Setting Application Limits
To avoid system-thrash due to an unmanageable load -- caused either by malicious users, or by accident -- the Capacity Scheduler enables you to place a static, configurable limit on the total number of concurrently active (both running and pending) applications at any one time. The maximum-applications configuration property is used to set this limit, with a default value of 10,000:
Property: yarn.scheduler.capacity.maximum-applications
          
Value: 10000
The limit for running applications in any specific queue is a fraction of this total limit, proportional to its capacity. This is a hard limit, which means that once this limit is reached for a queue, any new applications to that queue will be rejected, and clients will have to wait and retry later. This limit can be explicitly overridden on a per-queue basis with the following configuration property:
Property: 
            yarn.scheduler.capacity.<queue-path>.maximum-applications
Value: 
      absolute-capacity * yarn.scheduler.capacity.maximum-applications
 There is another resource limit that can be used to set a maximum percentage of 
            cluster resources allocated specifically to ApplicationMasters. The 
            maximum-am-resource-percent property has a default value of 10%, and 
            exists to avoid cross-application deadlocks where significant resources in the cluster 
            are occupied entirely by the Containers running ApplicationMasters. This property also 
            indirectly controls the number of concurrent running applications in the cluster, with 
            each queue limited to a number of running applications proportional to its capacity.
Property: 
            yarn.scheduler.capacity.maximum-am-resource-percent
Value: 0.1
As with maximum-applications, this limit can also be overridden on a per-queue basis:
Property: 
            yarn.scheduler.capacity.<queue-path>.maximum-am-resource-percent
Value: 0.1
All of these limits ensure that no single application, user, or queue can cause catastrophic failure, or monopolize the cluster and cause excessive degradation of cluster performance.

