Advanced actions
Upto now we have covered actions which focused on processing the entire data however big or small it may be. There are use cases where we might just need approximate values, or where we would like an asynchronous action which does not wait for the result, or even just the data from specific partitions. In this section,we will touch upon the appropriate actions that serve these specific requirements.
Approximate actions
In the previous chapter, we came across different methods in which RDD could be sampled to give a randomized output. This works fine as long as we want to debug or test our application. However, in other scenarios we might not want to get results which are accurate but which take a long time to execute, but rather require an approximate result within a certain percentage of error and in a time-bound manner. Spark has introduced approximate algorithms to cater to such needs, where the job can guarantee a result within a stipulated timeframe or/and within an error...
