I have k csv files (5 csv files for example), each file has m fields which produce a key and n values. I need to produce a single csv file with aggregated data. I'm looking for the most efficient solution for this problem, speed mainly. I don't think by the way that we will have memory issues. Also I would like to know if hashing is really a good solution because we will have to use 64 bit hashing solution to reduce the chance for a collision to less than 1% (we are having around 30000000 rows per aggregation). For example file 1: f1,f2,f3,v1,v2,v3,v4 a1,b1,c1,50,60,70,80 a3,b2,c4,60,60,80,90 file 2: f1,f2,f3,v1,v2,v3,v4 a1,b1,c1,30,50,90,40 a3,b2,c4,30,70,50,90 result: f1,f2,f3,v1,v2,v3,v4 a1,b1,c1,80,110,160,120 a3,b2,c4,90,130,130,180 algorithm that we thought until now: hashing (using concurentHashTable) merge sorting the files DB: using mysql or hadoop or redis. The solution needs to be able to handle Huge amount of data (each file more than two million rows) a better example: file 1 country,city,peopleNum england,london,1000000 england,coventry,500000 file 2: country,city,peopleNum england,london,500000 england,coventry,500000 england,manchester,500000 merged file: country,city,peopleNum england,london,1500000 england,coventry,1000000 england,manchester,500000 The key is: country,city. This is just an example, my real key is of size 6 and the data columns are of size 8 - total of 14 columns. We would like that the solution will be the fastest in regard of data processing.