As You Seek – How Search Enables Big Data Analytics

The Briefing Room
As You Seek—How Search Enables Big Data Analytics

Twitter Tag: #briefr The Briefing Room
Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com

!   Reveal the essential characteristics of enterprise software,
good and bad
!   Provide a forum for detailed analysis of today s innovative
technologies
!   Give vendors a chance to explain their product to savvy
analysts
!   Allow audience members to pose serious questions... and get
answers!
Mission

JUNE: Database
July: CLOUD
August: HIGH PERFORMANCE ANALYTICS
September: ANALYTICS

Database
Better SEARCH
Faster INSIGHT

Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group

robin.bloor@bloorgroup.com

! MarkLogic is an enterprise-class NoSQL database company
!   Key features of its database include ACID transactions,
horizontal scaling, real-time indexing, high availability,
disaster recovery, and government-grade security
!   Its platform provides full-text query and search capabilities,
application services and big data analytics
MarkLogic

David Gorbet
David Gorbet is Vice President of Engineering for
MarkLogic, where he also runs the Support
organization. Gorbet brings two decades of
experience delivering some of the highest-volume
applications and enterprise software in the world.
Prior to MarkLogic, Gorbet helped pioneer
Microsoft’s business online services strategy by
founding and leading the SharePoint Online
team. Gorbet holds a Bachelor of Applied
Science degree in Systems Design Engineering
with an additional major in Psychology from the
University of Waterloo, and an MBA from the
University of Washington Foster School of
Business.

MarkLogic: What it is, how it works
David Gorbet, VP Engineering

Copyright © 2013 MarkLogic® Corporation. All rights reserved.
WE ARE THE
NEW GENERATION
DATABASE
Any Structure Era
“For all your data!”
• Schema-agnostic
• Massive scale
• Query and search
• Analytics
• Application services
• Faster time-to-results
Relational Era
“For all your structured
data!”
• Normalized, tabular
model
• Application-
independent query
• User control
Hierarchical Era
For your application
data!
• Application- and
hardware-specific

Real Value From Big Data
Make The World More Secure
Provide Access To Valuable Information
Create New Revenue Streams
Gain Insights to Increase Market Share
Reduce Bottom Line Expense

The MarkLogic Advantage
Only Enterprise NoSQL Database
 ACID compliant
 Big data search
 High availability
 Replication
 Point in-time recovery
 Government-grade security
 Real-time your Hadoop
 Proven customer success

How Does It Work?
Schema-agnostic design
Real-time indexing and query
Event processing and alerting
Scale-out shared-nothing cluster topology
Analytics and Visualization
High availability and disaster recovery

Hierarchical Data Model
 MarkLogic Server is a document-centric database
 Supports any-structured data via hierarchical data model
Document
Title
Author
Section
Section Section Section Section
First
Last
Metadata
Trade
Cashflows
Party
Identifier
Net
Payment
Payment
Date
Party
Reference
Payer
Party
trade
ID
Payment
Amount
Receiver
Party

MarkLogic is Schema Agnostic
JSON and XML are self-describing
<article>
<title>MarkLogic Server:… </title>
<author>
<first-name>John</first-name>
<last-name>Doe</last-name>
</author>
<abstract>
. . . .<company>MarkLogic</company>. . . .
</abstract>
<body>
<section>
<section>. . . .</section>
</section>
<section>…index…</section>
</body>
<copyright>Copyright © … </copyright>
</article>

MarkLogic is Schema Agnostic
JSON and XML are self-describing
<article>
<title>
MarkLogic Server:…
<author>
<first-name>
John
<last-name>
Doe
<abstract>
. . . .
<company>
MarkLogic
. . . .
<body>
<section>
<section>
. . . .
<section>
…index…
<copyright>
Copyright © …

“brown” 123, 125, 129, 152, 344, 491, …
“mice” 123, 125, 126, 129, 130, 152, …
“brown mice” 125, 152, 516, 522, 765, 890, …
STEM “mouse” 123, 125, 126, 129, 130, 152, …
STEM “brown mouse” 125, 152, 516, 522, 765, 890, …
<article> …
<article>/<abstract> …
<section>/<paragraph> …
<animal>mouse</animal> …
<year>1950</year> …
Collection:Draft …
Role:Editor + Action:Read …
… …
… …
… …
Universal Index
Term Term List
MarkLogic indexes…
 Words
 Phrases
 Stemming
 Structure
 Values
 Collections
 Security Permissions
Document
References
125, 516, 890, …
Which draft articles contain the phrase brown mice?

“brown” 123, 125, 129, 152, 344, 491, …
“mice” 123, 125, 126, 129, 130, 152, …
“brown mice” 125, 152, 516, 522, 765, 890, …
STEM “mouse” 123, 125, 126, 129, 130, 152, …
STEM “brown mouse” 125, 152, 516, 522, 765, 890, …
<article> …
<article>/<abstract> …
<section>/<paragraph> …
<animal>mouse</animal> …
<year>1950</year> …
Collection:Draft …
Role:Editor + Action:Read …
… …
… …
… …
Scalar Queries
Term Term List Document
References
125, 516, 890, …
Which draft articles that contain the phrase brown mice were written before 2010?

Range Indexes
Value ID
2002 3
2003 10
2004 5
2004 11
2007 4
2007 17
2009 1
2011 8
… …
… …
… …
ID Value
1 2009
3 2002
4 2007
5 2004
8 2011
10 2003
11 2004
17 2007
… …
… …
… …
Map document IDs to
values, and vice-versa in
a compact in-memory
representation

Geospatial Index:
A 2-Dimensional Range Index
Fully composable with all other indexes!
 Built-in support for:
 Point
 Box
 Circle
 Polygon
 Complex Polygon
 Polygon Intersection
 Polygon Containment

Reverse Indexes (Alerting)
1. Load serialized queries as query documents
2. For a given data document, find all queries that match
 Can provide real-time alerts during loads
 With no significant performance impact!
 Can let documents store values as "ranges"
 Documents about cities self-defining their geo boundaries
 Person documents defining birthdays as ranges, sequences
 Can power classifiers and "matchmaker" queries

Value ID
2002 3
2003 10
2004 5
2004 11
2007 4
2007 17
2009 1
2011 8
… …
… …
… …
ID Value
1 2009
3 2002
4 2007
5 2004
8 2011
10 2003
11 2004
17 2007
… …
… …
… …
Range Indexes
Map document IDs to
values, and vice-versa in
a compact in-memory
representation
Range Indexes work like
a built-in in-memory
column store

Facets and Aggregation

Interactive Visualization

In-database Analytic Functions
Leverage ready-made
analytic built-ins for
commonly-used numeric
applications
 Variance
 Covariance
 Correlation
 Standard deviation
 Linear model
 Median
 Mode
 Percentile
 Rank
 Percent-rank
Benefits
 Faster analytics-based application
development
 Supports more users & more data
 Eliminates costs associated with
writing custom code

User-defined Functions
class InfluenceRank : public AggregateUDF
{
public:
struct Value {
double sum, sum_sq, count;
Value() : sum(0), sum_sq(0), count(0) {}
} value;
public:
AggregateUDF* clone() const { return new InfluenceRank (*this); }
void close() { delete this; }
void start(Sequence&, Reporter&) {}
void finish(OutputSequence& os, Reporter& reporter);
void map(TupleIterator& values, Reporter& reporter);
void reduce(const AggregateUDF* _o, Reporter& reporter);
void encode(Encoder& e, Reporter& reporter);
void decode(Decoder& d, Reporter& reporter);
};

• • •
• • •
In-database MapReduce
start
encode
decode
reduce
finish
decode
map
reduce
encode

SQL and BI Tools
ODBC
SQL
Range Indexes

SQL and BI Tools

HA/DR Features of MarkLogic

MarkLogic 6
Flexible
Indexes
Full Text
Search
Schema-
Agnostic
Scalable
Analytic
Functions
Hadoop
Distribution
Alerting
& Event
Processing
Geospatial
Query
In-
database
MapReduce
Visualization
Widgets
Transactions
Role-based
Security
Automated
Failover
Replication Journal
Archiving
Point-in-
time
Recovery
Database
Rollback
Backup/
Restore
Distributed
Transactions
Super-
clusters
Powerful
Everything you
need to deliver
business value
Trusted
Enterprise-
ready for
mission-critical
apps
REST &
Java APIs
JSON
Storage
Application
Builder
Information
Studio
Hadoop
Connector
Content
Pump
BI
Integration
SQL
Support
Monitoring
&
Management
OS
Support
Accessible
Leverage existing
tools, knowledge,
skills

Any Questions?

What is Semantics Technology?

Elasticity
 New tools to characterize and monitor the
resource requirements of your applications and
loads.
 Dynamic provisioning system that can add or
subtract resources on-the-fly to match the
loads.
 Distributed & virtualized environments including
VMWare, Amazon AWS and Hadoop are
supported to scale-out.
 Make the cloud a first-class citizen: Use Hadoop
HDFS or Amazon S3 for backup
Aligning infrastructure + demand, continually

Tiered storage
ML
SSD
local
HDFS
amzn s3
Benefits
 Keep data on tiers appropriate to
access needs = lower costs
 Detach and reattach storage when
needed. Fewer compute nodes
required = lower costs
 Leverage Hadoop HDFS investment
Choose infrastructure based on
value of data stored.
 100% online with different tiers
at different SLAs/topologies
 On-line/near-line mix utilizing
mount on-demand and
dynamic node spin-up.
Tiered Storage New Constructs
• Range partitions by Date/Scalar
manage group of forests by
range (“Q1” or “1990-1995”)
• Super Databases federate
queries across multiple
databases

Tiered Storage
96 504 1,044
592 2,066 2,080
Total Size (TB)
Total Cost ($000)
Operational
$25
Effective Unit Cost ($/GB)
$4
Compliance
$1.50
Analytic

Perceptions & Questions
Analyst:
Robin Bloor

The Bloor Group
Database Innovation
Database used to be a “zero-innovation market.”
Now it is the opposite.
Traditional (relational)
database is now seen
(rightly) as inadequate
in many respects
Big Data is, mainly, new
data posing new
problems
New products are
emerging and some
older products are
being given a make-over
(and gaining popularity)
Hadoop has changed
perceptions and
thinking about database

The Bloor Group
Multiple Database Roles
HAVE INCREASED SIGNIFICANTLY…

The Bloor Group
The Analytics Issue

The Bloor Group
The Origin of Big Data

The Bloor Group
NoSQL Confusion
As the graph indicates
NoSQL is a very
confusing descriptor.
WHAT CAN A GIVEN
DATABASE ACTUALLY
DO?
The important question is

The Bloor Group
The Joys and Sorrows of SQL
SQL:
Very good for set manipulation
Works for OLTP and many query
environments
Not good for nested data structures
(documents, web pages, etc.)
Not good for ordered data sets
Not good for data graphs (networks of
values)

The Bloor Group
!   In my view we have reached a situation where
there will be multiple “data engines.” Is that
MarkLogic’s view?
!   Specifically, are there data structures or
database contexts for which MarkLogic is
inappropriate?
!   What new features or capabilities are on the
MarkLogic roadmap?
!   In your view, is the “age of the data
warehouse” over?

The Bloor Group
!   Which sectors/businesses are currently in
MarkLogic’s “sweet spot”?
!   Data analytics involves much more than having
analytical functions in the database. It is more
than 50% data prep (merging, cleansing, joining,
transformation, etc.). How does MarkLogic
accommodate that?
!   What is MarkLogic’s attitude to the cloud?
Specifically, where would it recommend cloud
deployment?

July: CLOUD
August: HIGH PERFORMANCE ANALYTICS
September: ANALYTICS
Upcoming Topics
www.insideanalysis.com

Thank You
for Your
Attention

As You Seek – How Search Enables Big Data Analytics

Recommandé

Recommandé

Contenu connexe

Similaire à As You Seek – How Search Enables Big Data Analytics

Similaire à As You Seek – How Search Enables Big Data Analytics (20)

Plus de Inside Analysis

Plus de Inside Analysis (20)

Dernier

Dernier (20)

As You Seek – How Search Enables Big Data Analytics