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Hadoop YARN
Arnon Rotem-Gal-Oz
Director of technology research, Amdocs
let’s start by reviewing
Map/Reduce
ok, ok - K-Means
K=3
def perform_kmeans():
isStillMoving = 1
initialize_centroids()
while(isStillMoving):
recalculate_centroids()
isStillMoving = update_clusters()
return
put differently
In the map phase
• Read the cluster centers into memory from a File/HBase
• Iterate over each cluster center for each input key/value pair.
• Measure the distances and save the nearest center which has the lowest
distance to the vector
• Write the clustercenter with its vector to the context.
In the reduce phase
• Iterate over each value vector and calculate the average vector. (Sum
each vector and devide each part by the number of vectors we
received).
• This is the new center, save it into a File/HBase.
• Check if we need the different between runs is < epsilon
Run this whole damn thing again and again
until diff < epsilon
So you want to implement it
differently something that
doesn’t rely on map/reduce
Resource management is
tied to Map/Reduce
v
Yet Another Resource Negotiator
YARN takes Hadoop
beyond Map/Reduce
What is
an OS?
“…the function of the operating system is to present
the user with the equivalent of an extended machine or
virtual machine that is easier to program
that the underlying hardware”
“…The Operating System as a
Resource Manager… the job of the
operating system is to provide for an orderly and
controlled allocation of the processors, memories,
and/or devices among the various programs
competing for them”
With YARN
Hadoop becomes
a distributed OS
The Resource Manager
is essentially a scheduler
Containers are allocations
of physical resources
Each app instance spawns an
application manager (container 0)
- to negotiate resource and and
monitor app progress (tasks)
Node managers monitor
nodes and manage
containers lifecycle
Application Initiation
(or “how to get an App running in 11 easy steps”)
1. Client submits a job/app
2. Resource Manager (RM)
provides Application Id
3. Client provides context
(queue, resource requirements, files, security tokens etc.)
4. RM asks Node Manager to
launch Application Master
5. Node Manager launches
Application Master
6. Application Master
registers with RM
7. RM shares resource capabilities
with Application Master
8. Application Master
requests containers
9. RM assigns containers based on
policies and available resources
10. Application Master contacts assigned
node mageres to instantiate containers
(passing container contexts)
11. Node Manager initiate
container(s)
Congratulations your
Application is now running
Application Progress
(or “it doesn’t end there”)
1. continuous heartbeat & progress report
2. Request container status
3. Status response
1
2
3
Monitoring
1. Heartbeat also carries request for new container allocations / container
releases
2. Application master connects to node manger to activate allocated
containers
3. Container releases go through the Resource Manager
1
2
3
Lifecycle
YARN HA
(or “and you thought we were done”)
Still a work in progress
• Resource Manager - YARN 149 (patch available)
• Application Manager - YARN 1489
• Node Manager - YARN 1336
1. Active/Standby
YARN Limitation:
Manages memory & CPU
but not Disk IO or Network
YARN limitations
YARN Limitations:
Batch Focus
YARN Limitation :
Not daemon friendly
YARN Limitation: Relatively
complex to develop for
Apache Slider is an effort to
mitigate YARN gaps
Further Reading
YARN Source code
https://github.com/apache/hadoop-
common/tree/trunk/hadoop-yarn-project/hadoop-
yarn
Apache Hadoop YARN: Moving beyond
MapReduce and Batch Processing with
Hadoop 2 by Arun C. Murthy, Vinod Kumar
Vavilapalli, Doug Eadline, Joseph Niemiec &
Jeff Markham
Apache YARN site
http://hadoop.apache.org/docs/r2.3.0/hadoop-
yarn/hadoop-yarn-site/YARN.html
Apache Slider http://slider.incubator.apache.org
Image attributions:
slide 1 - Hadoop logo - http://hadoop.apache.org/docs/current/
slide 3 - adopted from pulp fiction
slide 4-5 http://pypr.sourceforge.net/kmeans.html
slide 10 - Elizabeth Moreau http://bornlibrarian.blogspot.co.il/2011/01/christmas-knitting.htm
slide 11 http://hortonworks.com/hadoop/yarn/
slide 14 http://developer.yahoo.com/blogs/ydn/posts/2007/07/yahoo-hadoop/
slide 32 https://upload.wikimedia.org/wikipedia/commons/a/a7/ColorfulFireworks.png
slide 39 http://justdan93.wordpress.com/2012/04/22/resource-categories/
slide 40 - http://dev2ops.org/2012/03/devops-lessons-from-lean-small-batches-improve-flow
slide 41 http://fc00.deviantart.net/fs71/i/2012/064/1/0/black_legion_daemon_prince_by_kny
Slide 42 By Christina Quinn https://www.flickr.com/photos/chrisser/7909860048/
Slide 43 By Paul L Dineen https://www.flickr.com/photos/pauldineen/757374092/in/photostr

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Hadoop YARN overview

Editor's Notes

  1. Also the partitioning to map and reduce is fixed and rigid (i.e. inefficient) And there are other limitations like max cluster size ~5000 nodes, up to 4000 processes (not a problem for most of us but still :) )
  2. Can’t balance effectively stream processes , doesn’t handle well point queries (impala, HBase)
  3. Doesn’t handle well long running processes (related to “batch focus”) Doesn’t work well with other daemons (see Cloudera Llama )
  4. Use frameworks : Spark, Pig etc. Or when you want to develop an App: Apache twill, Spring Hadoop etc.
  5. Still incubating (as of June 2014) 1. Simplify taking existing code unto YARN 2. Full capabilities of a YARN application - start/stop/multi-version, monitor, expand, security etc. (integrated with Ambari - > Nice move by HortonWorks to strengthen Ambari) Slider allows long-running applications, real-time services and online applications to easily integrate into a Hadoop environment.