YARN is the Hadoop second generation that not use the jobtracker daemon anymore, and substitute it with resource manager. But why, on mapred-site.xml hadoop 2 there is an mapreduce.jobtracker.address property?
in order to running Hadoop MapReduce Application from Eclipse , is there a plugin eclipse for yarn , because i find all plugins are spesified for jobtracker.
thanks in advance.
hadoop 2 is compatible to run on a Yarn cluster as well as MR cluster .
Just in case one needs to run hadoop 2 compiled code on a NON-YARN cluster one has to specify JT url:port .
hadoop 2 in binary compatible on both MR/YARN cluster .
Related
I'm studying about hadoop map-reduce on centos 6.5 and hadoop 2.7.2. I learned that hdfs is just distributed file system and Yarn administers map-reduce work, so I thought that if i don't turn on Yarn(resource manager, node manager), map-reduce doesn't work.
Therefore, I think, wordcount should not do map-reduce process in the system working only hdfs not yarn.
(on the pseudo distribute mode)
But when I turn hdfs on not Yarn as you see in the below, and execute wordcount example, it show 'map-reduce framework'. What's it meaning? Does it possible only hdfs process map-reduce without Yarn? Because Yarn manage resource and job, is it right that map-reduce doesn't work without Yarn?
bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar wordcount /user/input /user/output
With Hadoop 2.0 YARN takes responsibility of resource management, this is true. But even without YARN the Map Reduce applications can run using the older flavor.
The mapred-site.xml has a configuration - mapreduce.framework.name
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>local</value>
</property>
</configuration>
The above can be configured to choose whether to use YARN or not. The possible values for this property are - local, classic or yarn
The default value is "local". Set this to yarn, if you want to use YARN
What I know is YARN is introduced and it replaced JobTracker and TaskTracker.
I have seen is some Hadoop 2.6.0/2.7.0 installation tutorials and they are configuring mapreduce.framework.name as yarn and mapred.job.tracker property as local or host:port.
The description for mapred.job.tracker property is
"The host and port that the MapReduce job tracker runs at. If "local",
then jobs are run in-process as a single map and reduce task."
My doubt is why are configuring it if we are using YARN , I mean JobTracker shouldn't be running right?
Forgive me if my question is dumb.
Edit: These are the tutorials I was talking about.
http://chaalpritam.blogspot.in/2015/01/hadoop-260-multi-node-cluster-setup-on.html
http://pingax.com/install-apache-hadoop-ubuntu-cluster-setup/
https://chawlasumit.wordpress.com/2015/03/09/install-a-multi-node-hadoop-cluster-on-ubuntu-14-04/
This is just a guess, but either those tutorials talking about configuring the JobTracker in YARN are written by people who don't know what YARN is, or they set it in case you decide to stop working with YARN someday. You are right: the JobTracker and TaskTracker do not exist in YARN. You can add the properties if you want, but they will be ignored. New properties for each of the components replacing the JobTracker and the TaskTracker were added with YARN, such as yarn.resourcemanager.address to replace mapred.jobtracker.address.
If you list your Java processes when running Hadoop under YARN, you see no JobTrackeror TaskTracker:
10561 Jps
20605 NameNode
17176 DataNode
18521 ResourceManager
19625 NodeManager
18424 JobHistoryServer
You can read more about how YARN works here.
Iam new to Hadoop 2.5.1. As i have already installed Hadoop 1.0.4 previously, i thought installation process would be same so followed following tutorial.
http://www.michael-noll.com/tutorials/running-hadoop-on-ubuntu-linux-single-node-cluster/
Every thing was fine, even i have given these settings in core-site.xml
<name>fs.default.name</name>
<value>hdfs://localhost:54310</value>
But i have seen in several sites this value as 9000.
And also changes in yarn.xml.
Still everything works fine when i run a mapreduce job. But my question is
when i run command jps it gives me this output..
hduser#secondmaster:~$ jps
5178 ResourceManager
5038 SecondaryNameNode
4863 DataNode
5301 NodeManager
4719 NameNode
6683 Jps
I dont see task tracker and job tracker in jps. Where are these demons running.
And without these deamons how am i able to run Mapreduce job.
Thanks,
Sreelatha K.
From hadoop version hadoop 2.0 onwards, default processing framework has been changed to YARN from Classic Mapreduce. You are using YARN, where you cannot see Jobtracker, Tasker in YARN. Jobtracker and Tasktracker is replaced by Resource manager and Nodemanager respectively in YARN.
But still you have an option to use Classic Mapreduce framework instead of YARN.
In Hadoop 2 there is an alternative method to run MapReduce jobs, called YARN. Since you have made changes in yarn.xml, MapReduce processing happens using YARN, not using the traditional MapReduce framework. That's probably be the reason why you don't see TaskTracker and JobTracker listed after executing the jps command. Note that ResourceManager and NodeManager are the daemons for YARN.
YARN is next generation of Resource Manager who can able to integrate with Apache spark, storm and many more tools you can use to write map-reduce jobs
I Installed Hadoop 2.4.X. As expected there is no JobTracker and TaskTracker. Its Yarn based. Is there any way to make it use old JobTracker and TaskTracker for MapReduce and not based on Yarn ? In short can I make JT and TT daemons running on this ?
By default there is no configuration file for map reduce in the 2.4.x installation even though there is a file called mapred-site.xml.template.Rename the file to mapred-site.xml and remember to set the property mapred.framework.name to classic to use the job tracker and tasktracker.Also the start scripts start-all.sh cannot be used as it executes the scripts start-dfs.sh and start-yarn.sh.You need to execute the script that starts jobtracker and tasktracker.
As described above, there is no Jobtracker and Tasktracer in Hadoop 2.0 (yarn). It's better to follow this instruction (http://codesfusion.blogspot.in/2013/10/setup-hadoop-2x-220-on-ubuntu.html) to get the idea, and you will find the processes are as:
25578 ResourceManager
25411 SecondaryNameNode
447 Jps
29464 NameNode
25222 DataNode
25905 NodeManager
I am trying to understand how spark runs on YARN cluster/client. I have the following question in my mind.
Is it necessary that spark is installed on all the nodes in yarn cluster? I think it should because worker nodes in cluster execute a task and should be able to decode the code(spark APIs) in spark application sent to cluster by the driver?
It says in the documentation "Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster". Why does client node have to install Hadoop when it is sending the job to cluster?
Adding to other answers.
Is it necessary that spark is installed on all the nodes in the yarn
cluster?
No, If the spark job is scheduling in YARN(either client or cluster mode). Spark installation is needed in many nodes only for standalone mode.
These are the visualizations of spark app deployment modes.
Spark Standalone Cluster
In cluster mode driver will be sitting in one of the Spark Worker node whereas in client mode it will be within the machine which launched the job.
YARN cluster mode
YARN client mode
This table offers a concise list of differences between these modes:
pics source
It says in the documentation "Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client-side)
configuration files for the Hadoop cluster". Why does the client node have
to install Hadoop when it is sending the job to cluster?
Hadoop installation is not mandatory but configurations(not all) are!. We can call them Gateway nodes. It's for two main reasons.
The configuration contained in HADOOP_CONF_DIR directory will be distributed to the YARN cluster so that all containers used by the application use the same configuration.
In YARN mode the ResourceManager’s address is picked up from the
Hadoop configuration(yarn-default.xml). Thus, the --master parameter is yarn.
Update: (2017-01-04)
Spark 2.0+ no longer requires a fat assembly jar for production
deployment. source
We are running spark jobs on YARN (we use HDP 2.2).
We don't have spark installed on the cluster. We only added the Spark assembly jar to the HDFS.
For example to run the Pi example:
./bin/spark-submit \
--verbose \
--class org.apache.spark.examples.SparkPi \
--master yarn-cluster \
--conf spark.yarn.jar=hdfs://master:8020/spark/spark-assembly-1.3.1-hadoop2.6.0.jar \
--num-executors 2 \
--driver-memory 512m \
--executor-memory 512m \
--executor-cores 4 \
hdfs://master:8020/spark/spark-examples-1.3.1-hadoop2.6.0.jar 100
--conf spark.yarn.jar=hdfs://master:8020/spark/spark-assembly-1.3.1-hadoop2.6.0.jar - This config tell the yarn from were to take the spark assembly. If you don't use it, it will upload the jar from were you run spark-submit.
About your second question: The client node doesn't not need Hadoop installed. It only needs the configuration files. You can copy the directory from your cluster to your client.
1 - Spark if following s slave/master architecture. So on your cluster, you have to install a spark master and N spark slaves. You can run spark in a standalone mode. But using Yarn architecture will give you some benefits.
There is a very good explanation of it here : http://blog.cloudera.com/blog/2014/05/apache-spark-resource-management-and-yarn-app-models/
2- It is necessary if you want to use Yarn or HDFS for example, but as i said before you can run it in standalone mode.
Let me try to cut glues and make it short for impatient.
6 components: 1. client, 2. driver, 3. executors, 4. application master, 5. workers, and 6. resource manager; 2 deploy modes; and 2 resource (cluster) management.
Here's the relation:
Client
Nothing special, is the one submitting spark app.
Worker, executors
Nothing special, one worker holds one or more executors.
Master, & resource (cluster) manager
(no matter client or cluster mode)
in yarn, resource manager and master sit in two different nodes;
in standalone, resource manager == master, same process in the same node.
Driver
in client mode, sits with client
in yarn - cluster mode, sits with master (in this case, client process exits after submission of app)
in standalone - cluster mode, sits with one worker
Voilà!