I'm working with Windows and trying to set up Spark.
Previously I installed Hadoop in addition to Spark, edited the config files, run the hadoop namenode -format and away we went.
I'm now trying to achieve the same by using the bundled version of Spark that is pre built with hadoop - spark-1.6.1-bin-hadoop2.6.tgz
So far it's been a much cleaner, simpler process however I no longer have access to the command that creates the hdfs, the config files for the hdfs are no longer present and I've no 'hadoop' in any of the bin folders.
There wasn't an Hadoop folder in the spark install, I created one for the purpose of winutils.exe.
It feels like I've missed something. Do the pre-built versions of spark not include hadoop? Is this functionality missing from this variant or is there something else that I'm overlooking?
Thanks for any help.
By saying that Spark is built with Hadoop, it is meant that Spark is built with the dependencies of Hadoop, i.e. with the clients for accessing Hadoop (or HDFS, to be more precise).
Thus, if you use a version of Spark which is built for Hadoop 2.6 you will be able to access HDFS filesystem of a cluster with the version 2.6 of Hadoop via Spark.
It doesn't mean that Hadoop is part of the pakage and downloading it Hadoop is installed as well. You have to install Hadoop separately.
If you download a Spark release without Hadoop support, you'll need to include the Hadoop client libraries in all the applications you write wiìhich are supposed to access HDFS (by a textFile for instance).
I am also using same spark in my windows 10. What I have done create C:\winutils\bin directory and put winutils.exe there. Than create HADOOP_HOME=C:\winutils variable. If you have set all
env variables and PATH like SPARK_HOME,HADOOP_HOME etc than it should work.
Related
I am a new pyspark user.
I just downloaded and installed a spark cluster ("spark-2.0.2-bin-hadoop2.7.tgz")
after installation I wanted to access the file system (upload local files to cluster). But when I tried to type hadoop or hdfs in command it will say "no command found".
Am I gonna install hadoop/HDFS (I thought it's built in the spark, I don't get)?
Thanks in advance.
You have to install hadoop first to access HDFS.
Follow this http://www.michael-noll.com/tutorials/running-hadoop-on-ubuntu-linux-single-node-cluster/
Choose the latest version of hadoop from the apache site.
Once you done with hadoop setup go to spark http://d3kbcqa49mib13.cloudfront.net/spark-2.0.2-bin-hadoop2.7.tgz download this, Extract files. Setup java_home and hadoop_home in spark-env.sh.
You don't have hdfs or hadoop on classpath so this is the reason why you are getting message: "no command found".
If you run \yourparh\hadoop-2.7.1\bin\hdfs dfs -ls / it should works and show root content.
But, You can add your hadoop/bin (hdfs, hadoop ...) commands to classpath with something like this:
export PATH $PATH:$HADOOP_HOME/bin
where HADOOP_HOME is your env. variable with path to hadoop installation folder (download and install is required)
I'm new on Hadoop world, and I need install mesos with Hadoop HDFS to make a fault-tolerant distributed file system, but all installation references include necessary components for my scenario as for example: MapReduce.
Do you have any idea or references about this?
Absolutely possible. Don't think Hadoop as an installable program, it's just composed by a bunch of java processes running on different nodes inside a cluster.
If you use hadoop tar ball, you can just run NameNode and DataNodes processes if you only want HDFS.
If you use other hadoop distros (HDP for instance), I think HDFS and mapreduce come from different rpm packages, but it does harm to install both rpm packages. Again just run NameNode and DataNodes if you only need HDFS.
I'm using Hadoop 0.23.8 pseudo distributed and HBase 0.94.8. My HBase master is failing with:
Server IPC version 5 cannot communicate with client version 4
I think this is because HBase is using hadoop-core-1.0.4.jar in its lib folder.
Now http://cloudfront.blogspot.in/2012/06/how-to-configure-habse-in-pseudo.html#.UYfPYkAW38s suggests I should replace this jar by copying:
the hadoop-core-*.jar from your HADOOP_HOME ...
but there are no hadoop-core-*.jars in 0.23.8.
Will this process work for 0.23.8, and if so, which jars should I be using?
TIA!
I gave up with this and am using hadoop 2.2.0 which works well (ish) with HBase.
Currently, I have 3-node cluster running CDH 5.0 using MRv1. I am trying to figure out how to setup Hadoop on my Mac. So, I can submit jobs to the cluster. According to the "Managing Hadoop API Dependencies in CDH 5", you just need the files in /usr/lib/hadoop/client-0.20/* Do I need the following files too? Does Cloudera has hadoop-client in tarball?
- core-site.xml
- hdfs-site.xml
- mapred-site.xml
Yes, I'nk you can make use of cloudera tarball for setting up hadoop client, the same can be downloaded from the following path, configuration files are availble under etc/hadoop/ directory under Hadoop, just need to modify those files according to your environment.
http://archive-primary.cloudera.com/cdh5/cdh/5/hadoop-2.2.0-cdh5.0.0-beta-2.tar.gz
If the above link doesn't match your version, use the following link for getting the available hadoop versions
http://archive-primary.cloudera.com/cdh5/cdh/5/
I'm working on a hadoop cluster with CDH4.2.0 installed and ran into this error. It's been fixed in later versions of hadoop but I don't have access to update the cluster. Is there a way to tell hadoop to use this jar when running my job through the command line arguments like
hadoop jar MyJob.jar -D hadoop.mapreduce.client=hadoop-mapreduce-client-core-2.0.0-cdh4.2.0.jar
where the new mapreduce-client-core.jar file is the patched jar from the ticket. Or must hadoop be completely recompiled with this new jar? I'm new to hadoop so I don't know all the command line options that are possible.
I'm not sure how that would work as when you're executing the hadoop command you're actually executing code in the client jar.
Can you not use MR1? The issue says this issue only occurs when you're using MR2, so unless you really need Yarn you're probably better using the MR1 library to run your map/reduce.