...
REPLICATED caches with "ANY" policy should be eliminated as follows:
Code Block | ||||||
---|---|---|---|---|---|---|
| ||||||
(P1, :2) AND (ANY) => (P1, :2) (P1, :2) OR (ANY) => (P1, :2) |
If partition tree contain rules from different co-location groups, then exit.
TODO
TODO
TODO
This technique is used excessively by both distributed databases and classical RDBMS vendors (as a part of their partitioning/sharding solutions). First, query is analyzed and optionally re-written to allow for better partition pruning. Second, information of interesting partitions are extracted from query for every table (cache) and subquery (for complex multi-stage distributed queries).
WHERE conditions and JOIN-s are relatively simple targets for partition extraction. The main difficulty is subqueries, as they potentially introduce unlimited nesting that complicates analysis a lot. So the main goal of query rewrite is to minimize number of subqueries. Main techniques are JOIN conversion, correlation and materialization. Interestingly, all these techniques not only simplify partition extraction, but also allow optimizer to choose better execution plan for local execution, as JOINs are much easier to be analyzed and reordered than subqueries. As we shall see below, efforts are usually focused on non-correlated subqueries as they are the most costly for straightforward execution.
It is not easy to extract partitions from subqueries. But we can rewrite certain subqueries to joins with a technique called "join conversion". Important The goal is to convert a subquery to join if possible. Important prerequisite is that number of resulting rows is not changed. Outer(FK) → inner(PK) joins are possible. E.g. subqueries in SELECT (aka "derived table") and FROM clauses.
Example 1: JOIN conversion for derived table
...
Code Block | ||||||
---|---|---|---|---|---|---|
| ||||||
SELECT emp.name
FROM emp, dept
WHERE emp.salary>1000 AND emp.dept_id=dept.id AND dept.state='CA' |
Another example where inner subquery return at most one row is outer(PK) → inner(FK GROUP BY PK) case. Subquery is not eliminated in this case, but IN expression is replaced with more comfortable JOIN.
Example 4: Replacing IN with JOIN
Code Block | ||||||
---|---|---|---|---|---|---|
| ||||||
SELECT dept.name
FROM dept
WHERE dept.id IN (SELECT emp.dept_id FROM emp GROUP BY emp.dept_id HAVING COUNT(*)>10) |
Code Block | ||||||
---|---|---|---|---|---|---|
| ||||||
SELECT dept.name
FROM dept JOIN (SELECT emp.dept_id FROM emp GROUP BY emp.dept_id HAVING COUNT(*)>10) emp_subquery ON dept.id=emp_subquery.dept_id |
Uncorrelated subquery may need to scan huge result set. If underlying cache is PARTITIONED
, then multiple nodes need to be scanned. If underlying cache is REPLICATED
, then scan is local. If previous optimizations are not applicable, we can try to push-down condition from outer table to subquery. If condition is equality and both outer and inner columns are co-located, then potentially distributed query is reduced to co-located query. The downside is that subquery must be evaluated multiple times, once for every tuple from outer relation. This might be less than optimal comparing to subquery materialization explained below. MySQL refer to this as IN-to-EXISTS optimization [2].
Example 5: Converting non-correlated subquery to correlated via condition push-down
Code Block | ||||||
---|---|---|---|---|---|---|
| ||||||
SELECT emp.name FROM emp WHERE emp.dept_id IN (SELECT id FROM dept WHERE state='CA') | ||||||
Code Block | ||||||
| ||||||
SELECT emp.name FROM emp WHERE EXISTS (SELECT id FROM dept WHERE dept.id=emp.dept_id AND state='CA') |
...
This way some queries which were too complex to extract target partitions from may be reduced to simpler form, where extraction is possible.
[1] https://mariadb.com/kb/en/library/table-pullout-optimization/
[2] https://dev.mysql.com/doc/refman/8.0/en/subquery-optimization-with-exists.html
...
TODO
...
TODO
Jira | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
|