Tez big datacamp-la-bikas_saha

© Hortonworks Inc. 2013 Page 1
Apache Tez : Accelerating Hadoop
Query Processing
Bikas Saha
@bikassaha

© Hortonworks Inc. 2013
Tez – Introduction
Page 2
• Distributed execution framework
targeted towards data-processing
applications.
• Based on expressing a computation
as a dataflow graph.
• Highly customizable to meet a
broad spectrum of use cases.
• Built on top of YARN – the resource
management framework for
Hadoop.
• Open source Apache incubator
project and Apache licensed.

© Hortonworks Inc. 2012© Hortonworks Inc. 2013. Confidential and Proprietary.
Tez – Hadoop 1 ---> Hadoop 2
HADOOP 1.0
HDFS
(redundant, reliable storage)
MapReduce
(cluster resource management
& data processing)
Pig
(data flow)
Hive
(sql)
Others
(cascading)
HDFS2
(redundant, reliable storage)
YARN
(cluster resource management)
Tez
(execution engine)
HADOOP 2.0
Data Flow
Pig
SQL
Hive
Others
(Cascading)
Batch
MapReduce Real Time
Stream
Processing
Storm
Online
Data
Processing
HBase,
Accumulo
Monolithic
• Resource Management
• Execution Engine
• User API
Layered
• Resource Management – YARN
• Execution Engine – Tez
• User API – Hive, Pig, Cascading, Your App!

Tez – Empowering Applications
Page 4
• Tez solves hard problems of running on a distributed Hadoop environment
• Apps can focus on solving their domain specific problems
• This design is important to be a platform for a variety of applications
App
Tez
• Custom application logic
• Custom data format
• Custom data transfer technology
• Distributed parallel execution
• Negotiating resources from the Hadoop framework
• Fault tolerance and recovery
• Horizontal scalability
• Resource elasticity
• Shared library of ready-to-use components
• Built-in performance optimizations
• Security

Tez – End User Benefits
• Better performance of applications
• Built-in performance + Application define optimizations
• Better predictability of results
• Minimization of overheads and queuing delays
• Better utilization of compute capacity
• Efficient use of allocated resources
• Reduced load on distributed filesystem (HDFS)
• Reduce unnecessary replicated writes
• Reduced network usage
• Better locality and data transfer using new data patterns
• Higher application developer productivity
• Focus on application business logic rather than Hadoop internals
Page 5

Tez – Problems that it addresses
• Expressing the computation
• Direct and elegant representation of the data processing flow
• Interfacing with application code and new technologies
• Performance
• Late Binding : Make decisions as late as possible using real data from at
runtime
• Leverage the resources of the cluster efficiently
• Just work out of the box!
• Customizable engine to let applications tailor the job to meet their
specific requirements
• Operation simplicity
• Painless to operate, experiment and upgrade
Page 6

Tez – Simplifying Operations
• No deployments to do. No side effects. Easy and safe to try it out!
• Tez is a completely client side application.
• Simply upload to any accessible FileSystem and change local Tez
configuration to point to that.
• Enables running different versions concurrently. Easy to test new
functionality while keeping stable versions for production.
• Leverages YARN local resources.
Page 7
Client
Machine
Node
Manager
TezTask
Node
Manager
TezTaskTezClient
HDFS
Tez Lib 1 Tez Lib 2
Client
Machine
TezClient

Tez – Expressing the computation
Page 8
Aggregate Stage
Partition Stage
Preprocessor Stage
Sampler
Task-1 Task-2
Task-1 Task-2
Task-1 Task-2
Samples
Ranges
Distributed Sort
Distributed data processing jobs typically look like DAGs (Directed Acyclic
Graph).
• Vertices in the graph represent data transformations
• Edges represent data movement from producers to consumers

Tez – Expressing the computation
Page 9
Tez provides the following APIs to define the processing
• DAG API
• Defines the structure of the data processing and the relationship
between producers and consumers
• Enable definition of complex data flow pipelines using simple graph
connection API’s. Tez expands the logical DAG at runtime
• This is how all the tasks in the job get specified
• Runtime API
• Defines the interfaces using which the framework and app code interact
with each other
• App code transforms data and moves it between tasks
• This is how we specify what actually executes in each task on the cluster
nodes

Tez – DAG API
// Define DAG
DAG dag = new DAG();
// Define Vertex
Vertex Map1 = new Vertex(Processor.class);
// Define Edge
Edge edge = Edge(Map1, Reduce1,
SCATTER_GATHER, PERSISTED, SEQUENTIAL,
Output.class, Input.class);
// Connect them
dag.addVertex(Map1).addEdge(edge)…
Page 10
Defines the global processing flow
Map1 Map2
Reduce1 Reduce2
Join
Scatter
Gather
Scatter
Gather

Tez – DAG API
Page 11
• Data movement – Defines routing of data between tasks
– One-To-One : Data from the ith producer task routes to the ith consumer task.
– Broadcast : Data from a producer task routes to all consumer tasks.
– Scatter-Gather : Producer tasks scatter data into shards and consumer tasks
gather the data. The ith shard from all producer tasks routes to the ith consumer
task.
• Scheduling – Defines when a consumer task is scheduled
– Sequential : Consumer task may be scheduled after a producer task completes.
– Concurrent : Consumer task must be co-scheduled with a producer task.
• Data source – Defines the lifetime/reliability of a task output
– Persisted : Output will be available after the task exits. Output may be lost later
on.
– Persisted-Reliable : Output is reliably stored and will always be available
– Ephemeral : Output is available only while the producer task is running
Edge properties define the connection between producer and
consumer tasks in the DAG

Tez – Logical DAG expansion at Runtime
Page 12
Reduce1
Map2
Reduce2
Join
Map1

Tez – Runtime API
Flexible Inputs-Processor-Outputs Model
• Thin API layer to wrap around arbitrary application code
• Compose inputs, processor and outputs to execute arbitrary processing
• Event routing based control plane architecture
• Applications decide logical data format and data transfer technology
• Customize for performance
• Built-in implementations for Hadoop 2.0 data services – HDFS and YARN ShuffleService.
Built on the same API. Your impls are as first class as ours!
Page 13

Tez – Library of Inputs and Outputs
Page 14
Classical ‘Map’ Classical ‘Reduce’
Intermediate ‘Reduce’ for
Map-Reduce-Reduce
Map
Processor
HDFS
Input
Sorted
Output
Reduce
Processor
Shuffle
Input
HDFS
Output
Reduce
Processor
Shuffle
Input
Sorted
Output
• What is built in?
– Hadoop InputFormat/OutputFormat
– SortedGroupedPartitioned Key-Value
Input/Output
– UnsortedGroupedPartitioned Key-Value
Input/Output
– Key-Value Input/Output

Tez – Composable Task Model
Page 15
Hive Processor
HDFS
Input
Remote
File
Server
Input
HDFS
Output
Local
Disk
Output
Your Processor
HDFS
Input
Remote
File
Server
Input
HDFS
Output
Local
Disk
Output
Your Processor
RDMA
Input
Native
DB
Input
Kakfa
Pub-Sub
Output
Amazon
S3
Output
Adopt Evolve Optimize

Tez – Performance
• Benefits of expressing the data processing as a DAG
• Reducing overheads and queuing effects
• Gives system the global picture for better planning
• Efficient use of resources
• Re-use resources to maximize utilization
• Pre-launch, pre-warm and cache
• Locality & resource aware scheduling
• Support for application defined DAG modifications at runtime
for optimized execution
• Change task concurrency
• Change task scheduling
• Change DAG edges
• Change DAG vertices (TBD)
Page 16

Tez – Benefits of DAG execution
Faster Execution and Higher Predictability
• Eliminate replicated write barrier between successive computations.
• Eliminate job launch overhead of workflow jobs.
• Eliminate extra stage of map reads in every workflow job.
• Eliminate queue and resource contention suffered by workflow jobs that
are started after a predecessor job completes.
• Better locality because the engine has the global picture
Page 17
Pig/Hive - MR
Pig/Hive - Tez

Tez – Container Re-Use
• Reuse YARN containers/JVMs to launch new tasks
• Reduce scheduling and launching delays
• Shared in-memory data across tasks
• JVM JIT friendly execution
Page 18
YARN Container / JVM
TezTask Host
TezTask1
TezTask2
SharedObjects
YARN Container
Tez
Application Master
Start Task
Task Done
Start Task

Tez – Sessions
Page 19
Application Master
Client
Start
Session
Submit
DAG
Task Scheduler
ContainerPool
Shared
Object
Registry
Pre
Warmed
JVM
Sessions
• Standard concepts of pre-launch
and pre-warm applied
• Key for interactive queries
• Represents a connection between
the user and the cluster
• Multiple DAGs executed in the
same session
• Containers re-used across queries
• Takes care of data locality and
releasing resources when idle

Tez – Re-Use in Action
Task Execution Timeline
Containers
Time 

Tez – Customizable Core Engine
Page 21
Vertex-2
Vertex-1
Start
vertex
Vertex Manager
Start
tasks
DAG
Scheduler
Get Priority
Get Priority
Start
vertex
Task
Scheduler
Get container
Get container
• Vertex Manager
• Determines task
parallelism
• Determines when
tasks in a vertex
can start.
• DAG Scheduler
Determines priority
of task
• Task Scheduler
Allocates containers
from YARN and
assigns them to tasks

Tez – Event Based Control Plane
Page 22
Reduce Task 2
Input1 Input2
Map Task 2
Output1
Output2
Output3
Map Task 1
Output1
Output2
Output3
AM
Router
Scatter-Gather Edge
• Events used to communicate
between the tasks and between task
and framework
• Data Movement Event used by
producer task to inform the
consumer task about data location,
size etc.
• Input Error event sent by task to the
engine to inform about errors in
reading input. The engine then takes
action by re-generating the input
• Other events to send task completion
notification, data statistics and other
control plane information
Data Event
Error Event

Tez – Automatic Reduce Parallelism
Page 23
Map Vertex
Reduce Vertex
App Master
Vertex Manager
Data Size Statistics
Vertex State
Machine
Set Parallelism
Cancel Task
Re-Route
Event Model
Map tasks send data
statistics events to
the Reduce Vertex
Manager.
Vertex Manager
Pluggable application
logic that understands
the data statistics and
can formulate the
correct parallelism.
Advises vertex
controller on
parallelism

Tez – Theory to Practice
• Performance
• Scalability
Page 24

Tez – Hive TPC-DS Scale 200GB latency

0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Replicated
Join (2.8x)
Join +
Groupby
(1.5x)
Join +
Groupby +
Orderby
(1.5x)
3 way Split +
Join +
Groupby +
Orderby
(2.6x)
Timeinsecs
MR
Tez
Tez – Pig performance gains
• Demonstrates performance gains from a basic translation to a
Tez DAG
• Deeper integration in the works for further improvements

Tez – Observations on Performance
• Number of stages in the DAG
• Higher the number of stages in the DAG, performance of Tez (over MR)
will be better.
• Cluster/queue capacity
• More congested a queue is, the performance of Tez (over MR) will be
better due to container reuse.
• Size of intermediate output
• More the size of intermediate output, the performance of Tez (over MR)
will be better due to reduced HDFS usage.
• Size of data in the job
• For smaller data and more stages, the performance of Tez (over MR) will
be better as percentage of launch overhead in the total time is high for
smaller jobs.
• Offload work to the cluster
• Move as much work as possible to the cluster by modelling it via the job
DAG. Exploit the parallelism and resources of the cluster. E.g. MR split
calculation.
• Vertex caching
• The more re-computation can be avoided the better is the performance.
Page 27

Tez – Data at scale
Page 28
Hive TPC-DS
Scale 10TB

Tez – DAG definition at scale
Page 29
Hive : TPC-DS Query 64 Logical DAG

Tez – Container Reuse at Scale
78 vertices + 8374 tasks on 50 containers (TPC-DS Query 4)
Page 30

Tez – Real World Use Cases for the API
Page 31

Tez – Broadcast Edge
SELECT ss.ss_item_sk, ss.ss_quantity, avg_price, inv.inv_quantity_on_hand
FROM (select avg(ss_sold_price) as avg_price, ss_item_sk, ss_quantity_sk from store_sales
group by ss_item_sk) ss
JOIN inventory inv
ON (inv.inv_item_sk = ss.ss_item_sk);
Hive – MR Hive – Tez
M
M
M
M M
HDFS
Store Sales scan.
Group by and
aggregation
reduce size of this
input.
Inventory scan
and Join
Broadcast
edge
M M M
HDFS
Store Sales scan.
Group by and
aggregation.
Inventory and Store
Sales (aggr.) output
scan and shuffle join.
R R
R R
RR
M
MMM
HDFS
Hive :
Broadcast Join

Tez – Multiple Outputs
Page 33
Pig : Split & Group-by
f = LOAD ‘foo’ AS (x, y, z);
g1 = GROUP f BY y;
g2 = GROUP f BY z;
j = JOIN g1 BY group,
g2 BY group;
Group by y Group by z
Load foo
Join
Load g1 and Load g2
Group by y Group by z
Load foo
Join
Multiple outputs
Reduce follows
reduce
HDFS HDFS
Split multiplex de-multiplex
Pig – MR Pig – Tez

Tez – Multiple Outputs
FROM (SELECT * FROM store_sales, date_dim WHERE ss_sold_date_sk = d_date_sk and
d_year = 2000)
INSERT INTO TABLE t1 SELECT distinct ss_item_sk
INSERT INTO TABLE t2 SELECT distinct ss_customer_sk;
M MM
M
HDFS
Map join
date_dim/store sales
Two MR jobs to
do the distinct
M MM
M M
HDFS
RR
HDFS
M M M
R
M M M
R
HDFS
Broadcast Join
(scan date_dim,
join store sales)
Distinct for customer
+ items
Materialize join on
HDFS
Hive : Multi-insert
queries

Tez – One to One Edge
Page 35
Aggregate
Sample L
Join
Stage sample map
on distributed cache
l = LOAD ‘left’ AS (x, y);
r = LOAD ‘right’ AS (x, z);
j = JOIN l BY x, r BY x
USING ‘skewed’;
Load &
Sample
Aggregate
Partition L
Join
Pass through input
via 1-1 edge
Partition R
HDFS
Broadcast
sample map
Partition L and Partition R
Pig – MR Pig – Tez
Pig : Skewed Join

Tez – Custom Edge
SELECT ss.ss_item_sk, ss.ss_quantity, inv.inv_quantity_on_hand
FROM store_sales ss
JOIN inventory inv
ON (inv.inv_item_sk = ss.ss_item_sk);
M MM
M M
HDFS
Inventory scan
(Runs on cluster
potentially more
than 1 mapper)
Store Sales scan
and Join (Custom
vertex reads
both inputs – no
side file reads)
Custom edge
(routes outputs of
previous stage to
the correct
Mappers of the
next stage)M MM
M
HDFS
Inventory scan
(Runs as single
local map task)
Store Sales scan
and Join
(Inventory hash
table read as side
file)
HDFS
Hive : Dynamically
Partitioned Hash Join

Tez – Bridging the Data Spectrum
Page 37
Fact Table
Dimension
Table 1
Result
Table 1
Dimension
Table 2
Result
Table 2
Dimension
Table 3
Result
Table 3
Broadcast
Join
Shuffle
Join
Typical pattern in a
TPC-DS query
Fact Table
Dimension
Table 1
Dimension
Table 1
Dimension
Table 1
Broadcast join
for small data sets
Based on data size,
the query optimizer
can run either plan as
a single Tez job
Broadcast
Join

Tez – Current status
• Apache Incubator Project
–Rapid development. Over 1000 jiras opened. Over 700 resolved
–Growing community of contributors and users
–Latest release is 0.4
• Focus on stability
–Testing and quality are highest priority
–Code ready and deployed on multi-node environments at scale
• Support for a vast topology of DAGs
– Already functionally equivalent to Map Reduce. Existing Map Reduce
jobs can be executed on Tez with few or no changes
–Apache Hive 0.13 release supports Tez as an execution engine (HIVE-
4660)
–Apache Pig port to Tez close to completion (PIG-3446)
Page 38

Tez – Adoption Path
Pre-requisite : Hadoop 2 with YARN
Tez has zero deployment pain. No side effects or traces left
behind on your cluster. Low risk and low effort to try out.
• Using Hive, Pig, Cascading, Scalding
• Try them with Tez as execution engine
• Already have MapReduce based pipeline
• Use configuration to change MapReduce to run on Tez by setting
‘mapreduce.framework.name’ to ‘yarn-tez’ in mapred-site.xml
• Consolidate MR workflow into MR-DAG on Tez
• Change MR-DAG to use more efficient Tez constructs
• Have custom pipeline
• Wrap custom code/scripts into Tez inputs-processor-outputs
• Translate your custom pipeline topology into Tez DAG
• Change custom topology to use more efficient Tez constructs
Page 39

Tez – Roadmap
• Richer DAG support
– Addition of vertices at runtime
– Shared edges for shared outputs
– Enhance Input/Output library
• Performance optimizations
– Improve support for high concurrency
– Improve locality aware scheduling.
– Add framework level data statistics
– HDFS memory storage integration
• Usability
– Stability and testability
– API ease of use
– Tools for performance analysis and debugging
Page 40

Tez – Community
• Early adopters and code contributors welcome
– Adopters to drive more scenarios. Contributors to make them happen.
• Tez meetup for developers and users
– http://www.meetup.com/Apache-Tez-User-Group
• Technical blog series
– http://hortonworks.com/blog/apache-tez-a-new-chapter-in-hadoop-data-
processing
• Useful links
– Work tracking: https://issues.apache.org/jira/browse/TEZ
– Code: https://github.com/apache/incubator-tez
– Developer list: dev@tez.incubator.apache.org
User list: user@tez.incubator.apache.org
Issues list: issues@tez.incubator.apache.org
Page 41

Tez – Takeaways
• Distributed execution framework that models processing as
dataflow graphs
• Customizable execution architecture designed for extensibility
and user defined performance optimizations
• Works out of the box with the platform figuring out the hard
stuff
• No install – Safe and Easy to Evaluate and Experiment
• Addresses common needs of a broad spectrum of applications
and usage patterns
• Open source Apache project – your use-cases and code are
welcome
• It works and is already being used by Apache Hive and Pig
Page 42

Tez
Thanks for your time and attention!
Video with Deep Dive on Tez
http://goo.gl/BL67o7
http://www.infoq.com/presentations/apache-tez
Questions?
@bikassaha
Page 43

Tez big datacamp-la-bikas_saha

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