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Presentation architecting virtualized infrastructure for big data
- 1. © 2009 VMware Inc. All rights reserved Architecting Virtualized Infrastructure for Big Data Richard McDougall @richardmcdougll CTO, Application Infrastructure, Big Data Lead, VMware, Inc
- 2. 2 Cloud: Big Shifts in Simplification and Optimization 2. Dramatically Lower Costs to redirect investment into value-add opportunities 3. Enable Flexible, Agile IT Service Delivery to meet and anticipate the needs of the business 1. Reduce the Complexity to simplify operations and maintenance
- 3. 3 Infrastructure, Apps and now Data… Private Public Build Run Manage Simplify Infrastructure With Cloud Simplify App Platform Through PaaS Simplify Data
- 4. 4 Trend 1/3: New Data Growing at 60% Y/Y Source: The Information Explosion, 2009 medical(imaging,( sensors( cad/cam,(appliances,(machine(data,(digital(movies( digital(photos( digital(tv( audio( camera(phones,(rfid( satellite(images,(logs,(scanners,(twi7er( Exabytes of information stored 20 Zetta by 2015 1 Yotta by 2030 Yes, you are part of the yotta generation…
- 5. 5 Data Growth in the Enterprise
- 6. 6 Trend 2/3: Big Data – Driven by Real-World Benefit
- 7. 7 Trend 3/3: Value from Data Exceeds Hardware Cost ! Value from the intelligence of data analytics now outstrips the cost of hardware • Hadoop enables the use of 10x lower cost hardware • Hardware cost halving every 18mo Big Iron: $40k/CPU Commodity Cluster: $1k/CPU Value Cost
- 8. 8 A Holistic View of a Big Data System: ETL Real Time Streams Unstructured Data (HDFS) Real Time Structured Database (hBase, Gemfire, Cassandra) Big SQL (Greenplum, AsterData, Etc…) Batch Processin g Real-Time Processing (s4, storm) Analytics
- 9. 9 Big Data Frameworks and Characteristics Framework Scale of data Scale of Cluster Computable Data? Local Disks? File System: Gluster, Isilon, etc,… 10s PB 100s Some Yes, for cost Map-reduce: Hadoop 100s PB 1,000s Yes Yes, for cost, bandwidth and availability Big-SQL: Greenplum, Aster Data, Netezza, … PB’s 100s Some Yes, for cost and bandwidth No-SQL: Cassandra, hBase, … Trilions Of rows 100s Some Yes, for cost and availability In-Memory: Redis, Gemfire, Membase, … Billions of rows 10s-100s Yes Primarily Memory
- 10. 10 Cloud Infrastructure Data Platform Private Public Developer Frameworks The Unified Analytics Cloud Platform Analytics Tools vSphere Database/DataStore Cassandra Greenplum hBase Voldemort HDFS Data PaaS PaaS Hadoop Python Madlib Cloudfoundry Data Meer Karmasphere Spring Data-Director EMC Chorus Tableau
- 11. 11 Unifying the Big Data Platform using Virtualization ! Goals • Make it fast and easy to provision new data Clusters on Demand • Allow Mixing of Workloads • Leverage virtual machines to provide isolation (esp. for Multi-tenant) • Optimize data performance based on virtual topologies • Make the system reliable based on virtual topologies ! Leveraging Virtualization • Elastic scale • Use high-availability to protect key services, e.g., Hadoop’s namenode/job tracker • Resource controls and sharing: re-use underutilized memory, cpu • Prioritize Workloads: limit or guarantee resource usage in a mixed environment Cloud Infrastructure Private Public
- 12. 12 SQLCluster Unifed Analytics Infrastructure Hadoop Cluster Private Public Big SQL A Unified Analytics Cloud Significantly Simplifies HadoopNoSQL Decision Support Cluster NoSQL Cluster ! Simplify • Single Hardware Infrastructure • Faster/Easier provisioning ! Optimize • Shared Resources = higher utilization • Elastic resources = faster on-demand access
- 13. 13 Use Local Disk where it’s Needed SAN Storage $2 - $10/Gigabyte $1M gets: 0.5Petabytes 200,000 IOPS 1Gbyte/sec NAS Filers $1 - $5/Gigabyte $1M gets: 1 Petabyte 400,000 IOPS 2Gbyte/sec Local Storage $0.05/Gigabyte $1M gets: 20 Petabytes 10,000,000 IOPS 800 Gbytes/sec
- 14. 14 VMware is Commited to be the Best Virtual platform for Hadoop ! Performance Studies and Best Practices • Studies through 2010-2011 of Hadoop 0.20 on vSphere 5 • White paper, including detailed configurations and recommendations ! Making Hadoop run well on vSphere • Performance optimizations in vSphere releases • VMware engagement in Hadoop Community effort • Supporting key partners with their distibutions on vSphere • Contributing enhancements to Hadoop ! Hadoop Framework Integration • Spring Hadoop: Enabling Spring to simplify Map-Reduce Jobs • Spring Batch: Sophisticated batch management (Oozie on steroids)
- 15. 15 Extend Virtual Storage Architecture to Include Local Disk ! Shared Storage: SAN or NAS • Easy to provision • Automated cluster rebalancing ! Hybrid Storage • SAN for boot images, VMs, other workloads • Local disk for Hadoop & HDFS • Scalable Bandwidth, Lower Cost/GB Host Hadoop OtherVM OtherVM Host Hadoop Hadoop OtherVM Host Hadoop Hadoop OtherVM Host Hadoop OtherVM OtherVM Host Hadoop Hadoop OtherVM Host Hadoop Hadoop OtherVM
- 16. 16 Performance Analysis of Big Data (Hadoop) on Virtualization 0 0.2 0.4 0.6 0.8 1 1.2 RatiotoNative 1 VM 2 VMs Ratio of time taken – Lower is Better Tested on vSphere 5.0
- 17. 17 Simplify Hetrogeneous Data Management via Data PaaS Cloud Infrastructure Data Platform Developer Analytics Tools Databases File- system Big SQL Large- Scale NoSQL In- Memor y Data PaaS – Common Data Management Layer Provisioning Management Multi-tenancy Data Discovery Import/Export Cloud Infrastructure
- 18. 18 vFabric Data Director vFabric Data Director Powers Database-as-a-Service VMware vSphere Provisioning Backup/ Restore Clone One click HA Resource Mgmt Security Mgmt Database Templates Monitor DBA App Dev IT Admin Automation Self-Service Policy Based Control DBA Existing Applications New Applications
- 19. 19 Data Systems: Databases, file systems Cloud Infrastructure Data Platform Developer Analytics Tools Databases File- system Big SQL Large- Scale NoSQL In- Memor y Unstructured Structured
- 20. 20 Technology: Databases and Data Stores for Big Data File- system Big SQL Large- Scale NoSQL In- Memory Unstructured Structured Types of Data Log files, machine generated data, documents, device data, etc… Loosely typed device data, records, events, statistics, complex relations/graphs Structured, partitionable data Structured data Techno- logies NAS, HDFS, Blob (S3, Atmos, etc..) Cassandra, hBase, Voldemort Gemfire, Redis, Membase Greenplum, Sybase IQ, Aster Data, etc,. Values Store any data, easy to scale-out, can optimize for cost Easy to scale-out, flexible and dynamic schema’s High Throughput, low latency High performance for repetitive queries. Ease of query language.
- 21. 21 Simplified Developer Experience through PaaS Cloud Infrastructure Data Platform Developer Analytics Tools Databases Platform as a Service
- 22. 22 Spring Big Data Integrations ! NoSQL Integration • Spring data for MongoDB, Gemfire, Riak, Neo4j, Blob, Cassandra ! Spring Hadoop • Announced this week at Strata! • Provides support for developing applications based on Hadoop technologies by leveraging the capabilities of the Spring ecosystem. ! Spring Batch • Integration allows Hadoop jobs and HDFS operations as part of workflow
- 23. 23 Cloud Infrastructure Data Platform Private Public Developer Frameworks The Unified Analytics Cloud Platform Analytics Tools vSphere Database/DataStore Cassandra Greenplum hBase Voldemort HDFS Data PaaS PaaS Hadoop Python Madlib Cloudfoundry Data Meer Karmasphere Spring Data-Director EMC Chorus Tableau
- 24. 24 Summary ! Revolution in Big Data is under way • Data centric applications are now critical ! Hadoop on Virtualization • Proven performance • Cloud/Virtualization values apparent for Hadoop use ! Simplify through a Unified Analytics Cloud • One Platform for today’s and future big-data systems • Better Utilization • Faster deployment, elastic resources • Secure, Isolated, Multi-tenant capability for Analytics
- 25. 25 References ! Twitter • @richardmcdougll ! My CTO Blog • http://communities.vmware.com/community/vmtn/cto/cloud ! Hadoop on vSphere • Talk @ Hadoop World • Performance Paper – http://www.vmware.com/files/.../VMW-Hadoop-Performance-vSphere5.pdf ! Spring Hadoop • http://blog.springsource.org/2012/02/29/introducing-spring-hadoop