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更新时间：2024-10-14 GMT+08:00
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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);

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;

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)

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);

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)

经过SQL改写，性能由120s提升到7s，优化效果明显。

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