From Hadoop The Definitive Guide
The whole process is illustrated in Figure 7-1. At the highest level,
there are five independent entities:
• The client, which submits the MapReduce job.
• The YARN resource manager, which coordinates the allocation of
compute re‐ sources on the cluster.
• The YARN node managers, which launch and monitor the compute
containers on machines in the cluster.
• The MapReduce application master, which coordinates the tasks
running the Map‐ Reduce job. The application master and the MapReduce
tasks run in containers that are scheduled by the resource manager and
managed by the node managers.
What is the MapReduce application master?
In a MapReduce program written in Java, we need three things: a map function, a reduce function, and some code with main() function to run the job. Is the MapReduce application master the code with main() function to run a map reduce job?
main() function in typical Hadoop program usually does these things:
specifies the input/output path for the job
configures mappers/reducers/combiners/partitioners
configures memory
Then it creates an instance of Job interface, runs it and calls waitForCompletion, which blocks until job is finished. This call sends Yarn application request under the hood, which spawns AppMaster somewhere on the cluster.
AppMaster is responsible for creating Map/Reduce processes, tracking their status and reporting the progress. There's 1 instance of AppMaster for every job running on Hadoop cluster.
Related
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.
I have been trying to install Hadoop on a single node following the instructions written here. There are two sets of instructions, one for running a MapReduce job locally, and another for YARN.
What is difference between running a MapReduce job locally and running on YARN?
If you use local the map and reduce tasks are run in the same jvm. Usually this mode is used when we want to debug the code. Whereas if we use yarn resource manager which is in MRV2 comes into play and mappers and reducers will run in different nodes and different jvms with in the same node(if it is pseudo distributed mode).
I am using spark-1.6 with standalone resource manager in client mode. Now, as it is supported to run multiple executors per worker in spark. Can anyone tell me the pros and cons of running which one should be preferred for the production environment?
Moreover, when spark comes with the pre-built binaries of hadoop-2.x why do we need to setup another hadoop cluster to run it in the yarn mode. What's the point of packing those jars in the spark. And what's the point of using the yarn when flexibility of multiple executors per worker is given in standalone mode
I have successfully set up Mesos 0.22.1 cluster on 5 nodes. I can run Marathon and Chronos tasks on all slave nodes. Now I’m trying to run Hadoop jobs using Mesos Scheduler. I have followed very good tutorial and I could run wordcount test job. But when I try to run some larger job (loading data from Kafka to HDFS using Camus) job is running without the errors, but uses only one node with one task tracker, though it has in total 30 map jobs, and my nodes configured to run 2 map jobs in parallel.
What am I missing? Shouldn’t Jobtracker split task to run in parallel on all available nodes using 2 Map slots on eash node?
And what is strange - on Jobtracker webpage cluster summary reports only 1 available node. Is it correct behavior?
Any ideas are greatly appreciated!
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