Assuming default settings, which best describes the order of data provided to a reducer's reduce method:
You wrote a map function that throws a runtime exception when it encounters a control character in input data. The input supplied to your mapper contains twelve such characters totals, spread across five file splits. The first four file splits each have two control characters and the last split has four control characters.Indentify the number of failed task attempts you can expect when you run the job with mapred.max.map.attempts set to 4:
You want to populate an associative array in order to perform a map-side join. You've decided to put this information in a text file, place that file into theDistributedCache and read it in your Mapper before any records are processed.Indentify which method in the Mapper you should use to implement code for reading the file and populating the associative array?
You've written a MapReduce job that will process 500 million input records and generated 500 million key-value pairs. The data is not uniformly distributed. YourMapReduce job will create a significant amount of intermediate data that it needs to transfer between mappers and reduces which is a potential bottleneck. A custom implementation of which interface is most likely to reduce the amount of intermediate data transferred across the network?
Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated files inHDFS.
How are keys and values presented and passed to the reducers during a standard sort and shuffle phase of MapReduce?
