SlideShare a Scribd company logo
© Hortonworks Inc. 2013 Page 1
Apache Tez : Accelerating Hadoop
Query Processing
Bikas Saha@bikassaha
Hitesh Shah @hitesh1892
© 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.
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!
© Hortonworks Inc. 2013
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
© 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
Page 6
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
Page 7
© 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.
Page 8
Client
Machine
Node
Manager
TezTask
Node
Manager
TezTaskTezClient
HDFS
Tez Lib 1 Tez Lib 2
Client
Machine
TezClient
© Hortonworks Inc. 2013
Tez – Expressing the computation
Page 9
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
© Hortonworks Inc. 2013
Tez – Expressing the computation
Page 10
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
© Hortonworks Inc. 2013
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 11
Defines the global processing flow
Map1 Map2
Reduce1 Reduce2
Join
Scatter
Gather
Scatter
Gather
© Hortonworks Inc. 2013
Tez – DAG API
Page 12
• 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
© Hortonworks Inc. 2013
Tez – Logical DAG expansion at Runtime
Page 13
Reduce1
Map2
Reduce2
Join
Map1
© Hortonworks Inc. 2013
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 14
© Hortonworks Inc. 2013
Tez – Library of Inputs and Outputs
Page 15
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
© 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)
Page 16
© Hortonworks Inc. 2013
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
© 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
Page 18
YARN Container / JVM
TezTask Host
TezTask1
TezTask2
SharedObjects
YARN Container
Tez
Application Master
Start Task
Task Done
Start Task
© Hortonworks Inc. 2013
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
© Hortonworks Inc. 2013
Tez – Customizable Core Engine
Page 20
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
© Hortonworks Inc. 2013
Tez – Event Based Control Plane
Page 21
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
© Hortonworks Inc. 2013
Tez – Automatic Reduce Parallelism
Page 22
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
© 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
20
40
60
80
100
120
140
160
Prod script 1
25m vs 10m
5 MR Jobs
Prod script 2
34m vs 16m
5 MR Jobs
Prod script 3
1h 46m vs 48m
12 MR Jobs
Prod script 4
2h 22m vs 1h
21m
15 MR jobs
Timeinmins
MR
Tez
© 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 88 Logical DAG ( 39 MR jobs with Hive-10)
© Hortonworks Inc. 2013
Tez – Container Reuse at Scale
• 78 vertices + 8374 tasks on 50 containers (TPC-DS Query 4)
Page 29
© Hortonworks Inc. 2013
Tez – Real World Use Cases for the API
Page 30
© Hortonworks Inc. 2013
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
© Hortonworks Inc. 2013
Tez – Multiple Outputs
Page 32
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
© Hortonworks Inc. 2013
Tez – One to One Edge
Page 33
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
© Hortonworks Inc. 2013
Tez – Current status
• Apache Incubator Project
–Rapid development. Over 1100 jiras opened. Over 800 resolved
–Growing community of contributors and users
–Latest release is 0.4
• Support for a vast topology of DAGs
• Being used by multiple applications such as Apache Hive,
Apache Pig, Cascading.
Page 34
© Hortonworks Inc. 2013
Tez – Adoption Path
•Pre-requisite : Hadoop 2 with YARN
•Simple client-side install (no admin support needed)
–No side effects or traces left behind on your cluster. Low risk and low
effort to try out.
•Apache Hive – Already available in 0.13
•Apache Pig – Available on trunk ( slated for v0.14 )
•Cascading – version 3.0 ( coming soon )
•Run your MapReduce jobs using Tez runtime
• Change “mapreduce.framework.name” to “yarn-tez”
•ETL: Replace MR or custom pipelines with native Tez
•
Page 35
© Hortonworks Inc. 2013
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
–Tez UI (coming soon)
–Stability and testability
– API ease of use
– Tools for performance analysis and debugging
Page 36
© 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/incubator-tez
– Developer list: dev@tez.incubator.apache.org
User list: user@tez.incubator.apache.org
Issues list: issues@tez.incubator.apache.org
Page 37
© Hortonworks Inc. 2013
Tez – Takeaways
• Distributed execution framework that works on computations
represented as dataflow graphs
• Naturally maps to execution plans produced by query
optimizers
• 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
Page 38
© Hortonworks Inc. 2013
Tez
Thanks for your time and attention!
Video with Deep Dive on Tez
http://youtu.be/-7YhVwqky6M
http://www.infoq.com/presentations/apache-tez
Questions?
@bikassaha, @hitesh1892
Page 39

More Related Content

What's hot

Hadoop Security Architecture
Hadoop Security ArchitectureHadoop Security Architecture
Hadoop Security Architecture
Owen O'Malley
 
Optimizing Hive Queries
Optimizing Hive QueriesOptimizing Hive Queries
Optimizing Hive Queries
Owen O'Malley
 
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
mumrah
 
Internal Hive
Internal HiveInternal Hive
Internal Hive
Recruit Technologies
 
ORC File - Optimizing Your Big Data
ORC File - Optimizing Your Big DataORC File - Optimizing Your Big Data
ORC File - Optimizing Your Big Data
DataWorks Summit
 
How to understand and analyze Apache Hive query execution plan for performanc...
How to understand and analyze Apache Hive query execution plan for performanc...How to understand and analyze Apache Hive query execution plan for performanc...
How to understand and analyze Apache Hive query execution plan for performanc...
DataWorks Summit/Hadoop Summit
 
Top 5 Mistakes When Writing Spark Applications
Top 5 Mistakes When Writing Spark ApplicationsTop 5 Mistakes When Writing Spark Applications
Top 5 Mistakes When Writing Spark Applications
Spark Summit
 
Node Labels in YARN
Node Labels in YARNNode Labels in YARN
Node Labels in YARN
DataWorks Summit
 
Hive: Loading Data
Hive: Loading DataHive: Loading Data
Hive: Loading Data
Benjamin Leonhardi
 
Building robust CDC pipeline with Apache Hudi and Debezium
Building robust CDC pipeline with Apache Hudi and DebeziumBuilding robust CDC pipeline with Apache Hudi and Debezium
Building robust CDC pipeline with Apache Hudi and Debezium
Tathastu.ai
 
Deep Dive: Memory Management in Apache Spark
Deep Dive: Memory Management in Apache SparkDeep Dive: Memory Management in Apache Spark
Deep Dive: Memory Management in Apache Spark
Databricks
 
Apache Tez - A unifying Framework for Hadoop Data Processing
Apache Tez - A unifying Framework for Hadoop Data ProcessingApache Tez - A unifying Framework for Hadoop Data Processing
Apache Tez - A unifying Framework for Hadoop Data Processing
DataWorks Summit
 
Apache Spark overview
Apache Spark overviewApache Spark overview
Apache Spark overview
DataArt
 
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Databricks
 
Common issues with Apache Kafka® Producer
Common issues with Apache Kafka® ProducerCommon issues with Apache Kafka® Producer
Common issues with Apache Kafka® Producer
confluent
 
A Thorough Comparison of Delta Lake, Iceberg and Hudi
A Thorough Comparison of Delta Lake, Iceberg and HudiA Thorough Comparison of Delta Lake, Iceberg and Hudi
A Thorough Comparison of Delta Lake, Iceberg and Hudi
Databricks
 
Intro to HBase
Intro to HBaseIntro to HBase
Intro to HBase
alexbaranau
 
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...
Databricks
 
Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutori...
Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutori...Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutori...
Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutori...
Simplilearn
 
Spark and S3 with Ryan Blue
Spark and S3 with Ryan BlueSpark and S3 with Ryan Blue
Spark and S3 with Ryan Blue
Databricks
 

What's hot (20)

Hadoop Security Architecture
Hadoop Security ArchitectureHadoop Security Architecture
Hadoop Security Architecture
 
Optimizing Hive Queries
Optimizing Hive QueriesOptimizing Hive Queries
Optimizing Hive Queries
 
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
 
Internal Hive
Internal HiveInternal Hive
Internal Hive
 
ORC File - Optimizing Your Big Data
ORC File - Optimizing Your Big DataORC File - Optimizing Your Big Data
ORC File - Optimizing Your Big Data
 
How to understand and analyze Apache Hive query execution plan for performanc...
How to understand and analyze Apache Hive query execution plan for performanc...How to understand and analyze Apache Hive query execution plan for performanc...
How to understand and analyze Apache Hive query execution plan for performanc...
 
Top 5 Mistakes When Writing Spark Applications
Top 5 Mistakes When Writing Spark ApplicationsTop 5 Mistakes When Writing Spark Applications
Top 5 Mistakes When Writing Spark Applications
 
Node Labels in YARN
Node Labels in YARNNode Labels in YARN
Node Labels in YARN
 
Hive: Loading Data
Hive: Loading DataHive: Loading Data
Hive: Loading Data
 
Building robust CDC pipeline with Apache Hudi and Debezium
Building robust CDC pipeline with Apache Hudi and DebeziumBuilding robust CDC pipeline with Apache Hudi and Debezium
Building robust CDC pipeline with Apache Hudi and Debezium
 
Deep Dive: Memory Management in Apache Spark
Deep Dive: Memory Management in Apache SparkDeep Dive: Memory Management in Apache Spark
Deep Dive: Memory Management in Apache Spark
 
Apache Tez - A unifying Framework for Hadoop Data Processing
Apache Tez - A unifying Framework for Hadoop Data ProcessingApache Tez - A unifying Framework for Hadoop Data Processing
Apache Tez - A unifying Framework for Hadoop Data Processing
 
Apache Spark overview
Apache Spark overviewApache Spark overview
Apache Spark overview
 
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
 
Common issues with Apache Kafka® Producer
Common issues with Apache Kafka® ProducerCommon issues with Apache Kafka® Producer
Common issues with Apache Kafka® Producer
 
A Thorough Comparison of Delta Lake, Iceberg and Hudi
A Thorough Comparison of Delta Lake, Iceberg and HudiA Thorough Comparison of Delta Lake, Iceberg and Hudi
A Thorough Comparison of Delta Lake, Iceberg and Hudi
 
Intro to HBase
Intro to HBaseIntro to HBase
Intro to HBase
 
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...
 
Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutori...
Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutori...Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutori...
Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutori...
 
Spark and S3 with Ryan Blue
Spark and S3 with Ryan BlueSpark and S3 with Ryan Blue
Spark and S3 with Ryan Blue
 

Similar to Apache Tez - A New Chapter in Hadoop Data Processing

Apache Tez: Accelerating Hadoop Query Processing
Apache Tez: Accelerating Hadoop Query ProcessingApache Tez: Accelerating Hadoop Query Processing
Apache Tez: Accelerating Hadoop Query Processing
Hortonworks
 
Apache Tez : Accelerating Hadoop Query Processing
Apache Tez : Accelerating Hadoop Query ProcessingApache Tez : Accelerating Hadoop Query Processing
Apache Tez : Accelerating Hadoop Query Processing
Bikas Saha
 
Apache Tez -- A modern processing engine
Apache Tez -- A modern processing engineApache Tez -- A modern processing engine
Apache Tez -- A modern processing engine
bigdatagurus_meetup
 
Tez big datacamp-la-bikas_saha
Tez big datacamp-la-bikas_sahaTez big datacamp-la-bikas_saha
Tez big datacamp-la-bikas_saha
Data Con LA
 
YARN Ready: Integrating to YARN with Tez
YARN Ready: Integrating to YARN with Tez YARN Ready: Integrating to YARN with Tez
YARN Ready: Integrating to YARN with Tez
Hortonworks
 
February 2014 HUG : Tez Details and Insides
February 2014 HUG : Tez Details and InsidesFebruary 2014 HUG : Tez Details and Insides
February 2014 HUG : Tez Details and Insides
Yahoo Developer Network
 
Apache Hadoop YARN - The Future of Data Processing with Hadoop
Apache Hadoop YARN - The Future of Data Processing with HadoopApache Hadoop YARN - The Future of Data Processing with Hadoop
Apache Hadoop YARN - The Future of Data Processing with Hadoop
Hortonworks
 
Apache Tez : Accelerating Hadoop Query Processing
Apache Tez : Accelerating Hadoop Query ProcessingApache Tez : Accelerating Hadoop Query Processing
Apache Tez : Accelerating Hadoop Query Processing
Teddy Choi
 
Tez Data Processing over Yarn
Tez Data Processing over YarnTez Data Processing over Yarn
Tez Data Processing over Yarn
InMobi Technology
 
Introduction sur Tez par Olivier RENAULT de HortonWorks Meetup du 25/11/2014
Introduction sur Tez par Olivier RENAULT de HortonWorks Meetup du 25/11/2014Introduction sur Tez par Olivier RENAULT de HortonWorks Meetup du 25/11/2014
Introduction sur Tez par Olivier RENAULT de HortonWorks Meetup du 25/11/2014
Modern Data Stack France
 
Tez: Accelerating Data Pipelines - fifthel
Tez: Accelerating Data Pipelines - fifthelTez: Accelerating Data Pipelines - fifthel
Tez: Accelerating Data Pipelines - fifthel
t3rmin4t0r
 
Big Data Day LA 2015 - What's new and next in Apache Tez by Bikas Saha of Hor...
Big Data Day LA 2015 - What's new and next in Apache Tez by Bikas Saha of Hor...Big Data Day LA 2015 - What's new and next in Apache Tez by Bikas Saha of Hor...
Big Data Day LA 2015 - What's new and next in Apache Tez by Bikas Saha of Hor...
Data Con LA
 
Chapter 10
Chapter 10Chapter 10
Chapter 10
pavan penugonda
 
Get Started Building YARN Applications
Get Started Building YARN ApplicationsGet Started Building YARN Applications
Get Started Building YARN Applications
Hortonworks
 
Apache Tez – Present and Future
Apache Tez – Present and FutureApache Tez – Present and Future
Apache Tez – Present and Future
Rajesh Balamohan
 
Apache Tez – Present and Future
Apache Tez – Present and FutureApache Tez – Present and Future
Apache Tez – Present and Future
Jianfeng Zhang
 
Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015
Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015
Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015
Deanna Kosaraju
 
Data Analytics and IoT, how to analyze data from IoT
Data Analytics and IoT, how to analyze data from IoTData Analytics and IoT, how to analyze data from IoT
Data Analytics and IoT, how to analyze data from IoT
AmmarHassan80
 
Juniper Innovation Contest
Juniper Innovation ContestJuniper Innovation Contest
Juniper Innovation Contest
AMIT BORUDE
 
MHUG - YARN
MHUG - YARNMHUG - YARN
MHUG - YARN
Joseph Niemiec
 

Similar to Apache Tez - A New Chapter in Hadoop Data Processing (20)

Apache Tez: Accelerating Hadoop Query Processing
Apache Tez: Accelerating Hadoop Query ProcessingApache Tez: Accelerating Hadoop Query Processing
Apache Tez: Accelerating Hadoop Query Processing
 
Apache Tez : Accelerating Hadoop Query Processing
Apache Tez : Accelerating Hadoop Query ProcessingApache Tez : Accelerating Hadoop Query Processing
Apache Tez : Accelerating Hadoop Query Processing
 
Apache Tez -- A modern processing engine
Apache Tez -- A modern processing engineApache Tez -- A modern processing engine
Apache Tez -- A modern processing engine
 
Tez big datacamp-la-bikas_saha
Tez big datacamp-la-bikas_sahaTez big datacamp-la-bikas_saha
Tez big datacamp-la-bikas_saha
 
YARN Ready: Integrating to YARN with Tez
YARN Ready: Integrating to YARN with Tez YARN Ready: Integrating to YARN with Tez
YARN Ready: Integrating to YARN with Tez
 
February 2014 HUG : Tez Details and Insides
February 2014 HUG : Tez Details and InsidesFebruary 2014 HUG : Tez Details and Insides
February 2014 HUG : Tez Details and Insides
 
Apache Hadoop YARN - The Future of Data Processing with Hadoop
Apache Hadoop YARN - The Future of Data Processing with HadoopApache Hadoop YARN - The Future of Data Processing with Hadoop
Apache Hadoop YARN - The Future of Data Processing with Hadoop
 
Apache Tez : Accelerating Hadoop Query Processing
Apache Tez : Accelerating Hadoop Query ProcessingApache Tez : Accelerating Hadoop Query Processing
Apache Tez : Accelerating Hadoop Query Processing
 
Tez Data Processing over Yarn
Tez Data Processing over YarnTez Data Processing over Yarn
Tez Data Processing over Yarn
 
Introduction sur Tez par Olivier RENAULT de HortonWorks Meetup du 25/11/2014
Introduction sur Tez par Olivier RENAULT de HortonWorks Meetup du 25/11/2014Introduction sur Tez par Olivier RENAULT de HortonWorks Meetup du 25/11/2014
Introduction sur Tez par Olivier RENAULT de HortonWorks Meetup du 25/11/2014
 
Tez: Accelerating Data Pipelines - fifthel
Tez: Accelerating Data Pipelines - fifthelTez: Accelerating Data Pipelines - fifthel
Tez: Accelerating Data Pipelines - fifthel
 
Big Data Day LA 2015 - What's new and next in Apache Tez by Bikas Saha of Hor...
Big Data Day LA 2015 - What's new and next in Apache Tez by Bikas Saha of Hor...Big Data Day LA 2015 - What's new and next in Apache Tez by Bikas Saha of Hor...
Big Data Day LA 2015 - What's new and next in Apache Tez by Bikas Saha of Hor...
 
Chapter 10
Chapter 10Chapter 10
Chapter 10
 
Get Started Building YARN Applications
Get Started Building YARN ApplicationsGet Started Building YARN Applications
Get Started Building YARN Applications
 
Apache Tez – Present and Future
Apache Tez – Present and FutureApache Tez – Present and Future
Apache Tez – Present and Future
 
Apache Tez – Present and Future
Apache Tez – Present and FutureApache Tez – Present and Future
Apache Tez – Present and Future
 
Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015
Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015
Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015
 
Data Analytics and IoT, how to analyze data from IoT
Data Analytics and IoT, how to analyze data from IoTData Analytics and IoT, how to analyze data from IoT
Data Analytics and IoT, how to analyze data from IoT
 
Juniper Innovation Contest
Juniper Innovation ContestJuniper Innovation Contest
Juniper Innovation Contest
 
MHUG - YARN
MHUG - YARNMHUG - YARN
MHUG - YARN
 

More from DataWorks Summit

Data Science Crash Course
Data Science Crash CourseData Science Crash Course
Data Science Crash Course
DataWorks Summit
 
Floating on a RAFT: HBase Durability with Apache Ratis
Floating on a RAFT: HBase Durability with Apache RatisFloating on a RAFT: HBase Durability with Apache Ratis
Floating on a RAFT: HBase Durability with Apache Ratis
DataWorks Summit
 
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiTracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
DataWorks Summit
 
HBase Tales From the Trenches - Short stories about most common HBase operati...
HBase Tales From the Trenches - Short stories about most common HBase operati...HBase Tales From the Trenches - Short stories about most common HBase operati...
HBase Tales From the Trenches - Short stories about most common HBase operati...
DataWorks Summit
 
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
DataWorks Summit
 
Managing the Dewey Decimal System
Managing the Dewey Decimal SystemManaging the Dewey Decimal System
Managing the Dewey Decimal System
DataWorks Summit
 
Practical NoSQL: Accumulo's dirlist Example
Practical NoSQL: Accumulo's dirlist ExamplePractical NoSQL: Accumulo's dirlist Example
Practical NoSQL: Accumulo's dirlist Example
DataWorks Summit
 
HBase Global Indexing to support large-scale data ingestion at Uber
HBase Global Indexing to support large-scale data ingestion at UberHBase Global Indexing to support large-scale data ingestion at Uber
HBase Global Indexing to support large-scale data ingestion at Uber
DataWorks Summit
 
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixScaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
DataWorks Summit
 
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiBuilding the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
DataWorks Summit
 
Supporting Apache HBase : Troubleshooting and Supportability Improvements
Supporting Apache HBase : Troubleshooting and Supportability ImprovementsSupporting Apache HBase : Troubleshooting and Supportability Improvements
Supporting Apache HBase : Troubleshooting and Supportability Improvements
DataWorks Summit
 
Security Framework for Multitenant Architecture
Security Framework for Multitenant ArchitectureSecurity Framework for Multitenant Architecture
Security Framework for Multitenant Architecture
DataWorks Summit
 
Presto: Optimizing Performance of SQL-on-Anything Engine
Presto: Optimizing Performance of SQL-on-Anything EnginePresto: Optimizing Performance of SQL-on-Anything Engine
Presto: Optimizing Performance of SQL-on-Anything Engine
DataWorks Summit
 
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
DataWorks Summit
 
Extending Twitter's Data Platform to Google Cloud
Extending Twitter's Data Platform to Google CloudExtending Twitter's Data Platform to Google Cloud
Extending Twitter's Data Platform to Google Cloud
DataWorks Summit
 
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiEvent-Driven Messaging and Actions using Apache Flink and Apache NiFi
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
DataWorks Summit
 
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerSecuring Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
DataWorks Summit
 
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
DataWorks Summit
 
Computer Vision: Coming to a Store Near You
Computer Vision: Coming to a Store Near YouComputer Vision: Coming to a Store Near You
Computer Vision: Coming to a Store Near You
DataWorks Summit
 
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkBig Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
DataWorks Summit
 

More from DataWorks Summit (20)

Data Science Crash Course
Data Science Crash CourseData Science Crash Course
Data Science Crash Course
 
Floating on a RAFT: HBase Durability with Apache Ratis
Floating on a RAFT: HBase Durability with Apache RatisFloating on a RAFT: HBase Durability with Apache Ratis
Floating on a RAFT: HBase Durability with Apache Ratis
 
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiTracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
 
HBase Tales From the Trenches - Short stories about most common HBase operati...
HBase Tales From the Trenches - Short stories about most common HBase operati...HBase Tales From the Trenches - Short stories about most common HBase operati...
HBase Tales From the Trenches - Short stories about most common HBase operati...
 
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
 
Managing the Dewey Decimal System
Managing the Dewey Decimal SystemManaging the Dewey Decimal System
Managing the Dewey Decimal System
 
Practical NoSQL: Accumulo's dirlist Example
Practical NoSQL: Accumulo's dirlist ExamplePractical NoSQL: Accumulo's dirlist Example
Practical NoSQL: Accumulo's dirlist Example
 
HBase Global Indexing to support large-scale data ingestion at Uber
HBase Global Indexing to support large-scale data ingestion at UberHBase Global Indexing to support large-scale data ingestion at Uber
HBase Global Indexing to support large-scale data ingestion at Uber
 
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixScaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
 
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiBuilding the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
 
Supporting Apache HBase : Troubleshooting and Supportability Improvements
Supporting Apache HBase : Troubleshooting and Supportability ImprovementsSupporting Apache HBase : Troubleshooting and Supportability Improvements
Supporting Apache HBase : Troubleshooting and Supportability Improvements
 
Security Framework for Multitenant Architecture
Security Framework for Multitenant ArchitectureSecurity Framework for Multitenant Architecture
Security Framework for Multitenant Architecture
 
Presto: Optimizing Performance of SQL-on-Anything Engine
Presto: Optimizing Performance of SQL-on-Anything EnginePresto: Optimizing Performance of SQL-on-Anything Engine
Presto: Optimizing Performance of SQL-on-Anything Engine
 
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
 
Extending Twitter's Data Platform to Google Cloud
Extending Twitter's Data Platform to Google CloudExtending Twitter's Data Platform to Google Cloud
Extending Twitter's Data Platform to Google Cloud
 
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiEvent-Driven Messaging and Actions using Apache Flink and Apache NiFi
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
 
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerSecuring Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
 
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
 
Computer Vision: Coming to a Store Near You
Computer Vision: Coming to a Store Near YouComputer Vision: Coming to a Store Near You
Computer Vision: Coming to a Store Near You
 
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkBig Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
 

Recently uploaded

GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Tomaz Bratanic
 
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - HiikeSystem Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
Hiike
 
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStrDeep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
saastr
 
Skybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoptionSkybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoption
Tatiana Kojar
 
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Jeffrey Haguewood
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Malak Abu Hammad
 
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxOcean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
SitimaJohn
 
A Comprehensive Guide to DeFi Development Services in 2024
A Comprehensive Guide to DeFi Development Services in 2024A Comprehensive Guide to DeFi Development Services in 2024
A Comprehensive Guide to DeFi Development Services in 2024
Intelisync
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
saastr
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
Ivanti
 
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Tatiana Kojar
 
dbms calicut university B. sc Cs 4th sem.pdf
dbms  calicut university B. sc Cs 4th sem.pdfdbms  calicut university B. sc Cs 4th sem.pdf
dbms calicut university B. sc Cs 4th sem.pdf
Shinana2
 
5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides
DanBrown980551
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
Zilliz
 
Finale of the Year: Apply for Next One!
Finale of the Year: Apply for Next One!Finale of the Year: Apply for Next One!
Finale of the Year: Apply for Next One!
GDSC PJATK
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
ssuserfac0301
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
panagenda
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Brandon Minnick, MBA
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Safe Software
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Hiroshi SHIBATA
 

Recently uploaded (20)

GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
 
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - HiikeSystem Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
 
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStrDeep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
 
Skybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoptionSkybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoption
 
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
 
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxOcean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
 
A Comprehensive Guide to DeFi Development Services in 2024
A Comprehensive Guide to DeFi Development Services in 2024A Comprehensive Guide to DeFi Development Services in 2024
A Comprehensive Guide to DeFi Development Services in 2024
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
 
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
 
dbms calicut university B. sc Cs 4th sem.pdf
dbms  calicut university B. sc Cs 4th sem.pdfdbms  calicut university B. sc Cs 4th sem.pdf
dbms calicut university B. sc Cs 4th sem.pdf
 
5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
 
Finale of the Year: Apply for Next One!
Finale of the Year: Apply for Next One!Finale of the Year: Apply for Next One!
Finale of the Year: Apply for Next One!
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
 

Apache Tez - A New Chapter in Hadoop Data Processing

  • 1. © Hortonworks Inc. 2013 Page 1 Apache Tez : Accelerating Hadoop Query Processing Bikas Saha@bikassaha Hitesh Shah @hitesh1892
  • 2. © 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.
  • 3. © Hortonworks Inc. 2012© Hortonworks Inc. 2013. Confidential and Proprietary. 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!
  • 4. © Hortonworks Inc. 2013 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
  • 5. © 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
  • 6. © 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 Page 6 Look to the Future with an eye on the Past
  • 7. © 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 Page 7
  • 8. © 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. Page 8 Client Machine Node Manager TezTask Node Manager TezTaskTezClient HDFS Tez Lib 1 Tez Lib 2 Client Machine TezClient
  • 9. © Hortonworks Inc. 2013 Tez – Expressing the computation Page 9 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
  • 10. © Hortonworks Inc. 2013 Tez – Expressing the computation Page 10 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
  • 11. © Hortonworks Inc. 2013 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 11 Defines the global processing flow Map1 Map2 Reduce1 Reduce2 Join Scatter Gather Scatter Gather
  • 12. © Hortonworks Inc. 2013 Tez – DAG API Page 12 • 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
  • 13. © Hortonworks Inc. 2013 Tez – Logical DAG expansion at Runtime Page 13 Reduce1 Map2 Reduce2 Join Map1
  • 14. © Hortonworks Inc. 2013 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 14
  • 15. © Hortonworks Inc. 2013 Tez – Library of Inputs and Outputs Page 15 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
  • 16. © 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) Page 16
  • 17. © Hortonworks Inc. 2013 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
  • 18. © 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 Page 18 YARN Container / JVM TezTask Host TezTask1 TezTask2 SharedObjects YARN Container Tez Application Master Start Task Task Done Start Task
  • 19. © Hortonworks Inc. 2013 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
  • 20. © Hortonworks Inc. 2013 Tez – Customizable Core Engine Page 20 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
  • 21. © Hortonworks Inc. 2013 Tez – Event Based Control Plane Page 21 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
  • 22. © Hortonworks Inc. 2013 Tez – Automatic Reduce Parallelism Page 22 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
  • 23. © Hortonworks Inc. 2013 Tez – Theory to Practice • Performance • Scalability Page 23
  • 24. © Hortonworks Inc. 2013 Tez – Hive TPC-DS Scale 200GB latency
  • 25. Tez – Pig performance gains • Demonstrates performance gains from a basic translation to a Tez DAG 0 20 40 60 80 100 120 140 160 Prod script 1 25m vs 10m 5 MR Jobs Prod script 2 34m vs 16m 5 MR Jobs Prod script 3 1h 46m vs 48m 12 MR Jobs Prod script 4 2h 22m vs 1h 21m 15 MR jobs Timeinmins MR Tez
  • 26. © 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
  • 27. © Hortonworks Inc. 2013 Tez – Data at scale Page 27 Hive TPC-DS Scale 10TB
  • 28. © Hortonworks Inc. 2013 Tez – DAG definition at scale Page 28 Hive : TPC-DS Query 88 Logical DAG ( 39 MR jobs with Hive-10)
  • 29. © Hortonworks Inc. 2013 Tez – Container Reuse at Scale • 78 vertices + 8374 tasks on 50 containers (TPC-DS Query 4) Page 29
  • 30. © Hortonworks Inc. 2013 Tez – Real World Use Cases for the API Page 30
  • 31. © Hortonworks Inc. 2013 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
  • 32. © Hortonworks Inc. 2013 Tez – Multiple Outputs Page 32 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
  • 33. © Hortonworks Inc. 2013 Tez – One to One Edge Page 33 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
  • 34. © Hortonworks Inc. 2013 Tez – Current status • Apache Incubator Project –Rapid development. Over 1100 jiras opened. Over 800 resolved –Growing community of contributors and users –Latest release is 0.4 • Support for a vast topology of DAGs • Being used by multiple applications such as Apache Hive, Apache Pig, Cascading. Page 34
  • 35. © Hortonworks Inc. 2013 Tez – Adoption Path •Pre-requisite : Hadoop 2 with YARN •Simple client-side install (no admin support needed) –No side effects or traces left behind on your cluster. Low risk and low effort to try out. •Apache Hive – Already available in 0.13 •Apache Pig – Available on trunk ( slated for v0.14 ) •Cascading – version 3.0 ( coming soon ) •Run your MapReduce jobs using Tez runtime • Change “mapreduce.framework.name” to “yarn-tez” •ETL: Replace MR or custom pipelines with native Tez • Page 35
  • 36. © Hortonworks Inc. 2013 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 –Tez UI (coming soon) –Stability and testability – API ease of use – Tools for performance analysis and debugging Page 36
  • 37. © 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/incubator-tez – Developer list: dev@tez.incubator.apache.org User list: user@tez.incubator.apache.org Issues list: issues@tez.incubator.apache.org Page 37
  • 38. © Hortonworks Inc. 2013 Tez – Takeaways • Distributed execution framework that works on computations represented as dataflow graphs • Naturally maps to execution plans produced by query optimizers • 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 Page 38
  • 39. © Hortonworks Inc. 2013 Tez Thanks for your time and attention! Video with Deep Dive on Tez http://youtu.be/-7YhVwqky6M http://www.infoq.com/presentations/apache-tez Questions? @bikassaha, @hitesh1892 Page 39

Editor's Notes

  1. For anyone who has been working on MapReduce, there is this age-old problem around “how do I figure out the correct number of reducers?”. We guess some number at compile-time and usually that turns out to be incorrect at run-time. Let’s see how we can use the Tez model to fix that. So here is this Map Vertex and this Reduce Vertex, which have these tasks running and you have the Vertex Manager running inside the framework … [CLICK] The Map Tasks can send Data Size Statistics to the Vertex Manager, which can then extrapolate those statistics to figure out “what would be the final size of the data when all of these Maps finish?”. Based on that, it can realize that the data size is actually smaller than expected, and I can actually run two reduce tasks instead of three. [CLICK] The Vertex Manager sends a Set Paralellism command to the framework which changes the routing information in-between these two tasks and also cancels the last task.
  2. query 1: SELECT pageURL, pageRank FROM rankings WHERE pageRank > X
  3. 1.5x to 3x speedup on some of the Pigmix queries.
  4. Hive has written it’s own processor