1. ©
Hortonworks
Inc.
2015 Page
1
Apache
Tez
-­‐ Next
Generation
of
execution
engine
upon
hadoop
Jeff
Zhang
(@zjffdu)

2. ©
Hortonworks
Inc.
2015
Who’s
this
guy
• Start
use
pig
from
2009.
Become
Pig
committer
from
Nov
2009
• Join
Hortonworks
in
2014.
• Tez Committer
from
Oct
2014

3. ©
Hortonworks
Inc.
2015
Agenda
•Tez Introduction
•Tez Feature
Deep
Dive
•Tez Status
&
Roadmap

4. ©
Hortonworks
Inc.
2015
I/O
Synchronization
Barrier
I/O
Synchronization
Barrier
Job
1
(
Join a
&
b
)
Job
3 (
Group by
of
c
)
Job
2
(Group
by
of
a
Join b)
Job
4
(Join
of
S
& R
)
Hive
-­‐ MR
Example
of
MR
versus
Tez
Page
4
Single
Job
Hive
-­‐ Tez
Join a
&
b
Group
by
of
a
Join b
Group by
of
c
Job
4
(Join
of
S
& R
)

5. ©
Hortonworks
Inc.
2015
Tez
– Introduction
Page
5
• Distributed
execution
framework
targeted
towards
data-­‐processing
applications.
• Based
on
expressing
a
computation
as
a
dataflow
graph
(DAG).
• Highly
customizable
to
meet
a
broad
spectrum
of
use
cases.
• Built
on
top
of
YARN
– the
resource
management
framework
for
Hadoop.
• Open
source
Apache
project
and
Apache
licensed.

6. ©
Hortonworks
Inc.
2015
What
is
DAG
&
Why
DAG
Projection
Filter
GroupBy
…
Join
Union
Intersect
…
Split
…
• Directed
Acyclic
Graph
• Any
complicated
DAG
can
been
composed
of
the
following
3
basic
paradigm
– Sequential
– Merge
– Divide

7. ©
Hortonworks
Inc.
2015
Expressing
DAG
in
Tez API
• DAG
API
(Logic
View)
– Allowuser to
build
DAG
– Topological
structure
of
the
data
computation
flow
• Runtime
API
(Runtime
View)
– Application
logic
of
each
computation
unit
(vertex)
– How to move/read/write
data between vertices

8. ©
Hortonworks
Inc.
2015
DAG
API
(Logic
View)
Page
8
• Vertex
(Processor,
Parallelism,
Resource,
etc…)
• Edge (EdgeProperty)
– DataMovement
– Scatter
Gather
(Join,
GroupBy …
)
– Broadcast
(
Pig
Replicated
Join
/
Hive
Broadcast
Join
)
– One-­‐to-­‐One
(
Pig
Order
by
)
– Custom

9. ©
Hortonworks
Inc.
2015
Runtime
API
(Runtime
View)
Page
9
ProcessorInput Output
• Input
– Through
which
processor
receives
data
on
an
edge
– Vertex
can
have
multiple
inputs
• Processor
– Application
Logic
(One
vertex
one
processor)
– Consume
the
inputs
and
produce
the
outputs
• Output
– Through
which
processor
writes
data
to
an
edge
– One
vertex
can
have
multiple
outputs
• Example
of
Input/Output/Processor
– MRInput &
MROutput (InputFormat/OutputFormat)
– OrderedGroupedKVInput &
OrderedPartitionedKVOutput (Scatter
Gather)
– UnorderedKVInput &
UnorderedKVOutput (Broadcast
&
One-­‐to-­‐One)
– PigProcessor/HiveProcessor

10. ©
Hortonworks
Inc.
2015
Benefit
of
DAG
• Easier
to
express
computation
in
DAG
• No
intermediate
data
written
to
HDFS
• Less
pressure
on
NameNode
• No
resource
queuing
effort
&
less
resource
contention
• More
optimization
opportunity
with
more
global
context

11. ©
Hortonworks
Inc.
2015
Agenda
•Tez Introduction
•Tez Feature
Deep
Dive
•Tez Improvement
&
Debuggability
•Tez Status
&
Roadmap

12. ©
Hortonworks
Inc.
2015
Container-­‐Reuse
• Reuse
the
same
container
across
DAG/Vertices/Tasks
• Benefit
of
Container-­‐Reuse
– Less
resources
consumed
– Reduce
overhead
of
launching
JVM
– Reduce
overhead
of
negotiate with Resource
Manager
– Reduce
overhead
of
resource
localization
– Reduce
network
IO
– Object
Caching
(Object
Sharing)

13. ©
Hortonworks
Inc.
2015
Tez Session
• Multiple
Jobs/DAGs
in
one
AM
• Container-­‐reuse
across
Jobs/DAGs
• Data
sharing
between
Jobs/DAGs

14. ©
Hortonworks
Inc.
2015
Dynamic
Parallelism
Estimation
• VertexManager
– Listen
to
the
other
vertices
status
– Coordinate
and
schedule
its
tasks
– Communication
between
vertices

15. ©
Hortonworks
Inc.
2015
ATS
Integration
• Tez is
fully
integrated
with
YARN
ATS
(Application
Timeline
Service)
– DAG
Status,
DAG
Metrics,
Task
Status,
Task
Metrics
are
captured
• Diagnostics
&
Performance
analysis
– Data
Source
for
monitoring
&
diagnostics
– Data
Source
for
performance
analysis

16. ©
Hortonworks
Inc.
2015
Recovery
• AM
can
crash
in
corner
cases
– OOM
– Node
failure
– …
• Continue
from
the
last
checkpoint
• Transparent
to
end
users
AM
Crash

17. ©
Hortonworks
Inc.
2015
Order
By
of
Pig
f =
Load
‘foo’
as
(x,
y);
o =
Order
f
by
x;Load
Sample
（Calculate
Histogram)
HDFS
Partition
Sort
Broadcast
Load
Sample
（Calculate
Histogram)
Partition
Sort
One-­‐to-­‐One
Scatter
Gather
Scatter
Gather

18. ©
Hortonworks
Inc.
2015
Tez UI

19. ©
Hortonworks
Inc.
2015
Tez UI

20. Tez UI
20
Download
data from
ATS

21. ©
Hortonworks
Inc.
2015
RoadMap
• Shared
output
edges
– Same
output
to
multiple
vertices
• Local
mode
stabilization
• Optimizing
(include/exclude)
vertex
at
runtime
• Partial
completion
VertexManager
• Co-­‐Scheduling
• Framework
stats
for
better
runtime
decisions

22. ©
Hortonworks
Inc.
2015
Tez
– Adoption
• Apache
Hive
• Start
from
Hive
0.13
• set
hive.exec.engine =
tez
• Apache
Pig
• Start
from
Pig
0.14
• pig
-­‐x
tez
• Cascading
• Flink
Page
22

23. ©
Hortonworks
Inc.
2015
Tez Community
• Useful
Links
– http://tez.apache.org/
– JIRA
:
https://issues.apache.org/jira/browse/TEZ
– Code
Repository:
https://git-­‐wip-­‐us.apache.org/repos/asf/tez.git
– Mailing
Lists
– Dev List:
dev@tez.apache.org
– User
List:
user@tez.apache.org
– Issues
List:
issues@tez.apache.org
• Tez Meetup
– http://www.meetup.com/Apache-­‐Tez-­‐User-­‐Group

24. ©
Hortonworks
Inc.
2015
Thank You!
Questions & Answers
Page
24