There are two reasons why we might want to organize our tables (or partitions) into buckets. The first is to enable more efficient queries. Bucketing imposes extra structure on the table, which Hive can take advantage of when performing certain queries. In particular, a join of two tables that are bucketed on the same columns – which include the join columns – can be efficiently implemented as a map-side join.
The second reason to bucket a table is to make sampling more efficient. When working with large datasets, it is very convenient to try out queries on a fraction of your dataset while you are in the process of developing or refining them.
Let’s see how to tell Hive that a table should be bucketed. We use the CLUSTERED BY clause to specify the columns to bucket on and the number of buckets:
CREATE TABLE student (rollNo INT, name STRING) CLUSTERED BY (id) INTO 4 BUCKETS;
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Joins occur BEFORE WHERE CLAUSES. So, if we want to restrict the OUTPUT of a join, a requirement should be made in the WHERE clause, otherwise it should be in the JOIN clause. A big point of confusion for this issue is partitioned tables:
SELECT a.val, b.val FROM a LEFT OUTER JOIN b ON (a.key=b.key)
WHERE a.ds='2015-10-01' AND b.ds='2015-10-01'
Above query will join a on b, producing a list of a.val and b.val. The WHERE clause, however, can also reference other columns of a and b that are in the output of the join, and then filter them out. However, whenever a row from the JOIN has found a key for a and no key for b, all of the columns of b will be NULL, including the ds column. This is to say, you will filter out all rows of join output for which there was no valid b.key, and thus you have outsmarted your LEFT OUTER requirement. In other words, the LEFT OUTER part of the join is irrelevant if you reference any column of b in the WHERE clause. Instead, when OUTER JOINing, use this syntax:
SELECT a.val, b.val FROM a LEFT OUTER JOIN b
ON (a.key=b.key AND b.ds='2015-10-01' AND a.ds='2015-10-01')
Therefore, the result is that the output of the join is pre-filtered, and you won’t get post-filtering trouble for rows that have a valid a.key but no matching b.key. The same logic applies to RIGHT and FULL joins.