set hive.enforce.bucketing = true; -- (Note: Not needed in Hive 2.x onward) FROM user_id INSERT OVERWRITE TABLE user_info_bucketed PARTITION (ds='2009-02-25') SELECT userid, firstname, lastname WHERE ds='2009-02-25';
How does Hive distribute the rows across the buckets? In general, the bucket number is determined by the expression
hash_function(bucketing_column) mod num_buckets. (There's a '0x7FFFFFFF in there too, but that's not that important). The hash_function depends on the type of the bucketing column. For an int, it's easy,
hash_int(i) == i. For example, if user_id were an int, and there were 10 buckets, we would expect all user_id's that end in 0 to be in bucket 1, all user_id's that end in a 1 to be in bucket 2, etc. For other datatypes, it's a little tricky. In particular, the hash of a BIGINT is not the same as the BIGINT. And the hash of a string or a complex datatype will be some number that's derived from the value, but not anything humanly-recognizable. For example, if user_id were a STRING, then the user_id's in bucket 1 would probably not end in 0. In general, distributing rows based on the hash will give you a even distribution in the buckets.
So, what can go wrong? As long as you use the syntax above and
set hive.enforce.bucketing = true, and use the syntax above (for Hive 0.x and 1.x), the tables should be populated properly. Things can go wrong if the bucketing column type is different during the insert and on read, or if you manually cluster by a value that's different from the table definition.