What Is Hadoop update to include YARN, Tez, Spark, and Storm

1. Adam
Muise
–
Hortonworks
WELCOME
TO
HADOOP

2. Who
am
I?

3. Why
are
we
here?

4. Data

5. “Big
Data”
is
the
marke=ng
term
of
the
decade

6. What
lurks
behind
the
hype
is
the
democra=za=on
of
Data.

7. You
need
to
deal
with
Data.

8. You’re
probably
not
as
good
at
that
as
you
think.

9. Put
it
away,
delete
it,
tweet
it,
compress
it,
shred
it,
wikileak-­‐it,
put
it
in
a
database,
put
it
in
SAN/NAS,
put
it
in
the
cloud,
hide
it
in
tape…

10. Let’s
talk
challenges…

11. Volume
Volume
Volume
Volume

12. Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume

13. Volume
Volume
Volume
Volume
Volume
Volume
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Volume
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Volume
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Volume
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Volume

14. Volume
Volume
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Volume
Volume
Volume
Volume
Volume

15. Storage,
Management,
Processing
all
become
challenges
with
Data
at
Volume

16. Tradi=onal
technologies
adopt
a
divide,
drop,
and
conquer
approach

17. Another
EDW
Analy=cal
DB
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
The
solu=on?
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data
OLTP
Data
Data
Data
Data
Data
Data
Data
Data
Data
Yet
Another
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data

18. Another
EDW
Analy=cal
DB
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
OLTP
Ummm…you
dropped
something
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Yet
Another
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data

19. Analyzing
the
data
usually
raises
more
interes=ng
ques=ons…

20. …which
leads
to
more
data

21. Wait,
you’ve
seen
this
before.
Data
Data
Data
…
Sausage
Factory
Data
Data
Data
Data
Data
Data
Data
Data
Data
…
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data

22. Data
begets
Data.

23. What
keeps
us
from
Data?

24. “Prices,
Stupid
passwords,
and
Boring
Sta=s=cs.”
-­‐
Hans
Rosling
h"p://www.youtube.com/watch?v=hVimVzgtD6w

25. Your
data
silos
are
lonely
places.
EDW
Accounts
Customers
Web
Proper=es
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data

26. …
Data
likes
to
be
together.
EDW
Accounts
Customers
Data
Data
Web
Proper=es
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data

27. CDR
Data
Data
Data
Machine
Data
Facebook
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Weather
Data
TwiYer
Data
Data
likes
to
socialize
too.
Data
Data
EDW
Data
Data
Data
Data
Data
Data
Accounts
Data
Web
Proper=es
Data
Data
Data
Customers
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data

28. New
types
of
data
don’t
quite
fit
into
your
pris=ne
view
of
the
world.
Logs
Data
Data
Data
Data
Data
Data
Data
Machine
Data
Data
Data
Data
Data
Data
Data
Data
My
LiYle
Data
Empire
Data
?
Data
?
Data
Data
Data
Data
Data
?
?
Data
Data

29. To
resolve
this,
some
people
take
hints
from
Lord
Of
The
Rings...

30. …and
create
One-­‐Schema-­‐To-­‐
Rule-­‐Them-­‐All…
EDW
Data
Data
Data
Data
Data
Schema
Data
Data
Data
Data

31. ETL
Data
Data
Data
ETL
ETL
ETL
EDW
Data
Data
Data
Data
Data
Schema
Data
Data
Data
Data
…but
that
has
its
problems
too.
ETL
Data
Data
Data
ETL
ETL
ETL
EDW
Data
Data
Data
Data
Data
Schema
Data
Data
Data
Data

32. So
what
is
the
answer?

33. Enter
the
Hadoop.
………
hYp://www.fabulouslybroke.com/2011/05/ninja-­‐elephants-­‐and-­‐other-­‐awesome-­‐stories/

34. Hadoop
was
created
because
Big
IT
never
cut
it
for
the
Internet
Proper=es
like
Google,
Yahoo,
Facebook,
TwiYer,
and
LinkedIn

35. Tradi=onal
architecture
didn’t
scale
enough…
App
App
App
App
App
App
App
App
DB
DB
DB
SAN
App
App
App
App
DB
DB
DB
SAN
DB
DB
DB
SAN

36. Databases
become
bloated
and
useless

37. $upercompu=ng
Tradi=onal
architectures
cost
too
much
at
that
volume…
$/TB
$pecial
Hardware

38. How
would
you
fix
this?

39. If
you
could
design
a
system
that
would
handle
this,
what
would
it
look
like?

40. It
would
probably
need
a
highly
resilient,
self-­‐healing,
cost-­‐efficient,
distributed
file
system…
Storage
Storage
Storage
Storage
Storage
Storage
Storage
Storage
Storage

41. It
would
probably
need
a
completely
parallel
processing
framework
that
took
tasks
to
the
data…
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage

42. It
would
probably
run
on
commodity
hardware,
virtualized
machines,
and
common
OS
plaeorms
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage

43. It
would
probably
be
open
source
so
innova=on
could
happen
as
quickly
as
possible

44. It
would
need
a
cri=cal
mass
of
users

45. It
would
be
Apache
Hadoop

46. {Processing
+
Storage}
=
{MapReduce/Tez/YARN+
HDFS}

47. HDFS
stores
data
in
blocks
and
replicates
those
blocks
block1
Processing
Processing
Processing
Storage
Storage
Storage
block2
block2
Processing
Processing
Processing
block1
Storage
Storage
Storage
block3
block2
Processing
Storage
block3
Processing
Processing
block1
Storage
Storage
block3

48. If
a
block
fails
then
HDFS
always
has
the
other
copies
and
heals
itself
block1
Processing
Processing
Processing
block3
Storage
Storage
Storage
block2
block2
Processing
Processing
Processing
block1
Storage
Storage
Storage
block3
block2
Processing
Storage
block3
Processing
Processing
block1
Storage
Storage
X

49. MapReduce
is
a
programming
paradigm
that
completely
parallel
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Mapper
Mapper
Mapper
Mapper
Mapper
Reducer
Data
Data
Data
Reducer
Data
Data
Data
Reducer
Data
Data
Data

50. MapReduce
has
three
phases:
Map,
Sort/Shuffle,
Reduce
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Mapper
Mapper
Key,
Value
Key,
Value
Key,
Value
Reducer
Key,
Value
Key,
Value
Key,
Value
Mapper
Reducer
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Mapper
Reducer
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Mapper
Key,
Value
Key,
Value
Key,
Value

51. MapReduce
applies
to
a
lot
of
data
processing
problems
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Mapper
Mapper
Mapper
Mapper
Mapper
Reducer
Data
Data
Data
Reducer
Data
Data
Data
Reducer
Data
Data
Data

52. MapReduce
goes
a
long
way,
but
not
all
data
processing
and
analy=cs
are
solved
the
same
way

53. Some=mes
your
data
applica=on
needs
parallel
processing
and
inter-­‐
process
communica=on
Data
Data
Data
Data
Data
Data
Process
Data
Data
Data
Process
Data
Data
Data
Data
Data
Data
Data
Data
Data
Process
Process
Data
Data
Data
Data
Data
Data
Data
Data
Data

54. …like
Complex
Event
Processing
in
Apache
Storm

55. Some=mes
your
machine
learning
data
applica=on
needs
to
process
in
memory
and
iterate
Data
Data
Data
Data
Data
Data
Process
Data
Data
Data
Process
Data
Data
Data
Data
Data
Data
Data
Data
Data
Process
Process
Process
Process
Process
Data
Data
Data
Data
Data
Data

56. …like
in
Machine
Learning
in
Spark

57. Introducing
YARN

58. YARN
=
Yet
Another
Resource
Nego=ator

59. YARN
abstracts
resource
management
so
you
can
run
more
than
just
MapReduce
MapReduce
V2
MapReduce
V?
STORM
Giraph
Tez
YARN
HDFS2
MPI
HBase
…
and
more
Spark

60. Node
Manager
Resource
Manager
Container
Scheduler
Pig
AppMaster
Container
Resource
Manager
+
Node
Managers
=
YARN
Node
Manager
Container
Container
Storm
Node
Manager
Node
Manager
MapReduce
AppMaster
Container
Container
Container
Container
Container
AppMaster

61. YARN
turns
Hadoop
into
a
smart
phone:
An
App
Ecosystem
hortonworks.com/yarn/

62. Check
out
the
book
too…
Preview
at:
hortonworks.com/yarn/

63. YARN
is
an
essen=al
part
of
a
balanced
breakfast
in
Hadoop
2.x

64. Introducing
Tez

65. Tez
is
a
YARN
applica=on,
like
MapReduce
is
a
YARN
applica=on

66. Tez
is
the
Lego
set
for
your
data
applica=on

67. Tez
provides
a
layer
for
abstract
tasks,
these
could
be
mappers,
reducers,
customized
stream
processes,
in
memory
structures,
etc

68. Tez
can
chain
tasks
together
into
one
job
to
get
Map
–
Reduce
–
Reduce
jobs
suitable
for
things
like
Hive
SQL
projec=ons,
group
by,
and
order
by
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
TezMap
TezMap
TezReduce
TezReduce
Data
Data
Data
TezMap
TezReduce
TezReduce
Data
Data
Data
TezReduce
TezReduce
TezMap
TezMap
Data
Data
Data

69. Tez
can
provide
long-­‐running
containers
for
applica=ons
like
Hive
to
side-­‐step
batch
process
startups
you
would
have
with
MapReduce

70. Hadoop
has
other
open
source
projects…

71. Hive
=
{SQL
-­‐>
Tez
||
MapReduce}
SQL-­‐IN-­‐HADOOP

72. Pig
=
{PigLa=n
-­‐>
Tez
||
MapReduce}

73. HCatalog
=
{metadata*
for
MapReduce,
Hive,
Pig,
HBase}
*metadata
=
tables,
columns,
par==ons,
types

74. Oozie
=
Job::{Task,
Task,
if
Task,
then
Task,
final
Task}

75. Falcon
Feed
Feed
Feed
Feed
Hadoop
DR
Feed
Replica=on
Feed
Feed
Hadoop
Feed

76. Knox
REST
Client
REST
Client
Knox
Gateway
REST
Client
Hadoop
Cluster
Hadoop
Cluster
Enterprise
LDAP

77. Flume
Files
Flume
JMS
Weblogs
Events
Flume
Flume
Flume
Flume
Flume
Hadoop

78. Sqoop
DB
DB
Sqoop
Hadoop
Sqoop

79. Ambari
=
{install,
manage,
monitor}

80. HBase
=
{real-­‐=me,
distributed-­‐
map,
big-­‐tables}

81. Storm
=
{Complex
Event
Processing,
Near-­‐Real-­‐Time,
Provisioned
by
YARN
}

82. Tez
Storm
YARN
Pig
HDFS
MapReduce
Apache
Hadoop
HCatalog
Hive
HBase
Ambari
Knox
Sqoop
Falcon
Flume

83. Storm
Tez
Pig
YARN
HDFS
MapReduce
Hortonworks
Data
Plaeorm
HCatalog
Hive
HBase
Ambari
Knox
Sqoop
Falcon
Flume

84. What
else
are
we
working
on?
hortonworks.com/labs/

85. Hadoop
is
the
new
Modern
Data
Architecture