Download David Gibbs and Govardhan Tanniru Georgia State University Department of Computer Science

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 does not only relate to the size of data
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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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Primary sources of big data
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Meteorology
Complex physics simulations
Biology
Business
 Web searching
 Social networking
 Telecommunications
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Many programs for storage and processing
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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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Storage
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Transfer
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Developing a system in which very large amounts of
data can be stored securely and accessed quickly
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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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
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 Master/Slave architecture
 HDFS (Hadoop Distributed File System)
 P2P architecture
 Cassandra
 ColumnFamily data model
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Increased difficulty for data mining
 No Join operations
 Pulling in more data than needed
 Increased transfer times, processing power
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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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Google File System
Map Reduce
Big Table
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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.
->The 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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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 client’s append. (Without Additional Locking).
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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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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 -> Map -> Shuffle -> Reduction -> 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(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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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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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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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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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(highperformance 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.