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How To Achieve Non-Stop Hadoop
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Non-Stop Hadoop Enterprise Ready Hadoop Presentation for Big Data Meetup October 8, 2014
2 WWW.WANDISCO.COMREALIZING THE POSSIBILITIES OF BIG DATA
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
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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
Breaking Away from Active/Passive What’s in a NameNode
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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
Breaking Away from Active/Passive What’s in a Data Center
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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
1 100%
HDFS
Consistency
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Multi Datacenter Hadoop Disaster Recovery
WAN REPLICATION
Absolute Consistency Maximum Resource Use
Lower Recovery Time/Point
Replicate Only What You Want BeCer UFlizaFon of Power/Cooling
Lower TCO LAN Speed Performance
Technical Overview Hadoop Powered by WANdisco
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Periodic Synchronization DistCp
Parallel Data Ingest Load Balancer, Streaming
Multi Data Center Hadoop Today What's wrong with the status quo
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Periodic Synchronization DistCp
Multi Data Center Hadoop Today Hacks currently in use
• 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
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Parallel Data Ingest Load Balancer, Flume
Multi Data Center Hadoop Today Hacks currently in use
• 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
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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
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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• 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
How DConE Works WANdisco Active/Active Replication
TX id: 168 TX id: 169 TX id: 170 TX id: 171 TX id: 172 TX id: 173
TX id: 168 TX id: 169 TX id: 170 TX id: 171 TX id: 172 TX id: 173
TX id: 168 TX id: 169 TX id: 170 TX id: 171 TX id: 172 TX id: 173
Proposal 170
Agree 170
Agree 170
Proposal 171 Agree 172 Agree 173
Agree 171 Proposal 172 Proposal 173
B
A
C Agree 170 Agree 171 Agree 172
Agree 173
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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
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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
Use Case: Heterogeneous Hardware
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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
Use Case: Sub-Clusters
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Non-Stop Hadoop Demonstration
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Q & A
Question and Answer Feel free to submit your questions
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Thank you