wandisco non-stop hadoop: phxdataconference presentation oct 2014

Non-Stop Hadoop: Adding R-A-S to your Hadoop clusters using a Globally Consistent HDFS Namespace Presented by Chris Almond @ Phoenix Data Conference October 2014

2 WWW.WANDISCO.COMREALIZING THE POSSIBILITIES OF BIG DATA

For Today Who am I and what is this about?

At Work: [email protected]

On line: www.linkedin.com/in/chrisalmond/

www.twitter.com/calmo

Session Description: Hadoop has quickly evolved into the system of choice for storing and processing Big Data, and is now widely used to support mission-critical applications that operate within a ‘data lake’ style infrastructures. A critical requirement of such applications is the need for continuous operation even in the event of various system failures. This requirement has driven adoption of multi-data center Hadoop architectures, a.k.a geo-distributed or global Hadoop. In this session we will provide a brief introduction to WANdisco, then dig into how our Non-Stop Hadoop solution addresses real world use cases, and also a show live demonstration of Non-Stop namenode operation across two WAN connected hadoop clusters.


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)


Customers


Non-Stop Hadoop

Non-Intrusive Plugin

Provides Continuous Availability In the LAN / Across the WAN

Active/Active


Key Problem For Multi Cluster Hadoop LAN / WAN

+ =


•  Require Continuous Availability –  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

Enterprise Ready Hadoop Characteristics of Mission Critical Applications


The difficulty realizing the data lake…


… is that data spans the entire world


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


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


One Cluster Aproach

•  Example Applications

–  HBASE –  RT Query –  Map Reduce

•  Poor Resource Management

–  Data Locality Issues –  Network Use –  Complex

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

Multiple Clusters


Cluster Zones Zoning for Optimal Efficiency

1 100%

HDFS

Consistency


Multi Datacenter Hadoop Disaster Recovery

WAN REPLICATION

Absolute Consistency Maximum Resource Use

Lower Recovery Time/Point

Replicate Only What You Want BeEer UHlizaHon of Power/Cooling

Lower TCO LAN Speed Performance

Technical Overview Hadoop Powered by WANdisco


Periodic Synchronization DistCp

Parallel Data Ingest Load Balancer, Streaming

Multi Data Center Hadoop Today What's wrong with the status quo


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 us of the network


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


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.








PAXOS

Leslie Lamport: Any node that proposes aDer a decision has been reached must communicate with a node in the majority. The protocol guarantees that it will learn the previously agreed upon value from that majority. hEp://research.microsoW.com/en-‐us/um/people/lamport/pubs/pubs.html

hEp://research.microsoW.com/en-‐us/um/people/lamport/pubs/lamport-‐paxos.pdf

hEp://css.csail.mit.edu/6.824/2014/papers/paxos-‐simple.pdf








PAXOS

“Contrary to conventional wisdom, we were able to use Paxos to build a highly available system that provides reasonable latencies for interactive applications while synchronously replicating writes across geographically distributed datacenters.“ http://www.cidrdb.org/cidr2011/Papers/CIDR11_Paper32.pdf …


•  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



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


Architecture of a Non-Stop Hadoop


•  Data is as current as possible (no periodic synchs)

•  Doesn’t require monitoring and consistency checking

•  Virtually zero downtime to recover from regional data center failure

•  Regulatory compliance

Use Case: Disaster Recovery Use Cases


•  Ingest and analyze anywhere •  Analyze Everywhere

–  Fraud Detection –  Equity Trading Information –  New Business –  Etc…

•  Backup Datacenter(s) can be used for work

–  No idle resource

Use Case: Multi Data-Center Ingest and multi-tenant workloads


•  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: Zones


•  Mixed Hardware Profiles –  Memory, Disk, CPU –  Isolate memory-hungry

processing (Storm/Spark) from regular jobs

•  Share data, not processing –  Isolate lower priority (dev/

test) work

Use Case: Heterogeneous Hardware (Zones) In memory analytics


Data Ocean

Feeder Site

AccounHng Mart

Banking Mart

•  Data Marts –  Restrict access to relevant

data –  Create Quick Clusters

•  Feeder Sites (Data Tributaries) –  Ingest Only

Data Reservoir Use Cases


•  Basel III –  Consistency of Data

•  Data Privacy Directive –  Data Sovereignty

•  data doesn’t leave country of origin

Compliance

RegulaHon

Guidelines

Regulatory Compliance


5 Reasons your Hadoop Deployment Needs Wandisco


Non-Stop Hadoop Demonstration