1. © Hortonworks Inc. 2011 Running Non-MapReduce Applications on Apache Hadoop Hitesh Shah & Siddharth Seth Hortonworks Inc. Page 1

2. © Hortonworks Inc. 2011 Who am I? Hitesh Shah –Member of Technical Staff at Hortonworks Inc. –Apache Hadoop PMC member and committer –Apache Tez and Apache Ambari PPMC member and committer Siddharth Seth –Member of Technical Staff at Hortonworks Inc. –Apache Hadoop PMC member and committer –Apache Tez PPMC member and committer Page 2 Architecting the Future of Big Data

3. © Hortonworks Inc. 2011 Agenda Apache Hadoop v1 to v2 YARN Applications on YARN YARN Best Practices Page 3 Architecting the Future of Big Data

4. © Hortonworks Inc. 2011 Apache Hadoop v1 Page 4 Architecting the Future of Big Data Job Client Submit Job JobTracker TaskTracker Map Slot Reduce Slot

5. © Hortonworks Inc. 2011 Apache Hadoop v1 Pros: –A framework to run MapReduce jobs that allows you to run the same piece of code on a single node cluster to one spanning 1000s of machines. Cons: –It is a framework to run MapReduce jobs. Page 5 Architecting the Future of Big Data

6. © Hortonworks Inc. 2011 Apache Giraph Page 6 Architecting the Future of Big Data Iterative graph processing on a Hadoop cluster An iterative approach on MapReduce would require running multiple jobs. To avoid MR overheads, runs everything as a Map-only job. Map Task: Master Map Task: Master Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker Map Task: Worker

7. © Hortonworks Inc. 2011 Apache Oozie Page 7 Architecting the Future of Big Data Workflow scheduler system to manage Hadoop jobs. Running a PIG script through Oozie JobTracker Oozie MapTask: Pig Script Launcher MapTask: Pig Script Launcher Submit Job Submit Subsequent MR jobs 1 1 2 2 3 3

8. © Hortonworks Inc. 2011 Apache Hadoop v2 Page 8 Architecting the Future of Big Data

9. © Hortonworks Inc. 2011 YARN The Operating System of a Hadoop cluster Architecting the Future of Big Data Page 9

10. © Hortonworks Inc. 2011 The YARN Stack Page 10 Architecting the Future of Big Data

11. © Hortonworks Inc. 2011 YARN Glossary Page 11 Architecting the Future of Big Data Installer –Application Installer or Application Client Client –Application Client Supervisor –Application Master Workers –Application Containers

12. © Hortonworks Inc. 2011 YARN Architecture Page 12 Architecting the Future of Big Data ResourceManager NodeManager Client Submit Application NodeManager App Master App Master Container App Master App Master Container

13. © Hortonworks Inc. 2011 YARN Application Flow Page 13 Architecting the Future of Big Data Application Client Resource Manager Resource Manager Application Master NodeManager YarnClient App Specific API App Specific API Application Client Protocol AMRMClient NMClient Application Master Protocol Container Management Protocol App Container App Container

14. © Hortonworks Inc. 2011 YARN Protocols & Client Libraries Application Client Protocol: Client to RM interaction –Library: YarnClient –Application Lifecycle control –Access Cluster Information Application Master Protocol: AM – RM interaction –Library: AMRMClient / AMRMClientAsync –Resource negotiation –Heartbeat to the RM Container Management Protocol: AM to NM interaction –Library: NMClient/NMClientAsync –Launching allocated containers –Stop Running containers Page 14 Architecting the Future of Big Data

15. © Hortonworks Inc. 2011 Applications on YARN Architecting the Future of Big Data Page 15

16. © Hortonworks Inc. 2011 YARN Applications Page 16 Architecting the Future of Big Data Categorizing Applications –What does the Application do? –Application Lifetime –How Applications accept work –Language Application Lifetime –Job submit to complete. –Long-running Services Job Submissions – One job : One Application – Multiple jobs per application

17. © Hortonworks Inc. 2011 Language considerations Hadoop RPC uses Google Protobuf –Protobuf bindings: C/C++, GO, Java, Python… Accessing HDFS –WebHDFS –libhdfs for C –Python client by Spotify Labs: Snakebite YARN Application Logic –ApplicationMaster in Java and containers in any language Page 17 Architecting the Future of Big Data

18. © Hortonworks Inc. 2011 Tez ( App Submission) Page 18 Architecting the Future of Big Data Distributed Execution framework – computation is expressed as a DAG Takes MapReduce to the next level – where each job was limited to a Map and/or Reduce stage. YARNTasks Resource Manager DAG execution logic Task co-ordination Local Task Scheduling DAG execution logic Task co-ordination Local Task Scheduling Tez AM Node Manager(s) Launch AM AM Launched Job Submission Monitoring Job Submission Monitoring Tez Client Request Resources Allocated Resources Launch Tasks Launch AM Tasks Launched Heartbeat Submit DAG

19. © Hortonworks Inc. 2011 HOYA ( Long Running App ) Page 19 Architecting the Future of Big Data On Demand HBase cluster setup Share cluster resources – persist and shutdown the cluster when not needed Dynamically handles Node failures Allows re-sizing of a running HBase cluster

20. © Hortonworks Inc. 2011 Resource Manager Node Manager(s) YARN Get New Containers Kafka (Streams) Samza AM Task Container Task Task Container Task Task Container Task Container Finished Launch Container Samza on YARN ( Failure Handling App ) Page 20 Architecting the Future of Big Data Stream processing system – uses YARN as the execution framework Makes use of CGroups support in YARN for CPU isolation Uses Kafka as underlying store

21. © Hortonworks Inc. 2011 YARN Eco-system Page 21 Architecting the Future of Big Data Powered by YARN Apache Giraph – Graph Processing Apache Hama - BSP Apache Hadoop MapReduce – Batch Apache Tez – Batch/Interactive Apache S4 – Stream Processing Apache Samza – Stream Processing Apache Storm – Stream Processing Apache Spark – Iterative/Interactive applications Cloudera Llama DataTorrent HOYA – HBase on YARN RedPoint Data Management YARN Utilities/Frameworks Weave by Continuity REEF by Microsoft Spring support for Hadoop 2

22. © Hortonworks Inc. 2011 YARN Best Practices Architecting the Future of Big Data Page 22

23. © Hortonworks Inc. 2011 Best Practices Page 23 Architecting the Future of Big Data Use provided Client libraries Resource Negotiation –You may ask but you may not get what you want - immediately. –Locality requests may not always be met. –Resources like memory/CPU are guaranteed. Failure handling –Remember, anything can fail ( or YARN can pre-empt your containers) –AM failures handled by YARN but container failures handled by the application. Checkpointing –Check-point AM state for AM recovery. –If tasks are long running, check-point task state.

24. © Hortonworks Inc. 2011 Best Practices Page 24 Architecting the Future of Big Data Cluster Dependencies –Try to make zero assumptions on the cluster. –Your application bundle should deploy everything required using YARN’s local resources. Client-only installs if possible –Simplifies cluster deployment, and multi-version support Securing your Application –YARN does not secure communications between the AM and its containers.

25. © Hortonworks Inc. 2011 Testing/Debugging your Application Page 25 Architecting the Future of Big Data MiniYARNCluster –Regression tests Unmanaged AM –Support to run the AM outside of a YARN cluster for manual testing. Logs –Log aggregation support to push all logs into HDFS –Accessible via CLI, UI.

26. © Hortonworks Inc. 2011 Future work in YARN Page 26 Architecting the Future of Big Data ResourceManager High Availability and Work-preserving restart –Work-in-Progress Scheduler Enhancements –SLA Driven Scheduling, Gang scheduling –Multiple resource types – disk/network/GPUs/affinity Rolling upgrades Long running services –Better support to running services like HBase –Discovery of services, upgrades without downtime More utilities/libraries for Application Developers –Failover/Checkpointing http://hadoop.apache.org

27. © Hortonworks Inc. 2011 Questions? Architecting the Future of Big Data Page 27