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A STUDY ON MAPREDUCE FOR SIMPLIFIED PROCESSING OF BIG DATA
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International Journal of Modern Trends in Engineering and Research IJMTER. Volume 03 Issue 10 October 2016 ISSN Online 2349 9745 ISSN Print 2393 8161. understand how it impacts their business and resist the usage of the data until it is too late in. many cases, Virality measures and describes how quickly data is shared in a people to people peer. network Rate of spread is measured in time For example re tweets that are shared from an. original tweet is a good way to follow a topic or a trend. Big Data is non relational traditional, MapReduce is complementary to DBMS not a competing technology 3. Parallel DBMS are for efficient querying of large data sets 3. MR style systems are for complex analytics and ETL tasks. Parallel DBMS require data to fit into the relational paradigm of rows and columns. In contrast the MR model does not require that data files adhere to a schema defined using. the relational data model That is the MR programmer is free to structure their data in any. manner or even to have no structure at all,Big Data Pillars. Big Table Relational Tabular format rows columns 3. Big Text All kinds of unstructured data natural language grammatical data semantic data. Big Metadata Data about data taxonomies glossaries facets concepts entity. Big Graphs object connections semantic discovery degree of separation linguistic. analytic subject predicate, In the Big Data community MapReduce has been seen as one of the key enabling approaches. for meeting continuously increasing demands on computing resources imposed by massive data sets. The reason for this is the high scalability of the MapReduce paradigm which allows for massively. parallel and distributed execution over a large number of computing nodes. Data processing has been a complex subject to deal with since the primitive days of. computing The underlying reason for this stems from the fact that complexity is induced from the. instrumentation of data rather than the movement of data As a reaction to this complexity a new. abstraction was designed that allows to express the simple computations being tried to perform but. hides the messy details of parallelization fault tolerance data distribution and load balancing in a. library That abstraction is inspired by the map and reduce primitives Most of the computations. involved applying a map operation to each logical record in the input in order to compute a set of. intermediate key value pairs and then applying a reduce operation to all the values that shared the. same key in order to combine the derived data appropriately The use this functional model with. user specified map and reduce operations allows to parallelize large computations easily and to use. re execution as the primary mechanism for fault tolerance 1. The major contributions of this work are a simple and powerful interface that enables. automatic parallelization and distribution of large scale computations combined with an. implementation of this interface that achieves high performance on large clusters of commodity. II MAPREDUCE PROGRAMMING MODEL, MapReduce is a programming model designed for processing large volumes of data in.
parallel by dividing the work into a set of independent tasks A MapReduce program is composed of. a Map procedure that performs filtering and sorting such as sorting students by first name into. queues one queue for each name and a Reduce procedure that performs a summary operation. such as counting the number of students in each queue yielding name frequencies The. MapReduce System also called infrastructure or framework orchestrates the processing. by marshalling the distributed servers running the various tasks in parallel managing all. communications and data transfers between the various parts of the system and providing. for redundancy and fault tolerance 2,IJMTER 2016 All rights Reserved 186. International Journal of Modern Trends in Engineering and Research IJMTER. Volume 03 Issue 10 October 2016 ISSN Online 2349 9745 ISSN Print 2393 8161. MapReduce is a framework for processing parallelizable problems across huge datasets using. a large number of computers nodes collectively referred to as a cluster if all nodes are on the same. local network and use similar hardware or a grid if the nodes are shared across geographically and. administratively distributed systems and use more heterogeneous hardware Processing can occur. on data stored either in a files system unstructured or in a database structured MapReduce can. take advantage of locality of data processing it on or near the storage assets in order to reduce the. distance over which it must be transmitted 2,Map Reduce framework. Map Phase The master node takes the input divides, it into smaller sub problems and distribute them to. worker nodes A worker node may do this again in,turn leading to a multi level tree structure The. worker node processes the smaller problem and,passes the answer back to its master node 2.
Reduce Phase The master node then collects the, answers to all the sub problems and combine them in. some way to form the output the answer to the,problem it was originally trying to solve 2. Input reader It divides the input into appropriate. size in practice typically 64 MB to 512 MB as per,HDFS and the framework assigns one split to one. Map function The input reader reads the data from, stable storage typically as in our case Hadoop distributed file system and generates key value. Map function Each Map function takes a series of key value pairs processes each and generates. zero or more output key value pairs The input and output types of the map can be and often are. different from each other 2, Partition function Each Map function output is allocated to a particular reducer by the application.
partition function for sharing purposes The partition function is given the key and the number of. reducers and returns the index of desired reduce 2. Comparison function The input for every Reduce is fetched from the machine where the Map run. and sorted using comparison function 2, Reduce function The frame work calls the applications Reduce function for each unique key in the. sorted order It also itereates through the values that are associated with that key and produce zero or. more outputs 2, Output writer It writes the output of the Reduce function to stable storage usually a Hadoop. distributed file system 2,Their execution sequence can be seen as follows. Performance, MapReduce programs are not guaranteed to be fast The main benefit of this programming. model is to exploit the optimized shuffle operation of the platform and only having to write. the Map and Reduce parts of the program In practice the author of a MapReduce program however. has to take the shuffle step into consideration in particular the partition function and the amount of. data written by the Map function can have a large impact on the performance Additional modules. IJMTER 2016 All rights Reserved 187, International Journal of Modern Trends in Engineering and Research IJMTER.
Volume 03 Issue 10 October 2016 ISSN Online 2349 9745 ISSN Print 2393 8161. such as the Combiner function can help to reduce the amount of data written to disk and transmitted. over the network, When designing a MapReduce algorithm the author needs to choose a good tradeoff between. the computation and the communication costs Communication cost often dominates the computation. cost and many MapReduce implementations are designed to write all communication to distributed. storage for crash recovery,III BIG DATA ANALYTICS, The infrastructure required for analyzing big data must be able to support deeper analytics. such as statistical analysis and data mining on a wider variety of data types stored in diverse. systems scale to extreme data volumes deliver faster response times driven by changes in behavior. and automate decisions based on analytical models Most importantly the infrastructure must be able. to integrate analysis on the combination of big data and traditional enterprise data New insight. comes not just from analyzing new data but from analyzing it within the context of the old to. provide new perspectives on old problems For example analyzing inventory data from a smart. vending machine in combination with the events calendar for the venue in which the vending. machine is located will dictate the optimal product mix and replenishment schedule for the vending. The data analytics project life cycle stages are seen in the following diagram. IV CONCLUSION, There are many new technologies emerging at a rapid rate each with technological. advancements and with the potential of making ease in use of technology However one must be very. careful to understand the limitations and security risks posed in utilizing these technologies Neither. MapReduce like software nor parallel databases are ideal solutions for data analysis in the cloud. Hybrid solution that combines the fault tolerance heterogeneous cluster and ease of use out of the. box capabilities of MapReduce with the efficiency performance and tool plug ability of shared. nothing parallel systems could have a significant impact on the cloud market This paper analyzes the. concept of Big data and how it differs from traditional database It also clearly specifies the Hadoop. environment its architecture and how it can be implemented using MapReduce along with various. functions So it is sure that this paper helps the researches to understand the basic concepts of Big. data Hadoop and MapReduce to move further,REFERENCES. 1 J Dean and S Ghemawat Mapreduce Simplified data processing on large clusters in In Proceedings of. OSDI 04 Sixth Symposium on Operating System Design and Implementation December 2004. 2 V Patil V B Nikam Study of Mining Alogorithm in cloud computing using MapReduce Framework Journal of. Engineering Computers Applied Sciences JEC AS Vol 2 No 7 July 2013. 3 D Usha A P S Aslin Jenil A Survey of Big Data Processing in Perspective of Hadoop and Mapreduce. International Journal of Current Engineering and Technology Vol 4 No 2 April 2014. 4 M Deodhar C Jones and J Ghosh Parallel simultaneous co clustering and learning with Map Reduce In GrC. IJMTER 2016 All rights Reserved 188, International Journal of Modern Trends in Engineering and Research IJMTER.
Volume 03 Issue 10 October 2016 ISSN Online 2349 9745 ISSN Print 2393 8161. 5 Apache Apache Hadoop http hadoop apache org 2010. 6 T Sun C Shuy F Liy H Yuy L Ma and Y Fang An efficient hierarchical clustering method for large datasets. with Map Reduce In PDCAT 2009, 7 S Ghemawat et al The google file system ACM SIGOPS Operating Systems Review 37 5 29 43 2003. 8 Saptarshi Guha RHIPE R and Hadoop Integrated Processing Environment http www stat purdue edu. sguha rhipe 2010, 9 R Taylor An overview of the Hadoop MapReduce HBase framework and its current applications in. bioinformatics BMC bioinformatics 11 Suppl 12 S1 2010. 10 T White Hadoop The Definitive Guide Yahoo Press 2010.


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