How To Achieve Non-Stop Hadoop

1. Non-Stop Hadoop
Enterprise Ready Hadoop
Presentation for Big Data Meetup
October 8, 2014

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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 locations
– San Ramon (CA)
– Chengdu (China)
– Tokyo (Japan)
– Boston (MA)
– Sheffield (UK)
– Belfast (UK)

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Customers

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Non-Stop Hadoop
Non-Intrusive Plugin
Provides Continuous Availability
In the LAN / Across the WAN
Active/Active

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3 Key Problems For Multi Cluster Hadoop
LAN / WAN

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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

7. Breaking Away from Active/Passive
What’s in a NameNode
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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

8. Breaking Away from Active/Passive
What’s in a Data Center
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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

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

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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
Multiple Clusters

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Cluster Zones
Zoning for Optimal Efficiency
1
HDFS
100%
Consistency

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

13. Technical Overview
Hadoop Powered by WANdisco

14. Multi Data Center Hadoop Today
What's wrong with the status quo
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Periodic Synchronization
DistCp
Parallel Data Ingest
Load Balancer, Streaming

15. Multi Data Center Hadoop Today
Hacks currently in use
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Periodic Synchronization
DistCp
• Runs as Map reduce
• DR Data Center is read only
• Over time, Hadoop clusters
become inconsistent
• Manual and labor intensive
process to reconcile differences
• Inefficient use of the network

16. Multi Data Center Hadoop Today
Hacks currently in use
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Parallel Data Ingest
Load Balancer, Flume
• Hiccups in either of the Hadoop
cluster causes the two file
systems to diverge
• Potential to run out of buffer when
WAN is down
• Requires constant attention and
sys-admin hours to keep running
• Data created on the cluster is not
replicated
• Use of streaming technologies
(like flume) for data redirection are
only for streaming

17. PAXOS
Paxos is a family of protocols for solving consensus in a network of
unreliable processors.
Consensus is the process of agreeing on one result among a group of
participants.
This problem becomes difficult when the participants or their
communication medium may experience failures.
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DConE
Distributed Coordination Engine
• WANdisco’s patented WAN capable paxos implementation
– Mathematically proven
– Provides distributed co-ordination of File system metadata
• Active/Active (All locations)
• Create, Modify, Delete
• Shared nothing (No Leader)
• No restrictions on distance between datacenters
– US Patent granted for time independent implementation of Paxos
• 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
– Possible distribution of corrupted blocks

18. How DConE Works
WANdisco Active/Active Replication
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• 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
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Architecture of a Non-Stop Hadoop

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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, Storm, Map Reduce, 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

21. Use Case: Heterogeneous Hardware
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• 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

22. Use Case: Sub-Clusters
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• 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

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Non-Stop Hadoop Demonstration

24. Question and Answer
Feel free to submit your questions
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Q & A

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Thank you