Deploying Grid Services Using Hadoop

Deploying Grid Services
Using Hadoop

Allen Wittenauer
April 11, 2008

Yahoo! @ ApacheCon 1

Agenda

• Grid Computing At Yahoo!
• Quick Overview of Hadoop
– “Hadoop for Systems Administrators”
• Scaling Hadoop Deployments
• Yahoo!’s Next Generation Grid Infrastructure
• Questions (and maybe even Answers!)


Grid Computing at Yahoo!

• Drivers
– 500M unique users per month
– Billions of interesting events per day
– “Data analysis is the inner-loop at Yahoo!”
• Yahoo! Grid Vision and Focus
– On-demand, shared access to vast pool of resources
– Support for massively parallel execution (1000s of processors)
– Data Intensive Super Computing (DISC)
– Centrally provisioned and managed
– Service-oriented, elastic
• What We’re Not
– Not “Grid” in the sense of scientific community (Globus, etc)
– Not focused on public or 3rd-party utility (Amazon EC2/S3, etc)


Yahoo! Grid Services

• Operate multiple grids within Yahoo!
• 10,000s nodes, 100,000s cores, TBs RAM, PBs disk
• Support large internal user community
– Account management, training, etc
• Manage data needs
– Ingest TBs per day
• Deploy and manage software (Hadoop, Pig, etc)


“Grid” Computing

• What we really do is utility computing
– Nobody knows what “utility computing” is, but everyone has heard of
“grid computing”
• Grid computing implies sharing across resources owned by multiple,
independent organizations
• Utility computing implies sharing one owner’s resources by multiple,
independent customers
• Ultimate goal is to provide shared compute and storage resources
– Instead of going to a hardware committee to provision balkanized
resources, a project allocates a part of its budget for use on Yahoo!’s
shared grids
– Pay as you go
• Only buy 100 computers for 15 minutes of compute time vs. 100 computers
24x7


What is a Yahoo! Grid Service?

• Thousands of machines using basic network hardware
– It’s hard to program for many machines
• Clustering and sharing software
– Hadoop, HOD, Torque, Maui, and other bits...
• Petabytes of data
– It’s an engineering challenge to load so much data from many sources
• Attached development environment
– A clean, well lit place to interact with a grid
• User support
– Learning facilitation
• Usage tracking and billing
– Someone has to pay the bills...


Quick MapReduce Overview

• Application writer specifies Input 0 Input 1 Input 2
– two functions: Map and Reduce
– set of input files
• Workflow Map 0 Map 1 Map 2
– input phase generates a number of
FileSplits from input files (one per
Map Task) Shuffle
– Map phase executes user function
to transform key/value inputs into
new key/value outputs Reduce 0 Reduce 1
– Framework sorts and shuffles
– Reduce phase combines all k/v’s
with the same key into new k/v’s
– Output phase writes the resulting Out 0 Out 1
pairs to files
• cat * | grep | sort | uniq -c | cat > out

Hadoop MapReduce: Process Level

• Job
– Map Function + Reduce Function + List of inputs

Job
Job
Tracker

Task Task
Tracker Tracker

Task Task Task Task


Hadoop Distributed File System

Data
FSFS
FS
FS
Node

Data Status

Name
Metadata
Node
Data
Status

Data
FSFS
FS
FS
Node


Deploying Grid Services Using Hadoop

Gateways: Multi-user Land

• Provided for two main purposes
– Meaningful development interaction with a compute cluster
• High bandwidth, low latency, and few network barriers enable a tight
development loop when creating MapReduce jobs
– Permission and privilege separation
• Limit exposure to sensitive data
– Hadoop 0.15 and lower lack users, permissions, etc.
– Hadoop 0.16 has users and “weak” permissions
• Characteristics
– “Replacement” Lab machine
– World-writable local disk space
• Any single-threaded processing
• Code debugging Gateway


Compute Cluster and Name Nodes

• Compute Cluster Node
– Users cannot login!
– All nodes run both MapReduce and HDFS
frameworks
– usually 500 to 2000 machines
– Each cluster kept relatively homogenous
– Hardware configuration
Task
• 2xSockets (2 or 4 core)
Tracker
• 4x500-750G Name
• 6G-8G RAM Node
Data Node
• Name Node
– 16G RAM
• 14G Java heap = 18-20 million files


Queuing and Scheduling

• Hadoop does not have an advanced scheduling
system
– MapReduce JobTracker manages one or more jobs
running within a set of machines
– Works well for “dedicated” applications, but does not
work so well for shared resources
• Grid Services are intended to be a shared multi-user,
multi-application environment
– Need to combine Hadoop with an external queuing and
scheduling system...


Hadoop On Demand (HoD)

Resource
• Wrapper around PBS commands
Mgmt/Sched
– We use freely available Torque and Maui
• Big win: virtual private JobTracker clusters maui
– Job isolation
– Users create clusters of the size they need
pbs_server
– Submit jobs to their private JT
• Big costs:
– Lose data locality
pbs_mom
– Increased complexity
– Lose a node for private JobTracker
– Single reducer doesn’t free unused nodes
• ~ 30% efficiency lost!
• Looking at changing Hadoop scheduling
– Task scheduling flexibility combined with node elasticity


HoD Job Scheduling

1+3 Nodes 1+4 Nodes 1+5 Nodes

T T T T

J T T T J NN


The Reality of a 1000 Node Grid

200 400 500 10


Putting It All Together

Gateways
RM

JT DN
TT DN
JT DN
TT DN
TT DN
JT DN

NN


Network Configuration

Core1 Core2 Core3 Core4

GE

2xGE

Switch Switch Switch
40 hosts/racks H H H H H H H H H


Yahoo!’s Next Generation
Grid Infrastructure
A Work In Progress

19

Background Information

• Internal deployments
– Mostly Yahoo! proprietary technologies
• M45
– Educational outreach grid
– Non-Yahoo!’s using Yahoo! resources
• Legal required us not to use any Y! technology!
• Decision made to start from scratch!
– Hard to share best practices
– Potential legal issues
– Don’t want to support two ways to do the same operation
• Internal grids converting to be completely OSS as possible
– Custom glue code to deal with any Y!<-->OSS incompatibilities
• user and group data


Naming and Provisioning Services

• Naming services
– Kerberos for secure authentication
– DNS for host resolution
– LDAP for everything else
• ISC DHCP
– Reads table information from LDAP
– In pairs for redundancy
• Kickstart
– We run RHEL 5.x
– base image + bcfg2
• bcfg2
– host customization
– centralized configuration management


NFS for Multi-user Support

• NFS
– Home Directories
– Project Directories
• Group shared data

• Grids with service level agreements (SLAs) shouldn’t use NFS !
– Single point of failure
• HA-NFS == $$$
– Performance
– Real data should be in HDFS


Big Picture
Remote Data Center
LDAP/KRB5/ LDAP/KRB5/
MMR
DNS DNS
Read Only Replication

DHCP BCFG2/YUM/
LDAP/KRB Slaves
Data DHCP Farm

GRID1 GRID2 ... GRIDx

NFS:
home
project (per grid)

Self Healing/Reporting

• Torque
– Use the Torque node health mechanism to disable/fix ‘sick’ nodes
• Great reduction in amount of support issues
• Address problems in bulk

• Nagios
– Usual stuff
– Custom hooks into Torque

• Simon
– Yahoo!’s distributed cluster and application monitoring tools
– Similar to Ganglia
– On the roadmap to be open sourced


Node Usage Report


Ranges and Groups

• Range: group of hosts
– example: @GRID == all grid hosts
– custom tools to manipulate hosts based upon ranges:
• ssh -r @GRID uptime
– Report uptime on all of the hosts in @GRID

• Netgroup
– Used to implement ranges
– The most underrated naming service switch ever?
• Cascaded!
• Scalable!
• Supported in lots of useful places!
– PAM (e.g., _succeed_if on Linux)
– NFS


Some Links

• Apache Hadoop
– http://hadoop.apache.org/

• Yahoo! Hadoop Blog
– http://developer.yahoo.com/blogs/hadoop/

• M45 Press Release
– http://research.yahoo.com/node/1879

• Hadoop Summit and DISC Slides
– http://research.yahoo.com/node/2104


Deploying Grid Services Using Hadoop

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