yarn ready: integrating to yarn with tez
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© Hortonworks Inc. 2013 Page 1
Apache Tez : Accelerating Hadoop Data 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 project and Apache licensed.
Tez – Hadoop 1 ---> Hadoop 2Monolithic• Resource Management – MR • Execution Engine – MR
Layered• Resource Management – YARN• Engines – Hive, Pig, Cascading, Your App!
© Hortonworks Inc. 2013
Tez – Empowering Applications• 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
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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
© Hortonworks Inc. 2013
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
© Hortonworks Inc. 2013
Tez – Design considerations
Don’t solve problems that have already been solved. Or else you will have to solve them again!
• Leverage discrete task based compute model for elasticity, scalability and fault tolerance
• Leverage several man years of work in Hadoop Map-Reduce data shuffling operations
• Leverage proven resource sharing and multi-tenancy model for Hadoop and YARN
• Leverage built-in security mechanisms in Hadoop for privacy and isolation
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Look to the Future with an eye on the Past
© Hortonworks Inc. 2013
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
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© Hortonworks Inc. 2013
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.
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ClientMachine
NodeManager
TezTask
NodeManager
TezTaskTezClient
HDFSTez Lib 1 Tez Lib 2
ClientMachine
TezClient
© Hortonworks Inc. 2013
Tez – Expressing the computationDistributed 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
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Aggregate Stage
Partition Stage
Preprocessor Stage
Sampler
Task-1 Task-2
Task-1 Task-2
Task-1 Task-2
Samples
Ranges
Distributed Sort
© Hortonworks Inc. 2013
Tez – Expressing the computationTez 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 the connection of tasks in the job gets 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
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© Hortonworks Inc. 2013
Tez – DAG API
// Define DAG
DAG dag = DAG.create();
// Define Vertex
Vertex Map1 = Vertex.create(Processor.class);
// Define Edge
Edge edge = Edge.create(Map1, Reduce1, SCATTER_GATHER, PERSISTED, SEQUENTIAL, Output.class, Input.class);
// Connect them
dag.addVertex(Map1).addEdge(edge)…
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Defines the global processing flow
Map1 Map2
Reduce1 Reduce2
Join
Scatter Gather
Scatter Gather
© Hortonworks Inc. 2013
Tez – DAG API
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.
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Edge properties define the connection between producer and consumer tasks in the DAG
© Hortonworks Inc. 2013
Tez – Logical DAG expansion at Runtime
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Reduce1
Map2
Reduce2
Join
Map1
© Hortonworks Inc. 2013
Tez – Runtime APIFlexible Inputs-Processor-Outputs Model• Thin API layer to wrap around arbitrary application code• Compose inputs, processor and outputs to execute arbitrary processing• 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!
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© Hortonworks Inc. 2013
Tez – Library of Inputs and Outputs
Classical ‘Map’
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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
© Hortonworks Inc. 2013
Tez – Composable Task Model
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Hive Processor
HDFSInput
RemoteFile
ServerInput
HDFSOutput
LocalDisk
Output
Your Processor
HDFSInput
RemoteFile
ServerInput
HDFSOutput
LocalDisk
Output
Your Processor
RDMAInput
NativeDB
Input
KakfaPub-SubOutput
AmazonS3
Output
Adopt Evolve Optimize
© Hortonworks Inc. 2013
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)
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© Hortonworks Inc. 2013
Tez – Benefits of DAG executionFaster 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
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Pig/Hive - MRPig/Hive - Tez
© Hortonworks Inc. 2013
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
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YARN Container / JVM
TezTask Host
TezTask1
TezTask2
Shar
ed O
bjec
ts
YARN Container
Tez Application Master
Start Task
Task Done
Start Task
© Hortonworks Inc. 2013
Tez – Sessions
Page 20
Application Master
Client
Start Session
Submit DAG
Task Scheduler
Cont
aine
r Poo
lShared 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
© Hortonworks Inc. 2013
Tez – Current status• Apache Project
–Rapid development. Over 1100 jiras opened. Over 800 resolved–Growing community of contributors and users
• 0.5 being released. In voting for release–Developer release focused ease of development– Stable API–Better debugging–Documentation and code samples
• Support for a vast topology of DAGs• Being used by multiple applications such as Apache Hive,
Apache Pig, Cascading, Scalding
Page 21
© Hortonworks Inc. 2013
Tez – Adoption Path Pre-requisite : Hadoop 2 with YARNTez 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 22
© Hortonworks Inc. 2013
Tez – Theory to Practice• Performance• Scalability
Page 23
© Hortonworks Inc. 2013
Tez – Hive TPC-DS Scale 200GB latency
Tez – Pig performance gains
• Demonstrates performance gains from a basic translation to a Tez DAG
0
40
80
120
160
MRTez
Tim
e in
min
s
© Hortonworks Inc. 2013
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 26
© Hortonworks Inc. 2013
Tez – Data at scale
Page 27
Hive TPC-DS Scale 10TB
© Hortonworks Inc. 2013
Tez – DAG definition at scale
Page 28
Hive : TPC-DS Query 64 Logical DAG
© Hortonworks Inc. 2013
Tez – Container Reuse at Scale• 78 vertices + 8374 tasks on 50 containers (TPC-DS Query 4)
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© Hortonworks Inc. 2013
Tez – Bridging the Data Spectrum
Page 30
Fact Table Dimension Table 1
Result Table 1
Dimension Table 2
Result Table 2
Dimension Table 3
Result Table 3
BroadcastJoin
ShuffleJoin
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
BroadcastJoin
© Hortonworks Inc. 2013
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/tez – Developer list: dev@tez.apache.org
User list: user@tez.apache.org Issues list: issues@tez.apache.org
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© Hortonworks Inc. 2013
Tez – Takeaways• Distributed execution framework that works on computations
represented as dataflow graphs• Customizable execution architecture designed to enable
dynamic performance optimizations at runtime• Works out of the box with the platform figuring out the hard
stuff• Span the spectrum of interactive latency to batch, small to
large• Open source Apache project – your use-cases and code are
welcome• It works and is already being used by Hive and Pig
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© Hortonworks Inc. 2013
TezThanks for your time and attention!
Video with Deep Dive on Tez http://goo.gl/BL67o7http://www.infoq.com/presentations/apache-tez
Questions?
@bikassaha
Page 33
Next Steps
4Next Steps
1. Review YARN Resources
2. Review webinar recording
3. Attend Office Hours
4. Attend the next YARN webinar
ResourcesSetup HDP 2.1 environment
• Leverage Sandbox: Hortonworks.com/Sandbox
Get Started with YARN
• http://hortonworks.com/get-started/YARN
Video Deep Dive on Tez
• http://goo.gl/BL67o7 and http://www.infoq.com/presentations/apache-tez
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/tez
Developer list: dev@tez.apache.org User list: user@tez.apache.org
Hortonworks Office HoursYARN Office HoursDial in and chat with YARN experts
We plan Office Hours for September 11th and October 9th @ 10am PT (2nd Thursdays)Invitations will go out to those that attended or reviewed YARN webinars
These will also be posted to hortonworks.com/webinarsRegistration required.
YARN Ready Webinar Schedule
Native IntegrationSlider
Office Hours
August 14
Tez
August 21
Ambari
Sept. 4
Office Hours
Sept. 11
Scalding
Sept. 18
Spark
Oct. 2
Office Hours
Oct. 9
Upcoming Webinars
Office Hours
Recorded Webinars:
Timeline
Introductionto YARN Ready
Sign up here:For the Series, Individual webinar or office hourshttp://info.hortonworks.com/YarnReady-BigData-Webcast-Series.html
You can also visit http://hortonworks.com/webinars/#library
Thank you!
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