1. job. Proper tuning of the number of MapReduce tasks. In the code, one can configure JobConf variables. tasks property. (1 reply) I did a "select count(*) from", it's quite slow and I try to set mapred.reduce.tasks higher, but the reduce task turn out always unchanged and remain to 1(I can see it in the mapreduce administrator Web UI). The key and value classes have to be serializable by the framework and hence need to implement the Writable interface. You can modify using set mapred.map.tasks = b. mapred.reduce.tasks - The default number of reduce tasks per job is 1. 4.1.1 About Balancing Jobs Across Map and Reduce Tasks. In MapReduce job, if each task takes 30-40 seconds or more, then it will reduce the number of tasks. 4.1.1 About Balancing Jobs Across Map and Reduce Tasks. I have tried doubling the size of dfs.block.size. Number of mappers and reducers can be set like (5 mappers, 2 reducers):-D mapred.map.tasks=5 -D mapred.reduce.tasks=2 in the command line. In this case, reducer starts are scheduled as described in the following table: Ignored when mapred.job.tracker is "local". A typical Hadoop job has map and reduce tasks. When I submit a map/reduce job to process a file of size about 16 GB, in job.xml, I have the following mapred.map.tasks =242 mapred.min.split.size =0 dfs.block.size = 67108864 I would like to reduce mapred.map.tasks to see if it improves performance. 2.3. For example, jar word_count.jar com.home.wc.WordCount /input /output \ -D mapred.reduce.tasks = 20 The Map/Reduce framework operates exclusively on pairs, that is, the framework views the input to the job as a set of pairs and produces a set of pairs as the output of the job, conceivably of different types. A typical Hadoop job has map and reduce tasks. Hadoop also hashes the map-output keys uniformly across all reducers. a. mapred.map.tasks - The default number of map tasks per job is 2. Set mapred.compress.map.output to true to enable LZO compression. The number of reducers can be set in two ways as below: Using the command line: While running the MapReduce job, we have an option to set the number of reducers which can be specified by the controller mapred.reduce.tasks. Hadoop distributes the mapper workload uniformly across Hadoop Distributed File System (HDFS) and across map tasks while preserving the data locality. For example, assuming there is a total of 100 slots, to assign 100 reduce slots until 50% of 300 maps are complete, for Hadoop 1.1.1, you would specify options as follows: -Dmapred.reduce.tasks=100-Dmapred.reduce.slowstart.completed.maps=0.5. Update the driver program and set the setNumReduceTasks to the desired value on the job object. The key and value classes have to be serializable by the framework and hence need to implement the Writable interface. The Map-Reduce framework operates exclusively on pairs, that is, the framework views the input to the job as a set of pairs and produces a set of pairs as the output of the job, conceivably of different types. Hadoop also hashes the map-output keys uniformly across all reducers. In this way, it reduces skew in the mappers. A quick way to submit the debug script is to set values for the properties mapred.map.task.debug.script and mapred.reduce.task.debug.script, for debugging map and reduce tasks respectively. The mapper or reducer process involves following things: first, you need to start JVM (JVM loaded into the memory). These properties can also be set by using APIs JobConf.setMapDebugScript(String) and JobConf.setReduceDebugScript(String) . In this way, it reduces skew in the mappers. reduce. 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