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Hdfs fs –du

Hdfs fsck

Dskchk

The map-only checksum

Hadoop does not provide any tools to discover missing or corrupt data; there is not need because three replicas are kept for each data block

Automatically configures permissions for log files at & MAPRED_LOG_DIR/userlogs

Creates users for hdfs and mapreduce to facilitate role assignment

Creates directories for temp, hdfs, and mapreduce with the correct permissions

Creates a set of pre-configured Kerberos keytab files and their permissions

Creates and configures your kdc with default cluster values

The Mapper stores the intermediate data on the node running the Job’s ApplicationMaster so that it is available to YARN ShuffleService before the data is presented to the Reducer

The Mapper stores the intermediate data in HDFS on the node where the Map tasks ran in the HDFS /usercache/&(user)/apache/application_&(appid) directory for the user who ran the job

The Mapper transfers the intermediate data immediately to the reducers as it is generated by the Map Task

YARN holds the intermediate data in the NodeManager’s memory (a container) until it is transferred to the Reducer

The Mapper stores the intermediate data on the underlying filesystem of the local disk in the directories yarn.nodemanager.locak-DIFS

Run hdfs fs –du / and locate the DFS Remaining value

Run hdfs dfsadmin –report and locate the DFS Remaining value

Run hdfs dfs / and subtract NDFS Used from configured Capacity

Connect to http://mynamenode:50070/dfshealth.jsp and locate the DFS remaining value

ApplicationMaster

NodeManager

ApplicationManagerService

ResourceManager

When your workload generates a large amount of output data, significantly larger than the amount of intermediate data

When your workload consumes a large amount of input data, relative to the entire capacity if HDFS

When your workload consists of processor-intensive tasks

When your workload generates a large amount of intermediate data, on the order of the input data itself

Cached by the NodeManager managing the job containers, then written to a log directory on the NameNode

Cached in the YARN container running the task, then copied into HDFS on job completion

In HDFS, in the directory of the user who generates the job

On the local disk of the slave mode running the task

Because there is a more than a single job on the cluster, the FIFO Scheduler will enforce a limit on the percentage of resources allocated to a particular job at any given time

Tasks are scheduled on the order of their job submission

The order of execution of job may vary

Given job A and submitted in that order, all tasks from job A are guaranteed to finish before all tasks from job B

The FIFO Scheduler will give, on average, and equal share of the cluster resources over the job lifecycle

The FIFO Scheduler will pass an exception back to the client when Job B is submitted, since all slots on the cluster are use

Delete the /dev/vmswap file on the node

Delete the /etc/swap file on the node

Set the ram.swap parameter to 0 in core-site.xml

Set vm.swapfile file on the node

Delete the /swapfile file on the node