Apache Tez is a framework for building data processing applications on top of YARN. It allows expressing a computation as a directed acyclic graph (DAG) to optimize execution. Tez improves on MapReduce by avoiding intermediate data writes to HDFS and enabling optimizations across jobs. The presentation covered Tez features like container reuse, dynamic parallelism, and integration with YARN timeline service. It also discussed ongoing work to improve performance through speculation, intermediate file formats, and shuffle optimizations.

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Apache Tez – Present and Future
Jeff Zhang (@zjffdu)
Rajesh Balamohan (@rajeshbalamohan)

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Agenda
•Tez Introduction
•Tez Feature Deep Dive
•Tez Improvement & Debuggability
•Tez Status & Roadmap

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

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

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

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Expressing DAG in Tez API
• DAG API (Logic View)
–Allow user to build DAG
–Topological structure of the data computation flow
• Runtime API (Runtime View)
–Application logic of each computation unit (vertex)
–How to move/read/write data between vertices

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

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

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

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Agenda
•Tez Introduction
•Tez Feature Deep Dive
•Tez Improvement & Debuggability
•Tez Status & Roadmap

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

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Tez Session
• Multiple Jobs/DAGs in one AM
• Container-reuse across Jobs/DAGs
• Data sharing between Jobs/DAGs

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Dynamic Parallelism Estimation
• VertexManager
–Listen to the other vertices
status
–Coordinate and schedule its
tasks
–Communication between
vertices

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

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Recovery
• AM can crash in corner cases
–OOM
–Node failure
–…
• Continue from the last checkpoint
• Transparent to end users
AM Crash

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

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Agenda
•Tez Introduction
•Tez Feature Deep Dive
•Tez Improvement & Debuggability
•Tez Status & Roadmap

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• Performance
–Speculation
–Intermediate File Improvements
–Better use of JVM Memory
–Shuffle Improvements
• Debuggability
–Tez UI
–Local mode
–Job Analysis Tools
–Shuffle Performance Analysis Tool

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Speculation
• Good for clusters having good/slow nodes or heterogeneous
hardware.
• Maintains periodic runtime statistics of tasks
• Triggers speculative attempt when estimated runtime > mean
runtime

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Intermediate File Format Improvements
• Used for storing intermediate data
in Tez
• Drawbacks of earlier format
–Needs larger buffer in memory (due to
duplicate keys)
–Bigger file size in disk
–Not ideal for all use cases
• New Intermediate File Format
–Works based on (K, List<V>)
–Provides 57% memory efficiency and
23% improvement in disk storage
Task
Spill 1 Spill 2 Spill 3
Merged Spill
………………………
New IFile Format
Key
Len
K1Value Len V1
Value Len V2 V_ENDRLE Value Len V3 …
Key
Len
K2Value Len V1
Value Len V5 V_ENDRLE Value Len V6 …
Old IFile Format
Key
Len
Value Len K1 V1
Key
Len
Value Len K1 V2
Key
Len
Value Len K1 V3
Key
Len
Value Len K2 V1
………………………
Key
Len
Value Len K2 V5
Key
Len
Value Len K2 V6

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Better use of JVM Memory
• BytesWritable Improvements
–Provides FastByteSerialization
–Saves 8 bytes per key-value pair
–Reduces IFile size by 25%
–Reduces SERDE costs
• PipelinedSorter can support > 2 GB sort
buffers
–Containers with higher RAM no longer
limited by 2 GB sort buffer limits
–Avoids unnecessary spills in large jobs
• Reduced key comparison costs in
PipelinedSorter
Key Valu
e
Key Size Bytes Val Size Bytes
Key Size BytesSize Val Size BytesSize
Serialize to memory Serialize to memory
Serialize to disk Serialize to disk

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Better use of JVM Memory - Contd
• Enabled RLE in reducer codepath
–Reduced key comparisons in merge codepath
–Improved Job Runtime (observed 10% improvement)
–Reduced CPU cost
Without Fix
691 seconds
With Fix
621 seconds

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Better use of JVM Memory - Contd
• WeightedMemoryDistributor for better memory management
in tasks
–Observed 26% runtime improvement in tasks

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Source Task
….
….
Broadcast Shuffle Improvements
Task 1
Task 2
Task N
…
Task 1
Task 2
Task N
…
Task 1
Task 2
Task N
…
Broadcast
From local disk
From local disk
Source Task
….
….
Task 1
Task 2
Task N
…
Task 1
Task 2
Task N
…
Task 1
Task 2
Task N
…
Broadcast
Before Fix After Fix

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PipelinedShuffle Improvments
• Final merge in source
task is avoided.
– Less IO
• Consumers are
informed about spill
events in advance
– Better usage of
network bandwidth
– Overlap CPU with
network
– For sorted/unsorted
outputs, send data to
consumers in chunks
• Observed 20% runtime
improvement in
queries involving heavy
skews
Task 1
Spill 1
Task 2
Reduce Task 1 Reduce Task 1Reduce Task 1Reduce Task 1Reduce Task N
…..
…..
…..
…..
Spill 1 Spill 2 Spill 3
Task 1
Spill 1
Task 2
Spill 1 Spill 2 Spill 3
Reduce Task 1 Reduce Task 1Reduce Task 1Reduce Task 1Reduce Task N
…..
…..
…..
…..
Merged Spill
Normal Shuffle Path
Pipelined Shuffle Path

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PipelinedShuffle Improvements
Job Runtime : 925 seconds Job Runtime : 680 seconds
- 26% improvement
- Avoids final merge (less IO, CPU cost)
- Downstream can consume data whenever a spill
is generated

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• Performance
–Speculation
–Better use of JVM Memory
–Intermediate File Improvements
–Shuffle Improvements
• Debuggability
–Tez UI
–Local mode
–Job Analysis Tools
–Shuffle Performance Analysis Tool

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Tez UI

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Tez UI

30. Tez UI
30
Download data from ATS

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Better Debuggability– Local Mode
• Test Tez Jobs without Hadoop Cluster
• Enables Fast Prototyping
• Fast Unit Testing
• Runs on Single JVM (easy for debugging)
• Scheduling / RPC invocations Skipped

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Job Analysis Tools
• DAG Swimlane
–“$TEZ_HOME/tez-tools/swimlanes/sh yarn-swimlanes.sh <app_id>”
Prewarm
Container Reuse
Remote Reads

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Shuffle Performance Analysis Tools
• Analyze Tez logs in Hadoop
• Analyze shuffle performance between source / destination
nodes Data transferred
from node 7 to
rest of the nodes are slow

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Shuffle Performance Analysis Tools
• Analyze shuffle performance between source / destination
nodes

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

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Tez – Adoption
• Apache Hive
• Start from Hive 0.13
• set hive.exec.engine = tez
• Apache Pig
• Start from Pig 0.14
• pig -x tez
• Cascading
• Flink
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Tez Community
• Useful Links
–http://tez.apache.org/
–JIRA : https://issues.apache.org/jira/browse/TEZ
–Code Repository: https://git-wip-us.apache.org/repos/asf/tez.git
–Mailing Lists
– Dev List: dev@tez.apache.org
– User List: user@tez.apache.org
– Issues List: issues@tez.apache.org
• Tez Meetup
–http://www.meetup.com/Apache-Tez-User-Group

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Thank You!
Questions & Answers
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