What's a Grid

• A collection of computer nodes
• Shared workload
• General purpose

(Short) History of Grids

• Appeared mid 90's
• Commonly used in scientific computations
• "Distributed Supercomputer"
• BOINC
• Beowulf

Grid architecture

• Scheduler assigns tasks to nodes
• Assignment based on attributes (architecture) and load
• Unassigned tasks wait in queue

Grid responsibilities

• Schedule and distribute tasks to nodes
• Load balance nodes
• Retry failed tasks
• Monitor nodes
• Collect stdout, stderr
• Maintain job history

HPC Grids

• Offline batch jobs
• No API
• Simple, generic task placement
• non preemtive
• Slot based resource assignment
• No code distribution mechanism
• No isolation/containment

Google Borg

• In production circa 2003
• Online apps  & Offline batch tasks
• Granular resource control
• Dense
• API, sophisticated scheduling logic
• Application specific logic
• Preemptive

And everyone followed

• Facebook - Tupperware
• Twitter - Mesos
• Yahoo - YARN

It aint easy

Input, output

• Binaries, data
• Copy to local
• Logs

Elastic workload

• Add/release resources dynamically
• Resume dead tasks
• E.g. MapReduce, stateless web apps

Rigid workload

• Can't change number of tasks
• Task termination halts job
• Partitioned data/state
• E.g. MPI, Sharded database

Task lifespan

• Short tasks easier to balance
• Preemption (!!!!)
• Task creation overhead

Automation

• Deployment*
• Process management
• Supervision
• Server management

Density

• Shared resources
• Complementary workloads
• Priorities

Abstraction

Why do you care about

• IP addresses
• Servers
• Disks
• Racks

YARN

One grid to rule them all

One grid to find them

Motivation

• MRv1 didn't scale
• Bad resource utilization
• Multitenancy
• Not only MR

History

• Hadoop On Demand successor
• Development started 2011
• Released in Hadoop 2.2

Architecture

Architeture (summary)

• Monolithic scheduler
• App specific callbacks (app master)
• Single (pluggable) executor

Mesos

One grid to bring them all

and in the darkness bind them

History

• Berkeley reasearch project (2008)
• Lots of input from the Google Borg guys
• Early adoption by Twitter and AirBnB (2010)
• Apache project since 2013

Architecture (cont)

Architecture (summary)

• Two stage scheduler
• Frameworks, resource offers
• Multiple executor

Fight!

YARN

• Data processing workloads
• Many data apps already support it
• Quirky Docker support
• Generic apps (Apache Slider)

Mesos

• Generic workloads
• Hadoop (MRv1)
• Docker support
• Spark, Storm
• Plenty of frameworks
• But some apps need extra coding to work
• No file distribution mechanism*

Workload

YARN

• Hadoop sidekick
• Partial docs
• Apache Slider

Mesos

• Independent product
• Mesosphere DCOS
• Good docs
• Aurora, Marathon, Chronos
• Project Myriad*

Ecosystem

YARN

• Quirky API
• Not well documented
• Monolithic

Mesos

• Good API
• Built to be extended

Extending

YARN

• You already have it ;-)
• Almost all big data apps support it out of the box
• Works pretty well for Hadoop

Mesos

• Generic workload
• Easy to extend
• Production workhorse

Bottom line

Right choice if you want a grid for everything

Right choice if you want a grid for data processing