Key trends in Big Data and new reference architecture from Hewlett Packard Enterprise / Gilles Noisette (Hewlett Packard)
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The document discusses HPE's Big Data Reference Architecture (BDRA) and its modern, flexible approach to Hadoop implementation, highlighting its scalability, efficiency, and support for new features like YARN labeling and HDFS tiering. It emphasizes the challenges of managing big data infrastructure, including siloed data and the need for improved elasticity and power efficiency. Case studies from companies like Yahoo and eBay illustrate the practical applications and advantages of utilizing HPE’s optimized systems in big data environments.
![Hadoop YARN Labelling
Running applications on particular set of nodes
YARN Labelling (Node-labels / Hadoop 2.6 / jira YARN-796)
Capability to create groups of similar nodes to run different types of
applications with different workload, each, on the most appropriate group
of node
• Admin tags nodes with labels (e.g.: GPU, Storm)
− One node can have more than one label (e.g.: GPU, m710)
• Applications can include labels in container requests
Enabling the next Generation of Hadoop Applications . . .
NodeManager
[Storm]
Application
Master
I want a GPU
NodeManager
[GPU, m710]
HPE Moonshot cartridge
NodeManager
[Analytic, XL170r]
HPE Apollo blades](https://image.slidesharecdn.com/6-160115112002/85/Key-trends-in-Big-Data-and-new-reference-architecture-from-Hewlett-Packard-Enterprise-Gilles-Noisette-Hewlett-Packard-7-320.jpg)
![HOT COLD
Independent scaling of compute and storage
[ HPE ProLiant DL380 Gen9 ] vs [ HPE Moonshot for Computing + HPE ProLiant Apollo 4200 for Storage ]
HPE Big Data Reference Architecture
Traditional
Architecture
2.8x compute
97% of the storage capacity
4x the memory
1.6x compute
1.5x the storage capacity
2.5x the memory
90% of the compute
2.1x the storage capacity
1.5x the memory](https://image.slidesharecdn.com/6-160115112002/85/Key-trends-in-Big-Data-and-new-reference-architecture-from-Hewlett-Packard-Enterprise-Gilles-Noisette-Hewlett-Packard-18-320.jpg)
Key trends in Big Data and new reference architecture from Hewlett Packard Enterprise / Gilles Noisette (Hewlett Packard)
- 1. A modern, flexible approach to Hadoop implementation HPE Big Data Reference Architecture Gilles Noisette HPE EMEA Big Data Center Of Excellence November 2015
- 2. Agenda • Big Data IT infrastructure trends • Hadoop Evolution & Architecture trends • Hadoop YARN Labelling • Hadoop Storage Tiering • New HPE Architecture approach to Big Data • HPE Big Data Reference Architecture • Scaling Hadoop more efficiently • HPE BDRA Components • HPE BDRA in a virtualized context • HPE Big Data Architecture long term view
- 3. IT infrastructures must evolve to handle Big Data demands • Multiple silos with multiple copies of the same data • Difficult to standardize on a consistent server architecture • Less elastic than other virtualized or converged infrastructure • Large scale makes density, cost and power problematic Challenges
- 5. The Pace of Change
- 6. The Pace of Change And how people are buying Hadoop is changing also….
- 7. Hadoop YARN Labelling Running applications on particular set of nodes YARN Labelling (Node-labels / Hadoop 2.6 / jira YARN-796) Capability to create groups of similar nodes to run different types of applications with different workload, each, on the most appropriate group of node • Admin tags nodes with labels (e.g.: GPU, Storm) − One node can have more than one label (e.g.: GPU, m710) • Applications can include labels in container requests Enabling the next Generation of Hadoop Applications . . . NodeManager [Storm] Application Master I want a GPU NodeManager [GPU, m710] HPE Moonshot cartridge NodeManager [Analytic, XL170r] HPE Apollo blades
- 8. YARN Labels are used in production YARN Labelling case studies Vinod Vavilapalli – @Tshooter Yahoo! uses machines with GPUs on #Hadoop clusters (#YARN) to model 'beautiful' images on Flickr. #hadoopsummit 1:43 AM - 16 Apr 2015 Vinod Vavilapalli – @Tshooter .@pcnudde talking about #Yahoo using custom #Hadoop #YARN apps together with Node labels / High CPU machines for learning. #hadoopsummit 1:49 AM - 16 Apr 2015 Yahoo uses YARN labels eBay cluster use YARN labels to • Separate Machine Learning workloads from regular workloads • Separate licensed software to some machines • Enable GPU workloads • Separate organizational workloads Mayank Bansal, ebay
- 9. Hadoop Storage tiering Hadoop Architecture trends HDFS Tiering / Heterogeneous Storage Tiers (HDFS-2832) Allows a single cluster to have multiple storage tiers such as ARCHIVE, DISK, SSD, RAM-disk. Awareness of storage media allow HDFS to make better decisions about the placement of block data with input from applications. Distribution of replicas could be based on its performance and durability requirements. • Phase2: –HDFS-5682 - Application APIs for heterogeneous storage –HDFS-7228 - SSD storage tier –HDFS-5851 - Memory as a storage tier HDFS Archival Storage Design (HDFS-6584) – Introduces a new concept of storage policies. For accommodating future storage technology and different cluster characteristics, cluster administrators will be able to modify the predefined storage policies and/or define custom storage policies. – Data policy names : Very Hot Hot Warm Luke Warm Cold
- 10. Ebay use Tiered Storage for its Hadoop cluster HDFS Tiering case study 40 PB / 2000 nodes cluster was getting full HDFS Tiering features • Data reside on same cluster in a standard HDFS • Data could easily move back and forth, to and from, the Archive • Tiered storage is operated using storage types and storage policies • Archival policy is based on access pattern – Antony Benoy, ebay 40 PB / 2000 nodes DISK 10 PB / 48 nodes ARCHIVAL HDFS
- 11. Hadoop gets asymmetric but I thought we were taking the work to the data… B App L1 L1 L1 Isolate A A A nodes labels Hot All replicas on DISK Warm 1 replica on DISK, others on ARCHIVE Cold All replicas on ARCHIVE Hadoop cluster DISK DISK DISK DISK DISK DISK DISK DISK DISK ARCHIVE ARCHIVE ARCHIVE ARCHIVE ARCHIVE ARCHIVE ARCHIVE ARCHIVE ARCHIVE Yarn Labels Allows applications running in yarn containers to be constrained to designated nodes in the cluster HDFS Tiering Allows the creation of pools of storage for SSD, HDD and Archive, RAM-disk, leveraging different server configurations What about Data Locality ?
- 12. New complementary approach to address Big Data demands Storage Optimized Servers
- 13. Benefits of HPE Big Data Reference Architecture HPE Moonshot and Apollo servers address a variety of enterprise big data needs Cluster consolidation Multiple big data environments can directly access a shared pool of data Flexibility to scale Scale compute and storage independently Maximum elasticity Rapidly provision compute without affecting storage Breakthrough economics Significantly better density, cost and power through workload optimized components
- 14. DFSIO testing on Big Data Reference architecture Better numbers with optimized IO Servers for HDFS
- 15. HPE Big Data Reference Architecture Hadoop and its ecosystem take advantage of the BDRA 17 Ethernet Network Switches East - West Networking Impala
- 16. HPE Hadoop Traditional vs HPE Big Data Reference Architecture 2X Hadoop MapReduce performance with the same footprint 2.5X HBase performance with the same footprint Note: Comparison configuration is ProLiant DL380 Gen9 servers 2 x Higher Density 2.4 x Memory Density 46% Less Power (Watts) Traditional architecture Big Data Reference Architecture versus
- 17. 1.5PB configuration example Comparable Hadoop performance and raw compute (SpecInt) power Compared to 2U rackmount BDRA Acquisition cost 3% lower Power 54% lower Density (total rack U) 2x density 5 year power/cooling savings (assume $.20/kWh) $472K
- 18. HOT COLD Independent scaling of compute and storage [ HPE ProLiant DL380 Gen9 ] vs [ HPE Moonshot for Computing + HPE ProLiant Apollo 4200 for Storage ] HPE Big Data Reference Architecture Traditional Architecture 2.8x compute 97% of the storage capacity 4x the memory 1.6x compute 1.5x the storage capacity 2.5x the memory 90% of the compute 2.1x the storage capacity 1.5x the memory
- 20. Hadoop performance density > 2 times better - Power consumption = 0.5 HPE Big Data Reference Architecture Scale-Out Building blocks HPE Apollo Scalable System Storage optimized servers Cost-effective industry standard storage server purpose built for big data with converged infrastructure that offers high density energy- efficient storage HPE Network Switches East – West Networking HPE Moonshot System with 45 x m710 Compute nodes HPE Apollo 2200 with 4 x XL170r Gen9 High Compute nodes Compute optimized servers Front Rear
- 21. HPE Moonshot 1500 28 2 internal switches 45 hot-plug cartridges • 1-node = 45 servers in a chassis • 4-nodes =180 servers in a chassis • HP Moonshot-45G (45 x1Gb port) • HP Moonshot-180G (180 x1Gb port) • HP Moonshot-45XG (45 x10Gb port) Web-cache 64-bit ARM m400 Remote PCs XenDesktop m700 Big Data, Hadoop Video transcoding m710p Real-time analytics Telecom, finance m800 Web-hosting 180 servers in 4.3U m350 Full WEB-infrastructure in a single chassis Dedicated hosting m300 45 Hadoop Low-power Hadoop compute nodes per enclosure !
- 22. Big Data Compute Node
- 23. Big data Storage Node HPE Apollo 4200 - Bringing Big Data storage server density to enterprise
- 24. Big data Storage Node for Backup or Archival HPE Apollo 4510 - Very High density Big Data storage server Scalable density Lower TCO Workload optimized Rack-scale storage server density Up to 5.44 PB in 42U rack Rack-scale extreme density – 5.44 PB per Rack! Cost effective 68 LFF HDDs/SSDs in 4U server chassis for low-cost, power & space efficient solutions Configuration flexibility Balance capacity, cost and throughput with flexible options for disks, CPUs , I/O and interconnects
- 25. HPE BDRA in a Virtualized context Usage example 33
- 26. HPE BDRA used for multi-tenancy or Hadoop as a Service Multi-tenancy or Hadoop as a service, are made easier when separating the data processing service and the storage management service as it brings Often based on a Virtualized environment – Better workload isolation between YARN applications – More flexibility by scaling compute and storage independently – Full elasticity on the computing side – Rapidly provision and decommission compute without affecting storage
- 27. VMDK HPE BDRA used in a fully Elastic Virtualized environment Compute and Storage nodes are virtualized in a different manner 363PARF400 3PARF400 3PARF400 VMDK VMDK Ext4 Ext4 Ext4 Hadoop DataNode Virtualization Hosts 3PARF400 3PARF400 3PARF400 3PARF400 3PARF400 3PARF400 HadoopComputeNode HadoopComputeNode HadoopComputeNode HadoopComputeNode VMDKExt4 HostVM BDRAStorageNode BDRAComputeNodes
- 28. Summarizing & HPE Big Data Architecture long term view 37
- 29. HPE Big Data Reference Architecture – The HPE BDRA is a complementary Hadoop reference Architecture that brings • Elasticity extreme elasticity brought to Hadoop • Flexibility adaptive architecture that makes IT more responsive • Efficiency scale compute and storage independantly – It takes advantage of new Hadoop trends and features like • Hadoop YARN Labels • Hadoop HDFS Tiering – The target customers are • Mature Hadoop customers who want to consolidate clusters • People who need virtualization, multi-tenancy, Elasticity or want to build a smart Data Lake • People who want to optimize the density and the power consumption (breakthrough economics) – The BDRA works with fully standard Hadoop stacks (no patches, not proprietary) • Cloudera Enterprise 5 • Hortonworks Data Platform 2 • MapR M5
- 30. HPE BDRA Optimized Compute & Storage nodes Support multiple compute and storage blocks
- 31. Converged Infrastructure benefits for Big Data Hadoop Node Labels feature (jira YARN-796) • Combined with the HPE Big Data Reference Architecture, compute nodes can be dynamically assigned as there is no need for data repartitioning • HPE contributed IP into the Hadoop trunk, working with Hortonworks • Allows scheduling of YARN containers to specific pools of nodes
- 32. HPE BDRA CI for Big Data long term view Evolve to support multiple compute and storage blocks Multi-temperate Storage using HDFS Tiering and ObjectStores Workload Optimized compute nodes to accelerate various big data software
- 33. Thankyou! Learn more on how your organization can benefit from HPE Big Data Reference Architecture HPE Big Data Reference Architecture: Overview HPE Big Data Reference Architecture: Hortonworks implementation HPE Big Data Reference Architecture: Cloudera implementation HPE Big Data Reference Architecture: MapR implementation Running HBase on the HPE Big Data Reference Architecture http://www.hpe.com/go/hadoop