Clusterpoint is a privately held database software company founded in 2006 with 32 employees. Their product is a hybrid operational database, analytics, and search platform that provides secure, high-performance distributed data management at scale. It reduces total cost of ownership by 80% over traditional relational databases by providing blazing fast performance, unlimited scalability, and bulletproof transactions with instant text search and security. Clusterpoint also offers their database software as a cloud database as a service to instantly scale databases on demand.

1. High-performance database technology
for rock-solid IoT solutions
Gints Ernestsons, Clusterpoint founder
LATA conference, 28.01.2016.

2. Key facts about Clusterpoint
Founded: 2006
Team size: 32
Engineering: 25
Privately held, VC backed
4.8 m investments to date
Product: database software
Market share: 100s of installations
Partners: 7, Cloud partners: 2
Cloud DBaaS : from Q1/2015

3. Founder,
Visionary
Gints
Ernestsons
CTO,
Founder
Jurgis
Orups
DB Software
Architect
Janis
Sermulins
CEO
Zigmars
Rasscevskis
Business
Dev Director
Peteris
Janovskis
Key Personnel
15 years CTO in
Lursoft; 8 years
CEO in
Clusterpoint;
25 years as a
technology
entrepreneur
and investor
8 years in
Google;
Engineering
manager of the
Web search
backend (Zurich);
IMO silver medal
9 years runs
Clusterpoint
core software
engineering
team, expert
in C/C++,
NoSQL, Big
data search
5 years in
Google
MSc from MIT;
Intel Research
(USA)
IOI 2x Gold
medallist;
12 years in
Oracle;
Alliance &
Channel
Director Central
and East
Europe
Algorithms
Architect
Martins
Krikis
4 years in Intel
(USA), 4 years
in Tieto;
PhD from Yale
University;
Lecturer on
Algorithms

4. Selected list of our customers and partners
Ousting ORACLE, Microsoft SQL, MySQL and SEARCH platforms in 24/7
services

5. We operate cloud database infrastructure in Europe and USA
Dallas, US
Riga, Europe
Already > 5000
users, only 8
months
in a program
Cloud DBaaS
started in
Q1/2015

6. Gartner, Inc. forecasts that 6.4 billion connected things will
be in use worldwide in 2016, up 30 percent from 2015, and
will reach 20.8 billion by 2020. In 2016, 5.5 million new
things will get connected every day.
0
5000
10000
15000
20000
25000
2014 2015 2016 2020
Internet!of!Things!Units!Installed!Base!by!
Category!(Millions!of!Units)!|!Gartner!Nov!2015
Consumer Business:!Cross-Industry Business:!Vertical-Specific
0
500
1000
1500
2000
2500
3000
3500
2014 2015 2016 2020
Internet!of!Things!Endpoint!Spending!by!Category!
(Billions!of!Dollars)!|!Gartner!Nov!2015
Consumer Business:!Cross-Industry Business:!Vertical-Specific
Explosion of IoT data is inevitable: we are at the very beginning!

7. Product: hybrid operational database, analytics and search platform
Secure, high-performance,
distributed data management at scale
Hyper converged platform
that uses open standards
XML
JSON
SIEM
WEB HPC
DWH
OLAP
OLTP
Use cases
MB ► GB ▶ TB ▶ PB
ACID
TEXT
HybridSQL

8. We solve performance problems where relational databases fail
Blazing fast
performance
Unlimited
scalability
Bulletproof transactions, instant text search and security
Reduces your TCO by 80% over your database life-time
Up to 1000x
faster MB ► GB ▶ TB ▶ PB

9.
ACID
Distributed architecture delivers high-performance computing
CLUSTERPOINTRDBMS
Time
Reliability of legacy RDBMS without its complexity, at 1000x its speed
Simultaneous
execution of
parallel
computing
tasks using
fast & secure
transactions

10. All-in-one platform: DBMS, SEARCH, one API and one COST
Document database with
JavaScript/SQL + high
performance transactions
Search platform with data relevance
ranking, including full-text &
geospatial data
Scalable high-availability
distributed computing (sharding,
replication)
Real-time online web and mobile
analytics in Big data (no need for
map-reduce)
Bulletproof ACID
transactions
(patent filed, US)

11. No systems integration requiredCustom “stitching” all platforms
Kill complexity! Boost performance! Nail search! Cut your cost!
RDBMS w ACID-
transactions
ONE
API:
JS/SQL
Cut 80% off your TCO
Up to 1000x faster
High availability
shards, replicas
Online analytics
platform
Search platform,
full-text index
Tons of your integration efforts
and application “spaghetti” code

12. Budget for 100-users company, in
$
Commercial
RDBMS + SEARCH
Open source
RDBMS + SEARCH
Clusterpoin
t database
DBMS software license (enterprise edition) 14 000 0 0
DBMS software maintenance (3 years) 20% / 3 x 2800 DIY / 0 3 x 7200
SEARCH PLATFORM (SEARCH) license 10 000 0 0
SEARCH PLATFORM maintenance fee (3 year) 20% / 3 x 2
000
DIY / 0 0
DBMS client software access licenses (100
users)
10 000 0 0
DBMS + SEARCH integration through custom
application software code (developer
months)
3m / 15 000 6m / 30 000 1m / 5000
DBMS high-availability clustering option or
custom HA integration (developer months)
2m / 10 000 4m / 20 000 0
Operate & scale integrated DBMS + SEARCH
+ custom application code (developer
9m / 45 000 18m / 90 000 0
Replace 2 software platforms with 1 to decrease your TCO by
80%

13. MySQL Multiple Bugs Let Remote Users
Access and Modify Data and Deny Service
Security Tracker
Attackers targeting Elasticsearch
remote code execution hole
The Register
US Department of Homeland Security
Calls On Computer Users to Disable Java
Forbes
The Odd Couple: Hadoop
and Data Security
ZDnet
Major security alert as 40,000 MongoDB
databases left unsecured on the internet
InformationAge
By using multiple platforms, your security problems are snowballing
Bash bug leaves Linux
users shellshocked
WindowsSecurity

14. Manage all your data, indexes and replicas with solid security
Ordinary relational SQL database
Big data cluster, replicas, backups
All your mission-critical data in one
DBMS, analytics and search platform
XML
JSON
ONE API:
JS/SQLACID transactions
Search and analytics data/indexes
BLOB

15. Develop your application software code scalable from day-one
OPEX, TCO
Database life-cycle
Save > 80% WRITE ONCE
and decrease
life-time cost
of your web or
mobile
application
Test Year 1 Year 2 Year 3 Year 4 Year N

16. replica 1
replica 2
replica 3
Why pay extra for high-end features? Use out-of-the-box!
LOAD BALANCING
FAULT-TOLERANCE HIGH-AVAILABILITY
SCALE OUT ABILITY

17. Why document-oriented database architecture? Flexibility!
Easily includes other data models: tables, text, pictures, graphs, links etc
Manage all your data in open
industry standards:
XML and JSON

18. Life
time
Ordinary RDBMS: cost of changes escalates with software
stack
10
20
4015
Cumulative cost
ORM
45 d
Search
+ 90 d
Analytics & Reporting
+ 6 months
High availability clustering
+ 1 year
35
75
OPEX cost
Relational database
( ORM software model )
Launch
5
40

19. Document database: cost of changes goes down to minimum
20
40
Cumulative cost
Life
time
HA
+45 d
Search
+ 90 d
Analytics & Reporting
+ 6 months
Document model (de-
normalization)
1 year (rebuild application)
70
60
OPEX cost
Document database
( XML / JSON data model )
75
5
Launch

20. Ordinary database stores individual
measurements (1000s per meter)
Smart IoT meters: storing data in documents vs database raws
Document database stores all data on
individual meters as rich text objects
Fast degrading
performance
Billions of
measurements
Millions of smart
metersMeter Time Volts Amps Cost
1 10:00 220 0.25 0.05
2 10.00 230 0.50 0.10
3 10:00 180 0.30 0.03
... ... ... ... ...
1 10:15 240 0.65 0.10
2 10.15 230 0.50 0.10
3 10:15 180 0.30 0.03
... ... ... ... ...
Instant search
Top performance
Meter A day, a month or a year(s) data
1 00:00 { ... } ... 10:00 {220 0.25
0.05 } 10:15 { 240 0.65 0.10 } 10:30
{ ... } ... 23:45 { ... } address
2 00:00 { ... } ... 10:00 {230 0.50
0.10 } 10:15 { 230 0.50 0.10 } 10:30
{ ... } ... 23:45 { ... } ... name
3 00:00 { ... } ... 10:00 { Not
available } 10:15 { Signal loss }
10:30 { ... } ... 23:45 { ... } ... photo

21. Ordinary database indexing model
<id>
<title>
</title>
indexes
Full database content indexing
Automatically create and maintain fast full-text search index
Web-style free text SEARCH and
analytical JS/SQL queries
Complex queries requiring
steep learning curve
SQL query: tens of seconds Our query: milliseconds
RANKING INDEX

22. Your original data
in documents
Index tree is organized into a graph, enabling you
to set up your own search ranking (weighting)
rules
Distributed storage
architecture
words
strings
numbers
dates
names tags
values
relations
XML
&
JSON
Ultra-fast database index for ranked search and online
analytics
RANKING
INDEX%

23. Ranking index delivers endless scale out ability to your data
Organized as a modular graph, it enables to distribute data and computing
MB►GB▶TB▶PB

24.
Ordinary databases overload and overwhelm users with data
Two main performance problems with ordinary databases

25.
Disrupt your competition with fast and relevant full-text search
Use ranking
Relevance
of search results
Free text queries
at subsecond latency
Programmable filter that delivers superior search relevance
Having
billions of
data?

26. Scientists: ranking is a game-changing technology in
databases
Very Large Data Bases
Conference
7th International Workshop on
Ranking in Databases, 2013
“ the sheer amount of data makes it almost impossible to process queries
in the traditional compute-then-sort approach ”
“ Facing explosion of data ... the user would be overwhelmed by too many
unranked results “

27. Map
application
Address Product
AddressProduct
Company Company
100% 100%
75% 75%
50% 50%
Ranking delivers superior search experience in your database
Shop
application
Same data,
different ranking
rules for your free
text search
queries (think
voice in future)

28. Least relevant
Address
Company
Easily configure your own ranking rules for your business
needs
Email
Category
Most relevant
Product
Your own data items (fields) in
your XML or JSON database
100%0% 50%
100%0% 50%
100%0% 50%
100%0% 50%
100%0% 50%
When free text search hits data with higher rankings, results are sorted up-front

29. Simple,
super-fast,
user-friendly
web-style
SEARCH
Enjoy instantly relevant search in your data using only free text
Plain text:
Phrases:
Wildcards:
Patterns:
java developer London
“John Smith”
Joh* Smi* or “John Smi*”
John Sm?th
Substitutes: John Sm[iy]th
<query>
</query>

30. Two problems solved
w1^100% w2^+30% w3^-20%
integer 0 ..... 232
( when tag weightings are equal )
With ranking you can implement ranked pagination: 1 2 3 . . .
RANKING
INDEX
Real-time Big Data SEARCH
milli-
seconds
<id>
<title>
<document>
</title>
50%
Body
10%
Comments
100%
Ranking your database structure
Title
Ranking your documents
Ranking your search query terms
..w1...w2 ........ w3 ........
Ranking density of context
hits

31. Ranked pagination (1 2 3 ..) solves information overload
problem
Limited screen
estate
Limited network
bandwidth
Limited
waiting time
by users
Fast and relevant database search in your web and mobile applications
Page: 1 2 3 4 5 more

32. Constant query latency enables real-time Big data search and analytics
PB
GB
TB
MB
Milliseconds for a
JavaScript/SQL query in
Clusterpoint database
Minutes ... hours
for a SQL query in
legacy RDBMS
Scale to billions of documents without search performance
loss
RANKING
INDEX
XML
JSON

33. Clusterpoint Cloud Database as a Service (DBaaS)
We safely and efficiently manage
your databases for you AND
We instantly scale on-demand!

34. Clusterpoint Cloud is always ON, 99.99% available &
reliable
REST API JS/SQL
http(s) tcp/ip

35. Our cloud computing is using cost-efficient on-demand
model
Cost-Efficient
Model, $
Resources
Time
Conventional
Provisioning
Model, $
Save 3x-10x
DB

36. Thank you!