Conversely, values as high as 1.0 have been effective for reduces whose input can fit entirely in memory. In map and reduce tasks, performance may be influenced by adjusting parameters influencing the concurrency of operations and the frequency with which data will hit disk. If the value is set true, the task profiling is enabled. Hadoop can be implemented on any Windows OS version, but the installation process differs slightly. a) MapReduce tries to place the data and the compute as close as possible Hadoop Tutorial. The Hadoop framework application works in an environment that provides distributed storage and computation across clusters of computers. The framework then calls reduce(WritableComparable, Iterable, Context) method for each pair in the grouped inputs. These properties can also be set by using APIs Configuration.set(MRJobConfig.MAP_DEBUG_SCRIPT, String) and Configuration.set(MRJobConfig.REDUCE_DEBUG_SCRIPT, String). shell utilities) as the mapper and/or the reducer. Hadoop MapReduce 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. Google published two Tech Papers: one is on Google FileSystem (GFS) in October 2003 and another on MapReduce Algorithm in Dec 2004. This threshold influences only the frequency of in-memory merges during the shuffle. More details about the command line options are available at Commands Guide. Hadoop Streaming is a utility which allows users to create and run jobs with any executables (e.g. We’ll learn more about Job, InputFormat, OutputFormat and other interfaces and classes a bit later in the tutorial. a) MapReduce This section provides a reasonable amount of detail on every user-facing aspect of the MapReduce framework. The value for mapreduce. The value can be set using the api Configuration.set(MRJobConfig.TASK_PROFILE, boolean). b) Map Task in MapReduce is performed using the Mapper() function This is, however, not possible sometimes. Notice that the inputs differ from the first version we looked at, and how they affect the outputs. Although the Hadoop framework is written in Java, it allows developers to deploy custom- written programs coded in Java or any other language to process data in a parallel fashion across hundreds or thousands of commodity servers. a) Hadoop Strdata Point out the correct statement. The percentage of memory relative to the maximum heapsize in which map outputs may be retained during the reduce. Users/admins can also specify the maximum virtual memory of the launched child-task, and any sub-process it launches recursively, using mapreduce.{map|reduce}.memory.mb. Cloudera is the world’s most popular Hadoop distribution platform. 4. The map function helps to filter and sort data whereas reduce function deals with integrating the output results of the map function. Apache Pig and Spark expose higher level user interfaces like Pig Latin and a SQL variant respectively. The framework groups Reducer inputs by keys (since different mappers may have output the same key) in this stage. Hadoop Streaming is a utility which allows users to create and run jobs with any executables (e.g. Applications can control if, and how, the intermediate outputs are to be compressed and the CompressionCodec to be used via the Configuration. The DistributedCache can also be used to distribute both jars and native libraries for use in the map and/or reduce tasks. d) JobTracker After co… Since Job.setGroupingComparatorClass(Class) can be used to control how intermediate keys are grouped, these can be used in conjunction to simulate secondary sort on values. A ________ node acts as the Slave and is responsible for executing a Task assigned to it by the JobTracker. d) All of the mentioned Overall, Mapper implementations are passed the Job for the job via the Job.setMapperClass(Class) method. The profiler information is stored in the user log directory. The Mapper implementation, via the map method, processes one line at a time, as provided by the specified TextInputFormat. A lower bound on the split size can be set via mapreduce.input.fileinputformat.split.minsize. 3. Apache Software Foundation Note that the value set here is a per process limit. b) Map It is designed to scale up from a single server to thousands of machines, each offering local computation and storage. c) Both Mapper and Reducer Here it allows the user to specify word-patterns to skip while counting. The scaling factors above are slightly less than whole numbers to reserve a few reduce slots in the framework for speculative-tasks and failed tasks. If the mapreduce. Although the Hadoop framework is implemented in Java, MapReduce applications can be written in other programming languages (R, Python, C# etc). Since map outputs that can’t fit in memory can be stalled, setting this high may decrease parallelism between the fetch and merge. a) Mapper Comprising three main components with HDFS as storage, MapReduce as processing, and YARN as resource management, Hadoop has been successfully implemented across multiple industry verticals. {files |archives}. The properties can also be set by APIs Job.addCacheFile(URI)/ Job.addCacheArchive(URI) and [Job.setCacheFiles(URI[])](../../api/org/apache/hadoop/mapreduce/Job.html)/ [Job.setCacheArchives(URI[])](../../api/org/apache/hadoop/mapreduce/Job.html) where URI is of the form hdfs://host:port/absolute-path\#link-name. Hadoop Streaming is a utility which allows users to create and run jobs with any executables (e.g. Reducer reduces a set of intermediate values which share a key to a smaller set of values. • Hadoop Pipes is a SWIG- compatibleC++ API to implement MapReduce applications (A) Hadoop do need specialized hardware to process the data (B) Hadoop 2.0 allows live stream processing of real time data (C) In Hadoop programming framework output files are divided in to lines or records (D) None of the above Applications can then override the cleanup(Context) method to perform any required cleanup. The transformed intermediate records do not need to be of the same type as the input records. A record larger than the serialization buffer will first trigger a spill, then be spilled to a separate file. In such cases, the task never completes successfully even after multiple attempts, and the job fails. {files |archives}. It is optimized for contiguous read requests (streaming reads), where processing consists of scanning all the data. More details about the job such as successful tasks and task attempts made for each task can be viewed using the following command $ mapred job -history all output.jhist. Of course, the framework discards the sub-directory of unsuccessful task-attempts. The script is given access to the task’s stdout and stderr outputs, syslog and jobconf. See SkipBadRecords.COUNTER_MAP_PROCESSED_RECORDS and SkipBadRecords.COUNTER_REDUCE_PROCESSED_GROUPS. information to the job-clients.Although hadoop framework is implemented in java, MapReduce application need not be written in java. OutputCommitter describes the commit of task output for a MapReduce job. Hadoop needs Java to run, and the Java and Hadoop versions must fit together. Provide the RecordWriter implementation used to write the output files of the job. Job provides facilities to submit jobs, track their progress, access component-tasks’ reports and logs, get the MapReduce cluster’s status information and so on. If TextInputFormat is the InputFormat for a given job, the framework detects input-files with the .gz extensions and automatically decompresses them using the appropriate CompressionCodec. It also adds an additional path to the java.library.path of the child-jvm. View Answer, 5. c) Reducer It is undefined whether or not this record will first pass through the combiner. Hadoop data processing is done by using its MapReduce program. Increasing the number of reduces increases the framework overhead, but increases load balancing and lowers the cost of failures. shell utilities) as the mapper and/or the reducer. Setting the queue name is optional. Users can control the grouping by specifying a Comparator via Job.setGroupingComparatorClass(Class). The gzip, bzip2, snappy, and lz4 file format are also supported. For example, if mapreduce.map.sort.spill.percent is set to 0.33, and the remainder of the buffer is filled while the spill runs, the next spill will include all the collected records, or 0.66 of the buffer, and will not generate additional spills. Job setup/cleanup tasks occupy map or reduce containers, whichever is available on the NodeManager. Cloudera helps enterprises get the most out of the Hadoop framework, thanks to its packaging of the Hadoop tool in a much easy-to-use system. However, for this class we will use Java. After c… The child-jvm always has its current working directory added to the java.library.path and LD_LIBRARY_PATH. Although the Hadoop framework is implemented in Java TM, Map-Reduce applications need not be written in Java. The MapReduce framework provides a facility to run user-provided scripts for debugging. Hadoop Streaming is a utility which allows users to create and run jobs with any executables (e.g. It uses the MapReduce framework introduced by Google by leveraging the concept of map and reduce functions well known used in Functional Programming. Typically InputSplit presents a byte-oriented view of the input, and it is the responsibility of RecordReader to process and present a record-oriented view. Hadoop data processing is done by using its MapReduce program. The user can specify additional options to the child-jvm via the mapreduce. These files are shared by all tasks and jobs of the specific user only and cannot be accessed by jobs of other users on the slaves. Minimally, applications specify the input/output locations and supply map and reduce functions via implementations of appropriate interfaces and/or abstract-classes. WordCount is a simple application that counts the number of occurrences of each word in a given input set. • Hadoop Streaming is a utility which allows users to create and run jobs with any executables (e.g. 1. b) Reducer By default, the specified range is 0-2. Users can optionally specify a combiner, via Job.setCombinerClass(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. Google published two Tech Papers: one is on Google FileSystem (GFS) in October 2003 and another on MapReduce Algorithm in Dec 2004. The map function helps to filter and sort data whereas reduce function deals with integrating the output results of the map function. d) All of the mentioned shell utilities) as the mapper and/or the reducer. c) HashPartitioner For example, create the temporary output directory for the job during the initialization of the job. To increase the number of task attempts, use Job.setMaxMapAttempts(int) and Job.setMaxReduceAttempts(int). Job setup is done by a separate task when the job is in PREP state and after initializing tasks. The bug may be in third party libraries, for example, for which the source code is not available. If the task has been failed/killed, the output will be cleaned-up. Although the Hadoop framework is implemented in Java, MapReduce applications need not be written in Java. c) Applications typically implement the Mapper and Reducer interfaces to provide the map and reduce methods Output files are stored in a FileSystem. Although the Hadoop framework is implemented in Java, MapReduce applications need not be written in Java (Hadoop Streaming run jobs with any executables (e.g. View Answer, 2. This set of Multiple Choice Questions & Answers (MCQs) focuses on “Introduction to Mapreduce”. View Answer, 7. But the Hadoop Streaming API provides options to write MapReduce jobs in other languages. shell utilities), Hadoop Pipes is a SWIG-compatible C++ API). If more than one file/archive has to be distributed, they can be added as comma separated paths. However, this also means that the onus on ensuring jobs are complete (success/failure) lies squarely on the clients. WordCount also specifies a combiner. It is designed to scale up from a single server to thousands of machines, each offering local computation and storage. Although the Hadoop framework is implemented in Java, MapReduce applications need not be written in Java (Hadoop Streaming run jobs with any executables (e.g. View Answer, 4. Applications can control compression of intermediate map-outputs via the Configuration.set(MRJobConfig.MAP_OUTPUT_COMPRESS, boolean) api and the CompressionCodec to be used via the Configuration.set(MRJobConfig.MAP_OUTPUT_COMPRESS_CODEC, Class) api. b) C Hadoop is an Open Source implementation of a large-scale batch processing system. In some applications, component tasks need to create and/or write to side-files, which differ from the actual job-output files. This should help users implement, configure and tune their jobs in a fine-grained manner. The number of records skipped depends on how frequently the processed record counter is incremented by the application. Comprising three main components with HDFS as storage, MapReduce as processing, and YARN as resource management, Hadoop has been successfully implemented across multiple industry verticals. We can use mrjobs Python package to write MapReduce jobs that … {map|reduce}.java.opts are used only for configuring the launched child tasks from MRAppMaster. MAP REDUCE ARCHITECTURE Fig 2: Map Reduce 1.4 Hadoop Hadoop is a free; Java based prioritizing method that supports the transformation of huge data sets in shared computing surroundings. MapReduce framework and HDFS run on same set of nodes - can schedule tasks on nodes where data is already present •Must specify input/output locations and supply Map and Reduce functions •Although Hadoop framework is implemented in Java, the Map and Reduce functions don't need to be written in Java (can be Python, Ruby, C++, etc) If the file has no world readable access, or if the directory path leading to the file has no world executable access for lookup, then the file becomes private. The framework does not sort the map-outputs before writing them out to the FileSystem. In other words, if the user intends to make a file publicly available to all users, the file permissions must be set to be world readable, and the directory permissions on the path leading to the file must be world executable. Now, lets plug-in a pattern-file which lists the word-patterns to be ignored, via the DistributedCache. Users can control which keys (and hence records) go to which Reducer by implementing a custom Partitioner. Applications can use the Counter to report its statistics. Inputs and Outputs. The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell scripts. This usually happens due to bugs in the map function. Although the Hadoop framework is implemented in Java, any programming language can be used with Hadoop Streaming to implement the “map” and “reduce” functions. C B. C# C. Java D. None of the above. 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