我试图让这个查询在过去两天有效地运作。我已经了解了更多关于Oracle索引行为的信息，我认为我在这一点上很困惑什么应该有效，哪些无效。
基本上，查询是汇总值并与昨天和上周的值进行比较。
我玩弄了它，我玩弄了我的思维分析查询和改变指数的顺序，但似乎没有任何效果。我所有的测试都放在一张有500K行的桌子上，只要我在一张有2000万行的桌子上运行它就会永远。
非常感谢任何帮助。
我修改了原帖以帮助您。 ：）
CREATE TABLE TABLE_1
(ORDER_LINE_ID NUMBER, OFFSET NUMBER, BREAK_ID NUMBER, ZONE NUMBER, NETWORK NUMBER, HOUR_OF_DAY NUMBER, START_TIME DATE, END_TIME DATE, SUCCESS NUMBER
CONSTRAINT "TABLE_1_PK" PRIMARY KEY (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, HOUR_OF_DAY))
-- SUCCESS is already aggregated during the insert
-- These are last week's records
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS)
VALUES (1,0,1, 1, 1, 2016042001,'04/20/2016 00:00:00', '04/20/2016 02:00:00', 1);
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS)
VALUES (1,30,1, 1, 1, 2016042001,'04/20/2016 00:00:00', '04/20/2016 02:00:00', 2);
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS)
VALUES (2,0,1, 1, 1, 2016042001,'04/20/2016 00:00:00', '04/20/2016 02:00:00', 1);
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS)
VALUES (2,30,1, 1, 1, 2016042001,'04/20/2016 00:00:00', '04/20/2016 02:00:00', 1);
-- These are yesterday's records
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS)
VALUES (3,0,1, 1, 1, 2016042601,'04/26/2016 00:00:00', '04/26/2016 02:00:00', 1);
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS)
VALUES (3,30,1, 1, 1, 2016042601,'04/26/2016 00:00:00', '04/26/2016 02:00:00', 2);
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS)
VALUES (4,0,1, 1, 1, 2016042601,'04/26/2016 00:00:00', '04/26/2016 02:00:00', 1);
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS)
VALUES (4,30,1, 1, 1, 2016042601,'04/26/2016 00:00:00', '04/26/2016 02:00:00', 1);
-- This is today's records
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS)
VALUES (5,0,1, 1, 1, 2016042701,'04/27/2016 00:00:00', '04/27/2016 02:00:00', 1);
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS)
VALUES (5,30,1, 1, 1, 2016042701,'04/27/2016 00:00:00', '04/27/2016 02:00:00', 1);
-- Original twice join query
SELECT BREAK_ID, ORDER_LINE_ID, HOUR_OF_DAY, OFFSET, ZONE, NETWORK, START_TIME, END_TIME, SUM(SUCCESS), SUM(YESTERDAY_SUCCESS), SUM(LAST_WEEK_SUCCESS)
FROM TABLE_1 CURRENT_DAY
LEFT OUTER JOIN (
SELECT SUM(SUCCESS) YESTERDAY_SUCCESS, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME FROM TABLE_1
GROUP BY ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME
) YESTERDAY
ON YESTERDAY.START_TIME + 1 = CURRENT_DAY.START_TIME
AND YESTERDAY.END_TIME + 1 = CURRENT_DAY.END_TIME
AND YESTERDAY.HOUR_OF_DAY = CURRENT_DAY.HOUR_OF_DAY
AND YESTERDAY.NETWORK = CURRENT_DAY.NETWORK
AND YESTERDAY.ZONE = CURRENT_DAY.ZONE
LEFT OUTER JOIN (
SELECT SUM(SUCCESS) LAST_WEEK_SUCCESS, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME FROM TABLE_1
GROUP BY ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME
) LAST_WEEK
ON YESTERDAY.START_TIME + 7 = CURRENT_DAY.START_TIME
AND YESTERDAY.END_TIME + 7 = CURRENT_DAY.END_TIME
AND YESTERDAY.HOUR_OF_DAY = CURRENT_DAY.HOUR_OF_DAY
AND YESTERDAY.NETWORK = CURRENT_DAY.NETWORK
AND YESTERDAY.ZONE = CURRENT_DAY.ZONE
GROUP BY BREAK_ID, ORDER_LINE_ID, HOUR_OF_DAY, OFFSET, ZONE, NETWORK, START_TIME, END_TIME;
-- Using Analytic Query (thank you to MT0)
SELECT BREAK_ID, ORDER_LINE_ID, HOUR_OF_DAY, OFFSET, ZONE, NETWORK, START_TIME, END_TIME, SUM(SUCCESS), SUM(YESTERDAY_SUCCESS), SUM(LAST_WEEK_SUCCESS)
FROM (
SUM( SUCCESS )
OVER ( PARTITION BY ZONE, NETWORK, HOUR_OF_DAY, TO_CHAR(START_TIME, 'HH24:MI:SS'), TO_CHAR(END_TIME, 'HH24:MI:SS')
ORDER BY START_TIME
RANGE BETWEEN INTERVAL '1' DAY PRECDEDING AND INTERVAL '1' DAY PRECEDING
) AS YESTERDAY_SUCCESS,
SUM ( SUCCESS )
OVER ( PARTITION BY ZONE, NETWORK, HOUR_OF_DAY, TO_CHAR(START_TIME, 'HH24:MI:SS'), TO_CHAR(END_TIME, 'HH24:MI:SS')
ORDER BY START_TIME
RANGE BETWEEN INTERVAL '7' DAY PRECDEDING AND INTERVAL '7' DAY PRECEDING
) AS LAST_WEEK_SUCCESS
FROM TABLE_1
) T1
WHERE SYSDATE - INTERVAL '12' HOUR <= START_TIME
AND START_TIME < SYSDATE - INTERVAL '1' HOUR
GROUP BY BREAK_ID, ORDER_LINE_ID, HOUR_OF_DAY, OFFSET, ZONE, NETWORK, START_TIME, END_TIME;
我必须说谢谢你帮助将这个问题提升到我希望更容易理解的问题。一切都按预期工作，但性能可以使用一些调整。
在500K行的桌子上1.8秒
在具有2000万行的表格上400秒
我还想添加Oracle提供的一些执行计划。我在调整性能方面遇到了麻烦。
-- using twice self join
--------------------------------------------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | Writes | OMem | 1Mem | O/1/M |
--------------------------------------------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 50 |00:00:00.84 | 99875 | 217 | 1705 | | | |
| 1 | HASH GROUP BY | | 1 | 6711 | 50 |00:00:00.84 | 99875 | 217 | 1705 | 1616K| 995K| |
|* 2 | FILTER | | 1 | | 119K|00:00:00.65 | 99875 | 0 | 0 | | | |
| 3 | NESTED LOOPS OUTER | | 1 | 54M| 119K|00:00:00.64 | 99875 | 0 | 0 | | | |
|* 4 | HASH JOIN OUTER | | 1 | 109 | 119K|00:00:00.52 | 99875 | 0 | 0 | 13M| 2093K| 1/0/0|
| 5 | TABLE ACCESS BY INDEX ROWID| TABLE_1_IDX | 1 | 109 | 119K|00:00:00.14 | 85908 | 0 | 0 | | | |
|* 6 | INDEX RANGE SCAN | START_TIME_IDX | 1 | 109 | 119K|00:00:00.02 | 320 | 0 | 0 | | | |
| 7 | VIEW | | 1 | 1250 | 29311 |00:00:00.23 | 13967 | 0 | 0 | | | |
| 8 | HASH GROUP BY | | 1 | 1250 | 29311 |00:00:00.22 | 13967 | 0 | 0 | 3008K| 1094K| 1/0/0|
|* 9 | FILTER | | 1 | | 88627 |00:00:00.20 | 13967 | 0 | 0 | | | |
|* 10 | TABLE ACCESS FULL | TABLE_1 | 1 | 1250 | 88627 |00:00:00.19 | 13967 | 0 | 0 | | | |
| 11 | VIEW | | 119K| 499K| 0 |00:00:00.10 | 0 | 0 | 0 | | | |
| 12 | SORT GROUP BY | | 119K| 499K| 0 |00:00:00.08 | 0 | 0 | 0 | 1024 | 1024 | 1/0/0|
|* 13 | FILTER | | 119K| | 0 |00:00:00.02 | 0 | 0 | 0 | | | |
| 14 | TABLE ACCESS FULL | TABLE_1 | 0 | 499K| 0 |00:00:00.01 | 0 | 0 | 0 | | | |
--------------------------------------------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - filter(SYSDATE@!-17<SYSDATE@!-16)
4 - access("YESTERDAY"."ZONE"="T1"."ZONE" AND "YESTERDAY"."NETWORK"="T1"."NETWORK" AND "YESTERDAY"."HOUR_OF_DAY"="T1"."HOUR_OF_DAY"
AND "T1"."END_TIME"=INTERNAL_FUNCTION("YESTERDAY"."END_TIME")+1 AND
"T1"."START_TIME"=INTERNAL_FUNCTION("YESTERDAY"."START_TIME")+1)
6 - access("T1"."START_TIME">=SYSDATE@!-17 AND "T1"."START_TIME"<SYSDATE@!-16)
9 - filter(SYSDATE@!-17<SYSDATE@!-16)
10 - filter((INTERNAL_FUNCTION("START_TIME")+1>=SYSDATE@!-17 AND INTERNAL_FUNCTION("START_TIME")+1<SYSDATE@!-16))
13 - filter(("YESTERDAY"."ZONE"="T1"."ZONE" AND "YESTERDAY"."NETWORK"="T1"."NETWORK" AND "YESTERDAY"."HOUR_OF_DAY"="T1"."HOUR_OF_DAY"
AND "T1"."END_TIME"=INTERNAL_FUNCTION("YESTERDAY"."END_TIME")+7 AND
"T1"."START_TIME"=INTERNAL_FUNCTION("YESTERDAY"."START_TIME")+7))
使用Analytic Query的另一个执行计划（再次感谢MT0）
-- using analytic query
-------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | O/1/M |
-------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 50 |00:00:01.51 | 13967 | | | |
| 1 | HASH GROUP BY | | 1 | 499K| 50 |00:00:01.51 | 13967 | 98M| 7788K| |
|* 2 | VIEW | | 1 | 499K| 119K|00:00:01.15 | 13967 | | | |
| 3 | WINDOW SORT | | 1 | 499K| 499K|00:00:01.43 | 13967 | 66M| 2823K| 1/0/0|
|* 4 | FILTER | | 1 | | 499K|00:00:00.16 | 13967 | | | |
| 5 | TABLE ACCESS FULL| TABLE_1 | 1 | 499K| 499K|00:00:00.12 | 13967 | | | |
-------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
2 - filter(("T1"."START_TIME">=SYSDATE@!-INTERVAL'+17 00:00:00' DAY(2) TO SECOND(0) AND
"T1"."START_TIME"<SYSDATE@!-INTERVAL'+16 00:00:00' DAY(2) TO SECOND(0)))
4 - filter(SYSDATE@!-INTERVAL'+17 00:00:00' DAY(2) TO SECOND(0)<SYSDATE@!-INTERVAL'+16 00:00:00' DAY(2) TO
SECOND(0))
正如您所看到的，我在start_time上添加了一个索引，自我加入查询会从中受益，但估计与实际值相关。 Analytic Query只是决定它与索引无关。非常感谢任何想法，参考点或帮助。提前谢谢大家。
答案 0 :(得分：1)
目前还不清楚为什么只有在今天和昨天（或上周）有完全相同的行时加入，但如果你只想要在某些时间之间的行，那么你可以消除所有的自连接和做：
SELECT order_line,
zone,
network,
sum(
CASE WHEN SYSDATE - INTERVAL '12' HOUR <= start_time
AND start_time < SYSDATE - INTERVAL '1' HOUR
THEN success
END
) AS total_successes_today,
sum(
CASE WHEN SYSDATE - INTERVAL '12' HOUR <= start_time
AND start_time < SYSDATE - INTERVAL '1' HOUR
THEN error
END
) AS total_errors_today,
sum(
CASE WHEN SYSDATE - INTERVAL '36' HOUR <= start_time
AND start_time < SYSDATE - INTERVAL '25' HOUR
THEN success
END
) AS total_successes_yesterday,
sum(
CASE WHEN SYSDATE - INTERVAL '180' HOUR <= start_time
AND start_time < SYSDATE - INTERVAL '169' HOUR
THEN success
END
) AS total_successes_last_week
FROM table_1
WHERE ( SYSDATE - INTERVAL '12' HOUR <= start_time
AND start_time < SYSDATE - INTERVAL '1' HOUR ) -- today
OR ( SYSDATE - INTERVAL '36' HOUR <= start_time
AND start_time < SYSDATE - INTERVAL '25' HOUR ) -- yesterday = today + 24 hours
OR ( SYSDATE - INTERVAL '180' HOUR <= start_time
AND start_time < SYSDATE - INTERVAL '169' HOUR ) -- last week = today + 7*24 hours
但是，如果您确实希望在开始和结束时间保持联接，那么您可以使用分析查询：
SELECT order_line,
zone,
network,
SUM( success ),
SUM( error ),
SUM( yesterday_success ),
SUM( last_week_success )
FROM (
SELECT t.*,
SUM( success )
OVER ( PARTITION BY id,
TO_CHAR( start_time, 'HH24:MI:SS' ),
TO_CHAR( end_time, 'HH24:MI:SS' )
ORDER BY start_time
RANGE BETWEEN INTERVAL '1' DAY PRECEDING AND INTERVAL '1' DAY PRECEDING
) AS yesterday_success,
SUM( success )
OVER ( PARTITION BY id,
TO_CHAR( start_time, 'HH24:MI:SS' ),
TO_CHAR( end_time, 'HH24:MI:SS' )
ORDER BY start_time
RANGE BETWEEN INTERVAL '7' DAY PRECEDING AND INTERVAL '7' DAY PRECEDING
) AS last_week_success
FROM TABLE_1 t
)
WHERE SYSDATE - INTERVAL '12' HOUR <= start_time
AND start_time < SYSDATE - INTERVAL '1' HOUR
GROUP BY
order_line,
zone,
network
ORDER BY
order_line,
zone,
network
您可以通过在TO_CHAR( start_time, 'HH24:MI:SS' )和TO_CHAR( end_time, 'HH24:MI:SS' )上使用基于函数的索引来查看是否可以提高速度。