Unseating the Giants

Monte ZwebenCEO, Splice Machine

October 16, 2014

2

The Big SqueezeData growing much faster than IT budgets

Source: 2013 IBM Briefing Book

Source: Gartner, Worldwide IT, Spending forecast, 3Q13 Update

Traditional RDBMSs Giants Overwhelmed…Scale-up becoming cost-prohibitive

Splice Machine | Proprietary & Confidential

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Scale-Out: The Future of DatabasesDramatic improvement in price/performance

Scale Up(Increase server size)

Scale Out(More small servers)

vs.$ $ $ $ $ $

5

Unseating the Giants

vs.

Scale-Up Giants

Scale-Out Challengers

6

Scale-Out Example #1

New application chooses NoSQL

Splice Machine | Proprietary & Confidential

8

ADVERTISER ROCKET FUEL

145RTB advertisingsupply partners

21,103,424Websites

19BnDaily impressions

###MM WW CONSUMERS91,999 DEVICES

Rocket Fuel: New Application

AdExchange

Rocket Fuel Platform

Auto Optimization

Real-Time Bidding

Publishers

Exchanges

Ad networks

Advertisers

Data Providers

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$2.38965$0.6782$1.7234

$0.09$1.78964$1.6782$1.7234$0.809$2.421.25

$2.11$1.26

$2.178$2.056$0.809$2.421.25

$2.11$1.26$2.78$1.56

$1.809$2.421.25

$2.11$1.26$2.78$0.56$2.421.25

$2.11$1.26$2.78

$0.756$0.809$2.421.25

$2.11$1.26$2.78

$1.256$1.809$2.421.25

$2.11$1.26$2.78

$0.586$2.009

1.25$2.11$1.26$2.78$1.56

$0.00

[ + ][ + ]

Site/PageGeo/WeatherTime of DayBrand AffinityUser

12

Hourly

Refresh

RTB

EXCHANGES

Bid Servers

HB

AS

E

User Profile Store

Ad ServersPixel Servers

Direct Publishers

H D F S

Master

Slaves

ETL

Bidder Logs Ad Server Logs

LikelihoodScores & Bid Value

Master Database

Apollo Ad-hoc Analytics Tools

Campaign Framework

Response Prediction

Models

Eligible Ads

ExchangePublishers

Apollo Reporting & Campaign Tools

Bid Call

Tag for Selected Ad & Bid

Rocket Fuel Ad Tag

Ad Creative Tag

Response Prediction

Models

UserLookup

UserLookup

& Update

Evaluate Ads

Load Balancer

Load Balancer

Hourly

Refresh

Ad-Rejection

1

2

3

45

6

7

8

9

13Splice Machine Proprietary and Confidential

HBase: Proven Scale-Out

Auto-sharding Scales with commodity hardware Cost-effective from GBs to PBs

High availability thru failover and replication

LSM-trees

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Rocket Fuel: Results

Facebook likes

Searches on Google

Bid Requests Considered by Rocketfuel

5 B

6 B

45 B

Requests per day

World class request velocity on over 10 PBs of data

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Scale-Out Example #2

Web application replaces Oracle

Splice Machine | Proprietary & Confidential

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Before Architecture: OracleOracle too expensive, too slow, and too difficult to scale and modify

Metadata Storage

Shutterfly Website

Photo File Storage

UploaderApp

Consumers

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After Architecture: MongoDBFlexibility and scalability of NoSQL ideal for simple web app

Metadata Storage

Shutterfly Website

Photo File Storage

UploaderApp

Consumers

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MongoDB ArchitectureDocument data model sharded across commodity servers

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MongoDB: Compelling Results vs. Oracle

⅕ costwith commodity scale out

9x fasterthrough parallelized queries

Increased agilitywith flexible schema and “shard on demand”

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Scale-Out Example #3

Splice Machine | Proprietary & Confidential

Existing OLTP & OLAP Apps Replace Oracle

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Before Architecture: Oracle RACOracle RAC too expensive and too slow, with queries up to ½ hour

• Operational Reports for Campaign Performance• Ad Hoc Audience

Segmentation

Social Feeds

Web/eCommerce Clickstreams

ETL

1st Party/CRM Data

3rd Party Data (e.g., Axciom)

POS Data

Email Marketing

Data Quality

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After Architecture: Hadoop RDBMSRDBMS functionality with proven scale-out from Hadoop

• Operational Reports for Campaign Performance• Ad Hoc Audience

Segmentation

Social Feeds

Web/eCommerce Clickstreams

ETL

1st Party/CRM Data

3rd Party Data (e.g., Axciom)

POS Data

Email Marketing

Data Quality

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Hadoop RDBMS: Best of Both Worlds

Scale-out on commodity servers Proven to 100s of petabytes Efficiently handle sparse data Extensive ecosystem

RDBMS ANSI SQL Real-time, concurrent updates ACID transactions ODBC/JDBC support

Hadoop

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Distributed, Parallelized Query Execution

Parallelized computation across clusterMoves computation to the dataUtilizes HBase co-processorsNo MapReduce

HBase Co-Processor

HBase Server Memory Space

L EG EN D

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Hadoop RDBMS: Compelling Results vs. Oracle

¼ costwith commodity scale out

3-7x fasterthrough parallelized queries

10-20x price/perfwith no application, BI or ETL rewrites

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Scale-Up vs. Scale-Out

Scale-Up: Top Reasons1. Willing to pay for engineered systems

2. Lots of custom code (e.g., PL/SQL)

3. Proven reliability

4. Avoid risk of newer technologies

5. Less migration required

Scale-Out: Top Reasons6. Reduce costs by 4x-10x

7. Increase performance by 3x-10x

8. Ease of scalability

9. Support for flexible schemas

10.Huge ecosystem of open source tools

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Unseating the Giants: Why is it different this time?It’s not just technology – user requirements fundamentally changed

Seismic User Shift• Budgets flat• Massive increase in data:

- Volume- Velocity- Variety

• No longer acceptable to throw data away

Disruptive Tech: Scale-Out

• Leverage commodity H/W• Reduce costs by 4-5x• Increase perf by 5-10x• Increase agility

Questions?

Monte ZwebenCEO, Splice Machine

mzeben@splicemachine.com

Visit Booth 246

www.splicemachine.com