Gabriele Lana discusses document-oriented databases and their functionalities, including mapping, filtering, and indexing of documents. He presents various examples and use cases, such as querying job roles, handling messages, and implementing replication strategies. Additionally, he touches on web and application servers, highlighting their roles in document management and application support.

1. Webtechcon Milano 10/11/2010

2. gabriele lana
gabriele.lana@cleancode.it
twitter: @gabrielelana

3. “i can’t tell you
how i work in
25’ but i can
tell you why i
am cool”

4. what i am #1
document
oriented
database

5. key -> document
name: “Gabriele”
1 job: “Programmer”
born: “02/07/1977”
company: “CleanCode”
name: “Paolo”
2 job: “Manager”
born: “17/12/1965”
company: “BadCode”
name: “Enzo”
3 job: “Plumber”
born: “22/04/1982”
company: “CleanPipe”

6. complex values
name: “Gabriele”
1 job: “Programmer”
born: [ 2, 7, 1977 ]
company: “CleanCode”

7. map as filter
function(document) {
if (document.job === "Programmer") {
emit(document.id, document)
}
}
1 ... 1 ...
2 ... 2 ...
3 ... 3 ...

8. map as filter
function(document) {
if (document.job === "Programmer") {
emit(document.id, document)
}
}
yep is javascript

9. map -> index
function(document) {
emit(document.born, document)
}
19970702 ...
19651217 ... 19820422 ...

10. range queries are fast
function(document) {
emit(document.born, document)
}
19970702 ...
19651217 ... 19820422 ...

11. map -> reduce -> index
from: “Gabriele”
1 to: “Chiara”
at: [2010,11,11,21,30]
message: “...”
How many messages
sended from gabriele?

12. map -> reduce -> index
function(document) { function(from, messages) {
emit(document.from, 1) sum(messages)
} }
“Gabriele” 10
“Chiara” 127 “Renato” 45

13. index with complex key
function(document) {
emit([ document.from ].concat(document.at)), 1)
}
[ “Gabriele”,2010,11,11,21,30 ] 1

14. reduce by group levels
[ “Gabriele”,2010,11,11,21,30 ] 1
level 3 [ “Gabriele”,2010,11 ] 4
level 2 [ “Gabriele”,2010 ] 7
level 1 [ “Gabriele” ] 10

15. logarithmic update
map/
reduce

16. logarithmic update
map/
reduce

17. logarithmic update
(real time queries)
map/
reduce

18. crash only policy
append
only
multiversion
rev/0 rev/1
concurrency
control

19. replication
• master -> slave

20. replication
• master -> slave
• master -> slaves

21. replication
• master -> slave
• master -> slaves
• master -> master

22. replication
• master -> slave
• master -> slaves
• master -> master
• PEEr -> peer

23. sharding

24. usage patterns
oltp olap
Relatively Often complex
standardized and queries involving
simple queries aggregations.
Returning Periodic batch
relatively few jobs refresh the
records data

25. what i am #2
web
server

26. web server
http
• crud on documents
• documents attachments
• long polling changes
• proper caching
• lots of libraries
• firewall friendly

27. what i am #3
application
server

28. application server

29. application server

30. application server
• show document
• list documents
• authentication
• url rewrite
• good for small applications
• good for back offices

31. i have good friends

32. books...

33. questions?

34. gabriele lana
gabriele.lana@cleancode.it
twitter: @gabrielelana