Hello,
I am interested in learning what the best solution/practices might be to
join 3 accumulo tables by running a map reduce job. Interested in getting
feedback on best practices and such. Heres a pseudo code of what I want to
accomplish:
AccumuloInputFormat accepts tableA
Global variable <table_list> has table names: tableB, tableC
In a mapper, for example, you would do something like this:
for each row in TableA
if (row.family == "abc" && row.qualifier == "xyz") value = getValue()
if (foundvalue) {
for each table in table_list
scan table with (this rowid && family = "def")
for each entry found in scan
write to final_table (rowid, value_as_family, tablename_as_qualifier,
entry_as_value_string)
}//end if foundvalue
}//end for loop
This is a simple version of what I want to do. In my non mapreduce java
code I would do this by calling a using different scanners per table in the
list. Couple questions:
- how bad/good is performance when using scanners withing mappers?
- if I get one mapper per range in tableA, do I reset scanners? how? or
would I set up a scanner in the setup() of mapper ? --> i have no clue how
this will play out so thinking out loud here.
- any optimization suggestions? or examples of creating join_tables/indexes
out there that I can refer to?
Thank you for all suggestions.
