Monthly Archives: August 2016

  • 2

How to integrate Ambari with ldap

Tags :

Category : Ambari

By default, Ambari uses an internal database as the user store for authentication and authorization. If you wish to add LDAP external authentication in addition for Ambari Web, you need to make some edits to the Ambari properties file.

  1. Collect following information :
  • ldap.primaryUrl=<ldap_server_name>:389
  • ldap.useSSL=false
  • ldap.usernameAttribute=sAMAccountName
  • ldap.baseDn=cn=Users,dc=<sreach_dir>,dc=com
  • ldap.bindAnonymously=false
  • ldap.managerDn=cn=ambari,cn=users,dc=<sreach_dir>,dc=com
  • ldap.managerPassword=/etc/ambari-server/conf/ldap-password.dat
  • ldap.userObjectClass=user
  • ldap.groupObjectClass=group
  • ldap.groupMembershipAttr=memberOf
  • ldap.groupNamingAttr=cn
  • ldap.referral=ignore
  • ldap.isConfigured=true
  • security=ldap
  • ldap.dnAttribute=dn
  1. Create ldap-password.dat file with ldap password under =/etc/ambari-server/conf :

[root@m1 ~]# vi /etc/ambari-server/conf/ldap-password.dat

  1. Run Ambari ldap integration command on Ambari server and provide all required information, which you have collected above:

[root@m1 ~]# ambari-server setup-ldap

  1. Once it is done then restart your Ambari server:

[root@m1 ~]# ambari-server restart

  1. There is a problem in Ambari which does not sync users automatically so for that we have create a file users.txt(put user’s windows id in this file) under home dir and then usersync command manually:

[root@m1 ~]# vi users.txt

[root@m1 ~]# sudo /usr/sbin/ambari-server sync-ldap –users /root/users.txt

Using python /usr/bin/python2

Syncing with LDAP…

Enter Ambari Admin login: admin

Enter Ambari Admin password:

Syncing specified users and groups….

 

Completed LDAP Sync.

Summary:

memberships:

removed = 0

created = 0

users:

updated = 0

removed = 0

created = 1

groups:

updated = 0

removed = 0

created = 0

Ambari Server ‘sync-ldap’ completed successfully.

When you have made the necessary edits to the properties file. Initially the users you have enabled will all have User privileges. Users can read metrics, view service status and configuration, and browse job information.


  • 0

Check high CPU Intensive process on your server

Tags :

Category : Bigdata

When you start utilizing your cluster heavily then you may encounter a 100% CPU utilize error on a specific server. But as you may have many jobs and process running on that server that time it would be very tough to identify a culprit process whcih is causing this issue. It is like finding a needle in haystack.

I have faced such scenario in my job so you should not worry as I have created following script which will help you to find culprit and then you can shoot them or can do anything with them whatever you want. Only thing you have to schedule this script in your cron and thats all.

[hdfs@m1.hdp22 ~]$ cat cpu_Usage.sh

dateTime=$(date +”%Y-%m-%d”)

for (( i=1; i <= 20; i++ ))

do ps -eo pcpu,pid,user,start,etime,args | sort -k 1 -r | head -5 >> /hdptmp/Metrics/CPU_Usage_$dateTime.log;

sleep 10;

done

Cron your job like below: 

[hdfs@m1.hdp22 ~]$ crontab -l

##CPU issue script

20 11 * * * /home/hdfs/cpu_Usage.sh >>/hdptmp/error.log 2>&1

You will your output file like below: 

[hdfs@m1.hdp22 ~]$ cat /hdptmp/Metrics/CPU_Usage_2016-08-30.log

%CPU   PID USER      STARTED     ELAPSED COMMAND

94.5 61100 hdpbatch 11:19:59       00:02 gzip -d 14-prod_2016-08-29.tsv.gz

78.5 60220 hdpbatch 11:19:52       00:09 bzip2 20-mowprod_2016-08-29.tsv

77.2 60221 hdpbatch 11:19:52       00:09 bzip2 21-mowprod_2016-08-29.tsv

77.0 60216 hdpbatch 11:19:52       00:09 bzip2 16-mowprod_2016-08-29.tsv

%CPU   PID USER      STARTED     ELAPSED COMMAND

84.9 60220 hdpbatch 11:19:52       00:19 bzip2 20-mowprod_2016-08-29.tsv

84.9 60216 hdpbatch 11:19:52       00:19 bzip2 16-mowprod_2016-08-29.tsv

84.8 60218 hdpbatch 11:19:52       00:19 bzip2 18-mowprod_2016-08-29.tsv

84.3 60219 hdpbatch 11:19:52       00:19 bzip2 19-mowprod_2016-08-29.tsv

%CPU   PID USER      STARTED     ELAPSED COMMAND

89.0 62082 root     11:20:17       00:05 xz -1 /var/spool/abrt/pyhook-2016-08-30-11:20:10-61697/sosreport-corpadmin-20160830112011.tar

81.7 60220 hdpbatch 11:19:52       00:30 bzip2 20-mowprod_2016-08-29.tsv

81.5 60218 hdpbatch 11:19:52       00:30 bzip2 18-mowprod_2016-08-29.tsv

81.3 60222 hdpbatch 11:19:52       00:30 bzip2 22-mowprod_2016-08-29.tsv

%CPU   PID USER      STARTED     ELAPSED COMMAND

94.0 62886 root     11:20:30       00:02 xz -1 /var/spool/abrt/pyhook-2016-08-30-11:20:22-62093/sosreport-corpadmin-20160830112023.tar

85.1 60218 hdpbatch 11:19:52       00:40 bzip2 18-mowprod_2016-08-29.tsv

85.0 60220 hdpbatch 11:19:52       00:40 bzip2 20-mowprod_2016-08-29.tsv

84.9 60213 hdpbatch 11:19:52       00:40 bzip2 13-mowprod_2016-08-29.tsv

%CPU   PID USER      STARTED     ELAPSED COMMAND

88.5 60220 hdpbatch 11:19:52       00:51 bzip2 20-mowprod_2016-08-29.tsv

88.3 60213 hdpbatch 11:19:52       00:51 bzip2 13-mowprod_2016-08-29.tsv

88.1 60218 hdpbatch 11:19:52       00:51 bzip2 18-mowprod_2016-08-29.tsv

88.0 60214 hdpbatch 11:19:52       00:51 bzip2 14-mowprod_2016-08-29.tsv

I hope it will help you to find culprit. Please fell free to give your feedback for any improvement.


  • 0

Tez job fails with ‘vertex failure’ error

When you run your hive job on tez execution engine then you may see job failure due to ‘vertex failure’ error. Or you may see following error in your logs.

Vertex failed, vertexName=Reducer 34, vertexId=vertex_1424999265634_0222_1_23, diagnostics=[Task failed, taskId=task_1424999265634_01422_1_23_000008, diagnostics=[AttemptID:attempt_1424999265634_01422_1_23_000008_0 Info:Error: java.lang.RuntimeException: java.lang.RuntimeException: Reduce operator initialization failed 
at org.apache.hadoop.hive.ql.exec.tez.TezProcessor.run(TezProcessor.java:188)
at org.apache.tez.runtime.LogicalIOProcessorRuntimeTask.run(LogicalIOProcessorRuntimeTask.java:307)
at org.apache.hadoop.mapred.YarnTezDagChild$5.run(YarnTezDagChild.java:564)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1594)
at org.apache.hadoop.mapred.YarnTezDagChild.main(YarnTezDagChild.java:553)
Caused by: java.lang.RuntimeException: Reduce operator initialization failed
at org.apache.hadoop.hive.ql.exec.tez.ReduceRecordProcessor.init(ReduceRecordProcessor.java:191)
at org.apache.hadoop.hive.ql.exec.tez.TezProcessor.run(TezProcessor.java:164)
… 6 more
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: : init not supported
at org.apache.hadoop.hive.ql.udf.generic.GenericUDAFStreamingEvaluator.init(GenericUDAFStreamingEvaluator.java:70)
at org.apache.hadoop.hive.ql.plan.PTFDeserializer.setupWdwFnEvaluator(PTFDeserializer.java:209)
at org.apache.hadoop.hive.ql.plan.PTFDeserializer.initializeWindowing(PTFDeserializer.java:130)
at org.apache.hadoop.hive.ql.plan.PTFDeserializer.initializePTFChain(PTFDeserializer.java:94)
at org.apache.hadoop.hive.ql.exec.PTFOperator.reconstructQueryDef(PTFOperator.java:145)
at org.apache.hadoop.hive.ql.exec.PTFOperator.initializeOp(PTFOperator.java:74)
at org.apache.hadoop.hive.ql.exec.Operator.initialize(Operator.java:376)
at org.apache.hadoop.hive.ql.exec.Operator.initialize(Operator.java:460)
at org.apache.hadoop.hive.ql.exec.Operator.initializeChildren(Operator.java:416)
at org.apache.hadoop.hive.ql.exec.ExtractOperator.initializeOp(ExtractOperator.java:40)
at org.apache.hadoop.hive.ql.exec.Operator.initialize(Operator.java:376)
at org.apache.hadoop.hive.ql.exec.tez.ReduceRecordProcessor.init(ReduceRecordProcessor.java:160)

This error is because Tez containers are not allocating enough memory to run the query.

Resolution: So now to solve this issue you have to increase memory for resources with adjusting following parameters .

tez.am.resource.memory.mb=4096 
tez.am.java.opts=-server -Xmx3276m -Djava.net.preferIPv4Stack=true -XX:+UseNUMA -XX:+UseParallelGC 
hive.tez.container.size=4096 
hive.tez.java.opts=-server -Xmx3276m -Djava.net.preferIPv4Stack=true -XX:+UseNUMA -XX:+UseParallelGC 

 


  • 0

heap size issue in Hive Metastore

Category : Hive

Sometime during your job running you may see job failure due to heap size. It might be because of metastore heap issue. It is encountering OutOfMemory errors, or is known to be insufficient to handle the cluster workload.

Resolution: To fix this issue you have to increase heap size for metastore in hive-end.sh (or hive-end.cmd) file. 

  • If the cluster is managed by Ambari, edit the configuration for the hive-end.sh or .cmd file in the Configuration tab for the Hive Service.
  • If the cluster is not managed by Ambari, edit the file directly and distribute it throughout the cluster
  • Locate the string that looks like this

export HADOOP_CLIENT_OPTS=”-Xmx${HADOOP_HEAPSIZE}m $HADOOP_CLIENT_OPTS ${HIVEMETASTORE_JMX_OPTS}”​ 

  • Change the last line (covering the HADOOP_CLIENT_OPTS to declare the requested heap size. For example, to allocate up to 2048m of heap, the last line would be changed to this

export HADOOP_CLIENT_OPTS=”$HADOOP_CLIENT_OPTS ${HIVEMETASTORE_JMX_OPTS} –Xmx2048m

 


  • 6

Hadoop Admin most lovable commands

If you are working on hadoop and you want to know about your cluster or you want to control your hadoop cluster then following commands should be handy to you. In this article i have tried to explain few commands which will help you a lot to do your day to day works.

  1. hdfs dfsadmin -report :  It will give you summarize view of your hadoop cluster like size,live nodes and their utilization.

[hdfs@m1]$ hdfs dfsadmin -report

Configured Capacity: 51886964736 (48.32 GB)

Present Capacity: 27887029262 (25.97 GB)

DFS Remaining: 24417319950 (22.74 GB)

DFS Used: 3469709312 (3.23 GB)

DFS Used%: 12.44%

Under replicated blocks: 2

Blocks with corrupt replicas: 0

Missing blocks: 0

Missing blocks (with replication factor 1): 2

————————————————-

Live datanodes (3):

————————————————-

2. hdfs dfsadmin -safemode get|enter| leave : It will tell you whether your NN is in safemode or not. if NN is in safemode then you case leave option with main command. 

[hdfs@m1]$ hdfs dfsadmin -safemode get

Safe mode is OFF in m1.hdp22/192.168.56.41:8020

Safe mode is OFF in m2.hdp22/192.168.56.42:8020

3. hadoop version : It will help you to get which hadoop version you are using:

[hdfs@m1]$ hadoop version

Hadoop 2.7.1.2.3.4.0-3485

Subversion git@github.com:hortonworks/hadoop.git -r ef0582ca14b8177a3cbb6376807545272677d730

Compiled by jenkins on 2015-12-16T03:01Z

Compiled with protoc 2.5.0

From source with checksum cf48a4c63aaec76a714c1897e2ba8be6

This command was run using /usr/hdp/2.3.4.0-3485/hadoop/hadoop-common-2.7.1.2.3.4.0-3485.jar

4. classpath : This command will help you to know your hadoop class path, which will help you to get the Hadoop jar and the required libraries:

[hdfs@m1 ~]$ hadoop classpath

/usr/hdp/2.3.4.0-3485/hadoop/conf:/usr/hdp/2.3.4.0-3485/hadoop/lib/*:/usr/hdp/2.3.4.0-3485/hadoop/.//*:/usr/hdp/2.3.4.0-3485/hadoop-hdfs/./:/usr/hdp/2.3.4.0-3485/hadoop-hdfs/lib/*:/usr/hdp/2.3.4.0-3485/hadoop-hdfs/.//*:/usr/hdp/2.3.4.0-3485/hadoop-yarn/lib/*:/usr/hdp/2.3.4.0-3485/hadoop-yarn/.//*:/usr/hdp/2.3.4.0-3485/hadoop-mapreduce/lib/*:/usr/hdp/2.3.4.0-3485/hadoop-mapreduce/.//*::/usr/share/java/mysql-connector-java-5.1.17.jar:/usr/share/java/mysql-connector-java.jar:/usr/hdp/2.3.4.0-3485/tez/*:/usr/hdp/2.3.4.0-3485/tez/lib/*:/usr/hdp/2.3.4.0-3485/tez/conf

5. hadoop queue : This command will help you to get information about your yarn queue :

Usage: hadoop queue [-list] | [-info <job-queue-name> [-showJobs]] | [-showacls]

[hdfs@m1 ~]$ hadoop queue -list

DEPRECATED: Use of this script to execute mapred command is deprecated.

Instead use the mapred command for it.

16/08/09 05:44:35 INFO impl.TimelineClientImpl: Timeline service address: http://m2.hdp22:8188/ws/v1/timeline/

16/08/09 05:44:36 INFO client.ConfiguredRMFailoverProxyProvider: Failing over to rm2

======================

Queue Name : batch

Queue State : running

Scheduling Info : Capacity: 30.000002, MaximumCapacity: 60.000004, CurrentCapacity: 0.0

======================

Queue Name : default

Queue State : running

Scheduling Info : Capacity: 30.000002, MaximumCapacity: 90.0, CurrentCapacity: 0.0

======================

Queue Name : user

Queue State : running

Scheduling Info : Capacity: 40.0, MaximumCapacity: 40.0, CurrentCapacity: 0.0

    ======================

    Queue Name : ado

    Queue State : running

    Scheduling Info : Capacity: 40.0, MaximumCapacity: 100.0, CurrentCapacity: 0.0

    ======================

    Queue Name : aodp

    Queue State : running

    Scheduling Info : Capacity: 40.0, MaximumCapacity: 40.0, CurrentCapacity: 0.0

    ======================

    Queue Name : di

    Queue State : running

    Scheduling Info : Capacity: 20.0, MaximumCapacity: 23.0, CurrentCapacity: 0.0

Or you can get information about a specific queue. 

[hdfs@m1 ~]$ hadoop queue -info ado

DEPRECATED: Use of this script to execute mapred command is deprecated.

Instead use the mapred command for it.

16/08/09 05:49:14 INFO impl.TimelineClientImpl: Timeline service address: http://m2.hdp22:8188/ws/v1/timeline/

16/08/09 05:49:15 INFO client.ConfiguredRMFailoverProxyProvider: Failing over to rm2

======================

Queue Name : ado

Queue State : running

Scheduling Info : Capacity: 40.0, MaximumCapacity: 100.0, CurrentCapacity: 0.0

6. yarn job -kill <job_id> : It will help you to kill your running mapred job: 

yarn job -kill job_1462173172032_31967 or you can kill your running application by following command.

yarn application -kill application_1462173172032_31967

7. hadoop distcp : It will help you to copy file or directories recursively within cluster or from one cluster to another cluster: 

[hdfs@m1 ~]$ hadoop distcp hdfs://HDPINFHA/user/s0998dnz/input.txt hdfs://HDPTSTHA/tmp/

Note: HDPINFHA and HDPTSTHA both are namenode high availability id 

8. hadoop archive -archiveName <your_archive_name>.har -p <path_to_be_archive> <dir_to_be_archive> <destination>: This will hep you to hadoop archive yoru hdfs files. 

[hdfs@m1 ~]$ hadoop archive -archiveName testing.har -p /user saurabh /test

It will run a mapred job and will archive your dir.

[hdfs@m1 ~]$ hadoop fs -ls /test/

Found 1 items

drwxr-xr-x   – hdfs hdfs          0 2016-08-09 06:09 /test/testing.har

If you want to list out inside archival file then you can not read by normal ls command. You have to use -lsr like below:

[hdfs@m1 ~]$ hadoop fs -lsr /test/testing.har

lsr: DEPRECATED: Please use ‘ls -R’ instead.

-rw-r–r–   3 hdfs hdfs          0 2016-08-09 06:09 /test/testing.har/_SUCCESS

-rw-r–r–   5 hdfs hdfs        565 2016-08-09 06:09 /test/testing.har/_index

-rw-r–r–   5 hdfs hdfs         23 2016-08-09 06:09 /test/testing.har/_masterindex

-rw-r–r–   3 hdfs hdfs   20710951 2016-08-09 06:09 /test/testing.har/part-0

9. hadoop fsck / : fsck command is used to check the HDFS file system. There are different arguments that can be passed with this command to emit different results.

[hdfs@m1 ~]$ hadoop fsck /

Connecting to namenode via http://m1.hdp22:50070/fsck?ugi=hdfs&path=%2F

FSCK started by hdfs (auth:SIMPLE) from /192.168.56.41 for path / at Tue Aug 09 06:23:02 EDT 2016

……………………………………………………………………………………….

…………………………………………………………………………..Status: HEALTHY

Total size: 1161798713 B (Total open files size: 2242 B)

Total dirs: 11729

Total files: 1086

Total symlinks: 0 (Files currently being written: 4)

Total blocks (validated): 1056 (avg. block size 1100188 B) (Total open file blocks (not validated): 4)

Minimally replicated blocks: 1056 (100.0 %)

Over-replicated blocks: 0 (0.0 %)

Under-replicated blocks: 4 (0.37878788 %)

Mis-replicated blocks: 0 (0.0 %)

Default replication factor: 3

Average block replication: 2.9734848

Corrupt blocks: 0

Missing replicas: 18 (0.569981 %)

Number of data-nodes: 3

Number of racks: 1

FSCK ended at Tue Aug 09 06:23:05 EDT 2016 in 2764 milliseconds

The filesystem under path ‘/’ is HEALTHY

10. hadoop fsck / -files : It displays all the files in HDFS while checking. 

11. hadoop fsck / -files -blocksIt displays all the blocks of the files while checking.

12. hadoop fsck / -files -blocks -locations : It displays all the files block locations while checking.

13. hadoop fsck / -files -blocks -locations -racksThis command is used to display the networking topology for data-node locations.

14. hadoop fsck -deleteThis command will delete the corrupted files in HDFS. 

15. hadoop fsck -move :This command is used to move the corrupted files to a particular directory, by default it will move to the /lost+found directory. 

16. hadoop dfsadmin -metasave file_name.txt :This command is used to save the meta data that is present in the namenode in a file in the HDFS. 

17. hadoop dfsadmin -refreshNodesThis command is used to refresh the data nodes that are allowed to connect to the name node. 

18. hadoop fs -count -q /mydirChecks for the quota space for the specified directory or a file.

19. hadoop dfsadmin -setSpaceQuota 10M /dir_name :  This command is used to set the space quota space for a particular directory. Now we will set the directory quota to 10MB and then we will check it using the command hadoop fs -count -q /mydir. 

20. hadoop dfsadmin -clrSpaceQuota /mydir : This command is used to clear the allocated quota to a particular directory in HDFS. Now we will clear the quota which we have previously created and check the quota again.

 

I hope all the above commands will help you to control your cluster. Please fell free to give your feedback.


  • 0

Rack Awareness on Hadoop

Category : Bigdata

If you have Hadoop clusters of more than 30-40 nodes then it is better you have configured it with rack awarenwss because communication between two data nodes on the same rack is efficient than the same between two nodes on different racks.

It also have us to improve network traffic while reading/writing HDFS files, NameNode chooses data nodes which are on the same rack or a near by rack to read/write request (client node).

NameNode achieves this rack information by maintaining  rack ids of each data node. This concept of choosing closer data nodes based on racks information is called Rack Awareness in Hadoop.

Note : A default Hadoop installation assumes all the nodes belong to the same rack.

So in this article I have explained how to make your cluster rack aware.

Step 1: Create a topology data file anywhere in Master node(i.e NN) and insert all datanodes ip address corresponding to rack. 

[root@m1 ~]# vi topology.data

[root@m1 ~]# cat topology.data

192.168.56.51 01

192.168.56.52 02

192.168.56.53 01

192.168.56.54 02

192.168.56.55 01

192.168.56.56 02

Step 2: Now create rack-topology.sh for above data files. 

root@m1 ~]# vi rack-topology.sh

[root@m1 ~]# cat rack-topology.sh

#!/bin/bash

# Adjust/Add the property “net.topology.script.file.name”

# to core-site.xml with the “absolute” path the this

# file.  ENSURE the file is “executable”.

# Supply appropriate rack prefix

RACK_PREFIX=default

# To test, supply a hostname as script input:

if [ $# -gt 0 ]; then

CTL_FILE=${CTL_FILE:-“rack_topology.data”}

HADOOP_CONF=${HADOOP_CONF:-“/etc/hadoop/conf”}

if [ ! -f ${HADOOP_CONF}/${CTL_FILE} ]; then

  echo -n “/$RACK_PREFIX/rack “

  exit 0

fi

while [ $# -gt 0 ] ; do

  nodeArg=$1

  exec< ${HADOOP_CONF}/${CTL_FILE}

  result=””

  while read line ; do

    ar=( $line )

    if [ “${ar[0]}” = “$nodeArg” ] ; then

      result=”${ar[1]}”

    fi

  done

  shift

  if [ -z “$result” ] ; then

    echo -n “/$RACK_PREFIX/rack “

  else

    echo -n “/$RACK_PREFIX/rack_$result “

  fi

done

else

  echo -n “/$RACK_PREFIX/rack “

fi

Step 3: Add this property into core-site.xml or through ambari add following property. 

<property>
<name>topology.script.file.name</name>
<value>/home/hadoop/topology.sh</value>
</property>
or net.topology.script.file.name to your ambari.
Step 4: Now you need to restart your hdfs service to get it reflect. 
I hope this article helped you to make your cluster rack awareness. Please fell free to give your feedback.

  • 0

Namenode installation issue

When you install hdp and during installation if something goes wrong with hdfs components(like namenode) then you may see following errors.

File “/usr/lib/python2.6/site-packages/resource_management/core/shell.py”, line 140, in _call_wrapper
result = _call(command, **kwargs_copy)
File “/usr/lib/python2.6/site-packages/resource_management/core/shell.py”, line 291, in _call
raise Fail(err_msg)
resource_management.core.exceptions.Fail: Execution of ‘yes Y | hdfs –config /usr/hdp/current/hadoop-client/conf namenode -format’ returned 127.
/usr/hdp/current/hadoop-client/bin/hdfs: line 18: /usr/hdp/2.3.2.0-2950//hadoop-hdfs/bin/hdfs.distro: No such file or directory
yes: standard output: Broken pipe
yes: write error
stdout: /var/lib/ambari-agent/data/output-594.txt

ROOT CAUSE:
The packages were not installed correctly during cluster installation. There were many files that were missing under /usr/hdp/<HDP_VERSION>/hadoop-hdfs/bin.

RESOLUTION:

  • Run below command to check which package owns the missing file:
    • $ rpm -qf /usr/hdp/2.3.2.0-2950/hadoop-hdfs/bin/hdfs.distro
      hadoop_2_3_2_0_2950-hdfs-2.7.1.2.3.2.0-2950.el6.x86_64
  • Re-install the package by running below command.
    $ yum reinstall hadoop_2_3_2_0_2950-hdfs-2.7.1.2.3.2.0-2950.el6.x86_64

I hope it will help you to solve your namenode issue. Please feel free to give your feedback.

 


  • 0

How to debug distcp jobs

Tags :

Category : Bigdata

Some time when you run distcp jobs on cluster and you see some failure or performance then you want to debug it then you can go by using following command.

To turn on debug mode on the job level, issue the following command before executing the distcp job:

[root@m1.hdp22] export HADOOP_ROOT_LOGGER=hadoop.root.logger=Debug,console

To turn on debugmode on the mapper level, run distcp with mapper debug option as following:

[root@m1.hdp22] hadoop distcp -Dmapreduce.map.java.opts-="-Xmxyyyy -Dhadoop.root.logger=DEBUG,console"

  • 0

How to check contents of a JAR file

Tags :

Category : Bigdata

Many times we have to check what are the packages,classes included in one jar files, but due to black box(just a simple jar ) we face a trouble to check.

So with the help of following ways you can check it.

jar tf <PATH_TO_JAR

But if you are looking for a specific class or package then you can use following command.

jar tf <PATH_TO_JAR> | grep -i <PARTIAL_NAME_OF_CLASS>


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If you delete /hdp/apps/ dir from hdfs

There is situation when unfortunately and unknowingly you delete /hdp/apps/2.3.4.0-3485  with skipTrash then you will be in trouble and other services will be impacted. You will not be able to run hive,mapreduce or sqoop command, You will get following error.

[root@m1 ranger-hdfs-plugin]# hadoop fs -rmr -skipTrash /hdp/apps/2.3.4.0-3485

rmr: DEPRECATED: Please use ‘rm -r’ instead.

Deleted /hdp/apps/2.3.4.0-3485

So when I am trying to access to hive it is throwing below error.

[root@m1 admin]# hive

WARNING: Use “yarn jar” to launch YARN applications.

16/07/27 22:05:04 WARN conf.HiveConf: HiveConf of name hive.server2.enable.impersonation does not exist

Logging initialized using configuration in file:/etc/hive/2.3.4.0-3485/0/hive-log4j.properties

Exception in thread “main” java.lang.RuntimeException: java.io.FileNotFoundException: File does not exist: /hdp/apps/2.3.4.0-3485/tez/tez.tar.gz

at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:507)

 

Resolution: Don’t worry friends you can resolve this issue by following give steps.

Note: You have to replace version of your hdp.

Step 1: First you will have to create following required dirs :

hdfs dfs -mkdir -p /hdp/apps/<2.3.4.0-$BUILD>/mapreduce

hdfs dfs -mkdir -p /hdp/apps/<2.3.4.0-$BUILD>/hive

hdfs dfs -mkdir -p /hdp/apps/<2.3.4.0-$BUILD>/tez

hdfs dfs -mkdir -p /hdp/apps/<2.3.4.0-$BUILD>/sqoop

hdfs dfs -mkdir -p /hdp/apps/<2.3.4.0-$BUILD>/pig

Step 2: Now you have to copy required jars in related dir. 

hdfs dfs -put /usr/hdp/2.3.4.0-$BUILD/hadoop/mapreduce.tar.gz /hdp/apps/2.3.4.0-$BUILD/mapreduce/

hdfs dfs -put /usr/hdp/2.3.2.0-<$version>/hive/hive.tar.gz /hdp/apps/2.3.2.0-<$version>/hive/
hdfs dfs -put /usr/hdp/<hdp_version>/tez/lib/tez.tar.gz /hdp/apps/<hdp_version>/tez/
hdfs dfs -put /usr/hdp/<hdp-version>/sqoop/sqoop.tar.gz /hdp/apps/<hdp-version>/sqoop/
hdfs dfs -put /usr/hdp/<hdp-version>/pig/pig.tar.gz /hdp/apps/<hdp-version>/pig/

Step 3: Now you need to change dir owner and then change permission:

hdfs dfs -chown -R hdfs:hadoop /hdp
hdfs dfs -chmod -R 555 /hdp/apps/2.3.4.0-$BUILD

Now you will be able to start your hive CLI or other jobs.

[root@m1 ~]# hive

WARNING: Use “yarn jar” to launch YARN applications.

16/07/27 23:33:42 WARN conf.HiveConf: HiveConf of name hive.server2.enable.impersonation does not exist

Logging initialized using configuration in file:/etc/hive/2.3.4.0-3485/0/hive-log4j.properties

hive>

I hope it will help you to restore your cluster. Please feel free to give your suggestion.