Apache Hadoop YARN: Understanding the Data Operating System of Hadoop
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This deck covers concepts and motivations behind Apache Hadoop YARN, the key technology in Hadoop 2 to deliver a Data Operating System for the enterprise.
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Apache Hadoop YARN: Understanding the Data Operating System of Hadoop
- 1. YARN Apache Hadoop Next Generation Compute Platform Bikas Saha @bikassaha © Hortonworks Inc. 2013 Page 1
- 2. Apache Hadoop & YARN • Apache Hadoop – De facto Big Data open source platform – Running for about 5 years in production at hundreds of companies like Yahoo, Ebay and Facebook • Hadoop 2 – Significant improvements in HDFS distributed storage layer. High Availability, NFS, Snapshots – YARN – next generation compute framework for Hadoop designed from the ground up based on experience gained from Hadoop 1 – YARN running in production at Yahoo for about a year – YARN awarded Best Paper at SOCC 2013 © Hortonworks Inc. 2013 - Confidential Page 2
- 3. 1st Generation Hadoop: Batch Focus HADOOP 1.0 Built for Web-Scale Batch Apps Single App Single App INTERACTIVE ONLINE Single App Single App Single App BATCH BATCH BATCH HDFS HDFS All other usage patterns MUST leverage same infrastructure HDFS © Hortonworks Inc. 2013 - Confidential Forces Creation of Silos to Manage Mixed Workloads Page 3
- 4. Hadoop 1 Architecture JobTracker Manage Cluster Resources & Job Scheduling TaskTracker Per-node agent Manage Tasks © Hortonworks Inc. 2013 - Confidential Page 4
- 5. Hadoop 1 Limitations Lacks Support for Alternate Paradigms and Services Force everything needs to look like Map Reduce Iterative applications in MapReduce are 10x slower Scalability Max Cluster size ~5,000 nodes Max concurrent tasks ~40,000 Availability Failure Kills Queued & Running Jobs Hard partition of resources into map and reduce slots Non-optimal Resource Utilization © Hortonworks Inc. 2013 - Confidential Page 5
- 6. Our Vision: Hadoop as Next-Gen Platform Single Use System Multi Purpose Platform Batch Apps Batch, Interactive, Online, Streaming, … HADOOP 1.0 HADOOP 2.0 MapReduce Others (data processing) MapReduce YARN (cluster resource management & data processing) (cluster resource management) HDFS HDFS2 (redundant, reliable storage) (redundant, highly-available & reliable storage) © Hortonworks Inc. 2013 - Confidential Page 6
- 7. Hadoop 2 - YARN Architecture ResourceManager (RM) Central agent - Manages and allocates cluster resources Node Manager NodeManager (NM) Per-Node agent - Manages and App Mstr enforces node resource allocations ApplicationMaster (AM) Per-Application – Resource Manager Node Manager Client Container Manages application lifecycle and task scheduling MapReduce Status Job Submission Node Manager Node Status Resource Request © Hortonworks Inc. 2013 - Confidential Page 7
- 8. YARN: Taking Hadoop Beyond Batch Store ALL DATA in one place… Interact with that data in MULTIPLE WAYS with Predictable Performance and Quality of Service Applications Run Natively in Hadoop BATCH INTERACTIVE (MapReduce) (Tez) ONLINE (HBase) STREAMING (Storm, S4,…) GRAPH (Giraph) IN-MEMORY (Spark) HPC MPI (OpenMPI) OTHER (Search) (Weave…) YARN (Cluster Resource Management) HDFS2 (Redundant, Reliable Storage) © Hortonworks Inc. 2013 - Confidential Page 8
- 9. 5 Key Benefits of YARN 1. New Applications & Services 2. Improved cluster utilization 3. Scale 4. Experimental Agility 5. Shared Services © Hortonworks Inc. 2013 - Confidential Page 9
- 10. Key Improvements in YARN Framework supporting multiple applications – Separate generic resource brokering from application logic – Define protocols/libraries and provide a framework for custom application development – Share same Hadoop Cluster across applications Cluster Utilization – Generic resource container model replaces fixed Map/Reduce slots. Container allocations based on locality, memory (CPU coming soon) – Sharing cluster among multiple application © Hortonworks Inc. 2013 - Confidential Page 10
- 11. Key Improvements in YARN Scalability – Removed complex app logic from RM, scale further – State machine, message passing based loosely coupled design – Compact scheduling protocol Application Agility and Innovation – Use Protocol Buffers for RPC gives wire compatibility – Map Reduce becomes an application in user space unlocking safe innovation – Multiple versions of an app can co-exist leading to experimentation – Easier upgrade of framework and application © Hortonworks Inc. 2013 - Confidential Page 11
- 12. Key Improvements in YARN Shared Services – Common services needed to build distributed application are included in a pluggable framework – Distributed file sharing service – Remote data read service – Log Aggregation Service © Hortonworks Inc. 2013 - Confidential Page 12
- 13. YARN: Efficiency with Shared Services Yahoo! leverages YARN 40,000+ nodes running YARN across over 365PB of data ~400,000 jobs per day for about 10 million hours of compute time Estimated a 60% – 150% improvement on node usage per day using YARN Eliminated Colo (~10K nodes) due to increased utilization For more details check out the YARN SOCC 2013 paper © Hortonworks Inc. 2013 - Confidential Page 13
- 14. YARN as Cluster Operating System ResourceManager Scheduler NodeManager NodeManager NodeManager NodeManager map 1.1 nimbus0 vertex1.1.1 vertex1.2.2 NodeManager NodeManager NodeManager NodeManager map1.2 Batch Interactive SQL vertex1.1.2 nimbus2 NodeManager NodeManager NodeManager NodeManager nimbus1 Real-Time vertex1.2.1 reduce1.1 © Hortonworks Inc. 2013 - Confidential Page 14
- 15. Multi-Tenancy is Built-in • Queues • Economics as queue-capacity – Hierarchical Queues • SLAs ResourceManager – Cooperative Preemption Scheduler • Resource Isolation – Linux: cgroups – Roadmap: Virtualization (Xen, KVM) • Administration – Queue ACLs – Run-time re-configuration for queues Default Capacity Scheduler supports all features © Hortonworks Inc. 2013 - Confidential Hierarchical Queues root Mrkting 20% Dev 20% Adhoc 10% Prod 80% DW 70% Dev Reserved Prod 10% 20% 70% P0 70% P1 30% Capacity Scheduler Page 15
- 16. YARN Eco-system Applications 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 applications Elastic Search – Scalable Search Cloudera Llama – Impala on YARN DataTorrent – Data Analysis HOYA – HBase on YARN © Hortonworks Inc. 2013 - Confidential There's an app for that... YARN App Marketplace! Frameworks Powered By YARN Apache Twill REEF by Microsoft Spring support for Hadoop 2 Page 16
- 17. YARN Application Lifecycle Application Client Protocol Application Client YarnClient App Specific API Resource Manager NodeManager Application Master Protocol App Container Application Master AMRMClient Container Management Protocol NMClient © Hortonworks Inc. 2013 - Confidential Page 17
- 18. BYOA – Bring Your Own App 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 Use external frameworks like Twill/REEF/Spring © Hortonworks Inc. 2013 - Confidential Page 18
- 19. YARN Future Work • ResourceManager High Availability – Automatic failover – Work preserving failover • Scheduler Enhancements – SLA Driven Scheduling, Low latency allocations – Multiple resource types – disk/network/GPUs/affinity • Rolling upgrades • Generic History Service • Long running services – Better support to running services like HBase – Service Discovery • More utilities/libraries for Application Developers – Failover/Checkpointing © Hortonworks Inc. 2013 - Confidential Page 19
- 20. Key Take-Aways • YARN is a platform to build/run Multiple Distributed Applications in Hadoop • YARN is completely Backwards Compatible for existing MapReduce apps • YARN enables Fine Grained Resource Management via Generic Resource Containers. • YARN has built-in support for multi-tenancy to share cluster resources and increase cost efficiency • YARN provides a cluster operating system like abstraction for a modern data architecture © Hortonworks Inc. 2013 - Confidential Page 20
- 21. Apache YARN The Data Operating System for Hadoop 2.0 Flexible Efficient Shared Enables other purpose-built data processing models beyond MapReduce (batch), such as interactive and streaming Increase processing IN Hadoop on the same hardware while providing predictable performance & quality of service Provides a stable, reliable, secure foundation and shared operational services across multiple workloads Data Processing Engines Run Natively IN Hadoop BATCH MapReduce INTERACTIVE Tez ONLINE HBase STREAMING Storm, S4, … GRAPH Giraph MICROSOFT REEF SAS LASR, HPA OTHERS YARN: Cluster Resource Management HDFS2: Redundant, Reliable Storage © Hortonworks Inc. 2013 - Confidential Page 21
- 22. Thank you! http://hortonworks.com/products/hortonworks-sandbox/ Download Sandbox: Experience Apache Hadoop Both 2.0 and 1.x Versions Available! http://hortonworks.com/products/hortonworks-sandbox/ Questions? © Hortonworks Inc. 2013 - Confidential Page 22
Editor's Notes
- As Arun mentioned there are less JVMs to spin up per job management (1 instead of 3) as well as the RM and NM provisioning being fasterOriginally conceived & architected by the team at Yahoo!Arun Murthy created the original JIRA in 2008 and led the PMCThe team at Hortonworks has been working on YARN for 4 years: 90% of code from Hortonworks & Yahoo!YARN based architecture running at scale at Yahoo!Deployed on 35,000 nodes for 6+ monthsMultitude of YARN applications*********************On great public example of in production use of YARN, is at Yahoo!. They outlined some performance gains in a keynote address at Hadoop Summit this year. Yahoo uses YARN for three use cases, stream processing, iterative processing and shared storage. With Storm on YARN they stream data into a cluster and execute 5 second analytics windows. This cluster is only 320 nodes, but is processing 133,000 events per second and is executing 12000 threads. Their shared data cluster uses 1900 nodes to store 2PB of data.In all, Yahoo has over 30000 nodes running YARN across over 365PB of data. They calculate running about 400,000 jobs per day for about 10 million hours of compute time. They also have estimated a 60% – 150% improvement on node usage per day.ANDAt this point, over 50,000 Hadoop nodes have been upgraded at Yahoo from Hadoop 1.0 to Hadoop 2, yielding 50% improvement in cluster utilization & efficiency.This should be a big deal in terms of potential ROI.
- Reconfig already works (HDP-1.3 and HDP-2.0).Chargeback - coming in HDP-2.1https://issues.apache.org/jira/browse/YARN-415----- Meeting Notes (11/14/13 11:07) -----POINT AND CLICK TIME BASED RESOURCE ALLOCATION
- Graph processing – Giraph, HamaStream proessing – Smaza, Storm, Spark, DataTorrentMapReduceTez – fast query executionWeave/REEF – frameworks to help with writing applicationsList of some of the applications which already support YARN, in some form.Smaza, Storm, S4 and DataTorrent are streaming frameworksVarious types of graph processing frameworks – Giraph and Hama are graph processing systemsThere’s some github projects – caching systems, on-demand web-server spin up Wave and REEF are frameworks on top of YARN to make writing applications easier
- YarnClientApplication Lifecycle Control – submit, query state, kill - .e.g Submit, followed by poll state, and optionally connect to AM for further stateCluster Information – Query node status, Scheduler queues etcAMRMCLientRequest Resources from the RM – specifies a priority (within app only), location information, and whether strict locality is required, also number of such containers.Hides the details of the protocol – which is absolute (i.e. each call to the RM must provide complete information about a priority level)Once a container is allocated, provides a utility method to link it back to the entity which generated the request (entity optionally passed in while asking for containers)Allocate call doubles as a heartbeat – must be sent out ever so often.Also provides some node level information, available resources, etcNMClientOnce resources are obtained from the RM, an App uses this to communicate with the NM to start containers. Setup the ContainerLuanchContext – cmdLine, environment, resources.Authorization information is part of the contianers allocated by the RM – to prevent unauthorized usageOnly a single process can be launched on an allocated container, and one must be within a certain period otherwise the allocation times outContainer must launch a process with a certain time after being allocatedOnly a single process can be launched on an allocated container
- HA and work preserving – being actively worked upoin by the communitiy.Scheduler – Additional resources – specifically disk / network. Gang schedulingRolling upgrades – upgrading a cluster typically involves downtime. NM forgets containers across restartsLong Running – Enhandcement to log handling, security, multiple tasks per container, container resizingHA and work preserving restart are still being worked on in the community – YARN-128 and YARN-149.On scheduling – there’ve been requests for gang scheduling, meeting SLAs. Also TBD is support for scheduling additional resource types – disk/ network.Rolling Upgrades – some work pending. Big piece here, which ties in with work preserving restart – restarting a NodeManager should not cause processes started by the previous NM to be killedLong Running Services support – handling logs, security – specifically token expiryAdditional utility libraries to help AppWriters – primarily geared towards checkpointing in the AM, app history handling
- The first wave of Hadoop was about HDFS and MapReduce where MapReduce had a split brain, so to speak. It was a framework for massive distributed data processing, but it also had all of the Job Management capabilities built into it.The second wave of Hadoop is upon us and a component called YARN has emerged that generalizes Hadoop’s Cluster Resource Management in a way where MapReduce is NOW just one of many frameworks or applications that can run atop YARN. Simply put, YARN is the distributed operating system for data processing applications. For those curious, YARN stands for “Yet Another Resource Negotiator”.[CLICK] As I like to say, YARN enables applications to run natively IN Hadoop versus ON HDFS or next to Hadoop. [CLICK] Why is that important? Businesses do NOT want to stovepipe clusters based on batch processing versus interactive SQL versus online data serving versus real-time streaming use cases. They're adopting a big data strategy so they can get ALL of their data in one place and access that data in a wide variety of ways. With predictable performance and quality of service. [CLICK] This second wave of Hadoop represents a major rearchitecture that has been underway for 3 or 4 years. And this slide shows just a sampling of open source projects that are or will be leveraging YARN in the not so distant future.For example, engineers at Yahoo have shared open source code that enables Twitter Storm to run on YARN. Apache Giraph is a graph processing system that is YARN enabled. Spark is an in-memory data processing system built at Berkeley that’s been recently contributed to the Apache Software Foundation. OpenMPI is an open source Message Passing Interface system for HPC that works on YARN. These are just a few examples.