1. Big Data Platform Building Blocks: Serengeti, Resource Management, and Virtualization Extensions Abhishek Kashyap, Pivotal Kevin Leong, VMware VAPP5762 #VAPP5762

2. 22 Agenda Big Data, Hadoop, and What It Means to You The VMware Big Data Platform Operate Clusters Simply Share Infrastructure Efficiently Leverage Existing Investment Pivotal and VMware: Partnering to Virtualize Hadoop Conclusion and Q&A 3. 33 Big Data, Hadoop, and What It Means to You 4. 44 What is Hadoop? Framework that allows for distributed processing of large data sets across clusters of commodity servers Store large amount of data Process the large amount of data stored Inspired by Googles MapReduce and Google File System (GFS) papers Apache Open Source Project Initial work done at Yahoo! starting in 2005 Open sourced in 2009 there is now a very active open source community 5. 55 What is Hadoop? Storage & Compute in One Framework Open Source Project of the Apache Software Foundation Java-intensive programming required HDFS MapReduce Two Core Components Scalable storage in Hadoop Distributed File System Compute via the MapReduce distributed processing platform 6. 66 Why Hadoop? HDFS provides cheap and reliable storage on commodity hardware In-place data analysis, rather than moving from file systems to data warehouses Ability to analyze structured and unstructured data Enables better business decisions from more types of data at higher speeds and lower costs 7. 77 Use Case: Data Warehouse Augmentation / Offload Challenges Existing EDW used for low value and resource consuming ETL process Planned growth will far exceed compute capacity Hard to do analytics or even basic reporting on EDW system Objectives Reduce EDW Total Cost of Ownership Enable longer data retention to enable analytics and accelerate time to market Migrate ETL off EDW to free up compute resources 8. 88 Use Case: Retailer Trend Analysis Deep Historical Reporting for Retail Trends: Credit card company loads 10 years of data for all retailers (100s of TBs) Run Map/Reduce Job develop historical picture of retailers in a specific area Load results from Hadoop into data warehouse and further analyze with standard BI/statistics packages Why do this in Hadoop? Ability to store years of data cost effectively Data available for immediate recall (not on tapes or flat files) No need to ETL/normalize the data Data exists in its valuable, original format Offload intensive computation from DW Ability to combine structured and unstructured data 9. 99 Pivotal HD HDFS HBase Pig, Hive, Mahout Map Reduce Sqoop Flume Resource Management & Workflow Yarn Zookeeper Apache 10. 1010 Pivotal HD HDFS HBase Pig, Hive, Mahout Map Reduce Sqoop Flume Resource Management & Workflow Yarn Zookeeper Deploy, Configure, Monitor, Manage Command Center Hadoop Virtualization (HVE) Data Loader Pivotal HD Enterprise Apache Pivotal HD Enterprise 11. 1111 Pivotal HD HDFS HBase Pig, Hive, Mahout Map Reduce Sqoop Flume Resource Management & Workflow Yarn Zookeeper Deploy, Configure, Monitor, Manage Command Center Hadoop Virtualization (HVE) Data Loader Pivotal HD Enterprise Apache Pivotal HD Enterprise HAWQ Xtension Framework Catalog Services Query Optimizer Dynamic Pipelining ANSI SQL + Analytics HAWQ Advanced Database Services 12. 1212 Pivotal HD HDFS HBase Pig, Hive, Mahout Map Reduce Sqoop Flume Resource Management & Workflow Yarn Zookeeper Deploy, Configure, Monitor, Manage Command Center Data Loader Pivotal HD Enterprise Apache Pivotal HD Enterprise HAWQ Xtension Framework Catalog Services Query Optimizer Dynamic Pipelining ANSI SQL + Analytics HAWQ Advanced Database Services Spring XD Pivotal Analytics Pivotal Chorus & Alpine Miner MoreVRP Hadoop Virtualization (HVE) 13. 1313 The VMware Big Data Platform 14. 1414 The Big Data Journey in the Enterprise Stage 3: Cloud Analytics Platform Serve many departments Often part of mission critical workflow Fully integrated with analytics/BI tools Stage1: Hadoop Piloting Often start with line of business Try 1 or 2 use cases to explore the value of Hadoop Stage 2: Hadoop Production Serve a few departments More use cases Growing # and size of clusters Core Hadoop + components 10s 100s0 node Integrated Scale Standalone 15. 1515 Getting from Here to There Host Host Host Host Host Host Host Virtualization Shared File SystemData Layer Compute Layer Hadoop test/dev 16. 1616 Getting from Here to There Host Host Host Host Host Host Host Virtualization Shared File SystemData Layer Compute Layer Hadoop test/dev Hadoop production Hadoop production Hadoop experimentation 17. 1717 Getting from Here to There Host Host Host Host Host Host Host Virtualization Shared File SystemData Layer Compute Layer Hadoop test/dev HBase Hadoop production SQL on Hadoop HAWQ, Impala, Drill NoSQL Cassandra Mongo Other Spark Shark Solr Platfora 18. 1818 Benefits of Virtualization at Each Stage Stage 3: Cloud Analytics Platform Mixed workloads Right tool at the right time Flexible and elastic infrastructure Stage1: Hadoop Piloting Rapid deployment On the fly cluster resizing Flexible config Automation of cluster lifecycle Stage 2: Hadoop Production High Availability Consolidation Tiered SLAs Elastic Scaling 10s 100s0 node Integrated Scale Standalone 19. 1919 A Brief History of Project Serengeti (and Big Data at VMware) 20. 2020 Big Data Initiatives at VMware Serengeti vSphere Resource Management Hadoop Virtualization Extensions Virtualization changes for core Hadoop Contributed back to Apache Hadoop Advanced resource management on vSphere Big Data applications-specific extension to DRS Open source project Tool to simplify virtualized Hadoop deployment & operations 21. 2121 Clustered Workload Management: The Next Frontier ESXi Serengeti Hadoop Management Virtualization vCenter Source: http://www.conferencebike.com/image/generated/792.png 22. 2222 Serengeti Project History Serengeti 0.5 June 2012 Serengeti 0.6 August 2012 Serengeti 0.7 October 2012 Serengeti 0.8 April 2013 Hadoop in 10 min Highly Available Hadoop Time to insight Configuring Hadoop Compute elasticity Configuring placement and topology HBase MapR CDH4 Performance best practices Serengeti 0.9/ BDE Beta June 2013 Integrated GUI Automatic elasticity YARN/ Pivotal HD 23. 2323 Big Data Extensions: Serengeti-vCenter Integration ESXi Hadoop Management Virtualization Big Data Extensions + vCenter 24. 2424 Why Virtualize Hadoop Operate Clusters Simply Share Infrastructure Efficiently Leverage Existing Investment 25. 2525 Operate Clusters Simply Serengeti 26. 2626 What Does Nick Think About Hadoop? I dont want to be the bottleneck when it comes to provisioning Hadoop clusters I need sizing flexibility, because my Hadoop users dont know how large of a cluster they need I want to establish a repeatable process for deploying Hadoop clusters I dont really know that much about Hadoop I want to better manage the jumble of LOB Hadoop clusters in my enterprise Source: http://www.smartdraw.com/solutions/information-technology/images/nick.png 27. 2727 Choose Your Own Adventure Source: http://www.vintagecomputing.com/wp-content/images/retroscan/supercomputer_cyoa_large.jpg 28. 2828 Big Data Extensions Demo 29. 2929 Deploy Hadoop Clusters in Minutes Hadoop Installation and Configuration Network Configuration OS installation Server preparation From manual process To fully automated, using the GUI 30. 3030 How It Works BDE is packaged as a virtual appliance, which can be easily deployed on vCenter BDE works as a vCenter extension and establishes SSL connection with vCenter BDE clones VMs from the template and controls/configures VMs through vCenter Host Host Host Host Host Virtualization Platform Hadoop Node Hadoop Node vCenter Management Server Template Virtual Appliance VM Cloning 31. 3131 User-specified Customizations Using Cluster Specification File Storage configuration Choice of shared or local High Availability option Number of nodes and resource configuration VM placement policies 32. 3232 Deploy Customize Load data Execute jobs Tune configuration Scale Deploy, Manage, Run Virtual Hadoop with BDE 33. 3333 Agility and Operational Simplicity Host Host Host Host Host Host Host Virtualization Shared File SystemData Layer Compute Layer Hadoop test/dev 34. 3434 Share Infrastructure Efficiently vSphere Resource Management 35. 3535 Adult Supervision Required CLUSTERS OVER 10 NODES 36. 3636 Production Test Experimentation Dept A: recommendation engine Dept B: ad targeting Production Test Experimentation Log files Social dataTransaction data Historical cust behavior Pain Points: 1. Cluster sprawl 2. Redundant common data in separate clusters 3. Inefficient use of resources. Some clusters could be running at capacity while other clusters are sitting idle NoSQL Real time SQL On the horizon Challenges of Running Hadoop in the Enterprise 37. 3737 The Virtualization Advantage Experimentation Production Recommendation Engine Production Ad Targeting Test/Dev Production Test Production Test Experimentation Recommendation engine Ad targeting Experimentation One physical platform to support multiple virtual big data clusters 38. 3838 What Other Things Does Nick Think About Hadoop? Source: http://www.smartdraw.com/solutions/information-technology/images/nick.png I want to scale out when my workload requires it My Hadoop users ask for large Hadoop clusters, which end up underutilized I want to offer Hadoop-as-a- Service in my private cloud I want to get all Hadoop clusters into a centralized environment to minimize spend 39. 3939 Achieving Multi-tenancy Resource Isolation Control the greedy noisy neighbor Reserve resources to meet needs Version Isolation Allow concurrent OS, App, Distro versions Security Isolation Provide privacy between users/groups Runtime and data privacy required Host Host Host Host Host Host VMware vSphere + Serengeti Host 40. 4040 Combined Storage/ Compute VM Hadoop in VM VM lifecycle determined by Datanode Limited elasticity Limited to Hadoop Multi-Tenancy Storage Compute VM VM Separate Storage Separate compute from data Elastic compute Enable shared workloads Raise utilization Storage T1 T2 VM VM VM Separate Compute Tenants Separate virtual clusters per tenant Stronger VM-grade security and resource isolation Enable deployment of multiple Hadoop runtime versions Slave Node Separating Hadoop Data and Compute for Elasticity 41. 4141 Dynamic Hadoop Scaling Deploy separate compute clusters for different tenants sharing HDFS Commission/decommission compute nodes according to priority and available resources ExperimentationDynamic resourcepool Data layer Production recommendation engine Compute layer Compute VM Compute VM Compute VM Compute VM Compute VM Compute VM Compute VM Compute VM Compute VM Compute VM Compute VM Compute VM Compute VM Compute VM Compute VM Experimentation Production Compute VM Job Tracker Job Tracker VMware vSphere + Serengeti 42. 4242 Elastic Hadoop Demo 43. 4343 State, stats (Slots used, Pending work) Commands (Decommission, Recommission) Stats and VM configuration Serengeti Job Tracker vCenter DB Manual/Auto Power on/off Virtual Hadoop Manager (VHM) Job Tracker Task Tracker Task Tracker Task Tracker vCenter Server Serengeti Configuration VC state and stats Hadoop state and stats VC actions Hadoop actions Algorithms Cluster Configuration Resource Management Module 44. 4444 Combining Elasticity and Multi-tenancy Host Host Host Host Host Host Host Virtualization Shared File SystemData Layer Compute Layer Hadoop test/dev Hadoop production Hadoop production Hadoop experimentation 45. 4545 Leverage Existing Investment 46. 4646 What Is Nick Still Thinking About Hadoop? Source: http://www.smartdraw.com/solutions/information-technology/images/nick.png I want to use my existing infrastructure, not buy new hardware I want to leverage the tools I already have Hadoop on Amazon is costing too much My data is in shared storage; do I have to move it? I want a low-risk way of trying Hadoop 47. 4747 Use Storage That Meets Your Needs SAN Storage $2 - $10/Gigabyte $1M gets: 0.5 Petabytes 200,000 IOPS 8Gbyte/sec NAS Filers $1 - $5/Gigabyte $1M gets: 1 Petabyte 200,000 IOPS 10Gbyte/sec Local Storage $0.05/Gigabyte $1M gets: 10 Petabytes 400,000 IOPS 250 Gbytes/sec 48. 4848 Leveraging Isilon as External HDFS Time to results: Analysis of data in place Lower risk using vSphere with Isilon Scale storage and compute independently Data Layer Hadoop on Isilon Elastic Virtual Compute Layer 49. 4949 Hybrid Storage Model to Get the Best of Both Worlds Master nodes: NameNode, JobTracker on shared storage Leverage vSphere vMotion, HA and FT Slave nodes TaskTracker, DataNode on local storage Lower cost, scalable bandwidth Local StorageShared Storage 50. 5050 Achieving HA for the Entire Hadoop Stack Battle-tested HA technology Single mechanism to achieve HA for the entire Hadoop stack Simple to enable HA/FT HDFS (Hadoop Distributed File System) HBase (Key-Value store) MapReduce (Job Scheduling/Execution System) Pig (Data Flow) Hive (SQL) BI ReportingETL Tools ManagementServer Zookeepr(Coordination) HCatalog RDBMS Namenode Jobtracker Hive MetaDB Hcatalog MDB Server 51. 5151 Leveraging Other VMware Assets Monitoring with vCenter Operations Manager Gain comprehensive visibility Eliminate manual processes with intelligent automation Proactively manage operations Future: vCloud Automation Center, Software-defined Storage 52. 5252 Get Maximum Value from Existing Tools and Infrastructure Host Host Host Host Host Host Host Virtualization Shared File SystemData Layer Compute Layer Hadoop test/dev HBase Hadoop production SQL on Hadoop HAWQ, Impala, Drill NoSQL Cassandra Mongo Other Spark Shark Solr Platfora 53. 5353 Pivotal and VMware: Partnering to Virtualize Hadoop 54. 5454 Virtualization Benefits Multi-tenancy (users, business units) with strong vSphere-based isolation Multiple big data applications and compute engines can access common HDFS data Agility to scale Hadoop nodes at run-time Provide On-Demand Hadoop / Hadoop as a Service 55. 5555 Busting Myths About Virtual Hadoop Virtualization will add significant performance overhead Virtual Hadoop performance is comparable to bare metal Hadoop cannot work with shared storage Shared storage is a valid choice, especially for smaller clusters Virtualization necessitates the use of shared storage Shared storage is useful for HA, but virtual Hadoop on DAS is very common Hadoop distribution vendors dont support virtual implementations Pivotal HD is jointly tested, certified, and supported on vSphere Source: http://www.psychologytoday.com/files/u637/good-grief-charlie-brown.jpg, http://images2.wikia.nocookie.net/__cb20101130042247/peanuts/images/6/6d/Joe-cool-1-.jpg 56. 5656 Native versus Virtual Platforms, 32 hosts, 16 disks/host Source: http://www.vmware.com/resources/techresources/10360 57. 5757 Harness the Flexibility of Virtualization Hadoop Virtualization Extensions 58. 5858 You Need Hadoop Virtual Extensions Topology Extensions: Enable Hadoop to recognize additional virtualization layer for read/write/balancing for proper replica placement Enable compute/data node separation without losing locality Elasticity Extensions: Ability to dynamically adjust resources allocated (CPU, memory, map/reduce slots) to compute nodes Enables runtime elasticity of Hadoop nodes 59. 5959 Hadoop Virtual Extensions Topology Extensions: Enable Hadoop to recognize additional virtualization layer for read/write/balancing for proper replica placement Enable compute/data node separation without losing locality Elasticity Extensions: Ability to dynamically adjust resources allocated (CPU, memory, map/reduce slots) to compute nodes Enables runtime elasticity of Hadoop nodes 60. 6060 Current Hadoop Network Topology Not Virtualization Aware H1 H2 H3 R1 H4 H5 H6 R2 H7 H8 H9 R3 H10 H11 H12 R4 D1 D1 / D = data center R = rack H = host Multiple replicas may end up on same Physical Host in Virtual environments 61. 6161 HVE Adds a New Layer in Hadoop Network Topology D = data center R = rack NG = node group HG = node N13N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 N12 R1 R2 R3 R4 D1 D2 / NG1 NG2 NG3 NG4 NG5 NG6 NG7 NG8 62. 6262 Virtualization Aware Replica Placement Policy During Write Updated Policies: No replicas are placed on the same node or nodes under the same node group 1st replica is on the local node or one of nodes under the same node group of the writer 2nd replica is on a remote rack of the 1st replica 3rd replica is on the same rack as the 2nd replica Remaining replicas are placed randomly across rack to meet minimum restriction 63. 6363 Hadoop Virtual Extensions Topology Extensions: Enable Hadoop to recognize additional virtualization layer for read/write/balancing for proper replica placement Enable compute/data node separation without losing locality Elasticity Extensions: Ability to dynamically adjust resources allocated (CPU, memory, map/reduce slots) to compute nodes Enables runtime elasticity of Hadoop nodes 64. 6464 HVE Achieves Vertical Scaling of Hadoop Nodes VMs boundary is elastic already VM resource type: reserved (low limit) and maximum (up limit) If resource is tight, VMs compete for resource (between reserved and maximum) based on shares Stealing resources without notifying Apps sometimes cause very bad performance Thus, need to figure out a way to make app-aware resource change Current Hadoop resource schedulers are static MRV1 slots YARN resources (Memory for now, YARN-2 will include CPUs) HVE Elasticity patches Enable flexible resource model for each Hadoop node Change resources at runtime 65. 6565 Pivotal HD is the Best Suited for Virtualization Only distribution that ships with VMware Hadoop Virtual Extensions (HVE) Fully tested Ensures proper HDFS replication placement on vSphere Improves MapReduce performance through better data locality on vSphere Allows dynamic scaling of Hadoop Compute Nodes Certified on vSphere VMware Serengeti deploys and scales Pivotal HD on vSphere out- of-box Only YARN based distribution supported by Serengeti 66. 6666 Conclusion 67. 6767 Big Data Platform Building Blocks and Key Benefits Serengeti vSphere Resource Management Hadoop Virtualization Extensions Partnership 68. 6868 Thank You projectserengeti.org gopivotal.com/pivotal-products/pivotal-data-fabric/pivotal-hd Kevin Leong kleong@vmware.com Abhishek Kashyap akashyap@gopivotal.com 69. THANK YOU 70. Big Data Platform Building Blocks: Serengeti, Resource Management, and Virtualization Extensions Abhishek Kashyap, Pivotal Kevin Leong, VMware VAPP5762 #VAPP5762