Tag: YARN

Standalone vs YARN cluster for Flink

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Flink offers two options to setup a cluster, one is standalone cluster, and the other is based on YARN.

Here I’ll list the pros/cons, to do a comparison.

Standalone mode

pros

  • no dependency on external components;
  • easy to add/remove TaskManager in the cluster;
  • easy for debug, and log retrieve;

cons

  • No job isolation as slots share the same JVM, refer to Job Isolation on Flink;
  • Need to have a zookeeper for node failure recovery;

YARN mode

More specifically, you have two choices with YARN, see yarn setup

  • set up Flink session, similar as a virtual cluster;
  • run Flink job directly on YARN

pros

  • job isolation provided by YARN;
  • node failure auto-recovery;
  • flexible resource capacity per TaskManager for different jobs;

cons

  • external cost for YARN;
  • So far YARN is tied closed with a distribution file system, HDFS/AWS/GoogleCloud;

In our environment, we decide to go with YARN finally. As we value the isolation feature much more than others, to support multiple tenants.

Apache Hadoop NextGen MapReduce (YARN)

Mingmin

YARN. From Apache.Org

Apache Hadoop NextGen MapReduce (YARN)

MapReduce has undergone a complete overhaul in hadoop-0.23 and we now have, what we call, MapReduce 2.0 (MRv2) or YARN.

The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs.

The ResourceManager and per-node slave, the NodeManager (NM), form the data-computation framework. The ResourceManager is the ultimate authority that arbitrates resources among all the applications in the system.

The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the tasks.

MapReduce NextGen Architecture

The ResourceManager has two main components: Scheduler and ApplicationsManager.

The Scheduler is responsible for allocating resources to the various running applications subject to familiar constraints of capacities, queues etc. The Scheduler is pure scheduler in the sense that it performs no monitoring or tracking of status for the application. Also, it offers no guarantees about restarting failed tasks either due to application failure or hardware failures. The Scheduler performs its scheduling function based the resource requirements of the applications; it does so based on the abstract notion of a resource Container which incorporates elements such as memory, cpu, disk, network etc. In the first version, only memory is supported.

The Scheduler has a pluggable policy plug-in, which is responsible for partitioning the cluster resources among the various queues, applications etc. The current Map-Reduce schedulers such as the CapacityScheduler and the FairScheduler would be some examples of the plug-in.

The CapacityScheduler supports hierarchical queues to allow for more predictable sharing of cluster resources

The ApplicationsManager is responsible for accepting job-submissions, negotiating the first container for executing the application specific ApplicationMaster and provides the service for restarting the ApplicationMaster container on failure.

The NodeManager is the per-machine framework agent who is responsible for containers, monitoring their resource usage (cpu, memory, disk, network) and reporting the same to the ResourceManager/Scheduler.

The per-application ApplicationMaster has the responsibility of negotiating appropriate resource containers from the Scheduler, tracking their status and monitoring for progress.

MRV2 maintains API compatibility with previous stable release (hadoop-0.20.205). This means that all Map-Reduce jobs should still run unchanged on top of MRv2 with just a recompile.