Final answer:
In the MapReduce framework, the Map phase is the one that outputs the joinkey as the key for intermediate key-value pairs which are to be joined. The Map function tags each record with a common key, while the Reduce phase performs the actual join.
Step-by-step explanation:
When using MapReduce to implement the join operator, the phase that uses the joinkey as its output key is the Map phase. In a typical MapReduce join operation, the Map function processes each input record and emits intermediate key-value pairs where the key is the join key along with some sort of tag to identify the source dataset. The Reduce function then receives these key-value pairs, grouped by key, and is responsible for combining the values from both datasets based on the join condition.
In the Map phase, two sets of data are tagged with a common key for joining, and output key-value pairs are generated. During the Data Shuffle phase, the MapReduce framework automatically groups together all intermediate values associated with the same key, ensuring that all values that share a key are sent to the same Reducer. In the final Reduce phase, the join is completed as the reducer has access to all the values for each key, thus performing the actual join operation.
MapReduce join operations can be complex and depend on the specific requirements such as whether it's an inner join, left join, right join, or full outer join. The exact logic of the join is determined by the coding of the Map and Reduce functions. However, it is the Map phase that is critical for determining which keys will be used for joining the datasets.