agent.sinks=hpd
agent.sinks.hpd.type=hdfs
agent.sinks.hpd.channel=memoryChannel
agent.sinks.hpd.hdfs.path=hdfs://master:9000/user/hduser/gde
agent.sinks.hpd.hdfs.fileType=DataStream
agent.sinks.hpd.hdfs.writeFormat=Text
agent.sinks.hpd.hdfs.rollSize=0
agent.sinks.hpd.hdfs.batchSize=1000
agent.sinks.hpd.hdfs.fileSuffix=.i
agent.sinks.hpd.hdfs.rollCount=1000
agent.sinks.hpd.hdfs.rollInterval=0
I'm trying to use HDFS Sink to write events to HDFS. And have tried Size, Count and Time bases rolling but none is working as expected. It is generating too many small files in HDFS like:
-rw-r--r-- 2 hduser supergroup 11617 2016-03-05 19:37 hdfs://master:9000/user/hduser/gde/FlumeData.1457186832879.i
-rw-r--r-- 2 hduser supergroup 1381 2016-03-05 19:37 hdfs://master:9000/user/hduser/gde/FlumeData.1457186832880.i
-rw-r--r-- 2 hduser supergroup 553 2016-03-05 19:37 hdfs://master:9000/user/hduser/gde/FlumeData.1457186832881.i
-rw-r--r-- 2 hduser supergroup 2212 2016-03-05 19:37 hdfs://master:9000/user/hduser/gde/FlumeData.1457186832882.i
-rw-r--r-- 2 hduser supergroup 1379 2016-03-05 19:37 hdfs://master:9000/user/hduser/gde/FlumeData.1457186832883.i
-rw-r--r-- 2 hduser supergroup 2762 2016-03-05 19:37 hdfs://master:9000/user/hduser/gde/FlumeData.1457186832884.i.tmp
Please assist to resolve the given problem. I'm using flume 1.6.0
~Thanks
My provided configurations were all correct. The reason behind such behavior was HDFS. I had 2 data nodes out of which one was down. So, files were not achieving minimum required replication. In Flume logs one can see below warning message too:
"Block Under-replication detected. Rotating file."
To remove this problem one can opt for any of below solution:-
Up the data node to achieve required replication of blocks, or
Set property hdfs.minBlockReplicas accordingly.
~Thanks
You are now rolling the files for every 1000 items. You can try either of two methods mentioned below.
Try increasing hdfs.rollCount to much higher value, this value decides number of events contained in each rolled file.
Remove hdfs.rollCount and set hdfs.rollInterval to interval at which you want to roll your file. Say hdfs.rollInterval = 600 to roll file every 10 minutes.
For more information refer Flume Documentation
Related
Seeing nonsense values for user names in folder permissions for NFS mounted HDFS locations, while the HDFS locations themselves (using Hortonworks HDP 3.1) appear fine. Eg.
➜ ~ ls -lh /nfs_mount_root/user
total 6.5K
drwx------. 3 accumulo hdfs 96 Jul 19 13:53 accumulo
drwxr-xr-x. 3 92668751 hadoop 96 Jul 25 15:17 admin
drwxrwx---. 3 ambari-qa hdfs 96 Jul 19 13:54 ambari-qa
drwxr-xr-x. 3 druid hadoop 96 Jul 19 13:53 druid
drwxr-xr-x. 2 hbase hdfs 64 Jul 19 13:50 hbase
drwx------. 5 hdfs hdfs 160 Aug 26 10:41 hdfs
drwxr-xr-x. 4 hive hdfs 128 Aug 26 10:24 hive
drwxr-xr-x. 5 h_etl hdfs 160 Aug 9 14:54 h_etl
drwxr-xr-x. 3 108146 hdfs 96 Aug 1 15:43 ml1
drwxrwxr-x. 3 oozie hdfs 96 Jul 19 13:56 oozie
drwxr-xr-x. 3 882121447 hdfs 96 Aug 5 10:56 q_etl
drwxrwxr-x. 2 spark hdfs 64 Jul 19 13:57 spark
drwxr-xr-x. 6 zeppelin hdfs 192 Aug 23 15:45 zeppelin
➜ ~ hadoop fs -ls /user
Found 13 items
drwx------ - accumulo hdfs 0 2019-07-19 13:53 /user/accumulo
drwxr-xr-x - admin hadoop 0 2019-07-25 15:17 /user/admin
drwxrwx--- - ambari-qa hdfs 0 2019-07-19 13:54 /user/ambari-qa
drwxr-xr-x - druid hadoop 0 2019-07-19 13:53 /user/druid
drwxr-xr-x - hbase hdfs 0 2019-07-19 13:50 /user/hbase
drwx------ - hdfs hdfs 0 2019-08-26 10:41 /user/hdfs
drwxr-xr-x - hive hdfs 0 2019-08-26 10:24 /user/hive
drwxr-xr-x - h_etl hdfs 0 2019-08-09 14:54 /user/h_etl
drwxr-xr-x - ml1 hdfs 0 2019-08-01 15:43 /user/ml1
drwxrwxr-x - oozie hdfs 0 2019-07-19 13:56 /user/oozie
drwxr-xr-x - q_etl hdfs 0 2019-08-05 10:56 /user/q_etl
drwxrwxr-x - spark hdfs 0 2019-07-19 13:57 /user/spark
drwxr-xr-x - zeppelin hdfs 0 2019-08-23 15:45 /user/zeppelin
Notice the difference for users ml1 and q_etl that they have numerical user values when running ls on the NFS locations, rather then their user names.
Even doing something like...
[hdfs#HW04 ml1]$ hadoop fs -chown ml1 /user/ml1
does not change the NFS permissions. Even more annoying, when trying to change the NFS mount permissions as root, we see
[root#HW04 ml1]# chown ml1 /nfs_mount_root/user/ml1
chown: changing ownership of ‘/nfs_mount_root/user/ml1’: Permission denied
This causes real problems, since the differing uid means that I can't access these dirs even as the "correct" user to write to them. Not sure what to make of this. Anyone with more Hadoop experience have any debugging suggestions or fixes?
UPDATE:
Doing a bit more testing / debugging, found that the rules appear to be...
If the NFS server node has no uid (or gid?) that matches the uid of the user on the node accessing the NFS mount, we get the weird uid values as seen here.
If there is a uid associated to the username of the user on the requesting node, then that is the uid user that we see assigned to the location when accessing via NFS (even if that uid on the NFS server node is not actually for the requesting user), eg.
[root#HW01 ~]# clush -ab id ml1
---------------
HW[01,04] (2)
---------------
uid=1025(ml1) gid=1025(ml1) groups=1025(ml1)
---------------
HW[02-03] (2)
---------------
uid=1027(ml1) gid=1027(ml1) groups=1027(ml1)
---------------
HW05
---------------
uid=1026(ml1) gid=1026(ml1) groups=1026(ml1)
[root#HW01 ~]# exit
logout
Connection to hw01 closed.
➜ ~ ls -lh /hdpnfs/user
total 6.5K
...
drwxr-xr-x. 6 atlas hdfs 192 Aug 27 12:04 ml1
...
➜ ~ hadoop fs -ls /user
Found 13 items
...
drwxr-xr-x - ml1 hdfs 0 2019-08-27 12:04 /user/ml1
...
[root#HW01 ~]# clush -ab id atlas
---------------
HW[01,04] (2)
---------------
uid=1027(atlas) gid=1005(hadoop) groups=1005(hadoop)
---------------
HW[02-03] (2)
---------------
uid=1024(atlas) gid=1005(hadoop) groups=1005(hadoop)
---------------
HW05
---------------
uid=1005(atlas) gid=1006(hadoop) groups=1006(hadoop)
If wondering why I have, user on the cluster that have varying uids across the cluster nodes, see the problem posted here: How to properly change uid for HDP / ambari-created user? (note that these odd uid setting for hadoop service users was set up by Ambari by default).
After talking with someone more knowledgeable in HDP hadoop, found that the problem is that when Ambari was setup and run to initially install the hadoop cluster, there may have been other preexisting users on the designated cluster nodes.
Ambari creates its various service users by giving them the next available UID of a nodes available block of user UIDs. However, prior to installing Ambari and HDP on the nodes, I created some users on the to-be namenode (and others) in order to do some initial maintenance checks and tests. I should have just done this as root. Adding these extra users offset the UID counter on those nodes and so as Ambari created users on the nodes and incremented the UIDs, it was starting from different starting counter values. Thus, the UIDs did not sync and caused problems with HDFS NFS.
To fix this, I...
Used Ambari to stop all running HDP services
Go to Service Accounts in Ambari and copy all of the expected service users name strings
For each user, run something like id <service username> to get the group(s) for each user. For service groups (which may have multiple members), can do something like grep 'group-name-here' /etc/group. I recommend doing it this way as the Ambari docs of default users and groups does not have some of the info that you can get here.
Use userdel and groupdel to remove all the Ambari service users and groups
Then recreate all the groups across the cluster
Then recreate all the users across the cluster (may need to specify UID if nodes have other users not on others)
Restart the HDP services (hopefully everything should still run as if nothing happend, since HDP should be looking for the literal string (not the UIDs))
For the last parts, can use something like clustershell, eg.
# remove user
$ clush -ab userdel <service username>
# check that the UID you want to use is actually available on all nodes
$ clush -ab id <some specific UID you want to use>
# assign that UID to a new service user
$ clush -ab useradd --uid <the specific UID> --gid <groupname> <service username>
To get the lowest common available UID from each node, used...
# for UID
getent passwd | awk -F: '($3>1000) && ($3<10000) && ($3>maxuid) { maxuid=$3; } END { print maxuid+1; }'
# for GID
getent passwd | awk -F: '($4>1000) && ($4<10000) && ($4>maxuid) { maxuid=$4; } END { print maxuid+1; }'
Ambari also creates some /home dirs for users. Once you are done recreating the users, will need to change the permissions for the dirs (can also use something like clush there as well).
* Note that this was a huge pain and you would need to manually correct the UIDs of users whenever you added another cluster node. I did this for a test cluster, but for production (or even a larger test) you should just useKerberos or SSSD + Active Directory.
I have a hadoop MapReduce program that distributes keys unevenly.
Some reducers end up with two keys, some with one key, and some with none.
how do I force hadoop to distribute each partition with a certain key to a separate reducer. I have nine unique keys of the form:
0,0
0,1
0,2
1,0
1,1
1,2
2,0
2,1
2,2
and I set the job.setNumReduceTasks(9);
but the hashpartitioner seems to hash two keys to the same hashcode causing overlapped keys being sent to the same reducer and leaving some reducers idle.
Does a random partitioner resolve this? will it send each unique key to a random reducer guaranteeing each reducer receives a single key. How do I enable it and replace the default?
EDIT:
can someone please explain why my output looks like
-rw-r--r-- 1 user supergroup 0 2018-04-19 18:58 outbin9/_SUCCESS
drwxr-xr-x - user supergroup 0 2018-04-19 18:57 outbin9/_logs
-rw-r--r-- 1 user supergroup 869 2018-04-19 18:57 outbin9/part-r-00000
-rw-r--r-- 1 user supergroup 1562 2018-04-19 18:57 outbin9/part-r-00001
-rw-r--r-- 1 user supergroup 913 2018-04-19 18:58 outbin9/part-r-00002
-rw-r--r-- 1 user supergroup 1771 2018-04-19 18:58 outbin9/part-r-00003
-rw-r--r-- 1 user supergroup 979 2018-04-19 18:58 outbin9/part-r-00004
-rw-r--r-- 1 user supergroup 880 2018-04-19 18:58 outbin9/part-r-00005
-rw-r--r-- 1 user supergroup 0 2018-04-19 18:58 outbin9/part-r-00006
-rw-r--r-- 1 user supergroup 0 2018-04-19 18:58 outbin9/part-r-00007
-rw-r--r-- 1 user supergroup 726 2018-04-19 18:58 outbin9/part-r-00008
The larger groups part-r-00001 and part-r-00003 have received keys 1,0 and 2,2 / 0,0 and 1,2 respectively. And notice that part-r-00006 and part-r-00007 are empty.
HashPartitioner is the default partitioner in Hadoop, which creates one Reduce task for each unique “key”. All the values with the same key goes to the same instance of your reducer, in a single call to the reduce function.
If user is interested to store a particular group of results in different reducers, then the user can write his own partitioner implementation. It can be general purpose or custom made to the specific data types or values that you expect to use in user application.
Custom Partitioner is a process that allows you to store the results in different reducers, based on the user condition. By setting a partitioner to partition by the key, we can guarantee that, records for the same key will go to the same reducer. A partitioner ensures that only one reducer receives all the records for that particular key.
sample example link
I am trying to understand Namenode and I referred to online material and referring to book Hadoop: The definitive guide as well.
I understand that Namenode has concept like : "edit logs", "fsimage", and I can see the following files in my Namenode.
========================================================================
-rw-rw-r-- 1 vevaan24 vevaan24 1048576 Nov 23 22:53 edits_0000000000000000001-0000000000000000001
-rw-r--r-- 1 root root 1048576 Nov 23 23:42 edits_0000000000000000002-0000000000000000002
-rw-rw-r-- 1 vevaan24 vevaan24 1048576 Nov 24 00:07 edits_0000000000000000003-0000000000000000003
-rw-rw-r-- 1 vevaan24 vevaan24 1048576 Nov 24 21:03 edits_0000000000000000004-0000000000000000004
-rw-rw-r-- 1 vevaan24 vevaan24 1048576 Nov 24 22:59 edits_0000000000000000005-0000000000000000005
-rw-r--r-- 1 root root 1048576 Nov 24 23:00 edits_0000000000000000006-0000000000000000006
-rw-rw-r-- 1 vevaan24 vevaan24 1048576 Nov 25 21:15 edits_0000000000000000007-0000000000000000007
-rw-rw-r-- 1 vevaan24 vevaan24 1048576 Nov 25 21:34 edits_0000000000000000008-0000000000000000008
-rw-r--r-- 1 root root 1048576 Nov 26 02:13 edits_inprogress_0000000000000000009
-rw-rw-r-- 1 vevaan24 vevaan24 355 Nov 25 21:15 fsimage_0000000000000000006
-rw-rw-r-- 1 vevaan24 vevaan24 62 Nov 25 21:15 fsimage_0000000000000000006.md5
-rw-r--r-- 1 root root 355 Nov 26 00:12 fsimage_0000000000000000008
-rw-r--r-- 1 root root 62 Nov 26 00:12 fsimage_0000000000000000008.md5
-rw-r--r-- 1 root root 2 Nov 26 00:12 seen_txid
-rw-rw-r-- 1 vevaan24 vevaan24 201 Nov 26 00:12 VERSION
=========================================================================
As expected I see all these files in my namenode. However I haven't understood this concept, I have following questions, can anyone please help me understand this.
Q1) What are fsimage files? Why many fsimage files are present?
Q2) What are edit_000 file? Why many edit_000 file are present?
Q3) What are there .md5 files? What purpose do they serve?
I also read that NAMENODE keeps some data in MEMORY and some data it keeps in HARD-DISK, BUT it is bit confusing to understand what kind of information is stored in hard disk and what remains in memory.
Q4) Do Namenode memory have information taken from fsimage or edit_000 OR both?
Q5) When Namenode and Datanode is restarted, how is the meta-data constructed (that is, which file stored in which datanode, block etc.).
Ok I try to explain:
EditLog
The EditLog is a transactional log to record every change that occurs to file system metadata. For example Creating a new file, renaming the file and so on. This will always generate an entry in the EditLog.
FsImage
This file contains the entire file system namespace, including the mapping of blocks to files and file system properties. So wich file consists of which blocks. Which blocks are saved where and so on.
If you start your NameNode, Hadoop loads the complete FsImage file into your memory. After that applies all the transactions from the EditLog to the in-memory representation of the FsImage, and flushes out this new version into a new FsImage on disk. This only happens once (on startup). After that Hadoop is only working with the in-memory representation. The FsImage on your HDD ist not touched.
Some of your Questions
Q1) Why many fsimage files are present?
As is explaned the FsImage is loaded, EditLog is flushed and than a new Version is saved.
Q1) Why many edit_000 file are present?
After Hadoop flushed the EditLog and persist a new Version of FsImage it starts a new EditLog. This is called a checkpoint in Hadoop
Q3) What are there .md5 files? What purpose do they serve?
MD5 is a hash to check if the FsImage is not broken.
Q5) When Namenode and Datanode is restarted, how is the meta-data constructed (that is, which file stored in which datanode, block etc.).
The information is persisted in the FsImage.
I hope i could help.
With MapReduce v2, the output data that comes out from a map or a reduce task is saved in the local disk or the HDFS when all the tasks finish.
Since tasks end at different times, I was expecting that the data were written as a task finish. For example, task 0 finish and so the output is written, but task 1 and task 2 are still running. Now task 2 finish the output is written, and task 1 is still running. Finally, task 1 finish and the last output is written. But this does not happen. The outputs only appear in the local disk or HDFS when all the tasks finish.
I want to access the task output as the data is being produced. Where is the output data before all the tasks finish?
Update
After I have set these params in mapred-site.xml
<property><name>mapreduce.task.files.preserve.failedtasks</name><value>true</value></property>
<property><name>mapreduce.task.files.preserve.filepattern</name><value>*</value></property>
and these params in hdfs-site.xml
<property> <name>dfs.name.dir</name> <value>/tmp/data/dfs/name/</value> </property>
<property> <name>dfs.data.dir</name> <value>/tmp/data/dfs/data/</value> </property>
And this value in core-site.xml
<property> <name>hadoop.tmp.dir</name> <value>/tmp/hadoop-temp</value> </property>
but I still can't found where the intermediate output or the final output is saved as they are produced by the tasks.
I have listed all directories in hdfs dfs -ls -R / and in the tmp dir I have only found the job configuration files.
drwx------ - root supergroup 0 2016-08-11 16:17 /tmp/hadoop-yarn/staging/root/.staging/job_1470912033891_0002
-rw-r--r-- 1 root supergroup 0 2016-08-11 16:17 /tmp/hadoop-yarn/staging/root/.staging/job_1470912033891_0002/COMMIT_STARTED
-rw-r--r-- 1 root supergroup 0 2016-08-11 16:17 /tmp/hadoop-yarn/staging/root/.staging/job_1470912033891_0002/COMMIT_SUCCESS
-rw-r--r-- 10 root supergroup 112872 2016-08-11 16:14 /tmp/hadoop-yarn/staging/root/.staging/job_1470912033891_0002/job.jar
-rw-r--r-- 10 root supergroup 6641 2016-08-11 16:14 /tmp/hadoop-yarn/staging/root/.staging/job_1470912033891_0002/job.split
-rw-r--r-- 1 root supergroup 797 2016-08-11 16:14 /tmp/hadoop-yarn/staging/root/.staging/job_1470912033891_0002/job.splitmetainfo
-rw-r--r-- 1 root supergroup 88675 2016-08-11 16:14 /tmp/hadoop-yarn/staging/root/.staging/job_1470912033891_0002/job.xml
-rw-r--r-- 1 root supergroup 439848 2016-08-11 16:17 /tmp/hadoop-yarn/staging/root/.staging/job_1470912033891_0002/job_1470912033891_0002_1.jhist
-rw-r--r-- 1 root supergroup 105176 2016-08-11 16:14 /tmp/hadoop-yarn/staging/root/.staging/job_1470912033891_0002/job_1470912033891_0002_1_conf.xml
Where is the output saved? I am talking about the output that it is stored as it is being produced by the tasks, and not the final output that comes when all map or reduce tasks finish.
The output put of a task is in <output dir>/_temporary/1/_temporary.
HDFS /tmp directory mainly used as a temporary storage during mapreduce operation. Mapreduce artifacts, intermediate data etc will be kept under this directory. These files will be automatically cleared out when mapreduce job execution completes. If you delete this temporary files, it can affect the currently running mapreduce jobs.
Answer from this stackoverflow link:
It's not a good practice to depend on temporary files, whose location and format can change anytime between releases.
Anyway, setting mapreduce.task.files.preserve.failedtasks to true will keep the temporary files for all the failed tasks and setting mapreduce.task.files.preserve.filepattern to regex of the ID of the task will keep the temporary files for the matching pattern irrespective of the task success or failure.
There is some more information in the same post.
I have a local file
-rw-r--r-- 1 me developers 102445154 Oct 22 10:02 file1.csv
which I'm attempting to put to hdfs:
/usr/bin/hdfs dfs -put ./file1.csv hdfs://000.00.00.00/user/me/
which works fine, but the group is wrong
-rw-r--r-- 3 me me 102445154 2013-10-22 10:23 hdfs://000.00.00.00/user/file1.csv
How do I get the group developers to come with?
Use the chgrp option on the file.