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Neo4j on IBM POWER8

Philip RathleVP of ProductsNeo Technology

Keshav RanganathanSenior Offering Manager, Data & Analytics SolutionsIBM POWER Systems

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Neo4j on IBM POWER Systems

Key Takeaways:• Why Graphs & Why Now?• Unique Characteristics of Graph Data &

Architecture Implications• IBM Power Systems Overview• Why deploy Neo4j on IBM Power Systems • Q&A

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Neo4j on IBM Power Systems Solves Massive-Scale, Previously Unsolvable Problems

A paradigm shift accelerating time to insight and real-time decision making…Bringing big data insights into action

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A Walk Back in Time…

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Data Management in 1990

Paper Forms

Tiny RAM Spinning Platters(Low Capacity / Sequential IO)

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Traditional DBMS Technology

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Data Management in 2016

Dynamic Real-World Systems

Abundant RAM

Flash & CAPI

(High-Capacity Storage &Ultra-Fast Random I/O)

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Sep 2015May 2015Jan 2015Sep 2014May 2014Jan 2014Sep 2013May 2013

100

Popu

larit

y Ch

ange

s

500

600

700

200

300

400

Jan 2013

© DB-Engines.com 2015

Popular Movement

• Wide column stores• RDF stores• Document stores• Search engines• Native XML DBMS• Key-value stores• Object oriented DBMS• Multivalue DBMS• Times Series DBMS

Relational database

Graph Database

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“Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture.”

By the end of 2018, 70% of leading organizations will have one or more pilot or proof-of-concept efforts underway utilizing graph databases.

Analyst Perspective

“Forrester estimates that over 25% of enterprises will be using graph databases by 2017”

IT Market Clock for Database Management Systems, 2014https://www.gartner.com/doc/2852717/it-market-clock-database-management

TechRadar™: Enterprise DBMS, Q1 2014http://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-/E-RES106801

Making Big Data Normal with Graph Analysis for the Masses, 2015

http://www.gartner.com/document/3100219

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100 Best in Show 2015

Magic Quadrant for Operational DBMS 2015

Neo4j: World’s Leading Graph Database

Technology of the Year 2015, 2014

100 Companies that Matter the Most in Data 2015

Neo4j named most popular Graph Database, 2015

Neo4j declared“Champion”, 2015 & 2016

“Most Popular and Widely Deployed Database”

Winner of NoSQL: Graph Database Technologies

DB-Engines Rankings

Source: http://db-engines.com/en/ranking/graph+dbms

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Database Technology ArchitecturesA Portfolio View

Graph DB

Connected DataDiscrete Data

Relational DBMSOther NoSQL

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Queries can take non-sequential,arbitrary paths through data

Real-time queries need speed and consistent response times

Queries must run reliably with consistent results

Q

A single query can touch a lot of data

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Relationship Queries StrainTraditional Architectures

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UNIFIED, IN-MEMORY MAP

Lightning-fast queries due to

replicated in-memory architecture +

index-free adjacency

MACHINE 1 MACHINE 2 MACHINE 3

Slow queriesdue to

index lookups + network hops

Neo4j on IBM POWER8

Using Other NoSQL to Join DataQ R

Q R

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Data Relationship Queries

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Traversal Speeds• Realistic retail dataset from Amazon • Social recommendation (Java procedure) equivalent to:

MATCH (you)-[:BOUGHT]->(something)<-[:BOUGHT]-(other)-[:BOUGHT]->(reco)WHERE id(you)={id}RETURN reco

Threads Hops/second 1 3-4M

10 17-29M20 34-50M30 36-60M

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Write Scale

• Import highly connected Friendster dataset

• 1.8 billion relationships takes around 20 minutes

• That is 1M writes/second!

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Good News for Real-Time, In-Memory Graph Queries:Big RAM is Eating Big Data

Jure Leskovec (Stanford), GRADES 2016Szilard Pafka (Datascience.LA)

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Value from Data Relationships: Top Use Cases

Internal ApplicationsFraud Detection

Master Data Management Network and IT Operations

Customer-Facing ApplicationsReal-Time Recommendations

Graph-Based SearchIdentity and

Access Management

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Solving Massive-Scale Challenges: Recommendations

People, Places, Things +Interests +

Transactions + Activity

Each requires a new & higherlevel of scaling

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Solving Massive-Scale Challenges: Fraud Detection

Estimated cost in 2014 $16.31B 1

Fraud and the costs to prevent fraud are up 94% year over year 2

62% of companies subject to payment fraud 3

Nearly 1 out of 4 declined transactions are false positives 4

1 The Nilson Report, 2015; 2, 4 2015 LexisNexis True Cost of Fraud Survey; 3 2015 AFP Payments Fraud and Control Survey

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ProsSimpleStops rookies

Traditional Fraud Detection: Discrete Data Analysis

RevolvingDebt

INVESTIGATE

INVESTIGATE

Number of accounts

ConsFalse positivesFalse negatives

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What’s Needed: Connected Analysis

RevolvingDebt

Number of accounts

ADVANTAGESDetect fraud rings

Fewer false negatives

Stop fraud attempts in flight: Carry out graph “walks” in real-time for transactions &

key events, revealing suspicious “loops”

Speed manual fraud analysis: Empower fraud analysts with graph-based analysis, to

more quickly and accurately identify complex fraud

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Gartner’s Layered Fraud Prevention Approach (4)

(4) http://www.gartner.com/newsroom/id/1695014

Traditional Fraud Prevention: Batch Analysis

Analysis of users and their endpoints

Analysis ofnavigation

behavior and suspect patterns

Analysis of anomaly

behavior by channel

Analysis of anomaly behavior

correlated across channels

Analysis of relationships

to detect organized crime

and collusion

Layer 1

Endpoint-Centric

Navigation-Centric

Account-Centric

Cross-Channel

Entity Linking

Layer 2 Layer 3 Layer 4 Layer 5

DISCRETE DATA ANALYSIS CONNECTED ANALYSIS

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IBM Power Systems

Portfolio Overview and Value for Neo4j

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4X

Threads per core*4X

Mem. Bandwidth*4X

More cache* @ Lower Latency

SMT=Simultaneous Multi-Threading OLTP = On-Line Transaction Processing

These design decisions result in best performance for data centric workloads like: Database, NoSQL, Big Data Analytics, OLTP

POWER8: Designed for data to deliver breakthrough performance

POWER8SMT8

x86Hyperthread

Parallel Processing

POWER8pipe

Data flow

x86 pipe POWER8

x86 POWER8 + OpenPOWER

x86

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250 Worldwide members of

30 Hardware and technology providers100+

Collaborative

2,500+Linux ISVs

developing on POWER100,000

+Open source packages

innovations under way

The POWER of an open ecosystem

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Chip / SOC

Boards / Systems

I/O / Storage / Acceleration

System / Integration

Software

Implementation / HPC / Research

This is what a revolution looks like

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Power Systems Portfolio – Enterprise and Scale-out offerings

Offering OS capability Positioning in the Linux portfolio

Scale-up

E880E870E850

Equally run AIX, IBM i andLinux with IFLs

Enterprise systemsLeadership Performance and ReliabilityUtilization Guarantee (PowerVM – 70%/80%)Flexible, dynamic Capacity on Demand & Enterprise Pools

Scale-Out

S824S822S814

Equally Run AIX, IBM i and Linux

Scale out SystemsUtilization Guarantee (PowerVM – 65)High performance, availability and resiliency

L lineS824LS822LS812L

Linux OnlyScale out Linux SystemsPrice/Performance Leadership vs. X86PowerVM, KVM

LC lineS812LCS822LC BDS822LC HPCS821LC

Linux OnlyCluster-optimized Linux SystemsLowest cost Power SystemKVM

New

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- Design and cost optimized for deployments of multiples (cloud and cluster)

- Broad number of optimal solutions

- Co-Designed with the OpenPOWER Ecosystem

IBM SupportCommunity / 3rd Party Support

running

The LC Line

The L Line

PurePower

Enterprise& IFLs

IaaSScale-Out, Linux-Only

ConvergedInfrastructure

Scale-Up

- Enterprise level RAS for single system deployments

- Solutions for Big Data & Analytics

- Converged infrastructure offering

- Rapid time to value and simplicity of management

- Enterprise level robustness and IFL capability

- Solution editions for in memory databases

- (HANA, DB2 BLU)

- Hosted cloud and hybrid cloud solutions

- Rapid deployments and POCs

The IBM Power Systems Linux Portfolio

• Broad Linux portfolio deliver all your Linux deployment needs

• Expanding LC portfolio with two servers for data centric applications and 2nd generation HPC server

POWER8 is designed for the Big Data era and delivers price-performance leadership to the Linux Market!

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ONPOWER8 CAPI Coherent Accelerator Processor Interface

CustomHardware

Application

POWER8

CAPP

Coherence Bus

PSL

FPGA or ASIC

Customizable HardwareApplication Accelerator • Specific system SW, middleware, or user application• Written to durable interface provided by PSL

POWER8

PCIe Gen3Transport for encapsulated messages

Processor Service Layer (PSL)• Present robust, durable interfaces to applications• Offload complexity / content from CAPP

Virtual Addressing• Accelerator can work with same memory addresses that

the processors use• Pointers de-referenced same as the host application• Removes OS & device driver overhead

Hardware Managed Cache Coherence• Enables the accelerator to participate in “Locks” as a

normal thread Lowers Latency over IO communication model

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http://opencapi.org/

ONWhy CAPI is Better than Traditional PCIe

CAPP

PCIe

Power Processor

FPGA

AFU

IBM Supplied POWER Service Layer

Typical I/O Model Flow

Flow with a Coherent ModelShared Mem.

Notify Accelerator Acceleration Shared MemoryCompletion

DD Call Copy or PinSource Data

MMIO NotifyAccelerator Acceleration Poll / Int

CompletionCopy or Unpin

Result DataRet. From DD

Completion

Advantages of Coherent Attachment Over I/O Attachment• Virtual Addressing & Data Caching

– Shared Memory– Lower latency for highly referenced data

• Easier, More Natural Programming Model– Traditional thread level programming– Long latency of I/O typically requires

restructuring of application

• Enables Applications Not Possible on I/O– Pointer chasing, etc…

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Total ~13µs for data prep

Total 0.36µs

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IBM Data Engine for NoSQL is an integrated platform for large and fast growing NoSQL data stores. It builds on the CAPI capability of POWER8 systems and provides super-fast access to large flash storage capacity. It delivers high speed access to both RAM and flash storage which can result in significantly lower cost, and higher workload density for NoSQL deployments than a standard RAM-based system. The solution offers superior performance and price-performance to scale out x86 server deployments that are either limited in available memory per server or have flash memory with limited data access latency.

IBM Data Engine for NoSQLCost Savings for In-Memory NoSQL Data Stores

Up to 57TB of extended memory with one POWER8 server + CAPI attach FLASH

Power S822L / S812L Flash System 900 Power S822L / S812L / S822 LC

NEW

External Flash Configuration Integrated Flash Configuration

Up to 8TB of super-fast storage tier on one POWER8 server

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ONCAPI Unlocks the Next Level of Performance for Flash

Identical hardware with 3 different paths to data

FlashSystem

ConventionalI/O (FC) CAPI - E

Conventional CAPI - I CAPI - E0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

IOPS per Hardware Thread

Conventional CAPI - I CAPI - E0

20

40

60

80

100

120

140

160

180

200

Latency (microseconds)

IBM POWER S822L>3x better IOPS per HW thread Lower latency

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CAPI – I : Integrated CAPI Flash CardCAPI – E: CAPI attached External Flash

CAPI - I

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POWER8 with CAPI enabled acceleration running Neo4j delivers 1.82X the performance versus Intel Broadwell servers with NVMe

POWER8 x860

100

200

300

400

500

600

700

800

711

390

Rep

rese

ntat

ive

mix

ed w

orkl

oad

Thro

ughp

ut

IBM Power S822LC (20c/160t) x86 Broadwell Server (24c/48t)

82%More

Throughput

• Accelerate Graph Databases with CAPI on POWER8

• Real-World mixed graph transaction workload running Neo4j on IBM Power S822LC server delivers 1.82X the throughput versus Intel Xeon E5-2650 v4 server

– POWER8 (20 cores / 128 GB): 711 Ops/sec– Intel Xeon E5 2650 v4 processor (24 cores / 128

GB): 390 Ops/sec

• Based on IBM internal testing of single system and OS image running mixed graph transaction s based on 200 GB data model internal IBM and Neo4j workload. Conducted under laboratory condition, individual result can vary based on workload size, use of storage subsystems & other conditions. Data as of October 19, 2016• IBM Power System S822LC; 20 cores (2 x 10c chips) / 160 threads, POWER8; 128 GB memory (16 x 8GB), 1.6 TB CAPI NVMe adapter , Neo4j 3.0.4, Ubuntu 16.04. Competitive stack: HP Proliant DL380 Gen9; 24 cores (2 x 12c chips) / 48 threads; Intel E5-2650 v4; 128 GB memory,(16 x 8GB), 1.6 TB NVMe adapter, Neo4j 3.0.4, Ubuntu 15.10.

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POWER8 with CAPI enabled acceleration running Neo4j delivers 1.61X the price-performance versus Intel Xeon E5-2650 v4 with NVMe

IBM Power S822LC

(20-core, 128GB)

HP DL380 Gen9(24-core, 128GB)

Server price*-3-year warranty

$19,123 $16,911

Mixed graph transaction Workload(total operations per second)

711 390

1.61XPrice-Performance

1.82XPerformance

per Server

• Based on IBM internal testing of single system and OS image running mixed graph transaction s based on 200 GB data model internal IBM and Neo4j workload. Conducted under laboratory condition, individual result can vary based on workload size, use of storage subsystems & other conditions. Data as of October 19, 2016• IBM Power System S822LC; 20 cores (2 x 10c chips) / 160 threads, POWER8; 128 GB memory (16 x 8GB), 1.6 TB CAPI NVMe adapter , Neo4j 3.0.4, Ubuntu 16.04. Competitive stack: HP Proliant DL380 Gen9; 24 cores (2 x 12c chips) / 48 threads; Intel E5-2650 v4; 128 GB memory,(16 x 8GB), 1.6 TB NVMe adapter, Neo4j 3.0.4, Ubuntu 15.10. * Pricing is based bundled pricing for S822LC with Integrated CAPI Flash card (IBM ordering system) and HP Web price https://h22174.www2.hp.com/SimplifiedConfig/Index

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Neo4j on IBM Power Systems Solves Massive-Scale, Previously Unsolvable Problems

A paradigm shift accelerating time to insight and real-time decision making…Bringing big data insights into action

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Where Do I Go Next?

If you think that you have a graph problem

Let’s qualify your use case

• neo4j.com/contact-us

• Info@neotechnology.com

• Your local IBM representative

• OpenDB@us.ibm.com

Learn more…• About graphs & Neo4j @ http://neo4j.com

• Use cases / Case studies / Webinars / Training / Boot camp for your organization or team

• About IBM Power Systems @ http://www-03.ibm.com/systems/power

• About IBM Data Engine for NoSQL @ http://www-03.ibm.com/systems/power/solutions/bigdata-analytics/data-engine-nosql/