It seems like all queries expressed in SQL can be converted into MapReduce jobs. This is in essence what Spark SQL does. SparkSQL takes in SQL, converts it to a MapReduce job then executes the MapReduce job on Spark's runtime.
All questions which can be answered by SQL can be answered by MapReduce jobs. Can all MapReduce jobs also be written as SQL (maybe with custom user defined functions)? When does it make sense to use MapReduce over SQL or vice versa?
SQL is useful when you have structured data (e.g. tables, with clearly defined columns and, usually, data types). Using SQL with that structure you can select columns, join them, etc.
With MapReduce you can do that (Spark SQL will help you do that) but you can also do much more. A typical example is a word count app that counts the words in text files. Text files do not have any predefined structure that you can use to query them using SQL. Take into account that kind of applications are usually coded using Spark core (i.e. RDD) instead of Spark SQL, since Spark SQL needs also a structure.
Another maybe more real use case is processing large amounts of log files using MapReduce (again, log files does not have a relational structure such as the one required by SQL).
SQL and MapReduce also have their own advantages. To data analysist , they don't need to learn how to write the MapReduce Program. And from the perspective of developer, writing MapReduce program leave enough room to tuning the program ,like add random prefix to skewing data. And In the long run, with the development of SQL interpreters, use SQL over MapReduce/Spark RDD.
