Why NoSQL Makes Sense

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Former DoubleClick Founder and CTO Dwight Merriman wrote the first lines of MongoDB code in 2007. He drew upon his experiences building large scale, high availability, robust systems to create a new kind of database. This session will introduce some of the background to how and why MongoDB came about to address the need for a more scalable database, and provide some key use cases and rationale for a new product category in the database marketplace.

Tecnología Educación

Why NoSQL Makes Sense
Why NoSQL Makes Sense

NoSQL Now
Dwight Merriman / 10gen
Dwight Merriman / 10gen

signs we needed something different
signs we needed something different

• doubleclick ‐ 400,000 ads/second
• people writing their own stores
people writing their own stores
• caching is de rigueur
• complex ORM frameworks
• computer architecture trends
p
• cloud computing

the db space 2000
the db space 2000 ‐ 2010
+ great for complex
dh i
+ ad hoc queries easy transactions
+ SQL gives us a standard + great for tabular data
protocol for the interface + ad hoc queries easy
between clients and ‐ O<‐>R mapping hard
servers ‐ speed/scale challenges
+ scales horizontally ‐ not super agile
better than operational
dbs. some scale limits at BI / OLTP /
massive scale
i l reporting
i operational
i l
‐ schemas are rigid
‐ real time is hard; very
good at bulk nightly data
loads

the db space
the db space
+ fits OO programming
wellll
+ agile
+ speed/scale
‐ querying a little less
scalable
l bl add hoc
dd h
nonrelational ‐ not super transactional
BI / reporting (“nosql”) ‐ not sql

OLTP /
OLTP /
operational

as simple as possible
but no simpler
b l

as simple as possible
but no simpler
b l
• need a good degree of functionality to handle
a large set of use cases
– sometimes need strong consistency / atomicity
– secondary indexes
– ad hoc queries

as simple as possible
but no simpler
b l
• but, leave out a few things so we can scale
– no choice but to leave out relational
– distributed transactions are hard to scale

as simple as possible
but no simpler
b l
• to scale, need a new data model. some
options:
– key/value
– columnar / tabular
– document oriented (JSON inspired)
• opportunity to innovate ‐> agility
pp y g y

mongodb philosphy
mongodb philosphy
• No longer one‐size‐fits all. but not 12 tools either.
N l i fit ll b t t 12 t l ith
• By reducing transactional semantics the db provides, one can still solve an
interesting set of problems where performance is very important, and
horizontal scaling then becomes easier.
hori ontal scaling then becomes easier
• Non‐relational (no joins) makes scaling horizontally practical
• Document data models are good
• Keep functionality when we can (key/value stores are great, but we nee
more)
• Database technology should run anywhere, being available both for
running on your own servers or VMs, and also as a cloud pay‐for‐what‐
you‐use service. And ideally open source...

Questions?

http://blog.mongodb.org/
@mongodb
me
me ‐ @dmerr

www.mongodb.org
http://groups.google.com/group/mongodb‐user
htt // l / / db
irc://irc.freenode.net/#mongodb

thanks

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Why NoSQL Makes Sense

1. Why NoSQL Makes Sense Why NoSQL Makes Sense NoSQL Now Dwight Merriman / 10gen Dwight Merriman / 10gen

2. signs we needed something different signs we needed something different • doubleclick ‐ 400,000 ads/second • people writing their own stores people writing their own stores • caching is de rigueur • complex ORM frameworks • computer architecture trends p • cloud computing

3. the db space 2000 the db space 2000 ‐ 2010 + great for complex dh i + ad hoc queries easy transactions + SQL gives us a standard + great for tabular data protocol for the interface + ad hoc queries easy between clients and ‐ O<‐>R mapping hard servers ‐ speed/scale challenges + scales horizontally ‐ not super agile better than operational dbs. some scale limits at BI / OLTP / massive scale i l reporting i operational i l ‐ schemas are rigid ‐ real time is hard; very good at bulk nightly data loads

4. the db space 2000 the db space 2000 ‐ 2010 + great for complex dh i + ad hoc queries easy transactions + SQL gives us a standard + great for tabular data protocol for the interface + ad hoc queries easy between clients and ‐ O<‐>R mapping hard servers ‐ speed/scale challenges + scales horizontally ‐ not super agile better than operational dbs. some scale limits at BI / OLTP / massive scale i l reporting i operational i l ‐ schemas are rigid ‐ real time is hard; very good at bulk nightly data loads less issues here

5. the db space 2000 the db space 2000 ‐ 2010 + great for complex dh i + ad hoc queries easy transactions + SQL gives us a standard + great for tabular data protocol for the interface + ad hoc queries easy between clients and ‐ O<‐>R mapping hard servers ‐ speed/scale challenges + scales horizontally ‐ not super agile better than operational dbs. some scale limits at BI / OLTP / massive scale i l reporting i operational i l ‐ schemas are rigid caching ‐ real time is hard; very good at bulk nightly data loads app layer flat files partitioning map/reduce

6. the db space the db space + fits OO programming wellll + agile + speed/scale ‐ querying a little less scalable l bl add hoc dd h nonrelational ‐ not super transactional BI / reporting (“nosql”) ‐ not sql OLTP / OLTP / operational

7. data models

8. as simple as possible but no simpler b l

9. as simple as possible but no simpler b l • need a good degree of functionality to handle a large set of use cases – sometimes need strong consistency / atomicity – secondary indexes – ad hoc queries

10. as simple as possible but no simpler b l • but, leave out a few things so we can scale – no choice but to leave out relational – distributed transactions are hard to scale

11. as simple as possible but no simpler b l • to scale, need a new data model. some options: – key/value – columnar / tabular – document oriented (JSON inspired) • opportunity to innovate ‐> agility pp y g y

12. mongodb philosphy mongodb philosphy • No longer one‐size‐fits all. but not 12 tools either. N l i fit ll b t t 12 t l ith • By reducing transactional semantics the db provides, one can still solve an interesting set of problems where performance is very important, and horizontal scaling then becomes easier. hori ontal scaling then becomes easier • Non‐relational (no joins) makes scaling horizontally practical • Document data models are good • Keep functionality when we can (key/value stores are great, but we nee more) • Database technology should run anywhere, being available both for running on your own servers or VMs, and also as a cloud pay‐for‐what‐ you‐use service. And ideally open source...

13. Questions? http://blog.mongodb.org/ @mongodb me me ‐ @dmerr www.mongodb.org http://groups.google.com/group/mongodb‐user htt // l / / db irc://irc.freenode.net/#mongodb thanks

Why NoSQL Makes Sense

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