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Hadoop > Learn Hadoop > How can I improve my MapReduce job performance in the...
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How can I improve my MapReduce job performance in the Standalone mode?

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Technical Lead & Archiect - Azure & Databricks, Spark,Kafka,Snowflake,Scala,Pyspark,AWS Cloud,NoSQL

while running the jar file on command prompt you can use -m noofmapper which increase the performance .
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It depends upon the profile results generated by your job but without understanding I would suggest primary tuning techniques below: Memory optimization - set mapred.child.java.opts in your configuration file and try to allocate as much memory as you can without triggering swapping. Speculative Execution:...
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It depends upon the profile results generated by your job but without understanding I would suggest primary tuning techniques below: Memory optimization - set mapred.child.java.opts in your configuration file and try to allocate as much memory as you can without triggering swapping. Speculative Execution: MR jobs get impacted when tasks take long time to finish the execution. speculative execution would help by backing up slow tasks on alternate machines. set ‘mapreduce.map.tasks.speculative.execution’ and ‘mapreduce.reduce.tasks.speculative.execution’ to true for enabling speculative execution. This will reduce the job execution time if the task progress is slow due to memory unavailability. Not sure if you are working with SSD's but if not the take a look at disk I/O profile and minimise disk spill if possible. read less
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Configure your cluster correctly, Use LZO CompressionTune the number of map and reduce tasks appropriately,Write a Combiner,Use the most appropriate and compact Writable type for your data,Reuse Writables,Use “Poor Man’s Profiling” to see what your tasks are doing
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