hadoop training in hyderabad@kellytechnologies

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Distributed Computing Overviews
Presented By
Kelly Technologies
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Agenda

• What is distributed computing
• Why distributed computing
• Common Architecture
• Best Practice
• Case study
• Condor
• Hadoop HDFS and map reduce

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What is Distributed Computing/System?

• Distributed computing
• A field of computing science that studies
  distributed system.
• The use of distributed systems to solve
  computational problems.
• Distributed system
• Wikipedia
• There are several autonomous computational
  entities, each of which has its own local memory.
• The entities communicate with each other by
  message passing.
• Operating System Concept
• The processors communicate with one another
  through various communication lines, such as
  high-speed buses or telephone lines.
• Each processor has its own local memory.

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What is Distributed Computing/System?

• Distributed program
• A computing program that runs in a distributed
  system
• Distributed programming
• The process of writing distributed program

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What is Distributed Computing/System?

• Common properties
• Fault tolerance
• When one or some nodes fails, the whole system
  can still work fine except performance.
• Need to check the status of each node
• Each node play partial role
• Each computer has only a limited, incomplete view
  of the system. Each computer may know only one
  part of the input.
• Resource sharing
• Each user can share the computing power and
  storage resource in the system with other users
• Load Sharing
• Dispatching several tasks to each nodes can help
  share loading to the whole system.
• Easy to expand
• We expect to use few time when adding nodes. Hope
  to spend no time if possible.

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Why Distributed Computing?

• The nature of application
• Performance
• Computing intensive
• The task could consume a lot of time on
  computing. For example, p
• Data intensive
• The task that deals with a lot mount or large
  size of files. For example, Facebook, LHC(Large
  Hadron Collider).
• Robustness
• No SPOF (Single Point Of Failure)
• Other nodes can execute the same task executed on
  failed node.

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Common Architectures

• Communicate and coordinate works among concurrent
  processes
• Processes communicate by sending/receiving
  messages
• Synchronous/Asynchronous

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Common Architectures

• Master/Slave architecture
• Master/slave is a model of communication where
  one device or process has unidirectional control
  over one or more other devices
• Database replication
• Source database can be treated as a master and
  the destination database can treated as a slave.
• Client-server
• web browsers and web servers

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Common Architectures

• Data-centric architecture
• Using a standard, general-purpose relational
  database management system ?? customized
  in-memory or file-based data structures and
  access method
• Using dynamic, table-driven logic in ?? logic
  embodied in previously compiled programs
• Stored procedures ?? logic running in
  middle-tier application servers
• Shared databases as the basis for communicating
  between parallel processes ?? direct
  inter-process communication via message passing
  function

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Best Practice

• Data Intensive or Computing Intensive
• Data size and the amount of data
• The attribute of data you consume
• Computing intensive
• We can move data to the nodes where we can
  execute jobs
• Data Intensive
• We can separate/replicate data to difference
  nodes, then we can execute our tasks on these
  nodes
• Reduce data replication when executing tasks
• Master nodes need to know data location
• No data loss when incidents happen
• SAN (Storage Area Network)
• Data replication on different nodes
• Synchronization
• When splitting tasks to different nodes, how can
  we make sure these tasks are synchronized?

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Best Practice

• Robustness
• Still safe when one or partial nodes fail
• Need to recover when failed nodes are online. No
  further or few action is needed
• Condor restart daemon
• Failure detection
• When any nodes fails, master nodes can detect
  this situation.
• Eg Heartbeat detection
• App/Users dont need to know if any partial
  failure happens.
• Restart tasks on other nodes for users

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Best Practice

• Network issue
• Bandwidth
• Need to think of bandwidth when copying files
  from one node to other nodes if we would like to
  execute the task on the nodes if no data in these
  nodes.
• Scalability
• Easy to expand
• Hadoop configuration modification and start
  daemon
• Optimization
• What can we do if the performance of some nodes
  is not good?
• Monitoring the performance of each node
• According to any information exchange like
  heartbeat or log
• Resume the same task on another nodes

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Best Practice

• App/User
• shouldnt know how to communicate between nodes
• User mobility user can access the system from
  some point or anywhere
• Grid UI (User interface)
• Condor submit machine

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Case study - Condor

• Condor
• Computing intensive jobs
• Queuing policy
• Match task and computing nodes
• Resource Classification
• Each resource can advertise its attributes and
  master can classify according to this

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Case study - Condor
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Case study - Condor

• Role
• Central Manger
• The collector of information, and the negotiator
  between resources and resource requests
• Execution machine
• Responsible for executing condor tasks
• Submit machine
• Responsible for submitting condor tasks
• Checkpoint servers
• Responsible for storing all checkpoint files for
  the tasks

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Case study - Condor

• Robustness
• One execution machine fails
• We can execute the same task on other nodes.
• Recovery
• Only need to restart the daemon when the failed
  nodes are online

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Case study - Condor

• Resource sharing
• Each condor user can share computing power with
  other condor users.
• Synchronization
• Users need to take care by themselves
• Users can execute MPI job in a condor pool but
  need to think of the issues of synchronization
  and Deadlock.
• Failure detection
• Central manager can know when nodes fails
• Based on update notification sent by nodes
• Scalability
• Only execute few commands when new nodes are
  online.

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Case study - Hadoop

• HDFS
• Namenode
• manages the file system namespace and regulates
  access to files by clients.
• determines the mapping of blocks to DataNodes.
• Data Node
• manage storage attached to the nodes that they
  run on
• save CRC codes
• send heartbeat to namenode.
• Each data is split as a chunk and each chuck is
  stored on some data nodes.
• Secondary Namenode
• responsible for merging fsImage and EditLog

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Case study - Hadoop
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Case study - Hadoop

• Map-reduce Framework
• JobTracker
• Responsible for dispatch job to each tasktracker
• Job management like removing and scheduling.
• TaskTracker
• Responsible for executing job. Usually
  tasktracker launch another JVM to execute the
  job.

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Case study - Hadoop
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From Hadoop - The Definitive Guide
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Case study - Hadoop

• Data replication
• Data are replicated to different nodes
• Reduce the possibility of data loss
• Data locality. Job will be sent to the node where
  data are.
• Robustness
• One datanode fails
• We can get data from other nodes.
• One tasktracker failed
• We can start the same task on different node
• Recovery
• Only need to restart the daemon when the failed
  nodes are online

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Case study - Hadoop

• Resource sharing
• Each hadoop user can share computing power and
  storage space with other hadoop users.
• Synchronization
• No synchronization
• Failure detection
• Namenode/Jobtracker can know when
  datanode/tasktracker fails
• Based on heartbeat

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Case study - Hadoop

• Scalability
• Only execute few commands when new nodes are
  online.
• Optimization
• A speculative task is launched only when a task
  takes too much time on one node.
• The slower task will be killed when the other one
  has been finished

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Thank You
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