The problem is that there are 3,000 map tasks are launched to read the daily data and there are 250 distinct event types, so the mappers will produce
3,000 * 250 = 750,000files per day. That’s too much.
Reducing the number of map tasks is not always an option as it increases the execution time, and the number of files is still high (500 tasks will produce 125,000 files per day), while our goal is to have about 500-700 files daily for this single table.
A well-known solution for this problem is to force a reduce phase. But what if the data is highly skewed, for example, when there are few event types having hundreds of millions rows, while others just have a few million rows?
This is a combination of an interesting problem and an interesting solution.