MapReduce is a method to process vast sums of data in parallel without requiring the developer to write any code other than the mapper and reduce functions. The map function takes data in and churns out a result, which is held in a barrier.
Compared to MapReduce, which creates a DAG with two predefined stages - Map and Reduce, DAGs created by Spark can contain any number of stages. DAG is a strict generalization of MapReduce model.
mapreduce.shuffle.max.threads: Number of worker threads for copying the map outputs to reducers. mapreduce.reduce.shuffle.input.buffer.percent: How much of heap should be used for storing the map output, during the shuffle phase in the reducer.
MapReduce's use of input files and lack of schema support prevents the performance improvements enabled by common database system features such as B-trees and hash partitioning, though projects such as PigLatin and Sawzall are starting to address these problems.
For each input split a map task is spawned. So, over the lifetime of a mapreduce job the number of map tasks is equal to the number of input splits. mapred.map.tasks is just a hint to the InputFormat for the number of maps. In your example Hadoop has determined there are 24 input splits and will spawn 24 map tasks in total.
If you are using Hadoop Streaming, you can use the JobConf variables in a streaming job's mapper/reducer. As for the input file name of mapper, see the Configured Parameters section, the map.input.file variable (the filename that the map is reading from) is the one can get the jobs done. But note that: Note: During the execution of a streaming job, the names of the "mapred" parameters are ...
I am getting: FAILED: Execution Error, return code 2 from org.apache.hadoop.hive.ql.exec.MapRedTask While trying to make a copy of a partitioned table using the commands in the hive console: CREATE
Can any one explain me how secondary sorting works in hadoop ? Why must one use GroupingComparator and how does it work in hadoop ? I was going through the link given below and got doubt on how
