1.
Shubhendu Tripathi
PSE – Red Hat
GlusterFS and Hadoop

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Agenda
● What is BigData
● Hadoop and its Evolution
● Hadoop Acrchitecture and Components
● Hadoop and GlusterFS (glusterfs-hadoop plugin)
● Advantages of using GlusterFS with Hadoop
● References

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What is BigData
● Software solutions mostly capture, maintain and manage data
● Storing data
● Processing data
● Growing data size in current world – big data generators
● Sensors
● CC Cam
● Social networks
● Online shopping portals
● Airlines
● Hospitality

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Agenda
● What is BigData
● Hadoop and its Evolution
● Hadoop Acrchitecture and Components
● Hadoop and GlusterFS (glusterfs-hadoop plugin)
● Advantages of using GlusterFS with Hadoop
● References

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What is BigData
● 90% of total data today we have, got generated in last 2 years
● 1990
● HDD: 1-20 GB, RAM: 14-128 MB, Speed: 10kbps
● 2014
● HDD: 0.5-1 TB, RAM: 1-16 GB, Speed: 100 mbps
●
● 3 Factors which define BigData
● Volume
● Velocity
● Variety (unstructured and semi structured data)

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What is BigData
● SAN – Storage Area Network
● One option – Store the data on data centers and get them on need
basis and computation performed on them to process
● Computation is processor bound and a limit on the same
● As the size of the data increases we need more and more
computation as well and its not possible to perform the same on local
machine
● Solution - sending computation to the storage node and get the
processed data is better option (size of computation would be small)

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Hadoop Evolution
● Started with Google – white papers
● GFS (Google File System) 2003 - Storage
● MapReduce 2004 – Computation
● Yahoo
● HDFS (Hadoop Distributed File System) - 2006,7
● MapReduce (Computation mechanism) – 2007,8
● Doug Cutting and Michael Cafarrela from Yahoo
● Logo Elephant
● Apache foundation (2005 Yahoo donated)

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Hadoop Architecture /
Components
● Framework of tools – not an application in entirety
● Used for supporting running of applications on BigData
● Opensource'd set of tools distributed under Apache license
● Traditional Approach for handling huge data
● Powerful computer with big storage and computation capacity
● Limited by processing power of the computer with growing data
● Hadoop approach
● Break up data into smaller pieces and distribute to multiple
computers
● Breaks the computation as well into smaller pieces and distributes
them
● Combined results returned back

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Hadoop Architecture /
Components
● Map Reduce
● Job Tracker
● Task Tracker
● HDFS
● Name Node
● Data Node
● Applications contact the master node, a task is formed and submitted
to the Task Tracker
● Task Tracker maintains a queue of the tasks and gets them
processed using the Task Tracker and Data Nodes
● Consolidates the result and sends back to the application

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Hadoop Architecture /
Components
● Hadoop works on a distributed model
● Numerous low cost computers – commodity hardware
● Hadoop components
● Slaves
– Task Tracker – process smaller piece of task assigned
– Data Node – manage the piece of data distributed to this node
● Master
– Job Tracker – tracks the overall task
– Name Node – maintains the index of the data blocks stored on
different nodes
– Task Tracker
– Data Node

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Hadoop Architecture /
Components
Task
Tracker
Data
Node
Job
Tracker
Name
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Applications
Master
Slaves
Queue

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Hadoop Architecture /
Components
Task
Tracker
Data
Node
Job
Tracker
Name
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Applications
Master
Slaves

13.
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Hadoop Architecture /
Components
Task
Tracker
Data
Node
Job
Tracker
Name
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Applications
Master
Slaves

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Hadoop and GlusterFS
● GlusterFS is a general purpose scale-out distributed file-
system supporting thousands of clients
● Aggregates storage exports over network interconnect to
provide a single unified namespace
● File-system
completely in
userspace, runs on
commodity
hardware
● Layered on disk file
systems that
support extended
attributes

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● Hadoop contains set of daemons running in the system
● Name Node – centralized metadata node
● Job Tracker – overall task distribution across data nodes
● Task Tracker – on data nodes to maintain task
● Data Node – to store data
● Hadoop = Map Reduce framework + HDFS
● GlusterFS can be a replacement for HDFS
● glusterfs-hadoop-plugin
● Java module which implements Hadoop file system interface
● Simple a JAR file which could be kept in Hadoop libraries
● Replaces HDFS for glusterfs
Hadoop and GlusterFS

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Hadoop and GlusterFS
● Data locality is ensured by Job Tracker
● Using glusterfs-hadoop-plugin ensures data locality by getting the gluster
volumes mounted as fuse mount
● Effectively no name node involved
● Only clients where map-reduce job runs
● And data nodes to store data
● Glusterfs-hadoop-plugin talks to glusterfs using fuse mounts
● In absence of name node, plugin uses xfattrs mechanism to get the details
from volume and consolidates the data using the same
● Reads the data directly from the bricks and bypasses the volume as such for
improved performance

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Hadoop and GlusterFS
● As simple as to execute map reduce daemon and then submit the hadoop
task to use glusterfs as storage
● Analytics uses – using HDFS makes files moving around the nodes whereas
glusterfs just need to fuse mount the volume and no moving around the files

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Advantages
● Elimination of centralized metadata server (name node)
● Compatibility with MapReduce and Hadoop based applications
● Elimination of code rewrites for Hadoop enablement of glusterfs
● Fault tolerant file system
● Allows co-location of compute and data nodes and ability to run Hadoop jobs
across multiple namespaces using multiple glusterfs volumes
● Data access through serveral different mechanisms / protocols (Fuse, NFS,
SMB and SWIFT …. and of course Hadoop)

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References
● https://github.com/gluster/glusterfs-hadoop
● https://forge.gluster.org/hadoop/pages/Home
●
● shubhendu @ #gluster on freenode

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Deployment Scenario

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THANK YOU!