查询在具有1100万行的大型表上执行。在执行查询之前,我已经在表上执行了ANALYZE
。
查询1:
SELECT *
FROM accounts t1
LEFT OUTER JOIN accounts t2
ON (t1.account_no = t2.account_no
AND t1.effective_date < t2.effective_date)
WHERE t2.account_no IS NULL;
解释分析:
Hash Anti Join (cost=480795.57..1201111.40 rows=7369854 width=292) (actual time=29619.499..115662.111 rows=1977871 loops=1)
Hash Cond: ((t1.account_no)::text = (t2.account_no)::text)
Join Filter: ((t1.effective_date)::text < (t2.effective_date)::text)
-> Seq Scan on accounts t1 (cost=0.00..342610.81 rows=11054781 width=146) (actual time=0.025..25693.921 rows=11034070 loops=1)
-> Hash (cost=342610.81..342610.81 rows=11054781 width=146) (actual time=29612.925..29612.925 rows=11034070 loops=1)
Buckets: 2097152 Batches: 1 Memory Usage: 1834187kB
-> Seq Scan on accounts t2 (cost=0.00..342610.81 rows=11054781 width=146) (actual time=0.006..22929.635 rows=11034070 loops=1)
Total runtime: 115870.788 ms
估计费用为〜120万ozt_rstrong,实际花费的时间为〜1.9分钟。
查询2:
SELECT t1.*
FROM accounts t1
LEFT OUTER JOIN accounts t2
ON (t1.account_no = t2.account_no
AND t1.effective_date < t2.effective_date)
WHERE t2.account_no IS NULL;
解释分析:
Hash Anti Join (cost=480795.57..1201111.40 rows=7369854 width=146) (actual time=13365.808..65519.402 rows=1977871 loops=1)
Hash Cond: ((t1.account_no)::text = (t2.account_no)::text)
Join Filter: ((t1.effective_date)::text < (t2.effective_date)::text)
-> Seq Scan on accounts t1 (cost=0.00..342610.81 rows=11054781 width=146) (actual time=0.007..5032.778 rows=11034070 loops=1)
-> Hash (cost=342610.81..342610.81 rows=11054781 width=18) (actual time=13354.219..13354.219 rows=11034070 loops=1)
Buckets: 2097152 Batches: 1 Memory Usage: 545369kB
-> Seq Scan on accounts t2 (cost=0.00..342610.81 rows=11054781 width=18) (actual time=0.011..8964.571 rows=11034070 loops=1)
Total runtime: 65705.707 ms
估计费用为〜120万(再次),但实际花费的时间为 。
查询3:
SELECT *
FROM accounts
WHERE (account_no,
effective_date) IN
(SELECT account_no,
max(effective_date)
FROM accounts
GROUP BY account_no);
解释分析:
Nested Loop (cost=406416.19..502216.84 rows=2763695 width=146) (actual time=31779.457..917543.228 rows=1977871 loops=1)
-> HashAggregate (cost=406416.19..406757.45 rows=34126 width=43) (actual time=31774.877..33378.968 rows=1977425 loops=1)
-> Subquery Scan on "ANY_subquery" (cost=397884.72..404709.90 rows=341259 width=43) (actual time=27979.226..29841.217 rows=1977425 loops=1)
-> HashAggregate (cost=397884.72..401297.31 rows=341259 width=18) (actual time=27979.224..29315.346 rows=1977425 loops=1)
-> Seq Scan on accounts (cost=0.00..342610.81 rows=11054781 width=18) (actual time=0.851..16092.755 rows=11034070 loops=1)
-> Index Scan using accounts_idx2 on accounts (cost=0.00..2.78 rows=1 width=146) (actual time=0.443..0.445 rows=1 loops=1977425)
Index Cond: (((account_no)::text = ("ANY_subquery".account_no)::text) AND ((effective_date)::text = "ANY_subquery".max))
Total runtime: 918039.614 ms
估计费用为〜502,000 ,但实际花费的时间为〜15.3分钟!
EXPLAIN
输出的可靠性如何? EXPLAIN ANALYZE
来查看我们的查询如何对真实数据执行,而不会信任来确定查询计划者会花费多少? 最佳答案
它们是可靠的,除非不是。您无法真正概括。
看来它大大低估了它将发现的不同account_no的数量(认为它将发现34126实际上是在1977425找到的)。您的default_statistics_target可能不够高,无法获得此列的良好估算值。
关于sql - PostgreSQL解释计划中的成本计量有多可靠?,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/21154780/