text: Consider the following basic concepts for aggregated logging: 
  
  (cid:2) Cluster: A set of Elasticsearch nodes that distribute the workload. 
  
  (cid:2) Node: A container that is running an instance of Elasticsearch, which is part of the cluster. 
  
  (cid:2) Index: Collection of documents (container logs). 
  
  (cid:2) Shards and Replicas: Indexes can be divided into sets of data that contain the primary 
  
  copy of the documents that are stored (primary shards) or backups of that primary copies 
  (replica shards). Sharding allows the application to horizontally scale the information and 
  distributed/paralellized operations. Replication instead provides HA and also better search 
  throughput because searches are also run on replicas. 
  
  **Note:**Using NFS storage as a volume or a persistent volume (or by way of an NAS such 
  as Gluster) is not supported for Elasticsearch storage because Lucene relies on file 
  system behavior that NFS does not supply. Data corruption and other problems can occur. 
  
  Red Hat OpenShift Container Platform can gather metrics from kubelet and store the values 
  in Heapster. Red Hat OpenShift Container Platform Metrics provide the ability to view CPU, 
  memory, and network-based metrics and display the values in the user interface. These 
  metrics can allow for the horizontal autoscaling of pods based on parameters that are 
  provided by a Red Hat OpenShift Container Platform user. It is important to understand 
  capacity planning when metrics are deployed into an Red Hat OpenShift Container Platform 
  environment. 