Architecting virtualized infrastructure for big data presentation
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This document discusses architecting virtualized infrastructure for big data. It summarizes that big data is growing exponentially and new frameworks like Hadoop are enabling analysis of large, diverse data sets. Virtualization can simplify and optimize big data platforms by providing a unified analytics cloud that elastically provisions various data systems like Hadoop and SQL clusters on shared hardware infrastructure. This improves utilization and makes big data platforms faster and easier to deploy and manage.
Architecting virtualized infrastructure for big data presentation
- 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, videoconfercing, digital movies digital photos digital tv audio camera phones, rfid satellite images, games, scanners, twitter 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 Processing 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 No Yes, for cost Map-reduce: Hadoop 100s PB 1,000s Yes Yes, for cost and bandwidth Big-SQL: Greenplum, Aster Data, Netezza, … PB’s 100s No Yes, for cost and bandwidth No-SQL: Cassandra, hBase, … Trilions Of rows 100s Future Yes, for cost and availability In-Memory: Redis, Gemfire, Membase, … Billions of rows 10s-100s Hybrid Possible 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
- 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 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 Programming • 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- Memory 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- Memory 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