Reference Architecture and Best Practices for Virtualizing Hadoop Workloads Justin Murray VMware

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1 Reference Architecture and Best Practices for Virtualizing Hadoop Workloads Justin Murray ware

2 2

3 Agenda The Hadoop Journey Why Virtualize Hadoop? Elasticity and Scalability Performance Tests Storage Reference Architectures Isilon Architecture and Benefits vsphere Big Data Extensions Conclusion and Q&A

4 The Customer Journey with Hadoop

5 The Hadoop Journey Integrated Scale 0 node 10 s 100 s

6 Why Virtualize Hadoop?

7 Customer Example: Enterprise Adoption of Hadoop Production Production SLA: Jobs complete in 15 minutes Bandwidth limited to 30 nodes at peak Test Log files Test Issues: 1. Multiple clusters to manage 2. Redundant common data in separate clusters 3. Peak compute and I/O resource is limited to number of nodes in each independent cluster Experimentation Experimentation Dept A: recommendation engine Dept B: ad targeting Transaction data Social data Historical cust behavior

8 What if you could Recommendation engine Production Ad targeting Production One physical platform to support multiple virtual big data clusters Experimentation Test/Dev Test Test Production recommendation engine Production Ad Targeting Experimentation Experimentation Without Virtualization Multiple copies of common data (e.g. historical data, log data etc.) in separate Hadoop clusters Consolidate and virtualize Single copy of common data results in less storage requirements while maintaining good isolation between different MapReduce clusters

9 Big Data Extensions Value Propositions Operational Simplicity with Performance Maximize Resource Utilization Architect Scalable Platform Rapid Deployment Self service tools Performance Elastic scaling Avoid dedicated hardware -based isolation Increase resource utilization True multi-tenancy Deployment choice Maintain management flexibility at scale Control Costs Leverage vsphere features

10 Hadoop 2.0 Yet Another Resource Negotiator

11 A Virtualized Hadoop 2.0 Cluster

12 vsphere Big Data Extensions - Deploy Hadoop Clusters in Minutes Server preparation OS installation Network Configuration Hadoop Installation and Configuration From a manual process To fully automated, using the GUI

13 Elastic, Multi-Tenant Hadoop with Virtualization Hadoop Node Compute T1 T2 Combined Compute and Storage Storage Storage Unmodified Hadoop node in a lifecycle determined by Datanode Limited elasticity Separate Compute from Storage Separate compute from data Stateless compute Elastic compute Separate Virtual Compute Clusters per tenant Separate virtual compute Compute cluster per tenant Stronger -grade security and resource isolation

14 Performance and Reference Architectures

15 Native vs. Virtual, 32 hosts, 16 disks per host Source:

16 Reference Architecture: 32-Server Performance Test Up to four s per server vcpus per fit within socket size (e.g. 4 s x 4 vcpus, 2 X 8) Memory per - fit within NUMA node size 2013 Tests done using Hadoop

17 I/O Profile of a Hadoop MapReduce Job (TeraSort example application) Map Task Job Map Task Map Task Map Output file.out Reduce Reduce Spills Sort Map Task DFS Input Data 12% of Bandwidth Spills & Logs spill*.out 75% of Disk Bandwidth Shuffle Map_*.out Combine Intermediate.out DFS Output Data 12% of Bandwidth HDFS

18 The Combined Model Standard Deployment Hadoop Virtual Node NodeManager Datanode Virtualization Host OS Image DK DK DK DK DK DK DK DK DK Shared storage SAN/NAS Local disks

19 Combined Model Two Virtual Machines on a Host Server Hadoop Virtual Node 1 NodeManager DataNode Hadoop Virtual Node 2 NodeManager DataNode Virtualization Host OS Image DK OS Image DK DK DK DK DK DK DK DK DK Shared storage SAN/NAS Local disks

20 The Data-Compute Separation Deployment Model Hadoop Virtual Node 1 NodeManager Hadoop Virtual Node 2 DataNode Virtualization Host OS Image DK OS Image DK DK DK DK DK DK DK DK DK DK DK DK DK DK DK DK DK Shared storage SAN/NAS Local disks

21 Data Paths: Combined vs Data- Compute Separation Combined Model Separated Model Hadoop Virtual Node Hadoop Virtual Node 1 Hadoop Virtual Node 2 NodeManager NodeManager DataNode DataNode Virtualization Host Virtual Switch Virtualization Host Virtual Switch

22 Alternative Storage for Data/Compute Separation DataNode NodeManager Hadoop Virtual Node 1 Hadoop Virtual Node 2 Virtualization Host OS Image DK OS Image DK DK DK DK DK DK DK DK DK DK DK Shared storage SAN/NAS Local disks

23 DK Isolation for Performance NodeManager Datanode Hadoop Virtual Node 1 Hadoop Virtual Node 2 Virtualization Host OS Image DK OS Image DK DK DK DK DK DK DK DK Shared storage SAN/NAS Local disks for Temp Data JBOD Local disks for HDFS Data JBOD

24 Data/Compute Separation With Isilon ResourceManager NodeManager NodeManager Hadoop Virtual Node 1 Hadoop Virtual Node 2 Hadoop Virtual Node 3 Virtualization Host OS Image OS DK Image OS DK Image DK DK DK DK Temp Shared storage SAN/NAS Temp NN NN NN NN NN data node NN Isilon

25 Larger Architecture with Data Compute Separated 25

26 Hybrid storage model - the best of both worlds Shared Storage Local Storage NFS for HDFS Master nodes NameNode, ResourceManager, ZooKeeper etc. on shared storage Leverage vsphere vmotion, HA and FT Worker nodes NodeManager/DataNode on local storage Lower cost, scalable bandwidth Temp data is written to local storage for best performance NFS storage for HDFS data is a very good alternative to local

27 vsphere Big Data Extensions and Project Serengeti

28 Big Data Extensions - Highlights Serengeti Serengeti Open source project Tool to simplify virtualized Hadoop deployment & operations Virtualization changes for core Hadoop Contributed back to Apache Hadoop Hadoop Virtualization Extensions (HVE) Virtual Hadoop Manager (VHM) Advanced resource management on vsphere

29 Introducing vsphere Big Data Extensions (BDE) vcenter Plugin Hadoop as a Service with vcloud Automation Center

30 Brief Tour of Big Data Extensions

31 One Click to Scale out the Cluster on the Fly

32 BDE Allows Flexible Configurations Number of nodes and resource configuration Storage configuration Choice of shared or local High Availability option placement policies

33 External HDFS : Simple to Set Up

34 How BDE works

35 vsphere Configuration Provision the virtual machines at the right size Reserve 6% of physical memory on the ESXi Server for vsphere usage Avoid over-commitment Enable NUMA and keep the virtual machine memory and cpu size within the NUMA node NUMA scheduler is important for virtualized Hadoop performance Poor configuration can result in performance degradation Data preferably should be distributed across NUMA nodes

36 ware vsphere BDE and Hadoop Resources ware vsphere BDE web site Virtualized Hadoop Performance with ware vsphere Benchmarking Case Study of Virtualized Hadoop Performance on vsphere 5 Hadoop Virtualization Extensions (HVE) : Apache Hadoop High Availability Solution on ware vsphere 5.1

37 Conclusions Hadoop workloads work very well on ware vsphere Various performance studies have shown that any difference between virtualized performance and native performance is minimal Follow the general best practice guidelines that ware has published vsphere Big Data Extensions enhances your Hadoop experience on the ware virtualization platform Rapid provisioning tool for deployment of Hadoop components in virtual machines Algorithms for best layout of your Hadoop data and cluster components are built into the BDE HVE components Design patterns such as data-compute separation can be used to provide elasticity of your Hadoop cluster on demand. User self service available with Hadoop using tools such as vcloud Automation Center integrated with BDE

38 Thank You Justin Murray

39 Backup Slides

40 Today s Challenges on Hadoop Infrastructure Fixed compute and storage coupling Compute Node Compute leads to low utilization and inflexibility Node Compute Node Compute Node Compute and storage linked together Data Node Data Node Data with fixed ratio based on the hardware Node Storage Server Node Server specification Server Server Not all jobs are created equal (data vs. compute intensive) Inflexible infrastructure leads to waste Too little compute power slow processing Too much compute power sitting idle Problem compounds with larger clusters So what happens?

41 Getting more out of your infrastructure Compute Node Storage Node Server Compute layer Decouple the linkage between compute and storage Stateless compute can grow and shrink elastically Data locality is preserved, place the compute where data resides Run Hadoop Run other workloads Extra compute capacity can be used for other workloads Compute Compute Compute Compute Compute Compute Compute Storage layer Storage Storage Compute Storage Compute Storage Compute Storage Compute Storage Storage

42 Elasticity and Scalability

43 Elastic Scalability & Multiple Workloads Deploy separate compute clusters for different tenants sharing HDFS. Commission/decommission compute nodes according to priority and available resources Resource Manager Resource Manger Compute layer Compute Compute Compute Compute Compute Compute Compute Compute Dynamic resource pool Experimentation Production Production recommendation engine Data layer ware vsphere + Big Data Extensions

44 Hadoop 1.0 Job Input File JobTracker NameNode Split 1 64MB Split 2 64MB Worker Node 1 Worker Node 2 Worker Node 3 TaskTracker TaskTracker TaskTracker Split 3 64MB Task - 1 Task - 2 Task - 3 DataNode DataNode DataNode Block 1 64MB Block 2 64MB Block 3 64MB

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