Post rolling upgrade hdp2.5.3 deleting files from hdfs not recovered the space

Recently upgraded from hdp 2.3.2 to 2.5.3 using rolling upgrade method. Wile upgrade status in Pause status, pending for commit. HDFS preserving all data in hdfs even after using -skipTrash option.

hdfs dfs -du -s -h give correct size after deletion. But hdfs dfsadmin -report show higher value.

Identified root cause working with hwx is : It keeps data in “trash” folder in datanode disks allocated for dfs storage.

This will be cleared after commit the rolling upgrade through Ambari or manually ” dfsadmin -finalizeUpgrade”

 

 

MySQL password less access to root on localhost

mysql-config-editor is the answer to manage mysql without displaying password on prompt or passing plain text files.

This tool mysql_config_editor will created encrypted .mylogin.cnf file, this will resolve the age old problem of reading cleartext password for the mysql scripts.

Command has the following options:

set [command options] Sets user name/password/host name/socket/port
for a given login path (section).
remove [command options] Remove a login path from the login file.
print [command options] Print all the options for a specified
login path.
reset [command options] Deletes the contents of the login file.
help Display this usage/help information.

 

 

 

 

HDFS Disk Failures

Intresting discussion on hortonworks community on disk failures

how-to-alert-for-hdfs-disk-failures.html

> When a single drive fails on a worker node in HDFS, can this adversely affect performance of jobs running on this node?

The answer to this question is it depends. If this node is running a job that is accessing blocks on the failed volume, then yes. it is also possible that the job would be treated as failed if the dfs.datanode.failed.volumes.tolerated is not greater than 0. If it is not a value greater than zero, then HDFS treats a loss of a volume as catastrophic and marks the datanode as failed. If this is set to a value greater than zero, then node will work well until we lose more volumes.

> If this could cause a performance impact, how can our customers monitor for these drive failures in order to take corrective action?

Now this is a hard question to answer without further details. I am tempted to answer that the performance benefit you are going to get by monitoring and relying on a human being to take a corrective action is very doubtful. YARN / MR or whatever execution engine you are using is probably going to be much more efficient at re-scheduling your jobs.

>Or does the DataNode process quickly mark the drive and its HDFS blocks as “unusable”.

Datanode does mark the volume as failed , and namenode will learn that all the blocks on that failed volume are not available on that datanode any more. This happens via something called “block reports”. Once namenode learns that data node has lost the replica of a block then namenode will initiate appropriate replication. Since namenode knows about the loss of blocks, further jobs that need access to those block would most probably not be scheduled on that node. This again depends on the scheduler and its policies.

 

Microsoft cloud Azure hadoop component versions

Microsoft cloud Azure hadoop component versions

Component HDInsight version 3.5 HDInsight version 3.4 (Default) HDInsight Version 3.3 HDInsight Version 3.2 HDInsight Version 3.1 HDInsight Version 3.0
Hortonworks Data Platform 2.5 2.4 2.3 2.2 2.1.7 2.0
Apache Hadoop & YARN 2.7.3 2.7.1 2.7.1 2.6.0 2.4.0 2.2.0
Apache Tez 0.7.0 0.7.0 0.7.0 0.5.2 0.4.0
Apache Pig 0.16.0 0.15.0 0.15.0 0.14.0 0.12.1 0.12.0
Apache Hive & HCatalog 1.2.1.2.5 1.2.1 1.2.1 0.14.0 0.13.1 0.12.0
Apache HBase 1.1.2 1.1.2 1.1.1 0.98.4 0.98.0
Apache Sqoop 1.4.6 1.4.6 1.4.6 1.4.5 1.4.4 1.4.4
Apache Oozie 4.2.0 4.2.0 4.2.0 4.1.0 4.0.0 4.0.0
Apache Zookeeper 3.4.6 3.4.6 3.4.6 3.4.6 3.4.5 3.4.5
Apache Storm 1.0.1 0.10.0 0.10.0 0.9.3 0.9.1
Apache Mahout 0.9.0+ 0.9.0+ 0.9.0+ 0.9.0 0.9.0
Apache Phoenix 4.7.0 4.4.0 4.4.0 4.2.0 4.0.0.2.1.7.0-2162
Apache Spark 1.6.2 + 2.0 (Linux only) 1.6.0 (Linux only) 1.5.2 (Linux only/Experimental build) 1.3.1 (Windows-only)

 

 

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