Apache Tez : Accelerating Hadoop Data Processing

1. Apache Tez : Accelerating Hadoop Data Processing Bikas Saha @bikassaha

2. Tez – Introduction • 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.

3. Tez – Hadoop 1 ---> Hadoop 2 • Monolithic • Resource Management – MR • Execution Engine – MR Layered • Resource Management – YARN • Engines – Hive, Pig, Cascading, Your App!

4. 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 App • Custom application logic • Custom data format • Custom data transfer technology Tez • 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. 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

6. 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 Look to the Future with an eye on the Past

7. 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 to let applications tailor the job to meet their specific requirements • Operation simplicity • Painless to operate, experiment and upgrade

8. 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. HDFS Tez Lib 1 Tez Lib 2 Client Machine Node Manager Node Manager Client Machine TezTask TezTask TezClient TezClient

9. Tez – Expressing the computation 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 Preprocessor Stage Task-1 Task-2 Samples Sampler Partition Stage Ranges Task-1 Task-2 Distributed Sort Aggregate Stage Task-1 Task-2

10. Tez – Expressing the computation 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 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

11. Tez – DAG API Defines the global processing flow // 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)… Map1 Map2 Scatter Gather Reduce1 Reduce2 Scatter Gather Join

12. Tez – DAG API Edge properties define the connection between producer and consumer tasks in the DAG 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.

13. Tez – Logical DAG expansion at Runtime Map1 Map2 Reduce1 Reduce2 Join

14. 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 • 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 implementations are as first class as ours!

15. Tez – Task Composition Logical DAG Task A V-A Processor-A EdgeAB EdgeAC Output-3 Output-1 V-B V-C V-A = { Processor-A.class } V-B = { Processor-B.class} V-C = { Processor-C.class } EdgeAB = { V-A, V-B, Output-1.class, Input-2.class } Edge AC = { V-A, V-C, Output-3.class, Input-4.class } Input-2 Input-4 Processor-B Processor-C Task B Task C

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

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

18. 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

19. 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 Pig/Hive - Tez Pig/Hive - MR

20. 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 YARNContainer / JVM YARN Container Tez Application Master TezTask Host Start Task TezTask1 Task Done Shared Objects TezTask2 Start Task

21. Tez – Sessions • 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 Start Session SubmitDAG Client Application Master Task Scheduler Pre Warmed JVM Shared Object Registry Container Pool

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

23. Tez – Customizable Core Engine Start vertex • 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 Get container Vertex-1 GetPriority DAG Scheduler Task Scheduler Start vertex Vertex Manager Start tasks Vertex-2 GetPriority Get container

24. Tez – Event Based Control Plane • 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 Map Task 2 Map Task 1 Output1 Output1 Output2 Output2 Output3 Output3 Data Event AM Router Scatter-Gather Edge Error Event Reduce Task 2 Input1 Input2

25. Tez – Automatic Reduce Parallelism 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 App Master Data Size Statistics Vertex Manager Map Vertex Set Parallelism Vertex State Machine Re-Route Reduce Vertex Cancel Task

26. Tez – Reduce Slow Start/Pre-launch Event Model Map completion events sent to the Reduce Vertex Manager. Vertex Manager Pluggable application logic that understands the data size. Advises the vertex controller to launch the reducers before all maps have completed so that shuffle can start. App Master Task Completed Vertex Manager Map Vertex Start Tasks Vertex State Machine Reduce Vertex Start

27. Tez – Theory to Practice • Performance • Scalability

28. Tez – Hive TPC-DS Scale 200GB latency

29. Tez – Pig performance gains • Demonstrates performance gains from a basic translation to a Tez DAG • Deeper integration in the works for further improvements

30. 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.

31. Tez – Data at scale Hive TPC-DS Scale 10TB

32. Tez – DAG definition at scale Hive : TPC-DS Query 64 Logical DAG

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

34. Tez – Real World Use Cases for the API

35. 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 : Broadcast Join Store Sales scan. Group by and aggregation reduce size of this input. M M M M M M Store Sales scan. Group by and aggregation. R R R R Broadcast edge HDFS Inventory scan and Join M M M Inventory and Store Sales (aggr.) output scan and shuffle join. M M M HDFS R R HDFS

36. 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; Tez – Multiple Outputs Pig : Split & Group-by Load foo Load foo Split multiplex de-multiplex Multiple outputs Group by y Group by z Group by z Group by y HDFS HDFS Load g1 and Load g2 Reduce follows reduce Join Join

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

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

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

40. Tez – Bringing it all together Tez Session populates container pool Dimension table calculation and HDFS split generation in parallel Dimension tables broadcasted to Hive MapJoin tasks Final Reducer pre-launchedand fetches completed inputs TPCDS – Query-27 with Hive on Tez Architecting the Future of Big Data

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

42. Tez – Current status • Apache Project • Growing community of contributors and users • Latest release is 0.5.0 • Focus on stability • Testing and quality are highest priority • Code ready and deployed on multi-node environments at scale • Support for a vast topology of DAGs • Already functionally equivalent to Map Reduce. Existing Map Reduce jobs can be executed on Tez with few or no changes • Rapid adoption by important open source projects and by vendors

43. Tez – Developer Release • API stability – Intuitive and clean APIs that will be supported and be backwards compatible with future releases • Local Mode Execution – Allows the application to run entirely in the same JVM without any need for a cluster. Enables easy debugging using IDE’s on the developer machine • Performance Debugging – Adds swim-lanes analysis tool to visualize job execution and help identify performance bottlenecks • Documentation and tutorials to learn the APIs

44. Tez – Adoption • Apache Hive • Hadoop standard for declarative access via SQL-like interface (Hive 13) • Apache Pig • Hadoop standard for procedural scripting and pipeline processing (Pig 14) • Cascading • Developer friendly Java API and SDK • Scalding (Scala API on Cascading) (3.0) • Commercial Vendors • ETL : Use Tez instead of MR or custom pipelines • Analytics Vendors : Use Tez as a target platform for scaling parallel analytical tools to large data-sets

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

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

47. Tez – Community • Early adopters and code contributors welcome • Adopters to drive more scenarios. Contributors to make them happen. • Tez meetupfor 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

48. Tez – Takeaways • Distributed execution framework that models processing as dataflow graphs • Customizable execution architecture designed for extensibility and user defined performance optimizations • Works out of the box with the platform figuring out the hard stuff • Zero install – Safe and Easy to Evaluate and Experiment • Addresses common needs of a broad spectrum of applications and usage patterns • Open source Apache project – your use-cases and code are welcome • It works and is already being used by Apache Hive and Pig

49. Tez Thanks for your time and attention! Video with Deep Dive on Tez http://goo.gl/BL67o7 https://hortonworks.webex.com/hortonworks/lsr.php?RCID=b4382d626867fe57de70b218c74c03e4– WordCount code walk through with 0.5 APIs. Debugging in local mode. Execution and profiling in cluster mode. (Starts at ~27 min. Ends at ~37 min) Questions? @bikassaha