We have a 10 million row table exported from AS400 mainframe every day, the
table is exported as a csv text file, which is about 30GB in size, then the csv
file is imported into a RDBMS table which is dropped and recreated every day.
Now we want to find how many rows are updated during each export-import
interval, the table has a primary key, so deletes and inserts can be found
using RDBMS joins quickly, but we must do a column to column comparing in order
to find the difference between rows ( about 90%) with the same primary keys.
Our goal is to find a comparing process which takes no more than 10 minutes
with a 4-node cluster, each server in which has 4 4-core 3.0 GHz CPUs, 8GB
memory and a 300G local RAID5 array.
Bellow is our current solution:
The old data is kept in the RDBMS with index created on the primary key,
the new data is imported into HDFS as the input file of our Map-Reduce job.
Every map task connects to the RDBMS database, and selects old data from it for
every row, map tasks will generate outputs if differences are found, and there
are no reduce tasks.
As you can see, with the number of concurrent map tasks increasing, the RDBMS
database will become the bottleneck, so we want to kick off the RDBMS, but we
have no idea about how to retrieve the old row with a given key quickly from
HDFS files, any suggestion is welcome.
