Apache Hadoop 3. 0. Map. Reduce Tutorial. Purpose. This document comprehensively describes all user- facing facets of the Hadoop Map. Reduce framework and serves as a tutorial. Prerequisites. Ensure that Hadoop is installed, configured and is running. More details: Overview. Hadoop Map. Reduce is a software framework for easily writing applications which process vast amounts of data (multi- terabyte data- sets) in- parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault- tolerant manner. A Map. Reduce 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 framework takes care of scheduling tasks, monitoring them and re- executes the failed tasks. Typically the compute nodes and the storage nodes are the same, that is, the Map. Reduce framework and the Hadoop Distributed File System (see HDFS Architecture Guide) are running on the same set of nodes. This configuration allows the framework to effectively schedule tasks on the nodes where data is already present, resulting in very high aggregate bandwidth across the cluster. The Map. Reduce framework consists of a single master Resource. Manager, one worker Node. Manager per cluster- node, and MRApp. Master per application (see YARN Architecture Guide). Minimally, applications specify the input/output locations and supply map and reduce functions via implementations of appropriate interfaces and/or abstract- classes. These, and other job parameters, comprise the job configuration. The Hadoop job client then submits the job (jar/executable etc.) and configuration to the Resource. Manager which then assumes the responsibility of distributing the software/configuration to the workers, scheduling tasks and monitoring them, providing status and diagnostic information to the job- client. Although the Hadoop framework is implemented in Java. Additionally, the key classes have to implement the Writable. THIRD EDITION Hadoop: The Definitive Guide Tom White Beijing ! Hadoop MapReduce and Tez, execution engines in the Hadoop ecosystem, process workloads using frameworks that break down jobs into smaller pieces of work that can be distributed across nodes in your Amazon EMR cluster. This Hadoop tutorial for beginners describes how to refine data for a Trucking IoT Data Discovery use case using the Hortonworks Data Platform. Please confirm that you want to add Taming Big Data with MapReduce and Hadoop - Hands On! Learn how to create and run Python MapReduce jobs on Linux-based HDInsight clusters. 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Tokenizer. Configuration. import org. Path. import org. Int. Writable. import org. Text. import org. Job. import org. apache. Mapper. import org. Reducer. import org. File. Input. Format. File. Output. Format. Word. Count ? The - libjars option allows applications to add jars to the classpaths of the maps and reduces. The option - archives allows them to pass comma separated list of archives as arguments. These archives are unarchived and a link with name of the archive is created in the current working directory of tasks. More details about the command line options are available at Commands Guide. Running wordcount example with - libjars, - files and - archives: bin/hadoop jar hadoop- mapreduce- examples- < ver>. Here, myarchive. zip will be placed and unzipped into a directory by the name “myarchive. Users can specify a different symbolic name for files and archives passed through - files and - archives option, using #. For example,bin/hadoop jar hadoop- mapreduce- examples- < ver>. Here, the files dir. The archive mytar. Walk- through. The Word. Count application is quite straight- forward. Object key, Text value, Context context. IOException, Interrupted. Exception . It then splits the line into tokens separated by whitespaces, via the String. Tokenizer, 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>. We’ll learn more about the number of maps spawned for a given job, and how to control them in a fine- grained manner, a bit later in the tutorial. 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 *key*s. The output of the first map: < Bye, 1>. Hello, 1>. < World, 2>. The output of the second map: < Goodbye, 1>. Hadoop, 2>. < Hello, 1>. Text key, Iterable< Int. Writable> values. Context context. ) throws IOException, Interrupted. Exception . It then calls the job. For. Completion to submit the job and monitor its progress. We’ll learn more about Job, Input. Format, Output. Format and other interfaces and classes a bit later in the tutorial. Map. Reduce - User Interfaces. This section provides a reasonable amount of detail on every user- facing aspect of the Map. Reduce framework. This should help users implement, configure and tune their jobs in a fine- grained manner. However, please note that the javadoc for each class/interface remains the most comprehensive documentation available; this is only meant to be a tutorial. Let us first take the Mapper and Reducer interfaces. Applications typically implement them to provide the map and reduce methods. We will then discuss other core interfaces including Job, Partitioner, Input. Format, Output. Format, and others. Finally, we will wrap up by discussing some useful features of the framework such as the Distributed. Cache, Isolation. Runner etc. Payload. Applications typically implement the Mapper and Reducer interfaces to provide the map and reduce methods. These form the core of the job. Mapper. Mapper maps input key/value pairs to a set of intermediate key/value pairs. Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs. The Hadoop Map. Reduce framework spawns one map task for each Input. Split generated by the Input. Format for the job. Overall, mapper implementations are passed to the job via Job. Mapper. Class(Class) method. The framework then calls map(Writable. Comparable, Writable, Context) for each key/value pair in the Input. Split for that task. Applications can then override the cleanup(Context) method to perform any required cleanup. Output pairs do not need to be of the same types as input pairs. A given input pair may map to zero or many output pairs. Output pairs are collected with calls to context. Writable. Comparable, Writable). Applications can use the Counter to report its statistics. All intermediate values associated with a given output key are subsequently grouped by the framework, and passed to the Reducer(s) to determine the final output. Users can control the grouping by specifying a Comparator via Job. Grouping. Comparator. Class(Class). The Mapper outputs are sorted and then partitioned per Reducer. The total number of partitions is the same as the number of reduce tasks for the job. Users can control which keys (and hence records) go to which Reducer by implementing a custom Partitioner. Users can optionally specify a combiner, via Job. Combiner. Class(Class), to perform local aggregation of the intermediate outputs, which helps to cut down the amount of data transferred from the Mapper to the Reducer. The intermediate, sorted outputs are always stored in a simple (key- len, key, value- len, value) format. Applications can control if, and how, the intermediate outputs are to be compressed and the Compression. Codec to be used via the Configuration. How Many Maps? The number of maps is usually driven by the total size of the inputs, that is, the total number of blocks of the input files. The right level of parallelism for maps seems to be around 1. Task setup takes a while, so it is best if the maps take at least a minute to execute. Thus, if you expect 1. TB of input data and have a blocksize of 1. MB, you’ll end up with 8. Configuration. set(MRJob. Config. NUM. The framework then calls reduce(Writable. Comparable, Iterable< Writable> , Context) method for each < key, (list of values)> pair in the grouped inputs. Applications can then override the cleanup(Context) method to perform any required cleanup. Reducer has 3 primary phases: shuffle, sort and reduce. Shuffle. Input to the Reducer is the sorted output of the mappers. In this phase the framework fetches the relevant partition of the output of all the mappers, via HTTP. Sort. The framework groups Reducer inputs by keys (since different mappers may have output the same key) in this stage. The shuffle and sort phases occur simultaneously; while map- outputs are being fetched they are merged. Secondary Sort. If equivalence rules for grouping the intermediate keys are required to be different from those for grouping keys before reduction, then one may specify a Comparator via Job. Sort. Comparator. Class(Class). Since Job. Grouping. Comparator. Class(Class) can be used to control how intermediate keys are grouped, these can be used in conjunction to simulate secondary sort on values. Reduce. In this phase the reduce(Writable. Comparable, Iterable< Writable> , Context) method is called for each < key, (list of values)> pair in the grouped inputs. The output of the reduce task is typically written to the File. System via Context. Writable. Comparable, Writable). Applications can use the Counter to report its statistics. The output of the Reducer is not sorted. How Many Reduces? The right number of reduces seems to be 0. With 1. 7. 5 the faster nodes will finish their first round of reduces and launch a second wave of reduces doing a much better job of load balancing.
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OUT OF 1. 79 COUNTRIES Effects: Effects The chief economic consequences of corruption are the loss to the exchequer. Creates unhealthy climate for investment and an increase in the cost of government- subsidized services . Corruption in India is a major issue that adversely affects its economy. A study conducted by Transparency International in 2005 found that more than 62% of Indians had a first hand experience of paying bribes or influence peddling to get jobs done in public offices. The TI India study estimates the monetary value of petty corruption in 1. Power. Point Presentation: JAN LOKPAL BILL Anna Hazare spearheaded a nationwide campaign to demand a strong anti- corruption law Jan Lokpal Bill. WE WANT DEVELOPMENT WE WANT CORRUPTION FREE INDIA : WE WANT DEVELOPMENT WE WANT CORRUPTION FREE INDIA In Our Shining India, Over 5. Children Die Every Day From Hunger And Malnutrition. After reading this Do you still think india is a developing country? 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Selection process for the members and Chairperson of Jan Lokpal and Jan Lokayukta has been kept transparent and participatory Selected by a Selection Committee comprising: Prime Minister Leader of the opposition in Lok Sabha 2 youngest judges of Supreme Court (SC) 2 youngest Chief Justices of High Courts Comptroller and Auditor General (CAG) Chief Election Commissioner (CEC) Power. Point Presentation: 1. By ensuring that the right person is selected! The Selection Committee will make the above appointment from a pool of shortlisted candidates that has been identified by a Search Committee Search Committee is a 1. Selection Committee selects 5 members from retired Chief Election Commissioners and retired CAGs. Power. Point Presentation: 1. Ensuring that the right person is selected! When the Selection Committee chooses, those CECs and CAGs are ineligible to become part of Search Committee: who have any substantive allegation of corruption against them who have joined any political party after retirement who are still in any government appointment Power. Point Presentation: 1. Ensuring that the right person is selected! Search Committee will invite recommendations from various eminent people from reputed institutions Like for instance Directors of IITs ,IIMs , Editors and many others. Ensuring that the right person is selected! Selection committee will choose from this list, by consensus All the meetings of the Search Committee and Selection Committee shall be video recorded and will be made public. By ensuring that they work well! Jan Lokpal and Jan Lokayukta are directly accountable to people & transparent Every complaint will have to be heard No complaint can be rejected without giving a hearing to the complainant. By ensuring that they work well! Will have to publish every month on its website: Cases received and cases disposed Cases closed, reasons for closure List of cases pending All records of all works will be open to the public, except some which: Affect national security Endanger the whistle blower Impede the process of ongoing investigation. But after investigation is complete, these will have to be disclosed Power. Point Presentation: 3. By ensuring that Jan Lokpal and Jan Lokayukt are not influenced ! By ensuring that if not working well, can be removed! By ensuring that if not working well can be removed! Complaints against members and chair person of Lokpal and Lokayukta: Will be made to Supreme Court or respective High Court through a petition A five judge bench of respective Court, will hear the petition After hearing may order the formation of a Special Investigation Team (SIT) SIT will have to conduct inquiry and submit its report within 3 months On the basis of this enquiry report, the respective Court may order removal of the member or Chairperson. PROPOSED JAN LOKPAL- JAN LOKAYUKTA SYSTEM Under Jan Lokpal Bill, the full bench of Lokpal will give permission to file a case against any judge. Therefore, corruption in higher judiciary will also be checked through Jan Lokpal Bill. PROPOSED JAN LOKPAL- JAN LOKAYUKTA SYSTEM The punishment will range from: 1 year rigorous imprisonment to life sentence. EVIDENCE Comparison: Comparison CURRENT SYSTEM Presently there are multiple anti- corruption agencies like: CBI, CVC, ACB, state vigilance department All these are controlled by corrupt officials and politicians and are ineffective. PROPOSED JAN LOKPAL- JAN LOKAYUKTA SYSTEM At central govt level, anti- corruption branch of CBI, CVC and all internal vigilance wings of all dept should be merged into Jan Lokpal. At state level, anti- corruption branch of state police, the state vigilance department, internal vigilance wings of all departments and existing Lokayuktas in states should be merged into Jan Lokayukta of that state. This will do away with multiplicity of anti- corruption agencies. Multiple Anti- Corruption Agencies Power. Point Presentation: But Now Some People Saying! Judiciary should not be covered under Jan Lokpal. Lokpal should only investigate a few high profile cases of corruption! CBI, CVC and departmental vigilance should not be merged into Lokpal. 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