我有以下问题。
我有三张桌子 1.）记录所有运行实例的所有用户历史表 2.）记录所有实例的所有实例表 3.）实例表，我可以在其中识别活动实例
我的目标是让所有用户都成为活动实例的一部分。
问题是所有用户表都拥有1370亿条记录，因此无法通过一个查询加入它。
到目前为止我最好的查询：
SELECT allcontact.users FROM allcontact
WHERE EXISTS
(
SELECT 1
FROM allinstances
WHERE allinstances.instances = allcontact.instances
AND EXISTS
(
SELECT 1
FROM activeinstances
WHERE 1 = 1
AND activeinstances.end_date> CURRENT_TIMESTAMP
AND activeinstances.run_id = allinstances.run_id
AND activeinstances.run_date = allinstances.run_date))
QUALIFY ROW_NUMBER() OVER (PARTITION BY allcontact.users ORDER BY allcontact.users DESC)=1
目前它使用以下逻辑。它检查end_date大于当前日期的所有运行，然后从allinstance表中获取满足这些条件的所有实例。但是，此查询最终会出现假脱机空间问题。
我需要这样做的原因是一次运行可能包含activeinstances表中不存在的实例，因此我需要根据运行日期和run_id进行所有运行，并从所有实例表中查找。这个查询给了我正确的结果，但是如果我减少了在最终生产时无法做到的结果数量，我只能运行它。
如果我创建一个包含所有实例的volatile表，然后将其与allcontact表连接，我就可以运行它。但是，在这个查询应该去的最终产品中，我无法创建易失性表。
如果有人能提出如何使用一个查询来运行此建议，我将不胜感激。
环境是位于teradata之上的IBM活动。
谢谢！
编辑添加了更多内容
主键： allcontact表 PK：cntct_id
allinstances表 PK：实例
activeinstances表 PK：实例
解释计划：
SELECT allcontact.users FROM allcontact AS cntct
WHERE EXISTS ( SELECT 1 FROM allinstances WHERE allinstances.instances = allcontact.instances AND EXISTS(SELECT 1 FROM activeinstances WHERE 1 = 1 AND activeinstances.end_date > CURRENT_TIMESTAMP AND activeinstances.run_id = allinstances.run_id AND activeinstances.run_date = allinstances.run_date)) QUALIFY ROW_NUMBER() OVER (PARTITION BY allcontact.users ORDER BY allcontact.users DESC)=1;
This query is optimized using type 2 profile cp_rowkey, profileid
10006. 1) First, we lock ACTIVEINSTANCES for access, we
lock ALLCONTACT in view allcontact for
access, and we lock allinstances for access. 2) Next, we execute the following steps in parallel.
1) We do an all-AMPs RETRIEVE step from allinstances
by way of an all-rows scan with a condition of (
"allinstances.TRTMNT_TYPE_CODE <> 'I'") into
Spool 3 (all_amps), which is redistributed by the hash code
of (allinstances.RUN_DATE,
allinstances.RUN_ID) to all AMPs. The size of
Spool 3 is estimated with low confidence to be 4,612,364 rows
(119,921,464 bytes). The estimated time for this step is
0.50 seconds.
2) We do an all-AMPs RETRIEVE step from
ACTIVEINSTANCES by way of an all-rows
scan with a condition of (
"(CAST((ACTIVEINSTANCES.END_DATE)
AS TIMESTAMP(6) WITH TIME ZONE))> TIMESTAMP '2017-08-28
01:55:35.110000+00:00'") into Spool 4 (all_amps), which is
redistributed by the hash code of (
ACTIVEINSTANCES.RUN_DATE,
ACTIVEINSTANCES.RUN_ID) to all AMPs.
Then we do a SORT to order Spool 4 by row hash and the sort
key in spool field1 eliminating duplicate rows. The size of
Spool 4 is estimated with no confidence to be 132,623 rows (
4,907,051 bytes). The estimated time for this step is 0.01
seconds.
3) We do an all-AMPs RETRIEVE step from ALLCONTACT
in view allcontact by way of an all-rows scan
with no residual conditions into Spool 5 (all_amps) fanned
out into 17 hash join partitions, which is built locally on
the AMPs. The input table will not be cached in memory, but
it is eligible for synchronized scanning. The size of Spool
5 is estimated with high confidence to be 138,065,479,155
rows (3,451,636,978,875 bytes). The estimated time for this
step is 1 minute and 19 seconds. 3) We do an all-AMPs JOIN step from Spool 3 (Last Use) by way of an
all-rows scan, which is joined to Spool 4 (Last Use) by way of an
all-rows scan. Spool 3 and Spool 4 are joined using a single
partition inclusion hash join, with a join condition of (
"(TRTMNT_TYPE_CODE NOT IN ('I')) AND ((RUN_DATE =
RUN_DATE) AND (RUN_ID = RUN_ID ))"). The result goes into
Spool 7 (all_amps), which is redistributed by the hash code of (
allinstances.INSTANCES) to all AMPs. Then we do a
SORT to order Spool 7 by the sort key in spool field1 eliminating
duplicate rows. The size of Spool 7 is estimated with no
confidence to be 496,670 rows (12,416,750 bytes). The estimated
time for this step is 9.84 seconds. 4) We do an all-AMPs RETRIEVE step from Spool 7 (Last Use) by way of
an all-rows scan into Spool 6 (all_amps) fanned out into 17 hash
join partitions, which is duplicated on all AMPs. The size of
Spool 6 is estimated with no confidence to be 1,862,512,500 rows (
46,562,812,500 bytes). 5) We do an all-AMPs JOIN step from Spool 5 (Last Use) by way of an
all-rows scan, which is joined to Spool 6 (Last Use) by way of an
all-rows scan. Spool 5 and Spool 6 are joined using a inclusion
hash join of 17 partitions, with a join condition of ("INSTANCES =
INSTANCES"). The result goes into Spool 2 (all_amps), which is
built locally on the AMPs. The size of Spool 2 is estimated with
no confidence to be 34,652,542,903 rows (797,008,486,769 bytes).
The estimated time for this step is 23.71 seconds. 6) We do an all-AMPs STAT FUNCTION step from Spool 2 (Last Use) by
way of an all-rows scan into Spool 12 (Last Use), which is
redistributed by hash code to all AMPs. The result rows are put
into Spool 1 (group_amps), which is built locally on the AMPs.
The size is estimated with no confidence to be 650,694,038 rows (
24,075,679,406 bytes). 7) Finally, we send out an END TRANSACTION step to all AMPs involved
in processing the request. -> The contents of Spool 1 are sent back to the user as the result of
statement 1.
BEGIN RECOMMENDED STATS FOR FINAL PLAN->
-- "COLLECT STATISTICS COLUMN (RUN_ID ,RUN_DATE) ON
ACTIVEINSTANCES" (High Confidence)
-- "COLLECT STATISTICS COLUMN (CAST((END_DATE) AS
TIMESTAMP(6) WITH TIME ZONE)) AS
ACTIVEINSTANCES ON
ACTIVEINSTANCES" (High Confidence)
<- END RECOMMENDED STATS FOR FINAL PLAN
目前有效的查询：
SELECT Distinct t.users
FROM
(SELECT users, instances FROM allcontacts
JOIN
(SELECT DISTINCT Run_dt
FROM activeinstances
WHERE activeinstances.end_date> Cast(Current_Timestamp AS TIMESTAMP)
) AS drv on drv.Run_dt = allcontacts.run_dt) as t
JOIN
(
SELECT DISTINCT allinstances.instances
FROM allinstances
JOIN ( SELECT DISTINCT run_date, run_id
FROM activeinstances
WHERE activeinstances.end_date> Cast(Current_Timestamp AS TIMESTAMP)
) AS activeinstances
ON activeinstances.run_id = allinstances.run_id
AND activeinstances.run_date = allinstances.run_date
) AS dt
ON dt.instances = allcontact.instances
答案 0 :(得分：0)
这对评论来说太过分了：
此查询无法利用您当前的主要索引，对于两个较小的表格而言，这不是一个大问题，而是对于较大的一个。
您可以尝试使用派生表而不是双EXISTS来降低估计行数，这可能会更改最终的连接类型：
SELECT allcontact.users
FROM allcontact
JOIN
(
-- what's the estimated vs. actual number of rows?
-- check if one of the recommended stats helps to get closer to the actual number
SELECT DISTINCT allinstances.instances
FROM allinstances
JOIN ( SELECT DISTINCT run_date, run_id
FROM activeinstances
WHERE activeinstances.end_date> Cast(Current_Timestamp AS TIMESTAMP)
) AS activeinstances
ON activeinstances.run_id = allinstances.run_id
AND activeinstances.run_date = allinstances.run_date
) AS dt
ON dt.instances = allcontact.instances
-- Assuming you actual Select list includes other columns besides `allcontact.users`
-- otherwise you can switch to `DISTINCT` instead
QUALIFY Row_Number() Over (PARTITION BY allcontact.users ORDER BY allcontact.users DESC)=1