We are using hadoop-2.0.0-cdh4.0.0 and we start namenode using hadoop namenode, how do I start the hadoop processes in either 0.20 mode or 0.23 mode?
You'll need to install and configure these version of hadoop if you want to use them - there is no 'switch' to allow 2.0.0 (or any other hadoop version) to run in a lower version / mode.
Related
I'm writing a spark program that download different jars from maven based on the environment it runs on, each for a different version of Hadoop distribution (e.g. CDH, HDP, MapR).
This is necessary because some low-level APIs of HDFS and YARN are not shared between these distributions. However, I cannot find any public API of HDFS and YARN that tells their version.
Is it possible to do it only in Java? Or I have to run an external shell to know it?
In Java org.apache.hadoop.util.VersionInfo.getVersion() should work.
https://hadoop.apache.org/docs/current/api/org/apache/hadoop/util/VersionInfo.html
For the CLIs, you can use:
$ hadoop version
$ hdfs version
$ yarn version
I would like to make spark clustering with yarn.
Do i need
installing hadoop master and slaves with yarn config?
installing hadoop master/slaves and yarn master/slaves separately?
If 1 is ok, I'm going to work with this docker image(link). Is it suitable for this?
Installing hadoop master and slave with yarn config is sufficient in order to run spark over yarn but then you also need to make sure that spark version you are downloading supports yarn. once installed spark should be able to access yarn configurations and required jar files related to yarn are also in path of spark.
I have a cluster with 1 NameNode and 4 DataNodes on Red Hat Linux Enterprise 6. My HDP version is 2.1. Ambari version was 1.7 but I upgraded it to 2.1. I want to upgrade HDP to version 2.2. I read that if I want to upgrade HDP from 2.1 to 2.2 I have to do it before I upgrade Ambari to 2.1. When I am upgrading hdp to 2.2 ambari does not see any changes and everything is not working. I am using this tutorial:
http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.2.0/HDP_Man_Upgrade_v22/index.html#Item1
How can I do it? I tried to downgrade ambari to 1.7 but I got many errors. What if I try upgrade now hdp to 2.2 and then my ambari from 2.1 to 2.1.1. Will it work? The problem is that I have very little time.
Thank you in advance
I am upgrading from HDP-2.0/Hadoop-2.2 to HDP-2.2/Hadoop-2.6 (maybe temporarily, on the way to HDP-2.3) on a development/testing cluster. So far I have gotten the updated HDFS up and running. Not yet starting/stopping HDFS via Ambari, nor do I have YARN running yet.
Update: I got YARN, MapReduce, and Hive to run after finding HDP-2.2 documentation (current links added below).
Here are my rough notes on how I got this far:
upgrade ambari-1.4 to 1.7
update HDP yum repo file on each node and update with yum (cssh)
http://public-repo-1.hortonworks.com/HDP/centos6/2.x/updates/2.2.6.0/hdp.repo
hdp-select
sudo su -l hdfs -c "/usr/hdp/current/hadoop-client/sbin/hadoop-daemon.sh start namenode -upgrade"
/etc/hadoop/conf.empty/core-site.xml.rpmsave, hdfs-site.xml
sudo su -l hdfs -c "/usr/hdp/current/hadoop-hdfs-datanode/../hadoop/sbin/hadoop-daemon.sh start datanode"
hadoop dfsadmin -finalizeUpgrade
[update]
upgrade ambari-1.7 to 2.1
configure yarn & mapreduce - yarn-site.xml, yarn-env.sh[?], container-executor.cfg, mapred-site.xml
sudo ln -s /usr/hdp/2.2.6.0-2800/hadoop/libexec/ /usr/lib/hadoop/ # ambari insisting /usr/lib/hadoop
stop-start resourcemanager, start nodemanagers
hive-site.xml in both conf.dist & conf.server; manual start
The following resources were useful:
https://cwiki.apache.org/confluence/display/AMBARI/Install+Ambari+1.7.0+from+Public+Repositories
https://developer.ibm.com/hadoop/blog/2015/10/08/back-up-and-restore-ambari-server-postgresql-database/
http://docs.hortonworks.com/HDPDocuments/Ambari-2.1.0.0/bk_Installing_HDP_AMB/content/_hdp_stack_repositories.html
http://docs.hortonworks.com/HDPDocuments/Ambari-2.1.0.0/bk_upgrading_Ambari/content/_Upgrade_HDFS_mamiu.html
https://wiki.apache.org/hadoop/Hadoop_Upgrade
http://docs.hortonworks.com/HDPDocuments/Ambari-2.1.0.0/bk_upgrading_Ambari/content/_complt_upgrd_21-23_upgrade_hdfs.html
http://solaimurugan.blogspot.com/2014/11/upgrade-hadoop-with-latest-version.html
https://cwiki.apache.org/confluence/display/AMBARI/Install+Ambari+2.0.2+from+Public+Repositories
[update]
http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.2.0/bk_upgrading_hdp_manually/content/index.html
http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.2.0/bk_upgrading_hdp_manually/content/configure-yarn-mr-21.html
http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.2.0/bk_upgrading_hdp_manually/content/start-hive-hcat-21.html
https://brucebcampbell.wordpress.com/2014/12/11/hortonworks-fix-missing-jar-error-in-hive-after-upgrade-to-hdp-2-2/
When connecting to Hadoop cluster, how can I know which version of Hadoop this cluster is running? In particular this is important for proper configuration of libraries when compiling and packaging Hadoop Java jobs with Maven.
The simplest way if you have ssh access to hadoop node is by running command
$ hadoop version
If you are looking for CDH version then check /usr/lib/hadoop/cloudera/cdh_version.properties
In cdh, in the cluster I am using, there is not any cdh_version.properties (or I couldn't find it)
If your cluster uses "Parcels", you could check which version of cdh is used by doing:
/opt/cloudera/parcels
And you could see the version as the name of the folder:
CDH-5.5.1-1.cdh5.5.1.p0.11
Note: I know that this is a not a general rule for getting which cdh version is used. I am trying to show an alternative way that it worked to me.
We can check the installed version with the help of following command:
cat /usr/lib/hadoop/cloudera/cdh_version.properties
Hope this may help you.
I want to use lzo to compress map output but I can't run it! The version of Hadoop I used is 0.20.2. I set:
conf.set("mapred.compress.map.output", "true")
conf.set("mapred.map.output.compression.codec",
"org.apache.hadoop.io.compress.LzoCodec");
When I run the jar file in Hadoop it shows an exception that can't write map output.
Do I have to install lzo?
What do I have to do to use lzo?
LZO's licence (GPL) is incompatible with that of Hadoop (Apache) and therefore it cannot be bundled with it. One needs to install LZO separately on the cluster.
The following steps are tested on Cloudera's Demo VM (CentOS 6.2, x64) that comes with full stack of CDH 4.2.0 and CM Free Edition installed, but they should work on any Linux based on Red Hat.
The installation consists of the following steps:
Installing LZO
sudo yum install lzop
sudo yum install lzo-devel
Installing ANT
sudo yum install ant ant-nodeps ant-junit java-devel
Downloading the source
git clone https://github.com/twitter/hadoop-lzo.git
Compiling Hadoop-LZO
ant compile-native tar
For further instructions and troubleshooting see https://github.com/twitter/hadoop-lzo
Copying Hapoop-LZO jar to Hadoop libs
sudo cp build/hadoop-lzo*.jar /usr/lib/hadoop/lib/
Moving native code to Hadoop native libs
sudo mv build/hadoop-lzo-0.4.17-SNAPSHOT/lib/native/Linux-amd64-64/ /usr/lib/hadoop/lib/native/
cp /usr/lib/hadoop/lib/native/Linux-amd64-64/libgplcompression.* /usr/lib/hadoop/lib/native/
Correct version number with the version you cloned
When working with a real cluster (as opposed to a pseudo-cluster) you need to rsync these to the rest of the machines
rsync /usr/lib/hadoop/lib/ to all hosts.
You can dry run this first with -n
Login to Cloudera Manager
Select from Services: mapreduce1->Configuration
Client->Compression
Add to Compression Codecs:
com.hadoop.compression.lzo.LzoCodec
com.hadoop.compression.lzo.LzopCodec
Search "valve"
Add to MapReduce Service Configuration Safety Valve
io.compression.codec.lzo.class=com.hadoop.compression.lzo.LzoCodec
mapred.child.env="JAVA_LIBRARY_PATH=/usr/lib/hadoop/lib/native/Linux-amd64-64/"
Add to MapReduce Service Environment Safety Valve
HADOOP_CLASSPATH=/usr/lib/hadoop/lib/*
That's it.
Your MarReduce jobs that use TextInputFormat should work seamlessly with .lzo files. However, if you choose to index the LZO files to make them splittable (using com.hadoop.compression.lzo.DistributedLzoIndexer), you will find that the indexer writes a .index file next to each .lzo file. This is a problem because your TextInputFormat will interpret these as part of the input. In this case you need to change your MR jobs to work with LzoTextInputFormat.
As of Hive, as long as you don't index the LZO files, the change is also transparent. If you start indexing (to take advantage of a better data distribution) you will need to update the input format to LzoTextInputFormat. If you use partitions, you can do it per partition.