Apache Tez – Present and Future
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Apache Tez is a framework for executing data processing jobs on Hadoop clusters. It allows expressing jobs as directed acyclic graphs (DAGs) which enables optimizations like running jobs as a single logical unit rather than separate MapReduce jobs. The presentation covered Tez features like container reuse, dynamic parallelism, and integration with YARN and ATS for monitoring. It also discussed ongoing work to improve performance through speculation, intermediate file formats, and shuffle optimizations, as well as better debuggability using tools like the Tez UI.
Apache Tez – Present and Future
- 1. © Hortonworks Inc. 2015 Page 1 Apache Tez – Present and Future Jeff Zhang (@zjffdu) Rajesh Balamohan (@rajeshbalamohan)
- 2. © Hortonworks Inc. 2015 Agenda •Tez Introduction •Tez Feature Deep Dive •Tez Improvement & Debuggability •Tez Status & Roadmap
- 3. © Hortonworks Inc. 2015 I/O Synchronization Barrier I/O Synchronization Barrier Job 1 ( Join a & b ) Job 3 ( Group by of c ) Job 2 (Group by of a Join b) Job 4 (Join of S & R ) Hive - MR Example of MR versus Tez Page 3 Single Job Hive - Tez Join a & b Group by of a Join b Group by of c Job 4 (Join of S & R )
- 4. © Hortonworks Inc. 2015 Tez – Introduction Page 4 • 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.
- 5. © Hortonworks Inc. 2015 What is DAG & Why DAG Projection Filter GroupBy … Join Union Intersect … Split … • Directed Acyclic Graph • Any complicated DAG can been composed of the following 3 basic paradigm – Sequential – Merge – Divide
- 6. © Hortonworks Inc. 2015 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
- 7. © Hortonworks Inc. 2015 DAG API (Logic View) Page 7 • 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
- 8. © Hortonworks Inc. 2015 Runtime API (Runtime View) Page 8 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
- 9. © Hortonworks Inc. 2015 Benefit of DAG • Easier to express computation in DAG • No intermediate data written to HDFS • Less pressure on NameNode • No resource queuing effort & less resource contention • More optimization opportunity with more global context
- 10. © Hortonworks Inc. 2015 Agenda •Tez Introduction •Tez Feature Deep Dive •Tez Improvement & Debuggability •Tez Status & Roadmap
- 11. © Hortonworks Inc. 2015 Container-Reuse • Reuse the same container across DAG/Vertices/Tasks • Benefit of Container-Reuse –Less resources consumed –Reduce overhead of launching JVM –Reduce overhead of negotiate with Resource Manager –Reduce overhead of resource localization –Reduce network IO –Object Caching (Object Sharing)
- 12. © Hortonworks Inc. 2015 Tez Session • Multiple Jobs/DAGs in one AM • Container-reuse across Jobs/DAGs • Data sharing between Jobs/DAGs
- 13. © Hortonworks Inc. 2015 Dynamic Parallelism Estimation • VertexManager –Listen to the other vertices status –Coordinate and schedule its tasks –Communication between vertices
- 14. © Hortonworks Inc. 2015 ATS Integration • Tez is fully integrated with YARN ATS (Application Timeline Service) –DAG Status, DAG Metrics, Task Status, Task Metrics are captured • Diagnostics & Performance analysis –Data Source for monitoring & diagnostics –Data Source for performance analysis
- 15. © Hortonworks Inc. 2015 Recovery • AM can crash in corner cases –OOM –Node failure –… • Continue from the last checkpoint • Transparent to end users AM Crash
- 16. © Hortonworks Inc. 2015 Order By of Pig f = Load ‘foo’ as (x, y); o = Order f by x;Load Sample （Calculate Histogram) HDFS Partition Sort Broadcast Load Sample （Calculate Histogram) Partition Sort One-to-One Scatter Gather Scatter Gather
- 17. © Hortonworks Inc. 2015 Agenda •Tez Introduction •Tez Feature Deep Dive •Tez Improvement & Debuggability •Tez Status & Roadmap
- 18. © Hortonworks Inc. 2015 • Performance –Speculation –Intermediate File Improvements –Better use of JVM Memory –Shuffle Improvements • Debuggability –Tez UI –Local mode –Job Analysis Tools –Shuffle Performance Analysis Tool
- 19. © Hortonworks Inc. 2015 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
- 20. © Hortonworks Inc. 2015 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
- 21. © Hortonworks Inc. 2015 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
- 22. © Hortonworks Inc. 2015 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
- 23. © Hortonworks Inc. 2015 Better use of JVM Memory - Contd • WeightedMemoryDistributor for better memory management in tasks –Observed 26% runtime improvement in tasks
- 24. © Hortonworks Inc. 2015 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
- 25. © Hortonworks Inc. 2015 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
- 26. © Hortonworks Inc. 2015 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
- 27. © Hortonworks Inc. 2015 • Performance –Speculation –Better use of JVM Memory –Intermediate File Improvements –Shuffle Improvements • Debuggability –Tez UI –Local mode –Job Analysis Tools –Shuffle Performance Analysis Tool
- 28. © Hortonworks Inc. 2015 Tez UI
- 29. © Hortonworks Inc. 2015 Tez UI
- 30. Tez UI 30 Download data from ATS
- 31. © Hortonworks Inc. 2015 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
- 32. © Hortonworks Inc. 2015 Job Analysis Tools • DAG Swimlane –“$TEZ_HOME/tez-tools/swimlanes/sh yarn-swimlanes.sh <app_id>” Prewarm Container Reuse Remote Reads
- 33. © Hortonworks Inc. 2015 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
- 34. © Hortonworks Inc. 2015 Shuffle Performance Analysis Tools • Analyze shuffle performance between source / destination nodes
- 35. © Hortonworks Inc. 2015 RoadMap • Shared output edges –Same output to multiple vertices • Local mode stabilization • Optimizing (include/exclude) vertex at runtime • Partial completion VertexManager • Co-Scheduling • Framework stats for better runtime decisions
- 36. © Hortonworks Inc. 2015 Tez – Adoption • Apache Hive • Start from Hive 0.13 • set hive.exec.engine = tez • Apache Pig • Start from Pig 0.14 • pig -x tez • Cascading • Flink Page 36
- 37. © Hortonworks Inc. 2015 Tez Community • Useful Links –http://tez.apache.org/ –JIRA : https://issues.apache.org/jira/browse/TEZ –Code Repository: https://git-wip-us.apache.org/repos/asf/tez.git –Mailing Lists – Dev List: dev@tez.apache.org – User List: user@tez.apache.org – Issues List: issues@tez.apache.org • Tez Meetup –http://www.meetup.com/Apache-Tez-User-Group
- 38. © Hortonworks Inc. 2015 Thank You! Questions & Answers Page 38