Map Reduce is a parallel and distributed approach developed by Google for processing large data sets. It has two key components - the Map function which processes input data into key-value pairs, and the Reduce function which aggregates the intermediate output of the Map into a final result. Input data is split across multiple machines which apply the Map function in parallel, and the Reduce function is applied to aggregate the outputs.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
The document provides an overview of MapReduce, including:
1) MapReduce is a programming model and implementation that allows for large-scale data processing across clusters of computers. It handles parallelization, distribution, and reliability.
2) The programming model involves mapping input data to intermediate key-value pairs and then reducing by key to output results.
3) Example uses of MapReduce include word counting and distributed searching of text.
This document discusses various concepts related to Hadoop MapReduce including combiners, speculative execution, custom counters, input formats, multiple inputs/outputs, distributed cache, and joins. It explains that a combiner acts as a mini-reducer between the map and reduce stages to reduce data shuffling. Speculative execution allows redundant tasks to improve performance. Custom counters can track specific metrics. Input formats handle input splitting and reading. Multiple inputs allow different mappers for different files. Distributed cache shares read-only files across nodes. Joins can correlate large datasets on a common key.
The document provides an introduction to MapReduce, describing its motivation as a framework for simplifying large-scale data processing across distributed systems. It outlines MapReduce's programming model and main features, including automatic parallelization, fault tolerance, and locality. The document also provides a detailed example of counting letter frequencies in a large file to illustrate how MapReduce works.
This document provides an overview of the MapReduce paradigm and Hadoop framework. It describes how MapReduce uses a map and reduce phase to process large amounts of distributed data in parallel. Hadoop is an open-source implementation of MapReduce that stores data in HDFS. It allows applications to work with thousands of computers and petabytes of data. Key advantages of MapReduce include fault tolerance, scalability, and flexibility. While it is well-suited for batch processing, it may not replace traditional databases for data warehousing. Overall efficiency remains an area for improvement.
This document introduces MapReduce, including its architecture, advantages, frameworks for writing MapReduce programs, and an example WordCount MapReduce program. It also discusses how to compile, deploy, and run MapReduce programs using Hadoop and Eclipse.
This document summarizes Hadoop MapReduce, including its goals of distribution and reliability. It describes the roles of mappers, reducers, and other system components like the JobTracker and TaskTracker. Mappers process input splits in parallel and generate intermediate key-value pairs. Reducers sort and group the outputs by key before processing each unique key. The JobTracker coordinates jobs while the TaskTracker manages tasks on each node.
The document outlines the anatomy of MapReduce applications including common phases like input splitting, mapping, shuffling, and reducing. It then provides high-level and low-level views of how a word counting MapReduce job works, explaining that it takes a text corpus as input, maps words to counts of 1, shuffles to reduce by word, and outputs final word counts. The map and reduce functions are explained at a high-level, and then implementation details like MapRunner, RecordReader, and OutputCollector are described at a lower level.
Here is how you can solve this problem using MapReduce and Unix commands:
Map step:
grep -o 'Blue\|Green' input.txt | wc -l > output
This uses grep to search the input file for the strings "Blue" or "Green" and print only the matches. The matches are piped to wc which counts the lines (matches).
Reduce step:
cat output
This isn't really needed as there is only one mapper. Cat prints the contents of the output file which has the count of Blue and Green.
So MapReduce has been simulated using grep for the map and cat for the reduce functionality. The key aspects are - grep extracts the relevant data (map
This document provides an overview of MapReduce in Hadoop. It defines MapReduce as a distributed data processing paradigm designed for batch processing large datasets in parallel. The anatomy of MapReduce is explained, including the roles of mappers, shufflers, reducers, and how a MapReduce job runs from submission to completion. Potential purposes are batch processing and long running applications, while weaknesses include iterative algorithms, ad-hoc queries, and algorithms that depend on previously computed values or shared global state.
The document provides an introduction to MapReduce, including:
- MapReduce is a framework for executing parallel algorithms across large datasets using commodity computers. It is based on map and reduce functions.
- Mappers process input key-value pairs in parallel, and outputs are sorted and grouped by the reducers.
- Examples demonstrate how MapReduce can be used for tasks like building indexes, joins, and iterative algorithms.
• What is MapReduce?
• What are MapReduce implementations?
Facing these questions I have make a personal research, and realize a synthesis, which has help me to clarify some ideas. The attached presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.
The document presents an introduction to MapReduce. It discusses how MapReduce provides an easy framework for distributed computing by allowing programmers to write simple map and reduce functions without worrying about complex distributed systems issues. It outlines Google's implementation of MapReduce and how it uses the Google File System for fault tolerance. Alternative open-source implementations like Apache Hadoop are also covered. The document discusses how MapReduce has been widely adopted by companies to process massive amounts of data and analyzes some criticism of MapReduce from database experts. It concludes by noting trends in using MapReduce as a parallel database and for multi-core processing.
This document provides an overview of MapReduce, a programming model developed by Google for processing and generating large datasets in a distributed computing environment. It describes how MapReduce abstracts away the complexities of parallelization, fault tolerance, and load balancing to allow developers to focus on the problem logic. Examples are given showing how MapReduce can be used for tasks like word counting in documents and joining datasets. Implementation details and usage statistics from Google demonstrate how MapReduce has scaled to process exabytes of data across thousands of machines.
This document provides an overview of MapReduce, including:
- MapReduce is a programming model for processing large datasets in parallel across clusters of computers.
- It works by breaking the processing into map and reduce functions that can be run on many machines.
- Examples are given like word counting, distributed grep, and analyzing web server logs.
This document discusses data types and formats used in Hadoop MapReduce. It covers basic data types like IntWritable and Text that support serialization and comparability. It also describes common file formats like XML, JSON, SequenceFiles, Avro, Parquet, and how to implement custom formats like CSV. Input/output classes are discussed along with how different formats can be used in MapReduce jobs.
This document describes how to set up a single-node Hadoop installation to perform MapReduce operations. It discusses supported platforms, required software including Java and SSH, and preparing the Hadoop cluster in either local, pseudo-distributed, or fully-distributed mode. The main components of the MapReduce execution pipeline are explained, including the driver, mapper, reducer, and input/output formats. Finally, a simple word count example MapReduce job is described to demonstrate how it works.
The document describes MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. It allows users to specify map and reduce functions to process key-value pairs. The runtime system handles parallelization across machines, partitioning data, scheduling execution, and handling failures. Hundreds of programs have been implemented using MapReduce at Google to process terabytes of data on thousands of machines.
2004 map reduce simplied data processing on large clusters (mapreduce)anh tuan
The document describes MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. It allows users to specify map and reduce functions to process key-value pairs. The runtime system handles parallelization across machines, partitioning data, scheduling execution, and handling failures. Hundreds of programs have been implemented using MapReduce at Google to process terabytes of data on thousands of machines.
The document describes MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. MapReduce allows users to specify map and reduce functions to process key-value pairs. The runtime system automatically parallelizes and distributes the computation across clusters, handling failures and communication. Hundreds of programs have been implemented using MapReduce at Google to process terabytes of data on thousands of machines.
This document introduces MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. The key aspects are:
1. Users specify map and reduce functions that process key-value pairs. The map function produces intermediate key-value pairs and the reduce function merges values for the same key.
2. The system automatically parallelizes the computation by partitioning input data and scheduling tasks on a cluster. It handles failures, data distribution, and load balancing.
3. The implementation runs on large Google clusters and is highly scalable, processing terabytes of data on thousands of machines. Hundreds of programs use MapReduce daily at Google.
This document introduces MapReduce, a programming model for processing large datasets across distributed systems. It describes how users write map and reduce functions to specify computations. The MapReduce system automatically parallelizes jobs by splitting input data, running the map function on different parts in parallel, collecting output, and running the reduce function to combine results. It handles failures and distribution of work across machines. Many common large-scale data processing tasks can be expressed as MapReduce jobs. The system has been used to process petabytes of data on thousands of machines at Google.
This document provides an overview of Map & Reduce, a programming model for processing large datasets in parallel. It describes how Map & Reduce works by applying mapping functions to each element to generate intermediate key-value pairs, shuffling and sorting the data, then applying reduction functions to aggregate the values associated with each key. As an example, it walks through how the "word count" problem can be solved using Map & Reduce. Finally, it briefly discusses Google's implementation of MapReduce and the Apache Hadoop framework.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through HDFS and distributed processing via MapReduce. HDFS handles storage and MapReduce provides a programming model for parallel processing of large datasets across a cluster. The MapReduce framework consists of a mapper that processes input key-value pairs in parallel, and a reducer that aggregates the output of the mappers by key.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
MapReduce: Ordering and Large-Scale Indexing on Large ClustersIRJET Journal
This document discusses MapReduce, a programming model for processing large datasets across large clusters. It describes how MapReduce works, with a map function that processes input key-value pairs to generate intermediate pairs, and a reduce function that combines values for the same intermediate key. The document provides examples of applications like distributed grep, counting URL access frequencies, and building an inverted index. It then describes the implementation of MapReduce across thousands of machines, how it provides fault tolerance, optimizes for data locality, and handles failures. Performance is evaluated for searching a terabyte of data and sorting a terabyte.
In this presentation , i provide in depth information about the how MapReduce works. It contains many details about the execution steps , Fault tolerance , master / worker responsibilities.
The WordCount and Sort examples demonstrate basic MapReduce algorithms in Hadoop. WordCount counts the frequency of words in a text document by having mappers emit (word, 1) pairs and reducers sum the counts. Sort uses an identity mapper and reducer to simply sort the input files by key. Both examples read from and write to HDFS, and can be run on large datasets to benchmark a Hadoop cluster's sorting performance.
HDFS-HC: A Data Placement Module for Heterogeneous Hadoop ClustersXiao Qin
An increasing number of popular applications become data-intensive in nature. In the past decade, the World Wide Web has been adopted as an ideal platform for developing data-intensive applications, since the communication paradigm of the Web is sufficiently open and powerful. Data-intensive applications like data mining and web indexing need to access ever-expanding data sets ranging from a few gigabytes to several terabytes or even petabytes. Google leverages the MapReduce model to process approximately twenty petabytes of data per day in a parallel fashion. In this talk, we introduce the Google’s MapReduce framework for processing huge datasets on large clusters. We first outline the motivations of the MapReduce framework. Then, we describe the dataflow of MapReduce. Next, we show a couple of example applications of MapReduce. Finally, we present our research project on the Hadoop Distributed File System.
The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into
account for launching speculative map tasks, because it is
assumed that most maps are data-local. Unfortunately, both
the homogeneity and data locality assumptions are not satisfied
in virtualized data centers. We show that ignoring the datalocality issue in heterogeneous environments can noticeably
reduce the MapReduce performance. In this paper, we address
the problem of how to place data across nodes in a way that
each node has a balanced data processing load. Given a dataintensive application running on a Hadoop MapReduce cluster,
our data placement scheme adaptively balances the amount of
data stored in each node to achieve improved data-processing
performance. Experimental results on two real data-intensive
applications show that our data placement strategy can always
improve the MapReduce performance by rebalancing data
across nodes before performing a data-intensive application
in a heterogeneous Hadoop cluster.
MapReduce is a programming model for processing large datasets in a distributed environment. It consists of a map function that processes input key-value pairs to generate intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same key. It allows for parallelization of computations across large clusters. Example applications include word count, sorting, and indexing web links. Hadoop is an open source implementation of MapReduce that runs on commodity hardware.
This document provides an overview of Hadoop frameworks and concepts. It discusses distributed file systems like HDFS and how they are organized at large scale. It then explains the MapReduce execution model and how it allows distributed processing of large datasets in a fault-tolerant manner. Specific algorithms like matrix-vector multiplication are discussed as examples of how MapReduce can be used. Finally, it introduces Hadoop YARN, which separates resource management from job execution, allowing more flexible processing of different types of applications on Hadoop clusters.
This document provides an introduction to MapReduce and Hadoop. It begins by explaining the problems MapReduce aims to address like parallel database operations and processing unknown data schemas. It then describes the MapReduce programming model including the map and reduce functions. The rest of the document details how MapReduce is implemented in Hadoop, including the job launching process, use of mappers and reducers, and reading/writing of data. It provides an example word count program and discusses aspects like locality, fault tolerance, and optimizations in MapReduce and Hadoop.
MapReduce is a programming model for processing large datasets in parallel across clusters of machines. It involves splitting the input data into independent chunks which are processed by the "map" step, and then grouping the outputs of the maps together and inputting them to the "reduce" step to produce the final results. The MapReduce paper presented Google's implementation which ran on a large cluster of commodity machines and used the Google File System for fault tolerance. It demonstrated that MapReduce can efficiently process very large amounts of data for applications like search, sorting and counting word frequencies.
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Map reduce and Hadoop on windows
1. Map Reduce Muhammad UsmanShahidSoftware Engineer Usman.shahid.st@hotmail.com10/17/20111
2. Parallel ProgrammingUsed for performance and efficiency.Processing is broken up into parts and done concurrently.Instruction of each part run on a separate CPU while many processors are connected.Identification of set of tasks which can run concurrently is important.A Fibonacci function is Fk+2 = Fk + Fk+1.It is clear that Fibonacci function can not be parallelized as each computed value depends on previous.Now consider a huge array which can be broken up into sub-arrays.10/17/20112
3. Parallel Programming10/17/20113If each element required some processing, with no dependencies in the computation, we have an ideal parallel computing opportunity.
4. Google Data CenterGoogle believes buy cheap computers but numerous in number.Google has parallel processing concept in its data centers.Map Reduce is a parallel and distributed approach developed by Google for processing large data sets. 10/17/20114
5. Map Reduce IntroductionMap Reduce has two key components. Map and Reduce.Map function is used on input values to calculate a set of key/Value pairs.Reduce aggregates this data into a scalar.10/17/20115
6. Data Distribution Input files are split into M pieces on distributed file systems.Intermediate files are created from map tasks are written to local disks.Output files are written to distributed file systems.10/17/20116
8. Map Reduce FunctionMap Reduce function by an example see the query “Select Sum(stuMarks) from student group by studentSection”.In above query “select” phase is doing the same as Map do and “Group By” same as Reduce Phase.10/17/20118
9. Classical ExampleThe classical example of Map Reduce is the log file analysis.Big log files are split and mapper search for different web pages which are accessed.Every time a web page is found in the log a key/value pair is emitted to the reducer in such way that key = web page and value = 1.The reducer aggregates the number for a certain web pages. Result is the count of total hits for each web page.10/17/20119
10. Reverse Web Link GraphIn this example Map function outputs (URL target, source) from an input web page (source).Reduce function concatenates the list of all source URL(s) with a give target of URL and returns (target, list(source)).10/17/201110
11. Other Examples Map Reduce can be used for the lot of problems.For Example the Google used the Map Reduce for the calculation of page ranks.Word count in large set of documents can also be resolved by Map Reduce very efficiently.Google library for the Map Reduce is not open source but an implementation in java called hadoop is an open source.10/17/201111
12. Implementation of ExampleWord Count is a simple application that counts the number of occurrences of words in a given set of inputs.Hadoop library is used for its implementation.Code is given in the below attached file.10/17/201112
13. Usage of ImplementationFor example the input files are $ bin/hadoopdfs -ls /usr/joe/wordcount/input/ /usr/joe/wordcount/input/file01 /usr/joe/wordcount/input/file02 $ bin/hadoopdfs -cat /usr/joe/wordcount/input/file01 Hello World Bye World $ bin/hadoopdfs -cat /usr/joe/wordcount/input/file02 Hello Hadoop Goodbye HadoopRun the application.Word Count is straight forward problem.10/17/201113
14. Walk Through ImplementationThe Mapper implementation (lines 14-26), via the map method (lines 18-25), processes one line at a time, as provided by the specified TextInputFormat (line 49). It then splits the line into tokens separated by whitespaces, via the StringTokenizer, and emits a key-value pair of < <word>, 1>.For the given sample input the first map emits:< Hello, 1> < World, 1> < Bye, 1> < World, 1> The second map emits:< Hello, 1> < Hadoop, 1> < Goodbye, 1> < Hadoop, 1> 10/17/201114
15. Walk Through ImplementationWordCount also specifies a combiner (line 46). Hence, the output of each map is passed through the local combiner (which is same as the Reducer as per the job configuration) for local aggregation, after being sorted on the keys.The output of the first map:< Bye, 1> < Hello, 1> < World, 2> The output of the second map:< Goodbye, 1> < Hadoop, 2> < Hello, 1> 10/17/201115
16. Walk Through ImplementationThe Reducer implementation (lines 28-36), via the reduce method (lines 29-35) just sums up the values, which are the occurence counts for each key (i.e. words in this example).Thus the output of the job is:< Bye, 1> < Goodbye, 1> < Hadoop, 2> < Hello, 2> < World, 2> The run method specifies various facets of the job, such as the input/output paths (passed via the command line), key/value types, input/output formats etc., in theJobConf. It then calls the JobClient.runJob (line 55) to submit the and monitor its progress.10/17/201116
18. Map Reduce ExecutionMap Reduce library is the user program that first splits the input files in M pieces. Then it start ups many copies of the program on cluster of machines.One of the copy is special – The Master other are the workers. There are M Map tasks and R Reduce tasks to assign. The master picks the idle workers and assign them the Map task or Reduce Task.A worker who is assigned Map task reads the contents of corresponding input split. It parses the key value pair and pass it to user defined Map function this generates the intermediate key/value pairs buffered in the memory.Periodically, the buffered pairs are written to local disks. The locations of these buffered pairs on local disks are passed back to the master, who is responsible for forwarding them to the reducer workers.10/17/201118
19. Map Reduce Execution When master notify a reduce worker about these location, it uses RPC to access this local data, then it sorts the data.The reduce worker iterates over the sorted intermediate data, for each unique key it passes the key and values to the reduce function. The output is appended to the final output file.Many associated issues are handled by the library likeParallelizationFault Tolerance Data DistributionLoad Balancing10/17/201119
20. DebuggingOffer human readable status info on http server, user can see jobs In progress, Completed etc.Allows use of GDB and other debugging tools.10/17/201120
21. ConclusionsSimplifies large scale computations that fit this model.Allows user to focus on the problem without worrying about the details.It is being used by renowned companies like Google and Yahoo.Google library for Map Reduce is not open source but a project of Apache called hadoop is an open source library for Map Reduce.10/17/201121