LightDB – no_star_transformation

本文介绍: Li g h tDB 从23.4开始支持 no_star_transform a t i on hint。在Oracle 中no_s tar_transform a t i on hint 用于提示优化器不执行星型转换。Li g htDB 不支持星型转换，因此此h int总会起效，下面先介绍下星型转换，然后介绍下h int的用法。

Li g htDB 从23.4开始支持 no_s tar_transform ati on h int。
在Oracle 中no_s tar_transform ation h int 用于提示优化器不执行星型转换。Lig htDB 不支持星型转换，因此此h int总会起效，下面先介绍下星型转换，然后介绍下hint的用法。

星型转换是一种优化器转换，它避免了对星型模式中事实表的全表扫描(星型模式将数据分为事实表和维度表)。

在事实表和维度表的连接中，星型转换可以避免对事实表的全表扫描。星型转换通过位图索引来获取事实表上需要的行（由维度表过滤），从而提高性能。

在某些情况下，查询在维度表的非关联列上有过滤条件。通过组合这些过滤条件可以显著减少数据库需要处理的来自事实表的数据集。

转换后查询按如下两阶段执行：

CREATE TABLE times (

  time_id NUMBER PRIMARY KEY,

  calendar_quarter_desc VARCHAR2(20)

);


CREATE TABLE customers (

  cust_id NUMBER PRIMARY KEY,

  cust_city VARCHAR2(20),

  cust_state_province VARCHAR2(20)

);


CREATE TABLE channels (

  channel_id NUMBER PRIMARY KEY,

  channel_class VARCHAR2(20),

  channel_desc VARCHAR2(20)

);


CREATE TABLE sales (

  time_id NUMBER REFERENCES times(time_id),

  cust_id NUMBER REFERENCES customers(cust_id),

  channel_id NUMBER REFERENCES channels(channel_id),

  amount_sold NUMBER

);

-- oracle 中需要对事实表建立bitmap索引， LightDB 不支持bitmap 索引，不执行
--create bitmap index sales_i1 on sales(time_id);
--create bitmap index sales_i2 on sales(cust_id);
--create bitmap index sales_i3 on sales(channel_id);

lightdb@postgres=# EXPLAIN (COSTS false) SELECT/*+no_star_transformation*/ ch.channel_class, c.cust_city, t.calendar_quarter_desc,
       SUM(s.amount_sold) sales_amount
    FROM sales s, times t, customers c, channels ch
    WHERE s.time_id = t.time_id
    AND s.cust_id = c.cust_id
    AND s.channel_id = ch.channel_id
    AND c.cust_state_province = 'CA'
    AND ch.channel_desc in ('Internet','Catalog')
    AND t.calendar_quarter_desc IN ('1999-Q1','1999-Q2')
    GROUP BY ch.channel_class, c.cust_city, t.calendar_quarter_desc;
LOG:  lt_hint_plan:
used hint:
no_star_transformation
not used hint:
duplication hint:
error hint:

                                              QUERY PLAN                    
                           
----------------------------------------------------------------------------
---------------------------
 GroupAggregate
   Group Key: ch.channel_class, c.cust_city, t.calendar_quarter_desc
   ->  Sort
         Sort Key: ch.channel_class, c.cust_city, t.calendar_quarter_desc
         ->  Nested Loop
               ->  Nested Loop
                     ->  Hash Join
                           Hash Cond: (s.cust_id = c.cust_id)
                           ->  Seq Scan on sales s @"lt#0"
                           ->  Hash
                                 ->  Seq Scan on customers c @"lt#0"
                                       Filter: ((cust_state_province)::text 
= 'CA'::text)
                     ->  Index Scan using times_pkey on times t @"lt#0"
                           Index Cond: (time_id = s.time_id)
                           Filter: ((calendar_quarter_desc)::text = ANY ('{1
999-Q1,1999-Q2}'::text[]))
               ->  Index Scan using channels_pkey on channels ch @"lt#0"
                     Index Cond: (channel_id = s.channel_id)
                     Filter: ((channel_desc)::text = ANY ('{Internet,Catalog
}'::text[]))
(18 rows)