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Comment: Added version information on hive.enforce.bucketing

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Code Block
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';
Info
titleVersion 0.x and 1.x only

The command set hive.enforce.bucketing = true; allows the correct number of reducers and the cluster by column to be automatically selected based on the table. Otherwise, you would need to set the number of reducers to be the same as the number of buckets

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as in set mapred.reduce.tasks = 256; and have a CLUSTER BY ... clause in the select.

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.