I'm using giraph-1.3 built with yarn profile. For starting I configured 1 namenode and 2 datanodes on a ec2 cluster.
My application properly works because I see expected output in logs (and in output directory). I launched giraph with "-w 2" argument because I have two datanodes.
In userlogs of datanode1 I found log of first worker.
in userlogs of datanode2 I found log of second worker and log of master too.
I expected to find log of master in the namenode i.e. I expected that master runs on namenode. Is it right?
Maybe I have to configure another datanode and then I will find master logs on this new datanode?
I understood that hadoop/giraph works creating containers on datanodes. Hadoop creates a container for application master, then giraph creates a container for the master. Furthermore giraph creates a number of container for workers corresponding to -w parameter.
YARN always creates an Application Master for every job.
You can start as many "workers" as you want, depending on your workload, but since you only have 2 datanodes, you can only have 2 NodeManagers for maximum parallelism
A NodeManager has a maximum memory space available to it, and the YARN containers for the tasks of a job get a subsection of that in order to do processing.
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I've setup a Hadoop cluster with 4 nodes, one of which serves as the NameNode for HDFS as well as the Yarn master. This node is also the most powerful.
Now, I've distributed 2 text files, one on the node01 (namenode) and one on node03 (datanode). When running the basic WordCount MapReduce job, I can see in the logs that only node01 was doing any calculations.
My question is why Hadoop didn't decide to do MapReduce on node03 and transfer the result instead of transferring the entire book to node01. I also checked, duplication is disabled and the book is only available on node03.
So, how does Hadoop decide between transferring the data and setting up the jobs and in this decision, does it check which machine has more compute power (e.g. did it decide to transfer to node01 because node01 is a 4 core 4gig ram machine vs 2core 1 gig on node03)?
I couldn't find anything on this topic, so any guidance would be appreciated.
Thank you!
Some more clarifications:
node01 is running a NameNode as well as a DataNode and a ResourceManager as well as a NodeManager. Thus, it serves as "main node" as well as a "compute node".
I made sure to put one file on node01 and one file on node03 by running:
hdfs dfs -put sample1.txt samples on node01 and hdfs dfs -put sample02.txt samples on node03. As replication is disabled, this leads to the data - that was available locally on node01 respective node03 - only being stored there.
I verified this using the HDFS Webinterface. For sample1.txt, it says the blocks are only available on node01; for sample2.txt, it says the blocks are only available on node03.
Regarding #cricket_007:
My concern is that sample2.txt is only available on node03. The YARN Webinterface tells me that that for the Application Attempt, only one container was allocated on node01. If the map task for file sample2.txt, there would have been a container on node03 as well.
Thus, node01 needs to have fetched the sample2.txt file from node03.
Yes, I know Hadoop is not running well on 1gig of RAM, but I am working with a Raspberry Pi cluster just to fiddle around and learn a little. This is not for production usage.
The YARN application master picks a node at random to run the calculation based on information available from the Namenode where files are stored. DataNodes and NodeManagers should run on the same machines.
If your file isn't larger than the HDFS block size, there is no reason to fetch the data from other nodes.
Note: Hadoop services don't run that well on only 1G of RAM, and you need to adjust the YARN settings differently for different sized nodes.
For anyone else wondering:
At least for me, the HistoryServer UI (which needs to be started manually) shows correctly that node03 and node01 were running map jobs. Thus, my statement was incorrect. I still wonder why the application attempt UI speaks of one container, but I guess that doesn't matter.
Thank you guys!
I have created a diagram that represents how the MapReduce framework works. Could somebody please validate that this is an accurate representation?
P.S. For the purpose of this example, we are also interested in the system components shown in this diagram.
The MapReduce Architecture works in various different phases for executing a job. Here are the different stages of running a MapReduce Application -
The first stage involves the user writing his data into the HDFS for further processing. This data is stored on different nodes in the form of blocks in the HDFS.
Now the client submits its MapReduce job.
Then, the resource manager launches a container to start the App master.
The App master sends a resource request to the resource manager.
The resource manager now allocates containers on slaves via the node manager.
The App master starts respective tasks in the containers.
The job is now been executed in the container.
When the processing is complete, the resource manager deallocates the resources.
Source: Cloudera
JobTracker, TaskTracker, and MasterNode aren't real things in Hadoop 2+ w/ YARN. Jobs are submitted to a ResourceManager, which creates an ApplicationMaster on one of the NodeManagers.
"Slave Nodes" are commonly also your DataNodes because that is the core tenant of Hadoop - move the processing to the data.
The "Recieve the data" arrow is bi-directional, and there is no arrow from the NameNode to the DataNode. 1) Get the file locations from the NameNode, then locations are sent back to clients. 2) The clients (i.e. NodeManager processes running on a DataNode, or "slave nodes"), will directly read from the DataNodes themselves - the datanodes don't know directly where the other slave nodes exist.
That being said, HDFS and YARN are typically all part of the same "bubble", so the "HDFS" labelled circle you have should really be around everything.
I have installed a hadoop cluster with total 3 machines, with 2 nodes acting as datanodes and 1 node acting as Namenode and as well as a Datanode.
I wanted to clear certain doubts regarding hadoop cluster installation and architecture.
Here is a list of questions I am looking answers for----
I uploaded a data file around 500mb size in the cluster and then checked the hdfs report.
I noticed that the namenode I made is also occupying 500mb size in the hdfs, along with datanodes with a replication factor of 2.
The problem here is that I want the namenode not to store any data on it, in short i dont want it to work as a datanode as it is also storing the file I am uploading. So what is the way of making it only act as a Master Node and not like a datanode?
I tried running the command hadoop -daemon.sh stop on the Namenode to stop the datanode services on it but it wasnt of any help.
How much metadata does a Namenode generate for a filesize typically of 1 GB? Any approximations?
Go to conf directory inside your $HADOOP_HOME directory on your master. Edit the file named slaves and remove the entry corresponding to your name node from it. This way you are only asking the other two nodes to act as slaves and name node as only the master.
since the jobTracker in MapReduce1 is replaced by the Application Master and Resouce Manager in Yarn I wonder who is communication in Yarn with the namenode to find out where the data is stored in the different datanodes?
Is the Application Master doing so?
In YARN, the per-application ApplicationMaster is responsible for getting the information about the input splits from Namenode. Later when the task attempts are executed over the assigned nodes, the YarnChild fetches the respective splits from HDFS.
I'm curious if you could essentially separate the HDFS filesystem from the MapReduce framework. I know that the main point of Hadoop is to run the maps and reduces on the machines with the data in question, but I was wondering if you could just change the *.xml files to change the configuration of what machine the jobtracker, namenode and datanodes are running on.
Currently, my configuration is a 2 VMs setup: one (the master) with Namenode, Datanode, JobTracker, Tasktracker (and the SecondaryNameNode), the other (the slave) with DataNode, Tasktraker. Essentially, what I want to change is have the master with NameNode DataNode(s), JobTracker, and have the slave with only the TaskTracker to perform the computations (and later on, have more slaves with only TaskTrackers on them; one on each). The bottleneck will be the data transfer between the two VMs for the computations of maps and reduces, but since the data at this stage is so small I'm not primarily concerned with it. I would just like to know if this configuration is possible, and how to do it. Any tips?
Thanks!
You don't specify this kind of options in the configuration files.
What you have to do is to take care of what kind of deamons you start on each machine(you call them VMs but I think you mean machines).
I suppose you usually start everything using the start-all.sh script which you can find in the bin directory under the hadoop installation dir.
If you take a look at this script you will see that what it does is to call a number of sub-scripts corresponding to starting the datanodes, tasktrackers and namenode, jobtracker.
In order to achive what you've said, I would do like this:
Modify the masters and slaves files as this:
Master file should contain the name of machine1
Slaves should contain the name of machine2
Run start-mapred.sh
Modify the masters and slaves files as this:
Master file should contain the machine1
Slaves file should contain machine1
Run start-dfs.sh
I have to tell you that I've never tried such a configuration so I'm not sure this is going to work but you can give it a try. Anyway the solution is in this direction!
Essentially, what I want to change is have the master with NameNode DataNode(s), JobTracker, and have the slave with only the TaskTracker to perform the computations (and later on, have more slaves with only TaskTrackers on them; one on each).
First, I am not sure why to separate the computation from the storage. The whole purpose of MR locality is lost, thought you might be able to run the job successfully.
Use the dfs.hosts, dfs.hosts.exclude parameters to control which datanodes can connect to the namenode and the mapreduce.jobtracker.hosts.filename, mapreduce.jobtracker.hosts.exclude.filename parameters to control which tasktrackers can connect to the jobtracker. One disadvantage of this approach is that the datanodes and tasktrackers are started on the nodes which are excluded and aren't part of the Hadoop cluster.
Another approach is to modify the code to have a separate slave file for the tasktracker and the datanode. Currently, this is not supported in Hadoop and would require a code change.