更新时间：2022-06-13 GMT+08:00
案例：使排序下推
现象描述
在做场景性能测试时，发现某场景大部分时间是CN端在做window agg，占到总执行时间95%以上，系统资源不能充分利用。研究发现该场景的特点是：将两列分别求sum作为一个子查询，外层对两列的和再求和后做trunc，然后排序。
表结构如下所示：
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CREATE TABLE public.test(imsi int,L4_DW_THROUGHPUT int,L4_UL_THROUGHPUT int)
with (orientation = column) DISTRIBUTE BY hash(imsi);
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查询语句如下所示：
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SELECT COUNT(1) over() AS DATACNT,
IMSI AS IMSI_IMSI,
CAST(TRUNC(((SUM(L4_UL_THROUGHPUT) + SUM(L4_DW_THROUGHPUT))), 0) AS
DECIMAL(20)) AS TOTAL_VOLOME_KPIID
FROM public.test AS test
GROUP BY IMSI
order by TOTAL_VOLOME_KPIID DESC;
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执行计划如下：
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Row Adapter (cost=10.70..10.70 rows=10 width=12)
-> Vector Sort (cost=10.68..10.70 rows=10 width=12)
Sort Key: ((trunc((((sum(l4_ul_throughput)) + (sum(l4_dw_throughput))))::numeric, 0))::numeric(20,0))
-> Vector WindowAgg (cost=10.09..10.51 rows=10 width=12)
-> Vector Streaming (type: GATHER) (cost=242.04..246.84 rows=240 width=12)
Node/s: All datanodes
-> Vector Hash Aggregate (cost=10.09..10.29 rows=10 width=12)
Group By Key: imsi
-> CStore Scan on test (cost=0.00..10.01 rows=10 width=12)
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可以看到window agg和sort全部在CN端执行，耗时非常严重。
优化分析
尝试将语句改写为子查询。
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SELECT COUNT(1) over() AS DATACNT, IMSI_IMSI, TOTAL_VOLOME_KPIID
FROM (SELECT IMSI AS IMSI_IMSI,
CAST(TRUNC(((SUM(L4_UL_THROUGHPUT) + SUM(L4_DW_THROUGHPUT))),
0) AS DECIMAL(20)) AS TOTAL_VOLOME_KPIID
FROM public.test AS test
GROUP BY IMSI
ORDER BY TOTAL_VOLOME_KPIID DESC);
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将trunc两列的和作为一个子查询，然后在子查询的外面做window agg，这样排序就可以下推了，执行计划如下：
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Row Adapter (cost=10.70..10.70 rows=10 width=24)
-> Vector WindowAgg (cost=10.45..10.70 rows=10 width=24)
-> Vector Streaming (type: GATHER) (cost=250.83..253.83 rows=240 width=24)
Node/s: All datanodes
-> Vector Sort (cost=10.45..10.48 rows=10 width=12)
Sort Key: ((trunc(((sum(test.l4_ul_throughput) + sum(test.l4_dw_throughput)))::numeric, 0))::numeric(20,0))
-> Vector Hash Aggregate (cost=10.09..10.29 rows=10 width=12)
Group By Key: test.imsi
-> CStore Scan on test (cost=0.00..10.01 rows=10 width=12)
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经过SQL改写，性能由120s提升7s，优化效果明显。
父主题： 实际调优案例
