Non-Stop Hadoop Paul Scott-Murphy VP Field Techincal Service, APJ. Cloudera World Japan November 2014

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1 Non-Stop Hadoop Paul Scott-Murphy VP Field Techincal Service, APJ Cloudera World Japan November 2014

2 WANdisco Background WANdisco: Wide Area Network Distributed Computing Enterprise ready, high availability software solutions that enable globally distributed organizations to meet today s data challenges of secure storage, scalability and availability Leader in tools for software engineers: Subversion Apache Software Foundation sponsor Highly successful IPO, London Stock Exchange, June 2012 (LSE:WAND) US patented active-active replication technology granted, November 2012 Global organization 2

3 Hadoop not just batch processing The framework is growing and maturing Global Hadoop market growing at 59% CAGR in Asia 1 Key Drivers: Cost effective and fast data processing Large volumes of unstructured data Increasing demand for data analytics Top technology changes Real-time data ingest and processing Security Availability 1: 3

4 3 Key Problems For Multi Cluster Hadoop LAN / WAN 4

5 Enterprise Ready Hadoop Characteristics of Mission Critical Applications Require 100% Uptime of Hadoop SLA s, Regulatory Compliance Require HDFS to be Deployed Globally Share Data Between Data Centers Data is Consistent and Not Eventual Ease Administrative Burden Reduce Operational Complexity Simplify Disaster Recovery Lower RTO/RPO Allow Maximum Utilization of Resource Within the Data Center Across Data Centers 5

6 Breaking Away from Active/Passive What s in a NameNode Single Standby Inefficient utilization of resource Journal Nodes ZooKeeper Nodes Standby Node Performance Bottleneck Still tied to the beeper Limited to LAN scope Active / Active All resources utilized Only NameNode configuration Scale as the cluster grows All NameNodes active Load balancing Set resiliency (# of active NN) Global Consistency 6

7 Breaking Away from Active/Passive What s in a Data Center Standby Datacenter Idle Resource Single Data Center Ingest Disaster Recovery Only One way synchronization DistCp Error Prone Clusters can diverge over time Difficult to scale > 2 Data Centers Complexity of sharing data increases Active / Active DR Resource Available Ingest at all Data Centers Run Jobs in both Data Centers Replication is Multi-Directional active/active Absolute Consistency Single HDFS spans locations N Data Center support Global HDFS allows appropriate data to be shared 7

8 Multiple Clusters One Cluster Approach Example Applications HBASE RT Query Map Reduce Poor Resource Management Data Locality Issues Network Use Complex 8

9 Multiple Clusters Creating Multiple Clusters Example Applications HBASE RT Query Map Reduce Need to share data between clusters DistCp / Stale Data Inefficient use of storage and or network Some clusters may not be available 9

10 Cluster Zones Zoning for Optimal Efficiency 1 HDFS 100% Consistency 10

11 Multi Datacenter Hadoop Disaster Recovery Absolute Consistency Maximum Resource Use Lower Recovery Time/Point WAN REPLICATION Replicate Only What You Want Better Utilization of Power/Cooling Lower TCO LAN Speed Performance 11

12 Use Cases Non-Stop Hadoop

13 Non-Stop Hadoop provides unique benefits Zoning + selective replication + global consistency = Automate failover and recovery with no data loss Enable the cheaper cost of storage in Hadoop to be used in place of expensive traditional data warehousing Ensure compliance with data security regulations with selective replication Guaranteed availability of the Hadoop platform - no downtime and no single point of failure Span multiple data centers - maximize resource utilization across them Ingest data at all locations and perform processing at all locations - one logical cluster 13

14 Use Case 1: Major Global Bank Deployed Non-Stop Hadoop Global organization across 85 countries, with business groups in commercial banking, investment banking, retail and wealth management, and private banking. Time to market demands increasing technology proliferation, high delivery risks, low customer satisfaction, high incremental costs for analytics systems and limitations of processing capacity with growth of data sources. Reduced runtime of analytic jobs from 3 hours to 10 minutes Reduced TCO by 90% compared to current analytic platforms Payback period of 3 months Developed a single data platform that can provide a 360-degree view of clients, operations and products, with the capability to support production demands across the globe 14

15 Benefits for the global bank Ingest data at all locations and perform processing at all locations Load data anywhere and data is available for analysis anywhere (all one logical cluster). Span multiple data centers - maximize resource utilization across them No idle backup servers or data centers. 100% use of compute power. Asymmetric block replication allows a smaller hardware footprint in secondary data centers, reducing TCO. Automate failover and recovery with no data loss Active-active replication provides continuous hot backup and eliminates administrator error during recovery. RTO is close to 0 (recovery time), RPO is close to 0 (no data loss). 15

16 Use Case 2: Digital Media Company Deployed Non-Stop Hadoop Started with 1 cluster of 14 nodes. Added a second datacenter with 7 extra nodes for backup and real-time analytics. Constrained by capacity. Significant difference in workloads for both applications: batch processing vs real-time. Single logical cluster across sites. Automated backup and disaster recovery. Eliminated 35% overall cost for DR. Workload is distributed, bottlenecks are removed, significantly improved scalability for processing customer metrics 16

17 Benefits for the digital media company Single logical cluster across multiple sites and mixed hardware (different specs for memory, processing speed, and disk) Data is consistent and available across all sites. Virtual clusters within a cluster can be tuned for specific tasks (MapReduce vs. in-memory analytics). This can be achieved both within and across data centers. Enables heterogeneous hardware so the highest spec servers are only used where needed Automated backup and disaster recovery over a WAN is built in No spending on 3 rd party backup and recovery solutions. 35% of Hardware budget was previously for backup only. Now they get full active-active use of those servers. Workload is distributed Both batch and real-time analytics can be run against the same data at any location. Greater scalability and performance by eliminating the bottleneck of a single active NameNode in a single active data center. 17

18 Technical Overview Hadoop Powered by WANdisco

19 DConE Distributed Coordination Engine WANdisco s patented WAN capable paxos implementation Mathematically proven PAXOS Provides distributed co-ordination of File system metadata Active/Active (All locations) Create, Modify, Delete Paxos is a Shared family nothing of protocols (No Leader) for solving consensus in a network of unreliable processors. No restrictions on distance between datacenters Consensus US Patent is the granted process for time of agreeing independent on implementation one result among of Paxos a group of participants. Not based on SAN block device synchronization such as EMC SRDF SAN block replication has distance limits resulting from the inability of file systems such as NTFS and ext4 to tolerate long RTTs to block storage This problem becomes difficult when the participants or their communication medium may experience failures. Possible distribution of corrupted blocks 19

20 How DConE Works WANdisco Active/Active Replication Majority Quorum A fixed number of participants The Majority must agree for change Failure Failed nodes are unavailable Normal operation continue on nodes with quorum Recovery / Self Healing Nodes that rejoin stay in safe mode until they are caught up Disaster Recovery A complete loss can be brought back from another replica B TX id: 168 TX id: 169 Proposal Agree TX id: 170 Proposal Agree TX id: Agree TX id: 172 Agree TX id: 173 A C TX id: 168 TX id: 169 Agree TX id: 170 TX id: 171 TX id: 172 TX id: 173 TX id: 168 TX id: 169 Agree TX id: 170 Agree TX id: 171 Proposal Agree TX id: 172 Proposal Agree TX id:

21 Architecture of Non-Stop Hadoop 21

22 Use Cases Eliminate The Performance Bottleneck of a Single Active NameNode Multi Data-Center Ingest Information doesn't need to be sent to one DC and then copied back to the other using DistCP Parallel ingest methods don t require redirected data streams Ingest data at, or close to the source Global Analysis (Logs, Click Streams, etc ) Cluster Zones Efficient use of resource based on application profile HBASE, IMPALA, TEZ, MR, SPARK, etc Heterogeneous Clusters Supported Maximize Data Center Resource Utilization All datacenters can be used to run different jobs concurrently Disaster Recovery Data is as current as possible (no periodic synchs) Virtually zero downtime to recover from regional data center failure Regulatory compliance 22

23 Use Case: Heterogeneous Hardware Optimized hardware profiles for job specific tasks Batch Real-time NoSQL (HBASE) Set replication factors per sub-cluster Use at LAN or WAN scope Resilient to NameNode failures 23

24 Use Case: Sub-Clusters Maximize Resource Utilization No idle standby Isolate Dev and Test Clusters Share data not resource Carve off hardware for a specific group Prevents a bad map/reduce job from bringing down the cluster Guarantee Consistency and availability of data Data is instantly available 24

25 Non-Stop Hadoop Demonstration 25

26 Thank you ご 質 問 ご 要 望 は 下 記 にお 願 い 致 します APAC and Japan General Manager 小 川 研 之 26

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