Apache Hadoop YARN 2015: Present and Future

© Hortonworks Inc. 2015
Apache Hadoop YARN 2015
Present and Future
Vinod Kumar Vavilapalli
vinodkv [at] apache.org
@tshooter
Page 1

Who am I?
• 7.75 Hadoop-years old
– Don’t fall for the job postings asking
for 10 years #Hadoop Experience yet

• Past
– 2007: Last thing at School – a two
node Tomcat cluster. Three months
later, first thing at job, brought down a
800 node cluster ;)
– Team that ran Hadoop @ Yahoo!
• Present: @Hortonworks
• Two hats
– Hortonworks: Hadoop MapReduce
and YARN Development lead
– Apache: Apache Hadoop PMC,
Apache Member
• Worked/working on
– YARN, Hadoop MapReduce,
HadoopOnDemand,
CapacityScheduler, Hadoop security
– Apache Ambari: Kickstarted the
project’s first release
– Stinger: High performance data
processing with Hadoop/Hive
• Lots of trouble shooting on
clusters (@tshooter)
• 99% + code in Apache, Hadoop
– Open Source
– Community driven
Page 2
Architecting the Future of Big Data

Agenda
• Apache Hadoop YARN : Overview
• Past
• Present
• Future
Page 3

Overview
The Why and the What
Page 4

Why Hadoop YARN?
• Resource Management
• A messy problem
– Multiple apps, frameworks, their life-
cycles and evolution
• Varied expectations
– On isolation, capacity allocations,
scheduling
– Admin: “Best use of my cluster”
– Users: “Get me as much as possible,
as fast as possible”
• Tenancy
– “I am running this cluster for one
user”
– It almost never stops there
– Groups, Teams, Users
• Adhoc structures get bad real fast
• What’s different?
– Centered around Data
• ‘iIities
– Admission policies. Sharing. Security.
Elasticity. SLAs. ROI
Page 5
Data
?
Applications
Admins Users

What is Hadoop YARN?
Page 6
HDFS (Scalable, Reliable Storage)
YARN (Cluster Resource Management)
Applications (Running Natively in Hadoop)
• Store all your data in one place … (HDFS)
• Interact with that data in multiple ways … (YARN Platform + Apps)
• Scale as you go, shared, multi-tenant, secure … (The Hadoop Stack)
Queues Admins/Users
Cluster Resources
Pipelines

Past
A quick history
Page 7

A brief Timeline before the BigBang
• Sub-project of Apache Hadoop
• Releases tied to Hadoop releases
• Gmail like alphas and betas 
– In production at several large sites for
MapReduce already by that time
Page 8
1st line of Code Open sourced First 2.0 alpha First 2.0 beta
June-July 2010 August 2011 May 2012 August 2013

Apache Hadoop YARN releases
• 15 October, 2013
• The 1st GA release of Apache Hadoop 2.x
• YARN
– First stable and supported release of YARN
– Binary Compatibility for MapReduce applications built on Hadoop-1.x
– YARN level APIs solidified for the future
– Performance
– Scale from the get-go!
• Support for running Hadoop on Microsoft Windows
• Substantial amount of integration testing with rest of projects in the
ecosystem
Page 9
Apache Hadoop 2.2

Releases (contd)
• 24 February, 2014
• First post GA release for the year 2014
• Number of bug-fixes, enhancements
• Alpha features in YARN
– ResourceManager Failover
– Application History
Page 10
Apache Hadoop 2.3

Releases (contd)
• 07 April, 2014
• YARN
– ResourceManager Fail-over
– Preemption aided Scheduling
– Application History and Timeline Service V1
Page 11
Apache Hadoop 2.4

Releases (contd)
• 11 August, 2014
• YARN
– YARN's REST APIs
– Submitting & killing applications.
– Timeline Service V1 Security
Page 12
Apache Hadoop 2.5

Present
Page 13

Apache Hadoop releases (contd)
• 18 November 2014
• Last major release at the time of this talk
• YARN
– Support for rolling upgrades
– Support for long running services
– Support for node labels
– Alpha/Beta features: Time-based resource reservations, running applications
natively in Docker containers
Page 14
Apache Hadoop 2.6

Rolling Upgrades
At a click of a button
Page 15

Work preserving ResourceManager restart
Page 16
• ResourceManager remembers some state
• Reconstructs the remaining from nodes and apps

Work preserving NodeManager restart
Page 17
• NodeManager remembers state on each machine
• Reconnects to running containers

ResourceManager Fail-over
• Active/Standby Mode
• Depends on fast-recovery
Page 18
ZooKeeper

YARN Rolling Upgrades Workflow
Page 19
• Servers first
– Masters followed by Slaves
• Upgrade of Applications/Frameworks is decoupled!

YARN Rolling Upgrades Snapshot
Page 20

Stack Rolling Upgrades
Page 21
Rolling Updates Session by Sanjay Radia
Thursday April 16, 2015 11:45-12:25
@ Silver Hall

Services on YARN
Page 22

Long running services
• You could run them already before
2.6!
• Enhancements needed
– Logs
– Security
– Management/monitoring
– Sharing and Placement
– Discovery
• Resource sharing across
workload types
• Fault tolerance of long running
services
– Work preserving AM restart
– AM forgetting faults
• Service registry
• Project Slider:
http://slider.incubator.apache.org/
• HBase, Storm, Kafka already!
Page 23
“Bringing Long Running Services to Hadoop YARN”
by Steve Loughran
Thursday April 16, 2015 12:40-13:20
@ Copper Hall

Cluster Management Features
Page 24

Preemption aided Scheduling
• Admins
– “Make the best use of cluster resources”
• Users
– “Give me resources fast”
• Solution
– Elastic queues
– Loan idle capacities to others
– Take it back on demand
– Balance across queues: In
– Balance across users in a queue: WIP
Page 25

Fine-grain isolation for multi-tenancy
• Memory
– Custom monitoring
– Inelastic Resource
• CPU
– Cgroups on Linux
– Elastic Resource
• Support on Windows
– WIP
Page 26

Multi-resource scheduling
• Multi-dimensional bin-packing
– Application A says “I want 8GB RAM
and 2 CPUs”
– Application B says “I want 1GB RAM
and 10 CPUs”
• Today – memory & cpu
– Physical memory / virtual memory
– Cpu Cores – Virtual cores
• Scheduling constrained based on
the “bottleneck” resource
– Watch out for utilization drop on the
non-scarce resource
Page 27

Node Labels
• Partitions
– Admin: “I have machines of different
types”
– Impact on capacity planning: “Hey,
we bought those Windows machines”
• Types
– Exclusive: “This is my Precious!”
– Non-exclusive: “I get binding
preference. Use it for others when
idle”
• Constraints
– “Take me to a machine running JDK
version 9”
– No impact on capacity planning
– WIP
Page 28
Default Partition
Partition B
Linux
Partition C
Windows
JDK 8 JDK 7 JDK 7

Operational and Developer tooling
Page 29

Application History and Timeline Service
• Before
– Few MR specific implementations:
History and web-UI
• Not just MR anymore!
• History
– “Why was my application slow?”
– “Where did my containers run?”
– MapReduce specific Job History
Server
– Need a generic solution beyond
ResourceManager Restart
• Run analytics on historical apps!
– “User with most resource utilization”
– “Largest application run”
• Application Timeline
– Framework specific event collection
and UIs
– “Show me the Counters for my
running MapReduce task”
– “Show me the slowest Storm stream
processing bolt while it is running”
• Present
– A LevelDB based implementation
– Integrated into MapReduce, Apache
Tez, Apache Hive
Page 30

Other features
• Web Services
– No need for installed Hadoop Clients
– Submit an app
– Monitor / Kill it
• Multi-homing Environments
– Clients on a public networks
– Cluster traffic on a private network
– Fault tolerance
– Security
Page 31

Future
Page 32

Apache Hadoop releases (contd)
• Hadoop 2.7
– Likely April 19-24 week, 2014
– Moving to JDK 7 and beyond
• Future
Page 33
Apache Hadoop 2.7,
2.8 and beyond

Future: Timeline Service Next Generation
• Next generation
– Today’s solution helped understand the space
– Limited scalability and availability
• Analyzing Hadoop Clusters is a big-data problem
– Don’t want to throw away the Hadoop application metadata
– Large scale
– Enable near real-time analysis: “Find me the user who is hammering the
FileSystem with rouge applications. Now.”
• Timeline data stored in HBase and accessible to queries
Page 34

Future: Improved Usability
• Generic run-time information
– “What is my actual usage by the running container?”
– “How many rack local containers did I get”
– “How healthy is the scheduler”
– “Why is my application stuck? What limits did it hit?”
• With Timeline Service
– Why is my application slow?
– Why is my cluster slow?
– Why is my application failing?
– Why is my cluster down?
– What happened with my application? Succeeded?
– What happened in my clusters?
• Collect and use past data
– To schedule my application better
– To do better capacity planning
Page 35

Future: Containerized Applications
• Running Containerized
Applications on YARN
• Docker
• Multiple use-cases
– Run my existing service on YARN
– Slider + Docker
– Run my existing MapReduce
application on YARN via a docker
image
Page 36

Future: Scheduling
• Support priorities across
applications within the same
queue
• Policy Driven scheduling
– “I want app level fairness in queue A,
user level fairness in queue B, and
throughput focus in all other queues”
• Node anti-affinity
– “Do not run two copies of my service
daemon on the same machine”
• Gang scheduling
– “Run all of my app at once”
• Dynamic scheduling of containers
based on actual utilization
• Stabilized App Reservations
– “Create a reservation for my app with
X resources to run at 6AM tomorrow”
• Time based policies
– “10% cluster capacity for queue A
from 6-9AM, but 20% from 9-12AM”
• Prioritized queues
– Admin’s queue takes precedence
over everything else
• Lot more ..
Page 37

Future: More Resource Types
• Node level Isolation and Cluster
level Scheduling
• Disks
– Space
– IOPS: Read/Write
• Network
– Incoming bandwidth
– Outgoing bandwidth
Page 38

Thank you!
Page 39
Sandbox: Hadoop in a VM!
Questions Time!

Apache Hadoop YARN 2015: Present and Future

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