I know that Hadoop has the Fair Scheduler, where we can assign a job to some priority group and the cluster resources are allocated to the job based on priority. What I am not sure and what I ask is how a non map-red program is prioritized by the Hadoop cluster. Specifically how do the writes to Hadoop through external clients (say some standalone program which is directly opening HDFS file and streaming data to it) would be prioritized by Hadoop when the cluster is busy running map-red jobs.
The Resource Manager only can prioritize jobs submitted to it (such as MapReduce applications, Spark jobs, etc ...).
Other than distcp, HDFS operations only interact with the NameNode and Datanodes not the Resource Manager so they would be handled by the NameNode in the order they're received.
Related
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 am working on a solution providing run-time ressources addition to an Hadoop Yarn cluster. The purpose is to handle heavy peaks on our application.
I am not an expert and I need help in order to approve / contest what I understand.
Hadoop YARN
This application an run in a cluster-mode. It provides ressource management (CPU & RAM).
A spark a application, for example, ask for a job to be done. Yarn handles the request and provides an executor computing on the Yarn cluster.
HDFS - Data & Executors
The datas are not shared through executors, so they have to be stored in a file System. In my case : HDFS. That means I will have to run a copy of my spark streaming application in the new server (hadoop node).
I am not sure of this :
The yarn cluster and HDFS are different, writing on HDFS won't write on the new hadoop node local data (because it is not an HDFS node).
As I will only write on HDFS new data from a spark streaming application, creating a new application should not be a problem.
Submit the job to yarn
--- peak, resources needed
Instance new server
Install / configure Hadoop & YARN, making it a slave
Modifying hadoop/conf/slaves, adding it's ip adress (or dns name from host file)
Moddifying dfs.include and mapred.include
On host machine :
yarn -refreshNodes
bin/hadoop dfsadmin -refreshNodes
bin/hadoop mradmin -refreshNodes
Should this work ? refreshQueues sounds not really useful here as it seems to only take care of the process queue.
I am not sure if the running job will increase it's capacity. Another idea is to wait for the new ressources to be available and submit a new job.
Thanks for you help
Can someone pls tell what is the differece between MR1 and yarn and MR2
My understanding is MR1 will be having below components
Namenode,
secondary name node,
datanode,
job tracker,
task tracker
Yarn
Node manager
Resource Manager
Is Yarn consists of MR1 or MR2 ( or both MR2 and Yarn are same?)
sorry if i asked basic level question
MRv1 uses the JobTracker to create and assign tasks to task trackers, which can become a resource bottleneck when the cluster scales out far enough (usually around 4,000 clusters).
MRv2 (aka YARN, "Yet Another Resource Negotiator") has a Resource Manager for each cluster, and each data node runs a Node Manager. In MapReduce MRv2, the functions of the JobTracker have been split between three services. The ResourceManager is a persistent YARN service that receives and runs applications (a MapReduce job is an application) on the cluster. It contains the scheduler, which, as previously, is pluggable. The MapReduce-specific capabilities of the JobTracker have been moved into the MapReduce Application Master, one of which is started to manage each MapReduce job and terminated when the job completes. The JobTracker function of serving information about completed jobs has been moved to the JobHistory Server. The TaskTracker has been replaced with the NodeManager, a YARN service that manages resources and deployment on a host. It is responsible for launching containers, each of which can house a map or reduce task.
YARN is a generic platform for any form of distributed application to run on, while MR2 is one such distributed application that runs the MapReduce framework on top of YARN
A cluster which runs mapreduce 2 doesn't have a job tracker and instead it is split into two separate components, resource manager and job manager. However, these thing are transparent from a user and he doesn't need to know whether the cluster is running mapreduce 1 or 2 when submitting a mapreduce job.
The thing I cannot quite understand is Yarn application. How is it different from a regular mapreduce application? What's the advantage of running a mapreduce job as a yarn application, etc? Could someone shed some light on that for me?
MR1 has Job tracker and task tracker which takes care of Map reduce application.
In MR2 Apache separated the management of the map/reduce process from the cluster's resource management by using YARN. YARN is a better resource manger than we had in MR1. It also enables versatility. MR2 is built on top of YARN.
Apart from Map reduce, we can run applications like spark, storm, Hbase, Tex etc on top of Yarn, which we cannot do using MR1.
The following is the architecture for MR1 and MR2.
HDFS <---> MR
HDFS <----> Yarn <----> MR
I have a Hadoop cluster with 10 nodes. Out of the 10 nodes, on 3 of them, HBase is deployed. There are two applications sharing the cluster.
Application 1 writes and reads data from hadoop HDFs. Application 2 stores data into HBase. Is there a way in yarn to ensure that hadoop M/R jobs launched
by application 1 do not use the slots on Hbase nodes? I want only the Hbase M/R jobs launched by application 2 to use the HBase nodes.
This is needed to ensure enough resources are available for application 2 so that the HBase scans are very fast.
Any suggestions on how to achieve this?
if you run HBase and your applications on Yarn, the application masters (of HBase itself and the MR Jobs) can request the maximum of available resources on the data nodes.
Are you aware of the hortonworks project Hoya = HBase on Yarn ?
Especially one of the features is:
Run MR jobs while maintaining HBase’s low latency SLAs