Scalable Data Analytics, Distributed Data Analytics, Fault Tolerant Data Analytics, Big Data Analytics

1. Data
Science
Dr.
Ahmet
Bulut
ahmetbulut@gmail.com
Oct
4
/
2013,
İstanbul

2. Available,
Fault-­‐Tolerant,
and
Scalable
• High
Availability
(HA):
service
availability,
can
we
incur
no
down9me?
• Fault
Tolerance:
tolerate
failures,
and
recover
from
failures,
e.g,
so?ware,
hardware,
and
other.
• Scalability:
going
from
1
to
1,000,000,000,000
comfortably.

3. Towering
for
Civiliza9on

4. 1
User
Website
App
Server
DB

5. 1,000
Users
Website
Load
Balancer
App
Server
1
App
Server
2
DB

6. 1,000
Users
Website
Load
Balancer
App
Server
1
Hardware
Failure
App
Server
2
DB
1

7. 2
1,000
Users
Website
Load
Balancer
App
Server
1
App
Server
2
New
Hardware
DB
45
mins

8. 1,000,000
Users
Website
Load
Balancer
1
App
Server
1
Master
DB
Load
Balancer
2
App
Server
2
Copy
App
Server
N
Slave
DB

9. 1,000,000
Users
Website
Load
Balancer
1
App
Server
1
Hardware
Failure
Ana
DB
Load
Balancer
2
App
Server
2
Txn
Log
File
Ship
1
App
Server
N
Slave
DB

10. 1,000,000
Users
Website
Load
Balancer
1
App
Server
1
Load
Balancer
2
App
Server
2
2
Mins
2
App
Server
N
Promo9on
Master
DB

11. 1,000,000
Users
Website
Load
Balancer
1
App
Server
1
Slave
DB
Load
Balancer
2
App
Server
2
Copy
Backup
normal
3
App
Server
N
Master
DB
10
mins

12. 2
mins?
%99.99
=
4.32
mins
of
down9me
in
a
month!
%99.999
=
5.26
mins
of
down9me
in
a
year!

13. 100,000,000
Users
RAM
1
RAM
2
RAM
3
RAM
...
RAM
N-1
Big DB Server
RAM
N
Clustered
Cache

14. 100,000,000
Users
RAM
1
RAM
2
RAM
3
RAM
...
RAM
N-1
RAM
N
Clustered
Cache
So?ware
Upgrade

15. 100,000,000
Users
RAM
1
0
mins
RAM
2
RAM
3
RAM
...
RAM
N-1
RAM
N
Clustered
Cache
So?ware
Upgrade

16. Clustered
Cache

17. Clustered
Cache

18. Clustered
Cache

19. Towering
for
Civiliza9on

20. Distributed
File
System
My
Precious!!!

21. No
down9me?
Ankara
İstanbul
İzmir
Bakü

22. Army
of
machines
logging

23. A
simple
sum
over
the
incoming
web
requests...
•Query:
Find
the
most
issued
web
request!
•How
would
you
compute?

24. What
about
recommending
items?
•Collabora9ve
Filtering.
•Easy,
hard,
XXL-­‐hard?

25. Extract
Transform
and
Load
(ETL)
App
Server
1
App
Server
2
DB

26. Extract
Transform
and
Load
(ETL)
App
Server
1099
App
Server
77
App
Server
657
App
Server
45
App
Server
1
App
Server
2
DB

27. Working
with
data
small
|
big
|
extra
big
•Business
Opera9ons:
DBMS.
•Business
Analy9cs:
Data
Warehouse.
•I
want
interac9vity...
I
get
Data
Cubes!
•I
want
the
most
recent
news...
•How
recent,
how
o?en?
•Real
9me?
•Near
real
9me?

28. Sooo?
•Things
are
looking
good
except
that
we
have:
•DONT-­‐WANT-­‐SO-­‐MANY
database
objects.
•Database
objects
such
as
•tables,
•indices,
•views,
•logs.

29. Ship
it!
•Tradi9onal
approach
has
been
to
ship
data
to
where
the
queries
will
be
issued.
•The
new
world
order
demands
us
to
ship
“compute
logic”
to
where
data
is.

30. Ship
the
compute-­‐logic
App
Server
77
App
Server
77
App
Server
77
App
Server
77
App
Server
77
App
Server
77

31. Map/Reduce
(M/R)
Framework

32. What
does
M/R
give
me?
•Fine-­‐grained
fault
tolerance.
•Fine-­‐grained
determinis9c
task
model.
•Mul9-­‐tenancy.
•Elas9city.

33. M/R
based
plajorms
•Hadoop.
•Hive,
Pig.
•Spark,
Shark.
•...
(many
others).

34. Towering
for
Civiliza9on

35. Spark
Parallel Operations
Resilient Distributed Datasets

36. Resilient
Distributed
Dataset
(RDD)
•Read-­‐only
collec9on
of
objects
par99oned
across
a
set
of
machines
that
can
be
re-­‐built
if
a
par99on
is
lost.
•RDDs
can
always
be
re-­‐constructed
in
the
face
of
node
failures.
Parallel Operations
Resilient Distributed Datasets

37. Resilient
Distributed
Dataset
(RDD)
•RDDs
can
be
constructed
by:
•From
a
file
in
DFS,
e.g.,
Hadoop-­‐DFS
(HDFS).
•Slicing
a
collec9on
(an
array)
into
mul9ple
pieces
through
parallelizaAon.
•
Transforming
an
exis9ng
RDD.
An
RDD
with
elements
of
type
A
being
mapped
to
an
RDD
with
elements
of
type
B.
•Persis9ng
an
exis9ng
RDD
through
cache
and
save
opera9ons.

38. Parallel
Opera9ons
•reduce:
combining
data
elements
using
an
associa9ve
func9on
to
produce
a
result
at
the
driver.
•collect:
sends
all
elements
of
the
dataset
to
the
driver.
•foreach:
pass
each
data
element
through
a
UDF.
Parallel Operations
Resilient Distributed Datasets

39. Spark
•Let’s
count
the
lines
containing
errors
in
a
large
log
file
stored
in
HDFS:
val file = spark.textFile("hdfs://...")
val errs = file.filter(_.contains("ERROR"))
val ones = errs.map(_ => 1)
val count = ones.reduce(_+_)

40. Spark
Lineage
val file = spark.textFile("hdfs://...")
val errs = file.filter(_.contains("ERROR"))
val ones = errs.map(_ => 1)
val count = ones.reduce(_+_)

41. Towering
for
Civiliza9on

42. Shark
Architecture

43. SQL
Queries
SELECT [GROUP_BY_COLUMN], COUNT(*)
FROM lineitem GROUP BY [GROUP_BY_COLUMN]
SELECT * from lineitem l join supplier s
ON l.L_SUPPKEY = s.S_SUPPKEY
WHERE SOME_UDF(s.S_ADDRESS)

44. SQL
Queries
•Data
Size:
2.1
TB
Data
•Selec9vity:
2.5
million
of
dis9nct
groups!
Time: 2.5 mins

45. Machine
Learning
•LogisHc
Regression:
Search
for
a
hyperplane
w
that
best
separates
two
sets
of
points
(e.g.,
spammers
and
non-­‐
spammers).
•The
algorithm
applies
gradient
descent
op9miza9on
by
star9ng
with
a
randomized
vector
w.
•The
algorithm
updates
w
itera9vely
by
moving
along
gradients
towards
the
op9mal
w’’.

46. Machine
Learning
def logRegress(points: RDD[Point]): Vector {
var w = Vector(D, _ => 2 * rand.nextDouble - 1)
for (i <- 1 to ITERATIONS) {
val gradient = points.map
{
p => val denom = 1 + exp(-p.y * (w dot p.x)) (1 / denom - 1) * p.y * p.x
}.reduce(_ + _)
w -= gradient
}
w
}
val users = sql2rdd("SELECT * FROM user u JOIN comment c ON c.uid=u.uid")
val features = users.mapRows { row =>
new Vector(extractFeature1(row.getInt("age")), extractFeature2(row.getStr("country")),
...)}
val trainedVector = logRegress(features.cache())

47. Batch
and/or
Real-­‐Hme
Data
Processing

48. History

49. LinkedIn
Recommenda9ons
•Core
matching
algorithm
uses
Lucene
(customized).
•Hadoop
is
used
for
a
variety
of
needs:
•Compu9ng
collabora9ve
filtering
features,
•Building
Lucene
indices
offline,
•Doing
quality
analysis
of
recommenda9on.
•Lucene
does
not
provide
fast
real-­‐9me
indexing.
•To
keep
indices
up-­‐to
date,
a
real-­‐9me
indexing
library
on
top
of
Lucene
called
Zoie
is
used.

50. LinkedIn
Recommenda9ons
•Facets
are
provided
to
members
for
drilling
down
and
exploring
recommenda9on
results.
•Face9ng
Search
library
is
called
Bobo.
•For
storing
features
and
for
caching
recommenda9on
results,
a
key-­‐value
store
Voldemort
is
used.
•For
analyzing
tracking
and
repor9ng
data,
a
distributed
messaging
system
called
Ka3a
is
used.

51. LinkedIn
Recommenda9ons
•Bobo,
Zoie,
Voldemort
and
Kara
are
developed
at
LinkedIn
and
are
open
sourced.
•Kara
is
an
apache
incubator
project.
•Historically,
they
used
R
for
model
training.
Now
experimen9ng
with
Mahout
for
model
training.
•All
the
above
technologies,
combined
with
great
engineers
powers
LinkedIn’s
Recommenda9on
plajorm.

52. Live
and
Batch
Affair
•Using
Hadoop:
1.
Take
a
snapshot
of
data
(member
profiles)
in
produc9on.
2.
Move
it
to
HDFS.
3.
Grandfather
members
with
<ADDED-­‐VALUE>
in
a
mawer
of
hours
in
the
cemetery
(Hadoop).
4.
Copy
this
data
back
online
for
live
servers
(ResurrecHon).

53. Who
we
are?
•We
are
Data
Scien9sts.

54. Our
Culture
•Our
work
culture
relies
heavily
on
Cloud
Compu9ng.
•Cloud
Compu9ng
is
a
perspec9ve
for
us,
not
a
technology!

55. What
we
do?
•Distributed
Data
Mining.
•Computa9onal
Adver9sing.
•Natural
Language
Processing.
•Scalable
Data
Analy9cs.
•Data
Visualiza9on.
•Probabilis9c
Inference.

56. Ongoing
projects
•Data
Science
Team:
3
Faculty;
1
Doctoral,
6
Masters,
and
6
Undergraduate
Students.
•Vista
Team:
Me,
2
Masters
&
4
Undergraduate
Students.
•Türk
Telekom
funded
project
(T2C2):
Scalable
Analy9cs.
•Tübitak
1001
funded
project:
Computa9onal
Adver9sing.
•Tübitak
1005
(submi7ed):
Computa9onal
Adver9sing,
NLP.
•Tübitak
1003
(in
prepera:on):
Online
Learning.