancing in their Hadoop usage and are beginning to run business critical applications in multitenant clusters. This is a scenario where the organizations need to ensure that high-priority jobs do not get run over by jobs which are low on priority. This measure is a prerequisite for providing quality of service (QoS) for Hadoop, but the framework has not addressed it so far.
It is nuances similar to the ones mentioned above which are a must-know for a Hadoop professional. A Big Data Hadoop Administrator Training program educates and prepares you for Hadoop scenarios.
Coming back to the QoS concern, let us observe an example. Consider a simple three-node cluster where two different jobs are queued up to be scheduled by YARN Resource Manager. The resource manager enables the business critical HBase streaming job and the low priority ETL job to run simultaneously on the cluster and schedules them for execution.
Consider a runtime situation on the same cluster without QoS, where YARN determines that the cluster has enough resources to run a low-priority job and a business-critical job at the same time. In more situations than one it is expected that the business-critical job will be processed within the time period laid down in service-level agreement. Whereas there is no such expectation from the lower-priority job.
Understanding how to process data based on priorities is imperative for establishing an ideal Hadoop scenario. To get this understanding a professional needs to educate himself through programs like a Hadoop Administrator Training. There are number of academies which provide such training. Collabera TACT’s Big Data Hadoop Administrator Training is one of the finest by industry standards. Collabera TACT has been recognized as the top training provider on emerging technologies like Big Data and Hadoop. If Big Data Analytics fascinates you then acquiring Hadoop skills is the right move for you. Get started!!
