[13/JUL/2025, Yusuf Leo, Oracle SQL Performance Tuning Series]
我们经常会遇到从同一表中按不同维度取出不同区间的数据，再以相同的属性将这些数据分别汇总到一起的需求。这类需求往往迫使我们对同一个表反复去扫描，当原始数据量太大的时候，这就可能给我们带来程序性能上的困扰。行转列PIVOT语法或许会是较好的优化思路之一。
PIVOT需要Oracle 11g及以上版本支持。
下面我们来看看这个实例，这是某企业EBS客制化开发的一个报表，核心逻辑是从ASCP工作台按订单类型区分统计各物料的总需求、在库、在途、在购等数量。
- 优化前
1.1 主程序主游标
cursor c1 isselect aa.organization_id,aa.plan_id,aa.item_segments,aa.description,aa.uom_code,aa.minimum_order_quantity, --MOQaa.fixed_lot_multiplier, --SPQaa.full_lead_time, --Lead Timeget_order_qty(aa.plan_id, aa.item_segments, '现有量') pr_qty1,get_order_qty(aa.plan_id, aa.item_segments, '采购订单') pr_qty7,get_order_qty(aa.plan_id, aa.item_segments, '采购申请') pr_qty8from (select distinct mov.organization_id,mov.plan_id,mov.item_segments,mov.description,mov.uom_code,msi.inventory_item_id,msi.minimum_order_quantity, --MOQmsi.fixed_lot_multiplier, --SPQmsi.full_lead_timefrom MSC_ORDERS_V mov, MTL_SYSTEM_ITEMS_B MSIwhere 1 = 1and msi.organization_id = mov.organization_idand msi.segment1 = mov.item_segmentsand (trunc(mov.new_order_date) >= to_date(p_date_f, 'YYYY-MM-DD') orp_date_f is null)and (trunc(mov.new_order_date) <= to_date(p_date_e, 'YYYY-MM-DD') orp_date_e is null)and mov.item_segments like '%' || p_item_segments || '%'and mov.plan_id = p_plan_idand mov.organization_id = p_organizatino_id) aa;1.2 主程序次级游标
cursor c2(p_item_code VARCHAR2) isselect bb.new_order_date,bb.new_due_date,get_plan_qty(bb.plan_id,bb.item_segments,'计划单',bb.new_order_date,bb.new_due_date) po_qty, --采购数量get_need_qty(bb.plan_id, bb.item_segments, bb.new_due_date) need_qty --总需求数量from (select distinct mov.organization_id,mov.plan_id,mov.item_segments,to_char(mov.new_order_date, 'YYYY-MM-DD') new_order_date, --建议采购日期to_char(mov.new_due_date, 'YYYY-MM-DD') new_due_date --建议到期日from MSC_ORDERS_V mov, MSC_ORDERS_V mov1where 1 = 1and mov1.item_segments = mov.item_segmentsand mov1.new_due_date = mov.new_due_dateand mov1.new_order_date = mov.new_order_dateand mov1.order_type_text = '计划单' --物料有计划单的输出，没有计划单的排除and (trunc(mov.new_order_date) >=to_date(p_date_f, 'YYYY-MM-DD') or p_date_f is null)and (trunc(mov.new_order_date) <=to_date(p_date_e, 'YYYY-MM-DD') or p_date_e is null)and mov.plan_id = p_plan_idand mov.item_segments = p_item_codeand mov.organization_id = p_organization_idorder by new_due_date) bb;1.3 游标调用的子函数
-- get_order_qty 核心逻辑
select round(nvl(sum(mov.quantity_rate), 0), 2)from MSC_ORDERS_V movwhere 1 = 1and mov.category_set_id = 1001and mov.item_segments = p_item_codeand mov.order_type_text = p_order_typeand mov.plan_id = p_plan_id;-- get_plan_qty 核心逻辑
select round(nvl(sum(mov.quantity_rate), 0), 2)from MSC_ORDERS_V movwhere 1 = 1and trunc(mov.new_order_date) = to_date(p_order_date, 'YYYY-MM-DD')and trunc(mov.new_due_date) = to_date(p_due_date, 'YYYY-MM-DD')and mov.category_set_id = 1001and mov.item_segments = p_item_codeand mov.order_type_text = p_order_typeand mov.plan_id = p_plan_id;-- get_need_qty 核心逻辑
select abs(round(nvl(sum(mov.quantity_rate), 0), 2))from MSC_ORDERS_V movwhere 1 = 1and mov.new_due_date <= (to_date(p_due_date, 'YYYY-MM-DD') + 6)and mov.category_set_id = 1001and mov.item_segments = p_item_codeand mov.order_type_text in ('非标准任务需求','工作单需求','计划单需求','销售订单 MDS','预测 MDS')and mov.plan_id = p_plan_id;- 问题分析
该程序的主要逻辑是：主程序首先遍历主游标，从Msc_Oraders_V中取出符合参数条件的物料，再代入次级游标中进一步取出符合要求的明细数据以打印输出，并且这其中的很多数量数据是通过调用子函数计算。
我们在两个游标中都看到了很不友好的DISTINCT去重，进一步分析作者使用粗暴去重的原意发现，两层游标的设计也并非必要：次级游标中的“物料有计划单的输出，没有计划单的排除”这个筛选条件其实可以通过EXISTS手段并入主游标，而在主游标中先去重再调用子函数求值的方式则应考虑通过分组聚合的方式尝试简化写法。
除了程序结构设计的问题，该程序的性能问题还存在于对视图Msc_Oraders_V的反复扫描，这是一个带有UNION ALL拼接的大型视图，而程序中所有的数据其实都是来自这个视图，困扰作者的可能是并不能通过简单的分组聚合直接满足功能设计的需求，因为各汇总数据不仅是order_type_text不同，而是同时在其它字段上又有不同范围的限制（即三个子函数的区别）。
- 优化思路
大方向是1、两级游标整合成一级，2、拆解子函数入主游标
原次级游标能够决定代入来的主游标物料是否打印，则应把这个限制条件直接作为物料的筛选条件；
虽然子函数都是在读取Msc_Orders_V，但又略有不同，不能通过GROUP BY直接改写，考虑尝试PIVOT，原始扫描范围放为最大，各列统计时再分别限制其范围。
- 优化后
with plan_qtys as(select mov1.organization_id,mov1.plan_id,mov1.item_segments,trunc(mov1.new_order_date) new_order_date, --建议采购日期trunc(mov1.new_due_date) new_due_date, --建议到期日sum(casewhen mov1.category_set_id = 1001 thenmov1.quantity_rateelse0end) plan_qtyfrom MSC_ORDERS_V mov1where 1 = 1and mov1.new_due_date is not nulland mov1.new_order_date is not nulland mov1.order_type_text = '计划单' --物料有计划单的输出，没有计划单的排除and mov1.new_order_date >=nvl(to_date(p_date_f, 'YYYY-MM-DD'), mov1.new_order_date)and mov1.new_order_date <=nvl(to_date(p_date_e, 'YYYY-MM-DD') + .99999, mov1.new_order_date)and mov1.plan_id = p_plan_idand mov1.item_segments like '%' || p_item_segments || '%'and mov1.organization_id = p_organization_idgroup by mov1.organization_id,mov1.plan_id,mov1.item_segments,trunc(mov1.new_order_date),trunc(mov1.new_due_date)),
mov_data as(select organization_id,item_segments,description,uom_code,new_order_date,new_due_date,round(nvl(pr_qty1, 0), 2) pr_qty1,round(nvl(pr_qty7, 0), 2) pr_qty7,round(nvl(pr_qty8, 0), 2) pr_qty8,round(nvl(plan_qty, 0), 2) plan_qty,abs(round(nvl((need_qty_q2), 0), 2)) need_qtyfrom (select mov.organization_id,mov.item_segments,mov.description,mov.uom_code,casewhen order_type_text in ('非标准任务需求','工作单需求','计划单需求','销售订单 MDS','预测 MDS') then'需求'elseorder_type_textend as order_type_text,mov.quantity_rate order_qty,casewhen mov.new_due_date <= pq.new_due_date + 6 thenmov.quantity_rateelse0end order_qty2,pq.plan_qty,pq.new_order_date,pq.new_due_datefrom MSC_ORDERS_V mov, plan_qtys pqwhere mov.organization_id = pq.organization_idand mov.plan_id = pq.plan_idand mov.item_segments = pq.item_segmentsand mov.category_set_id = 1001)pivot(sum(order_qty), sum(order_qty2) as q2for order_type_text in('现有量' as pr_qty1,'采购订单' as pr_qty7,'采购申请' as pr_qty8,'需求' as need_qty)))
select mov.organization_id,mov.item_segments,mov.description,mov.uom_code, --单位msi.inventory_item_id,msi.minimum_order_quantity, --MOQmsi.fixed_lot_multiplier, --SPQmsi.full_lead_time,mov.pr_qty1,mov.pr_qty7,mov.pr_qty8,mov.need_qty,mov.plan_qty,mov.new_order_date,mov.new_due_datefrom mov_data mov, MTL_SYSTEM_ITEMS_B MSIwhere 1 = 1and msi.organization_id = mov.organization_idand msi.segment1 = mov.item_segmentsorder by item_segments, new_due_date优化前请求第二次运行（有缓存）用时14h51m42s，优化后请求同参数第二次运行（有缓存）用时54s，优化比例1:991
[END]
)
