Big Data Overview of storage and processing

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Big DataOverview of storage and processing

• David Gibbs and Govardhan Tanniru
• Georgia State University
• Department of Computer Science
• P.O. Box 3965 Atlanta, GA 30302-3965.

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Big Data

• Big Data does not only relate to the size of data
• Complexity missing information, dummy data,
  organization
• Processing Software, processing power, parallel
  and distributed computing
• Data Transfer Limitations of current systems,
  CPU intensive
• Storage Data sets beyond relational database,
  clusters, data centers, distributed data
• User Interaction Non-programmers need to perform
  complex information, real time GUI interfaces,
  visualization of data

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Where the Field is

• Primary sources of big data
• Meteorology
• Complex physics simulations
• Biology
• Business
• Web searching
• Social networking
• Telecommunications
• Many programs for storage and processing
• Most Popular HDFS, GFS, Hadoop, and MapReduce
• No standard for processing/storing data
• No common off the shelf software
• Increases the difficulty in mining data within a
  field or industry

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Difficulties

• Storage
• Developing a system in which very large amounts
  of data can be stored securely and accessed
  quickly
• Transfer
• Transfer from the storage site to the processing
  site
• Moving large amounts of data over TCP is costly
• Processing
• How powerful of a system is needed?
• There is a lot of data but no information
• Processing the data in an efficient manner and
  obtaining the correct information

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The Direction of Data Storage

• NoSQL
• Allows storage of massive data sets without the
  need for overwhelming tables and indexing
• Each cluster stores part of the data and
  replicates it on other clusters
• Master/Slave architecture
• HDFS (Hadoop Distributed File System)
• P2P architecture
• Cassandra
• ColumnFamily data model
• Increased difficulty for data mining
• No Join operations
• Pulling in more data than needed
• Increased transfer times, processing power

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More About NoSQL(ACID)..

• The key advantage of schema-free design is that
  it enables applications to quickly upgrade the
  structure of data without table rewrites.
• The data validity and integrity aspect is
  enforced at the data management layer.
• NoSQL typically does not maintain complete
  consistency across distributed servers because of
  the burden this places on databases,
• particularly in distributed systems.
• The Consistency, Availability, Partition (CAP)
  Theorem states that with consistency,
  availability, and partitioning tolerance, only
  two can be optimized at any time.
• Traditional relational databases enforce strict
  transactional semantics to preserve consistency,
  but many NoSQL databases have more scalable
  architectures that relax the consistency
  requirement.
• Some NoSQL databases put objects into a conflict
  state when this occurs. However, it is inevitably
  the responsibility of the application to deal
  with these conflicts.

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Important Papers by Google

• Google File System
• Map Reduce
• Big Table

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Google File System

• Google has reexamined traditional choices
  /Assumptions and explored radically different
  points in the design space.
• First, component failures are the norm rather
  than the exception.
• -gtThe system is built from many inexpensive
  commodity components that often fail. It must
  constantly monitor itself and detect, tolerate,
  and recover promptly from component failures on a
  routine basis
• Second, files are huge by traditional standards.
  Multi-GB files are common.
• Third, most files are mutated by appending new
  data rather than overwriting existing data.
• Fourth, co-designing the applications and the
  file system API benefits the overall system by
  increasing our flexibility .

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Consistancy Model.

• Random writes within a file are practically
  non-existent. Once written, the files are only
  read, and often only sequentially.
• A variety of data share these characteristics.
• Appending becomes the focus of performance
  optimization and atomicity guarantees, while
  caching data blocks in the client loses its
  appeal.
• Google has introduced an atomic append operation
  so that multiple clients can append concurrently
  to a file without extra synchronization between
  them.
• Snapshot creates a copy of a file or a directory
  treeat low cost.
• Record append allows multiple clients to append
  data to the same file concurrently while
  guaranteeing the atomicity of each individual
  clients append. (Without Additional Locking).

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GFS Architecture

• Master servers keep metadata on the various data
  files.
• Chunk servers store the actual data on disk. Each
  chunk is replicates across three different chunk
  servers to create redundancy in case of server
  crashes.
• Once directed by a master server, a client
  application retrieves files directly from chunk
  servers.

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Map Reduce Operation

• MapReduce is a programming model and an
  associated implementation for processing and
  generating large data sets.
• Users specify a map function that processes a
  key/value pair to generate a set of intermediate
  key/value pairs.
• A Reduce function that merges all intermediate
  values associated with the same intermediate key.
• The MapReduce system has three different types of
  servers.The Master server assigns user tasks to
  map and reduce servers. It also tracks the state
  of the tasks. - The Map servers accept user
  input and performs map operations on them. The
  results are written to intermediate files.
• The Reduce servers accepts intermediate files
  produced by map servers and performs reduce
  operation on them.
• The steps look like GFS -gt Map -gt Shuffle -gt
  Reduction -gt Store Results back into GFS.- In
  MapReduce a map maps one view of data to another,
  producing a key value pair,
• Data transferred between map and reduce servers
  is compressed. The idea is that because servers
  aren't CPU bound it makes sense to spend on data
  compression and decompression in order to save on
  bandwidth and I/O.

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Map and Reduce. (Contd..)

• map(String key, String value)
• // key document name
• // value document contents
• for each word w in value
• EmitIntermediate(w, "1")
• reduce(String key, Iterator values)
• // key a word
• // values a list of counts
• int result 0
• for each v in values
• result ParseInt(v)
• Emit(AsString(result))

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Big Table

• BigTable is a large scale, fault tolerant, self
  managing system that includes terabytes of memory
  and petabytes of storage. It can handle millions
  of reads/writes per second.
• BigTable is a distributed hash mechanism built on
  top of GFS. It is not a relational database. It
  doesn't support joins or SQL type queries.
• It provides lookup mechanism to access
  structured data by key. GFS stores opaque data
  and many applications needs has data with
  structure.
• Machines can be added and deleted while the
  system is running and the whole system just
  works.
• Each data item is stored in a cell which can be
  accessed using a row key, column key, or
  timestamp.
• BigTable has three different types of servers
  ( Master, Tablet ,Lock Servers)

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Hardware strategy

• Use ultra cheap commodity hardware and built
  software on top to handle their death.
• A 1,000-fold computer power increase can be had
  for a 33 times lower cost if you you use a
  failure-prone infrastructure rather than an
  infrastructure built on highly reliable
  components. You must build reliability on top of
  unreliability for this strategy to work.

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Mixed Architectures

• Many Papers focus on the integration of
  Traditional and Big Data Architectures.
• We need architectures to handle both the types of
  Data.
• Below is the diagram from Oracle white Paper.

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Other Areas of Focus

• Knowledge Discovery in Databases.
• Bringing the big data and big compute
  communities together is an active area of
  research.
• Hybrid Way of Storing Un Structuted Data(File
  Systems and DBMS).
• Efficient Data Transfer Protocols for Big
  Data(high-performance network data movement )
• Use of cloud computing for Big Data.
• Compression aspects I/O Performance Analysis for
  Big Data Clustering.
• Privacy Implications on Social Networking
  sites.(Friends tagging another person).
• Faults with HADOOP might help our research.

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Questions