Tree and Graph Processing On Hadoop

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Tree and Graph Processing On Hadoop

Ted Malaska

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Schedule

• Intro• Overview of Hadoop and Eco-System• Summarize Tree Rooting• MR Overview/Implementation Options• Hbase Overview/Implementation Options• Giraph Overview/Implementation Options• Spark Overview/Implementation Options• Summery• Quesitons

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Intro

• Hi there

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Overview of Hadoop and Eco-System

SearchNoSqlMachine LearningLFPRTQStreamingIngestionBatch

HDFSSecurity and Access Controls

Auditing and Monitoring

Map

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Hive

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In Scope for Tonight

SearchNoSqlMachine LearningLFPRTQStreamingIngestionBatch

HDFSSecurity and Access Controls

Auditing and Monitoring

Map

Red

uce

Pig

Crun

ch

Hive

Gira

ph

Sqoo

p

Flum

e

Kafk

a

Stor

m

Spar

k St

ream

ing

Spar

k

Impa

la

Mah

out

Ory

x

R Pyth

on S

trea

min

g

SAS

HBas

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Accu

mul

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NFS

Sear

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Summarize Tree Rooting

• Basic Tree

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1 1

22 2

2

3

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True Root

Leafs

Branches

Vertex

Edge

Depth

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Summarize Tree Rooting

• More Complex Tree

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11

22 2

2

3

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Circular Link

Multiple Parents

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Summarize Tree Rooting

• Merging Trees• Borderline True Graph Problem

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11

22 2

2

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Multi RootedVertex

True RootTrue Root

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Summarize Tree Rooting

• Know your data

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Basic Storage Format

• <NodeID>|<EdgeID>

• Example• 101• 101|201• 101|202• 201• 202|301• 301

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Preprocessing

• Terming Data• Nodes and edges have data• Data has weight• Normally linkage information is under 10% of true data size

• Organize Data by Partitioning

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Basic Solution

• Step 1: Identify Roots• Echo to all edges• Vertexes with that receive no echoes are roots• Root the root

• Step 2: Walk the tree• Echo from last newly rooted Vertex to all edges• If vertex is not already rooted then root it.

• 101• 101|201• 101|202• 201• 202|301• 301

• 101|R:101• 101|201|R:101• 101|202|R:101• 201|R:Null• 202|301|R:Null• 301|R:Null

• 101|R:101• 101|201|R:101• 101|202|R:101• 201|R:101• 202|301|R:101• 301|R:Null

• 101|R:101• 101|201|R:101• 101|202|R:101• 201|R:101• 202|301|R:101• 301|R:101

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Map Reduce

• Massive parallel processing on Hadoop• Based on the Google 2004 MapReduce white paper• Able to process PBs of data

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Map Reduce

Data Blocks

Data Blocks

Data Blocks

Mapper

Mapper

Mapper

Sort & Shuffle

Sort & Shuffle

Sort & Shuffle

Mapper

Mapper

Data Blocks

Data Blocks

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Map Reduce

• Self Joins• Always dumping two output:

• Newly Rooted• Still Un-Rooted

All Data

Un-Rooted

Newly Rooted

Un-Rooted

Newly Rooted

Old Rooted 0

MR - Stage0

Root Identifying

MR – Stage1

Rooting

Un-Rooted

Newly Rooted

Old Rooted 0

MR – Stage2

RootingOld Rooted 1

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Map Reduce

• Great for large batch operations• No memory limit• Not good at iterations

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HBase

• Largest and Most used NoSql Implementation in the World• Based on the Google 2006 BigTable white paper• Imagine it like a giant HashMap with keys and values• Handles 100k of operations a second on even a small 10 node cluster

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HBase Getting

Client

HBase Master

HBase Region Server HBase Region Server HBase Region Server

Block Cache Block Cache Block Cache

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HBase Putting

Client

HBase Master

HBase Region Server HBase Region Server HBase Region Server

WAL

MemStore

HFile

HFile

HFile

WAL

MemStore

WAL

MemStore

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HBase

• Good for graph traversing• Bad for large batch processing

• Scan rate about 8x slower then HDFS• Good for end of a long tail

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Giraph

• System built for Large Batch Graph Processing • Based on Pregel 2009 white paper• Hardened by LinkedIn and FaceBook• Recorded to handle up to a Trillion edges

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Giraph Loading

Data Blocks

Data Blocks

Data Blocks

Worker

Worker

Worker

Worker

Master

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Com

mun

icati

on

Giraph (Bulk Synchronous Parallel)

Worker Worker Worker

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Barrier synchronization

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Giraph

• Most mature bulk graph processing out there• Of all the solutions, most graph focused

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Spark

• At Berkeley around 2011 some asked is we could do better then MR• Take advantage of lower cost memory• Building on everything before

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Spark

WorkerDag Scheduler

(Like a queue planner

Spark Worker

RDD Objects

Task Threads

Block Manager

Rdd1.join(rdd2).groupBy(…).filter(…)

Task Scheduler

Threads

Block Manager

ClusterManager

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Spark

• Implementations• Onion MR approach with Basic Spark• Pregel approach with Bagel or GraphX

• Bagel is a Façade over Generic Spark Functionality• GraphX is an effort extend to Spark

• Less code• Learning curve • Its Raw will be changing a lot in the next year