我在下面写了查询，这个成本大约是 1770077 我想降低成本请提出最佳选择。
SELECT eco.operationalstatus,SI.storecode,count(SI.gustoreid), SI.storename,SI.storetype, PPK.guproductpkgid,PPK.code AS ProductCode, PPK.description as ProductName
FROM inv_storeinformation as SI
LEFT OUTER JOIN sms_ecotransaction as et on SI.gustoreid=et.currentgustoreid
LEFT OUTER JOIN sms_ecomaster as eco on et.refid=eco.ecoid
LEFT OUTER JOIN sms_productpackagemaster as PPK on eco.guproductid=PPK.guproductpkgid
Group by eco.operationalstatus,SI.storecode,SI.gustoreid, SI.storename,SI.storetype, PPK.guproductpkgid,PPK.code , PPK.description
order by SI.storecode,PPK.code
执行时，我得到以下实际执行计划成本
"GroupAggregate (cost=1770077.23..1798321.49 rows=16836 width=661)"
" -> Sort (cost=1770077.23..1772884.82 rows=1123036 width=661)"
" Sort Key: si.storecode, ppk.code, eco.operationalstatus, si.gustoreid, si.storename, si.storetype, ppk.guproductpkgid, ppk.description"
" -> Hash Left Join (cost=62583.69..336765.88 rows=1123036 width=661)"
" Hash Cond: ((eco.guproductid)::text = (ppk.guproductpkgid)::text)"
" -> Hash Right Join (cost=62577.94..321318.39 rows=1123036 width=634)"
" Hash Cond: ((et.currentgustoreid)::text = (si.gustoreid)::text)"
" -> Hash Left Join (cost=62576.43..305875.13 rows=1123036 width=39)"
" Hash Cond: ((et.refid)::text = (eco.ecoid)::text)"
" -> Seq Scan on sms_ecotransaction et (cost=0.00..24914.25 rows=779925 width=35)"
" -> Hash (cost=39803.30..39803.30 rows=1121130 width=40)"
" -> Seq Scan on sms_ecomaster eco (cost=0.00..39803.30 rows=1121130 width=40)"
" -> Hash (cost=1.23..1.23 rows=23 width=612)"
" -> Seq Scan on inv_storeinformation si (cost=0.00..1.23 rows=23 width=612)"
" -> Hash (cost=4.22..4.22 rows=122 width=44)"
" -> Seq Scan on sms_productpackagemaster ppk (cost=0.00..4.22 rows=122 width=44)"
EXPLAIN ANALYZE VERBOSE”如下
"GroupAggregate (cost=1770077.23..1798321.49 rows=16836 width=661) (actual time=154832.599..184209.736 rows=83 loops=1)"
" Output: eco.operationalstatus, si.storecode, count(si.gustoreid), si.storename, si.storetype, ppk.guproductpkgid, ppk.code, ppk.description, si.gustoreid"
" -> Sort (cost=1770077.23..1772884.82 rows=1123036 width=661) (actual time=154830.264..183333.793 rows=1096170 loops=1)"
" Output: eco.operationalstatus, si.storecode, si.storename, si.storetype, ppk.guproductpkgid, ppk.code, ppk.description, si.gustoreid"
" Sort Key: si.storecode, ppk.code, eco.operationalstatus, si.gustoreid, si.storename, si.storetype, ppk.guproductpkgid, ppk.description"
" Sort Method: external merge Disk: 109184kB"
" -> Hash Left Join (cost=62583.69..336765.88 rows=1123036 width=661) (actual time=988.629..4165.343 rows=1096170 loops=1)"
" Output: eco.operationalstatus, si.storecode, si.storename, si.storetype, ppk.guproductpkgid, ppk.code, ppk.description, si.gustoreid"
" Hash Cond: ((eco.guproductid)::text = (ppk.guproductpkgid)::text)"
" -> Hash Right Join (cost=62577.94..321318.39 rows=1123036 width=634) (actual time=988.508..3582.942 rows=1096170 loops=1)"
" Output: si.storecode, si.storename, si.storetype, si.gustoreid, eco.operationalstatus, eco.guproductid"
" Hash Cond: ((et.currentgustoreid)::text = (si.gustoreid)::text)"
" -> Hash Left Join (cost=62576.43..305875.13 rows=1123036 width=39) (actual time=988.472..3056.651 rows=1121492 loops=1)"
" Output: et.currentgustoreid, eco.operationalstatus, eco.guproductid"
" Hash Cond: ((et.refid)::text = (eco.ecoid)::text)"
" -> Seq Scan on public.sms_ecotransaction et (cost=0.00..24914.25 rows=779925 width=35) (actual time=0.184..316.954 rows=779925 loops=1)"
" Output: et.currentgustoreid, et.refid"
" -> Hash (cost=39803.30..39803.30 rows=1121130 width=40) (actual time=981.649..981.649 rows=1121130 loops=1)"
" Output: eco.operationalstatus, eco.ecoid, eco.guproductid"
" Buckets: 2048 Batches: 128 Memory Usage: 645kB"
" -> Seq Scan on public.sms_ecomaster eco (cost=0.00..39803.30 rows=1121130 width=40) (actual time=0.006..471.218 rows=1121130 loops=1)"
" Output: eco.operationalstatus, eco.ecoid, eco.guproductid"
" -> Hash (cost=1.23..1.23 rows=23 width=612) (actual time=0.026..0.026 rows=23 loops=1)"
" Output: si.storecode, si.storename, si.storetype, si.gustoreid"
" Buckets: 1024 Batches: 1 Memory Usage: 2kB"
" -> Seq Scan on public.inv_storeinformation si (cost=0.00..1.23 rows=23 width=612) (actual time=0.009..0.013 rows=23 loops=1)"
" Output: si.storecode, si.storename, si.storetype, si.gustoreid"
" -> Hash (cost=4.22..4.22 rows=122 width=44) (actual time=0.093..0.093 rows=122 loops=1)"
" Output: ppk.guproductpkgid, ppk.code, ppk.description"
" Buckets: 1024 Batches: 1 Memory Usage: 10kB"
" -> Seq Scan on public.sms_productpackagemaster ppk (cost=0.00..4.22 rows=122 width=44) (actual time=0.007..0.038 rows=122 loops=1)"
" Output: ppk.guproductpkgid, ppk.code, ppk.description"
"Total runtime: 184242.421 ms"
最佳答案
我不确定在 SELECT 部分中按 SI.gustoreid 分组是否有意义...如果这是你的错误并且你的意思是 count(*) 按 gustoreid 分组，请考虑下面的查询 - 子查询减少数量必须分组的列的数量，它可以降低查询的复杂性:
SELECT
eco.operationalstatus,
SI.storecode,
rowCount,
SI.storename,
SI.storetype,
PPK.guproductpkgid,
PPK.code AS ProductCode,
PPK.description as ProductName
FROM
(
SELECT
storecode,
count(*) AS rowCount,
gustoreid,
storename,
storetype
FROM
inv_storeinformation
GROUP BY
storecode,
storename,
storetype,
gustoreid
) as SI
LEFT OUTER JOIN sms_ecotransaction as et on SI.gustoreid=et.currentgustoreid
LEFT OUTER JOIN sms_ecomaster as eco on et.refid=eco.ecoid
LEFT OUTER JOIN sms_productpackagemaster as PPK on eco.guproductid=PPK.guproductpkgid
order by
SI.storecode,
PK.code
关于sql - 如何使用连接优化 postgresql 查询？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/29508092/