Slides for my talk at Developer Konferenz

1. NoSQL
An Introduction
Internet Briefing Developer Konferenz, 7/4/2010
Michael Marth
@michaelmarth | www.marth.ch
marth.software.services

2. “NoSQL” ???
Product? Technology?

3. “NoSQL” ???
Product? Technology?
persistence
Bunch of proje cts w ih share d interes t

4. relational non-relational
your requirements
Not only SQL

5. NoSQL Focus
data models scalability

6. NoSQL Focus
ly?
shard
al
ing
Re
read-
slave
vertic s
al sc
aling
data models scalability

7. NoSQL Focus
what is
scaling
1. Horizontal
scale:
scalability
more ser
vers crea
capacity tes more
2. Trans
parent t
applicati o the
on:
the busin
ess logic
app shou of the
ld be se
from con parated
cerns of
server re scaling
sources
3. No sin
gle point
no one s of failur
erver wh e:
lost, cau ich, if
ses down
the appl time of
ication
http://adam.blog.heroku.com/past/2009/7/6/sql_databases_dont_scale/

8. reme mber the
ambrian E xplosion?
C

9. according to http://nosql-database.org:
Column stores
Key Value / Tuple Store
Document stores
Eventually Consistent
Key Value Store
Graph Databases
XML Databases

10. Column stores Key Value / Tuple Store
Chordless
Hadoop / HBase Berkeley DB
Redis!
Cassandra MemcacheDB
Scalaris
Hypertable Mnesia
Tokyo Cabinet / Tyrant
LightCloud
GT.M
HamsterDB
Document stores Scalien
Jackrabbit Riak Eventually Consistent
CouchDB Terrastore
MongoDB ThruDB Key Value Store
Terrastore CloudKit
Voldemort
Dynomite a lm
o
KAI ope s t a l l
n s o a re
u rc
e
Graph Databases XML Databases
Neo4J Mark Logic Server
InfoGrid Sedna
EMC Documentum xDB !
Sones Xindice
Tamino
HyperGraphDB Berkeley DB XML
eXist

13. Background check
large contributions by web
content management system
vendor Day (Basel)

14. Data Model
stuc
ture mandatory fields
d field types
schem
allowed parents
a
etc.
unst
ruct
ured
look Ma, no schema!
s
schemales

15. 1 Data First
2 Structure Structure First
later
(maybe)

16. Data Model
title:
granular nosql
date:
20/3/2010
binary

17. Data Model
granular
binary
1
ordere d
2

18. Data Model
granular
binary
ordered
node-level access
control

19. Data Model
granular
binary
ordered
node-level access
control
versioning

20. The Sweet Spot
web content
http://www.flickr.com/photos/blentley/2951836266/

22. Background check
ideas and 1st
implementations by ex-Notes
developer

23. Data Model
Documents
{ schemaless
“_id”: “abc”,
“title”: “NoSQL”, JSON
“tags”:
[ binary attachments
“data model”,
“scalability”
],
“_rev”: 1234
}

24. Interface
ODBC?
JDBC?
HTTP! + JSON

25. Interface
read
GET /somedatabase/some_doc_id?rev=946B7D1C HTTP/1.0
write
POST /somedatabase/ HTTP/1.0
Content-Length: 245
Content-Type: application/json
{
"Subject":"I like Plankton",
"Author":"Rusty"
}
delete
DELETE /somedatabase/some_doc?rev=1582603387 HTTP/1.0

26. quer y
“No SQL”,
remember?
meet “map-reduce”

27. all documents results of map
1 map() reduce()
2 result 1
3
result 2 result of reduce
result 3
parallel

28. example: map-reduce
{
{ "total_rows":2,
id: “abc”, "offset":0,
title: “nosql”, "rows":
type: “talk” [
} function(doc) { {
if (doc.type == "talk") { "id":"abc",
emit(doc.title, doc); "key":"nosql",
} "value": {id: “abc”,
{ title: “nosql”, type: “talk”}
id: “xyz”, }
},
title: “ajax”, {
type: “talk” "id":"xyz",
} "key":"ajax",
map "value": {id: “xyz”,
title: “ajax”, type: “talk”}
}
]
}

29. example: map-reduce
{
"total_rows":2,
"offset":0,
"rows":
[
{
"id":"abc",
"key":"nosql", function (key, values, rereduce) {
"value": {id: “abc”, return sum(values);
title: “nosql”, type: “talk”} }
},
{
"id":"xyz",
"key":"ajax",
re duce
"value": {id: “xyz”,
title: “ajax”, type: “talk”}
}
]
}

30. Designed for Replication
t io n
e not the excep
co nflicts ar
master master
(incremental)
replication on demand
master

31. Distributed Address Book
server desktop
mobile
(partial replication)

32. The Sweet Spot
distributed databases with
schemaless data
http://www.flickr.com/photos/blentley/2951836266/

33. Eric Brewer:
CAP Theorem
Consistency choose any 2
Availability application
trade-off
Partition tolerance

34. client 1 client 2
write reads consistent value
db replicate db
1 transaction
1 Partition
2 Consistency

35. client 1 client 2
write reads inconsistent data
until replication
een
time betw
db replicate db clicks?
DNS
1 Partition
2 Availability

37. Background check
first developed by Facebook
for inbox search
now also used by Twitter,
Digg, ...

39. Data Model
k v
on s tero id s

40. Data Model
keyspace Twitter
column family Statuses
key columns
123 user_id: “abc” text: “i can haz cheesburger”
456 user_id: “abc” reply-to: “123” text: “nom nom”
plus super-co lumns
column family Users

41. High Lights
datacenter aware
• High availability
• Eventually consistent
• Tunable tradeoffs between consistency and latency
• No Single Point of Failure
no no de in the clus ter is spec
ial

42. The Sweet Spot
really large data sets,
really high availability
http://www.flickr.com/photos/blentley/2951836266/

43. I’ve seen this
before

44. reme mber the
ambrian E xplosion?
C
extinct

45. “Polyglot Programming”
Neal Ford, 2006
“Polyglot Persistence”
Scott Leberknight , 2009

46. questions
?
@michaelmarth | www.marth.ch