I have two clusters each running different version of Hadoop. I am working on a POC were I need to understand how YARN provides the capability to run multiple applications simultaneously which was not accomplished with Classic Map Reduce Framework.
Hadoop Classic:
I have a wordcount.jar file and executed on a single cluster (2 Mappers & 2 Reducers). I started two jobs in parallel, the one lucky started first got both mappers, completed the task and then second job started. This is the expected behavior.
Hadoop Yarn:
Same wordcount.jar with a different cluster (4 cores, so total 4 machines). As Yarn does not pre-assign mapper and reducer, any core can be used as mapper or reducer. Here also I submitted two jobs in parallel.
Expected Behavior: Both the jobs should start with 2 mappers each or whichever config as resource manager assigns but atleast both the jobs should start.
Reality: One job starts with 3 mappers and 1 reducers. second job waits untill first is completed.
Can someone please help me understand the behavior, as well as does the parallelism behavior best reflected with multinode cluster?
Not sure if this is the exact reason, but the Classic Hadoop and YARN architectures use a different scheduler. Classic Hadoop uses a JobQueueTaskScheduler, while YARN uses CapacityScheduler by default.
Related
I have been reading up on Hadoop, YARN and SPARK. What makes sense to me thus far is what I have summarized below.
Hadoop MapReduce: Client choses an input file and hands if off to
Hadoop (or YARN). Hadoop takes care of splitting the flie based on
user's InputFormat and stores it on as many nodes that are available
and configured Client submits a job (map-reduce) to YARN, which
copeies the jar to available Data Nodes and executes the job. YARN is
the orchestrator that takes care of all the scheduling and running of
the actual tasks
Spark: Given a job, input and a bunch of configuration parameters, it
can run your job, which could be a series of transformations and
provide you the output.
I also understand MapReduce is a batch based processing paradigm and
SPARK is more suited for micro batch or stream based data.
There are a lot of articles that talks about how Spark can run on YARN and how they are complimentary, but none have managed to help me understand how those two come together during an acutal workflow. For example when a client has a job to submit, read a huge file and do a bunch of transformations what does the workflow look like when using Spark on YARN. Let us assume that the client's input file is a 100GB text file. Please include as much details as possible
Any help with this would be greatly appreciated
Thanks
Kay
Let's assume the large file is stored in HDFS. In HDFS the file is divided into blocks of some size (default 128 MB).
That means your 100GB file will be divided into 800 blocks. Each block will be replicated and can be stored on different node in the cluster.
When reading the file with Hadoop InputFormat list of splits with location is obtained first. Then there is created one task per each splits. That you will get 800 parallel tasks that are executed by runtime.
Basically the input process is the same for MapReduce and Spark, because both of the use Hadoop Input Formats.
Both of them will process each InputSplit in separate task. The main difference is that Spark has more rich set of transformations and can optimize the workflow if there is a chain of transformations that can be applied at once. As opposed to MapReduce where is always map and reduce phase only.
YARN stands for "Yet another resource negotiator". When a new job with some resource requirement (memory, processors) is submitted it is the responsibility of YARN to check if the needed resources are available on the cluster. If other jobs are running on the cluster are taking up too much of the resources then the new job will be made to wait till the prevoius jobs complete and resources are available.
YARN will allocate enough containers in the cluster for the workers and also one for the Spark driver. In each of these containers JVM is started with given resources. Each Spark worker can process multiple tasks in parallel (depends on the configured number of cores per executor).
e.g.
If you set 8 cores per Spark executor, YARN tries to allocated 101 containers in the cluster tu run 100 Spark workers + 1 Spark master (driver). Each of the workers will process 8 tasks in parallel (because of 8 cores).
Hi I am a big data newbie. I searched all over the internet to find what exactly uber mode is. The more I searched the more I got confused. Can anybody please help me by answering my questions?
What does uber mode do?
Does it works differently in mapred 1.x and 2.x?
And where can I find the setting for it?
What is UBER mode in Hadoop2?
Normally mappers and reducers will run by ResourceManager (RM), RM will create separate container for mapper and reducer.
Uber configuration, will allow to run mapper and reducers in the same process as the ApplicationMaster (AM).
Uber jobs :
Uber jobs are jobs that are executed within the MapReduce ApplicationMaster. Rather then communicate with RM to create the mapper and reducer containers.
The AM runs the map and reduce tasks within its own process and avoided the overhead of launching and communicate with remote containers.
Why
If you have a small dataset or you want to run MapReduce on small amount of data, Uber configuration will help you out, by reducing additional time that MapReduce normally spends in mapper and reducers phase.
Can I configure an Uber for all MapReduce job?
As of now,
map-only jobs and
jobs with one reducer are supported.
Uber Job occurs when multiple mapper and reducers are combined to use a single container. There are four core settings around the configuration of Uber Jobs in the mapred-site.xml. Configuration options for Uber Jobs:
mapreduce.job.ubertask.enable
mapreduce.job.ubertask.maxmaps
mapreduce.job.ubertask.maxreduces
mapreduce.job.ubertask.maxbytes
You can find more details here: http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.1.15/bk_using-apache-hadoop/content/uber_jobs.html
In terms of hadoop2.x, Uber jobs are the jobs which are launched in mapreduce ApplicationMaster itself i.e. no separate containers are created for map and reduce jobs and hence the overhead of creating containers and communicating with them is saved.
As far as working (with hadoop 1.x and 2.x) is concerned, I suppose the difference is only observable when it comes to terminologies of 1.x and 2.x, no difference in working.
Configuration params are same as those mentioned by Navneet Kumar in his answer.
PS: Use it only with small dataset.
Pretty good answers are given for "What is Uber Mode?"
Just to add some more information for "Why?"
The application master decides how to run the tasks that make
up the MapReduce job. If the job is small, the application master may choose to run the tasks in the same JVM as itself. This happens when it judges the overhead of allocating and running tasks in new containers outweighs the gain in running them in parallel, when compared to running them sequentially on one node.
Now, the questions could be raised as "What qualifies as a small job?
By default, a small job is one that has less than 10 mappers, only one reducer, and an input size that is less than the size of one HDFS block.
I was searching about hadoop and mapreduce with respect to straggler problems and the papers in this problem
but yesterday I found that there is hadoop 2 with Yarn ,,
unfortunately no paper is talking about straggler problem in Yarn
So I want to know what is difference between MapReduce and Yarn in the part straggler?
is Yarn suffer from straggler problem?
and when MRmaster asks resource manger for resources , resource manger will give MRmaster all resources it needs or it is according to cluster computing capabilities ?
thanks so much,,
Here are the MapReduce 1.0 and MapReduce 2.0 (YARN)
MapReduce 1.0
In a typical Hadoop cluster, racks are interconnected via core switches. Core switches should connect to top-of-rack switches Enterprises using Hadoop should consider using 10GbE, bonded Ethernet and redundant top-of-rack switches to mitigate risk in the event of failure. A file is broken into 64MB chunks by default and distributed across Data Nodes. Each chunk has a default replication factor of 3, meaning there will be 3 copies of the data at any given time. Hadoop is “Rack Aware” and HDFS has replicated chunks on nodes on different racks. JobTracker assign tasks to nodes closest to the data depending on the location of nodes and helps the NameNode determine the ‘closest’ chunk to a client during reads. The administrator supplies a script which tells Hadoop which rack the node is in, for example: /enterprisedatacenter/rack2.
Limitations of MapReduce 1.0 – Hadoop can scale up to 4,000 nodes. When it exceeds that limit, it raises unpredictable behavior such as cascading failures and serious deterioration of overall cluster. Another issue being multi-tenancy – it is impossible to run other frameworks than MapReduce 1.0 on a Hadoop cluster.
MapReduce 2.0
MapReduce 2.0 has two components – YARN that has cluster resource management capabilities and MapReduce.
In MapReduce 2.0, the JobTracker is divided into three services:
ResourceManager, a persistent YARN service that receives and runs applications on the cluster. A MapReduce job is an application.
JobHistoryServer, to provide information about completed jobs
Application Master, to manage each MapReduce job and is terminated when the job completes.
TaskTracker has been replaced with the NodeManager, a YARN service that manages resources and deployment on a node. NodeManager is responsible for launching containers that could either be a map or reduce task.
This new architecture breaks JobTracker model by allowing a new ResourceManager to manage resource usage across applications, with ApplicationMasters taking the responsibility of managing the execution of jobs. This change removes a bottleneck and lets Hadoop clusters scale up to larger configurations than 4000 nodes. This architecture also allows simultaneous execution of a variety of programming models such as graph processing, iterative processing, machine learning, and general cluster computing, including the traditional MapReduce.
You say "Differences between MapReduce and YARN". MapReduce and YARN definitely different. MapReduce is Programming Model, YARN is architecture for distribution cluster. Hadoop 2 using YARN for resource management. Besides that, hadoop support programming model which support parallel processing that we known as MapReduce. Before hadoop 2, hadoop already support MapReduce. In short, MapReduce run above YARN Architecture. Sorry, i don't mention in part of straggler problem.
"when MRmaster asks resource manger for resources?"
when user submit MapReduce Job. After MapReduce job has done, resource will be back to free.
"resource manger will give MRmaster all resources it needs or it is according to cluster computing capabilities"
I don't get this question point. Obviously, the resources manager will give all resource it needs no matter what cluster computing capabilities. Cluster computing capabilities will influence on processing time.
There is no YARN in MapReduce 1. In MapReduce there is Yarn.
If for straggler problem you mean that if first guy waits 'something' which then causes more waits along a road who depends on that first guy then I guess there is always this problem in MR jobs. Getting allocated resources naturally participate to this problem along with all other things which may cause components to wait something.
Tez which is supposed to be a drop-in replacement for MR job runtime makes a things differently. Instead of doing task runs in a same way current MR Appmaster does it tries to use DAG of tasks which does a much better job of not getting into bad straggler problem.
You need to understand a relationship between MR and YARN. YARN is simply a dummy resource scheduler meaning it doesn't schedule 'tasks'. What it gives to MR Appmaster is a set or resources(in a sense it's only combination of memory and cpu and location). It's then MR Appmaster responsibility to decide what to do with those resources.
I am confused about the relationship between core instances and mappers each instance can have. How are these mappers created? If I set core instance count to 0, so that only master node is running, why can MapReduce jobs run without any task nodes?
Thanks in advance.
the number of cores means how many processors are implemented in each machine within a given cluster. Moreover, each core can run a mapper.
You don't have to worry about the creation of the mapper because the hadoop framework will do it for you.
That's a really good question. My guess is that what's happening is that EMR is smart enough to setup the Master node to run the MapReduce jobs in the event that there are no Core or Task nodes. That's a guess.
If you want to find out if I'm right, spin up a cluster. Then start a MapReduce job, while keeping an eye on the java processes via jps -lm and see if any mapper processes get launched on the Master node.
I'm new in hadoop development. I read about hadoop cluster structure and understood that there are one namenode, jobtracker, tasktracker and multiple datanodes.
When we write map-reduce programs we implement mapper and reducer. I also understood logic of these clasess. But I don't understand how are they executed in the hadoop cluster.
Is mapper executed in the namenode only?
Is reducer executed seperatly on the datanodes?
I need to make a lot of parralel computations and don't want to use HDFS, how can I be sure that each output collection (from mapper) executes seperatly in all datanodes?
Please explain me the connection between hadoop cluster and map/reduce logic.
Thanks a lot!
Map Reduce Jobs are executed by Job Tracker and Task Trackers.
Job Tracker initiates the Job the dividing the input file/files into splits. Tasktrackers are given these splits who run map tasks on the splits( One map task per split). After Mappers throws their output.This output will be passed on the reducer depending on the map output keys . Similar keys are sent to one reducer. Reducer can be more than 1 , depending upon your configuration. Reducer process also runs on one the tasktracker nodes only .
You can see stats of the Job on , jobtracker UI which by default runs on 50030 port.
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