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TITANScalable Graph Database

Matthias Broecheler@mbroechelerMarch 6th, MMXIII

Graph Database

distributed

real time

opensource

name: Herculestype: demigod

name: Cerberustype: monster

battled

time:12

Vertex

Edge Label

Edge

Property= key + value

name: Jupitertype: god

name: Herculestype: demigod

name: Cerberustype: monster

father father

mother

brother

brotherbattled

pet

time:12

name: Plutotype: godage: 4000

name: Neptunetype: godage: 4500

name: Alcmenetype: humanage: 45

name: Saturntype: titanage: 10000

name: Hydratype: monster

battledtime: 2

name: Jupitertype: god

name: Herculestype: demigod

name: Cerberustype: monster

father father

mother

brother

brotherbattled

pet

time:12

name: Plutotype: godage: 4000

name: Neptunetype: godage: 4500

name: Alcmenetype: humanage: 45

name: Saturntype: titanage: 10000

name: Hydratype: monster

battledtime: 2

g.Vg.E

v

name: Jupitertype: god

name: Herculestype: demigod

name: Cerberustype: monster

father father

mother

brother

brotherbattled

pet

time:12

name: Plutotype: godage: 4000

name: Neptunetype: godage: 4500

name: Alcmenetype: humanage: 45

name: Saturntype: titanage: 10000

name: Hydratype: monster

battledtime: 2

v = g.V.has(‘name’,’Hercules’)

v

name: Jupitertype: god

name: Herculestype: demigod

name: Cerberustype: monster

father father

mother

brother

brotherbattled

pet

time:12

name: Plutotype: godage: 4000

name: Neptunetype: godage: 4500

name: Alcmenetype: humanage: 45

name: Saturntype: titanage: 10000

name: Hydratype: monster

battledtime: 2

v.out(‘father’,’mother’)

v

name: Jupitertype: god

name: Herculestype: demigod

name: Cerberustype: monster

father father

mother

brother

brotherbattled

pet

time:12

name: Plutotype: godage: 4000

name: Neptunetype: godage: 4500

name: Alcmenetype: humanage: 45

name: Saturntype: titanage: 10000

name: Hydratype: monster

battledtime: 2

v.out(‘father’).out(‘brother’).name

v

name: Jupitertype: god

name: Herculestype: demigod

name: Cerberustype: monster

father father

mother

brother

brotherbattled

pet

time:12

name: Plutotype: godage: 4000

name: Neptunetype: godage: 4500

name: Alcmenetype: humanage: 45

name: Saturntype: titanage: 10000

name: Hydratype: monster

battledtime: 2

v.outE(‘battled’).has(‘time’,T.gt,5).inV.name

name: Jupitertype: god

name: Herculestype: demigod

name: Cerberustype: monster

father father

mother

brother

brotherbattled

pet

time:12

name: Plutotype: godage: 4000

name: Neptunetype: godage: 4500

name: Alcmenetype: humanage: 45

name: Saturntype: titanage: 10000

name: Hydratype: monster

battledtime: 2

g.V.has(‘age’,T.gt,4200)

name: Jupitertype: god

name: Herculestype: demigod

name: Cerberustype: monster

father father

mother

brother

brotherbattled

pet

time:12

name: Plutotype: godage: 4000

name: Neptunetype: godage: 4500

name: Alcmenetype: humanage: 45

name: Saturntype: titanage: 10000

name: Hydratype: monster

battledtime: 2

g.E.has(‘time’,T.lt,5)

name: Jupitertype: god

name: Herculestype: demigod

name: Cerberustype: monster

father father

mother

brother

brotherbattled

pet

time:12

name: Plutotype: godage: 4000

name: Neptunetype: godage: 4500

name: Alcmenetype: humanage: 45

name: Saturntype: titanage: 10000

name: Hydratype: monster

battledtime: 2

saturn.as('x').in('father').loop('x'){it.loops < 3}.next()

name: Jupitertype: god

name: Herculestype: demigod

name: Cerberustype: monster

father father

mother

brother

brotherbattled

pet

time:12

name: Plutotype: godage: 4000

name: Neptunetype: godage: 4500

name: Alcmenetype: humanage: 45

name: Saturntype: titanage: 10000

name: Hydratype: monster

battledtime: 2

g.V.sideEffect{it.rank = it.both.both.both.count()

}

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Titan DatabaseArchitecture Overview

Titan Features

I. Data Management

II. Vertex-Centric Indices

Titan Features

III. Graph Partitioning

IV. Edge Compression

Architecture Analogy

MyISAM

Flexible Persistence

Partitionability

AvailabilityConsistency

g.E.has(‘location’,WITHIN,Geoshape.circle(38,24,50)

Full text & Geo Search

I. Navigate Memory

Sequential Data Access

II. Manage Concurrency

Multiple users Units of work

Atomicity Isolation Consistency Distribution

Transactions

Vertex Representation

5

Property

Property

Out-Edge

In-Edge

Out-Edge

In-Edge

In-Edge

row indices for fastvertex centric queries

byte

ord

er

sort

ing

cell = column + value

row

key

Titan Storage Model

Adjacency list in onecolumn family

Row key = vertex id Each property and edge

in one column Denormalized, i.e. stored twice

Direction and label/key as column prefix Use slice predicate for quick retrieval

5

5

label id +

directionsort key

Δ vertex id

Δ edgeid

signaturepropertie

s

other propertie

s

Edge Representation

Column Value

compressed serialized objects

variable long encoding

Properties & Edges are atomic

Vertex-Centric Indices

Sort and index edges per vertex by sor tkey Sort key can be composite

Enables efficient focused traversals Only retrieve edges that

matter Uses push down

predicates for quick, index-driven retrieval

v

time: 1

foughtfoughtfather

mother

battled battled battled

battled

time: 3 time: 5

time: 9v.query()

v

time: 1

father

mother

battled battled battled

battled

time: 3 time: 5

time: 9v.query() .direction(OUT)

v

time: 1

battled battled battled

battled

time: 3 time: 5

time: 9v.query() .direction(OUT) .labels(‘battled’)

v

time: 1

battled battled

time: 3

v.query() .direction(OUT) .labels(‘battled’) .has(‘time’,T.lt,5)

v

time: 1

battled battled

time: 3

v.query() .direction(OUT) .labels(‘battled’) .has(‘time’,T.lt,5)

=

v.outE(‘battled’).has(‘time’,T.lt,5).inV

Query Optimization

Consistency

on eventually consistent storage backends, Titan can enforce consistency constraints by configuring types withUniquenessConsistency.LOCK Titan acquires locks to avoid conflicting

changes Acquiring locks is expensive use with

care Locking protocol used is configurable

reasonably safe implementation, not completely fail-safe

Token Ring

Graph Partitioning

assigns ids to map vertices into “optimal” token range

Lots of interesting questions forfuture work

uses BOP

Educating the Planet

Person

PersonStuden

tTeacher

Course

Institution

Concept

Discussion

Comment

Share

enrolledIn

teaches

relatesTo

hasCourse

belongsTo

follows

author

references

hasComment relatesTo

author

partOf

relatesTo

121 Billion Edges6.2 Billion Vertices

1 Million Universities3 . 5 Billion Students

Placement Group

hi1 .4xl

Setup

1.1 million edges / sec

using batch mode

Data Ingestion

80 m1 .medium

10,200 transactions / sec

16 randomly chosen complex traversal templates

Throughput

Titan Local Caching

Flexible Persistence

Partitionability

AvailabilityConsistency

Local Deployment

Application + TitanStorage Backend

Application + Titan + Storage Backend (embedded)

Remote Deployment

Application + Titan

Storage Backend Cluster

Server Deployment II

Application

Cluster of: (2 JVM)- Titan + Rexster- Storage Backend (via localhost)

Native BlueprintsImplementation

Gremlin QueryLanguage

Rexster Server any Titan graph can

be exposed as a REST endpoint

Titan Ecosystem

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FaunusBatch Graph Analytics

Hadoop-based GraphComputing Framework

Graph Analytics Breadth-first Traversals Global Graph Computations Batch Big Graph Data

Faunus Features

Faunus Architecture

g._()

Faunus Work Flow

Compressed HDFS Graphs stored in sequence files variable length encoding prefix compression

Degree Distribution

GitHub Network

g.V.sideEffect{it.degree = it.out(‘follows’).count()

}.degree.groupCount

Degree Distribution

P(k) ~ k-γ

γ = 2.2

Global Recommendations

gremlin> g.E.has('label','pushed','to').keep.V.out('pushed').out('to').in('to').in('pushed').sideEffect('{it.score =it.pathCounter}').score.order(F.decr,'name')

# Top 5:Jippi 60892182927garbear 30095282886FakeHeal 30038040349brianchandotcom 24684133382nyarla 15230275746

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Big PictureClosing Thoughts

Value in Relationshipslow high

Key-Value

Why Graph Databases?

K V

BigTable

K V V V V

Document

Relational

Graph

The value of data is proportional to the

number of meaningful relationships

Social Networks

Recommendations

Path Finding

Graph Search

Knowledge Graph

Markets & Risks

ECONOMY

Health & Medicine

HEALTH

June 14th

2012September

2012December

2012March2013

November2013

AlphaRelease

Titan0.1.0

Titan0.2.0

Titan0.3.0

Titan0.4.0

Experimental release of a distributed, open -source graph database

First stable release

Rewrite of coreIndexing & ElasticSearch

PerformanceFeature ExtensionFulgora

Faunus Release

What’s Coming

Creating and updating indexes Vertex-centric indexes Graph indexes

Log integration Tighter Titan-Faunus Integration Graph Partitioning Declarative Query Answering Usability Improvements

Aurelius Graph Cluster

OLTP OLAP

Hadoop MapReduce

Analysis resultsback into Titan

Apache 2

g.V.label.groupCountg.v(101).out

titan.thinkaurelius.com

faunus.thinkaurelius.com

aureliusgraphs@googlegroups.com

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