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Forcing the Join Order

글쓴이 : 모델광 날짜 : 2022-06-06 (월) 07:23 조회 : 432

Most relational databases have an architecture of the parser, transformer, optimizer and executor and it works well. Most of your queries will be OK. But there might be a few queries which do not have optimal excution plans and it only takes one to ruin the throughput of your application programs.

When the optimizer produces an execution plan, it tries to check almost all possible join orders. In most cases the default plan chosen by PostgreSQL is optimal but in some cases the plan is not optimal. Other DBMSs provide hints to allow an SQL developer to influence the query execution plan but PostgreSQL does not, which is a serious drawback. When you are certain that the optimizer made a wrong decision on the join order and you know the best possible join order, you have to tweak the query to produce the desired execution plan.

Often I hear application developers say,  "SQL is a declarative language. We don't have to care about how to retrieve it. If we specify what to retrieve, the DBMS will take care of it." Theoretically, we don't have to worry about how to retrieve data, but optimizers don't know the data the way we know it. That is why sometimes we have to dictate the planner to retrieve the data in a more efficient way.


In this note I will offer some methods to force the join order in PostgreSQL.

I am an ardent advocate for taking an evidence-based, scientific, and methodical approach to query performance problem solving. But to be honest, I often take the approach of "do whatever comes into my mind and make guesses until the problem goes away...ㅠㅠ;".


I will be using a database of PostgreSQL 14 in the example below. Before we dig into a practical example, it is necessary to create some tables that we can later use to perform our joins.


CREATE TABLE employee (

    empno numeric(5,0) NOT NULL,

    ename character varying(10),

    job character varying(9),

    mgr numeric(5,0),

    hiredate timestamp(0),

    sal numeric(7,2),

    comm numeric(7,2),

    deptno numeric(2,0),

    sido_nm character varying(100)

);


I added some rows to the table.


insert into employee

select i, chr(65+mod(i,26))||i::text||'NM'

      ,case when mod(i,10000)=0 then 'PRESIDENT'

            when mod(i,1000) = 0 then 'MANAGER'

            when mod(i,3)=0 then 'SALESMAN'

            when mod(i,3)=1 then 'ANALYST'

            when mod(i,3)=2 then 'CLERK'

        end as job

      ,case when mod(i,10000)= 0 then null

            when mod(i,1000)= 1 then 10000

            when i >= 9000 then 1000

            else ceiling((i+1000)/1000)*1000

        end as mgr

      , current_date - i

      , trunc(random() * 10000) as sal

      , trunc(random() * 10000) as com

      , mod(i,12)+1             as deptno

      , case when mod(i,3) = 0 then 'Jeonbuk'

             when mod(i,3) = 1 then 'Kangwon'

             else                   'Chungnam'

        end as sido_nm

from generate_series(1,10000) a(i);

 

                         

ALTER TABLE employee ADD CONSTRAINT employee_pk

  PRIMARY KEY (empno);

 

select pg_relation_size('employee');

0.9Mbytes


drop table offline_order;

create table offline_order (

ord_no numeric(10,0) not null,

cust_no numeric      not null,

ord_date timestamp(0) not null,

ord_dt   varchar(8)   not null,

ord_status_cd varchar(1) not null,

empno    numeric(5,0),

comment  varchar(100)

);

insert into offline_order

select i, mod(i,1000000) as cust_no

      ,current_date - mod(i,1000) as ord_date

      ,to_char((current_date - mod(i,1000)),'yyyymmdd') as ord_dt

      ,(mod(i,4) + 1) as ord_status_cd

      ,mod(i,10000) + 1 as empno

      ,lpad('y',100,'y')

  from generate_series(2,2000000,2) a(i);

alter table offline_order add constraint offline_order_pk

primary key (ord_no);

CREATE INDEX OFFLINE_ORDER_X01 ON OFFLINE_ORDER(CUST_NO);

CREATE INDEX OFFLINE_ORDER_X02 ON OFFLINE_ORDER(EMPNO);

select * from pg_relation_size('offline_order');

--174M


create table ord_item (

ord_no numeric(10,0) not null,

prod_id varchar(10) not null,

unit_price numeric(10) not null,

quantity  numeric(10) not null,

on_off_code varchar(2) not null,

oder_comment varchar(100)

);

--prod1  ~ prod200

insert into ord_item

select a.ord_no, 'prod'||(mod(ord_no,200)+1)::text as prod_id

      , trunc(100*random())+1 as unit_price

      , trunc(10*random())+1 as quantity

      , case when mod(ord_no,2)=0 then '01' else '02' end as on_off_code

      , lpad('c',100,'y')

  from

       (select ord_no from online_order

        union all

        select ord_no from offline_order

       ) a

;


Let us add some data to this table.


insert into ord_item

select a.ord_no, 'prod'||(mod(ord_no,200)+2)::text as prod_id

      , trunc(100*random())+1 as unit_price

      , trunc(10*random())+1 as quantity

      , case when mod(ord_no,2)=0 then '01' else '02' end as on_off_code

      , lpad('d',100,'q')

  from

       (select ord_no from online_order

        union all

        select ord_no from offline_order

       ) a

;

alter table ord_item add constraint ord_item_pk

primary key(ord_no, prod_id);

select * from ord_item where ord_no in (998,999);

select pg_relation_size('ord_item');

--648M


Here is the query we are interested in followed by its execution plan.


* SQL statement 1

select a.ename, a.job, b.ord_date, b.ord_status_cd, c.prod_id, c.quantity
  from employee a, offline_order b, ord_item c
 where a.empno between 1 and 5
   and a.empno = b.empno
   and b.ord_no = c.ord_no;


Gather  (cost=1008.88..27819.86 rows=940 width=36)
  Workers Planned: 2
  ->  Nested Loop  (cost=8.88..26725.86 rows=392 width=36)
        ->  Hash Join  (cost=8.45..26546.33 rows=208 width=30)
              Hash Cond: (b.empno = a.empno)
              ->  Parallel Seq Scan on offline_order b  (cost=0.00..25443.67 rows=416667 width=21)
              ->  Hash  (cost=8.39..8.39 rows=5 width=19)
                    ->  Index Scan using employee_pk on employee a  (cost=0.29..8.39 rows=5 width=19)
                          Index Cond: ((empno >= '1'::numeric) AND (empno <= '5'::numeric))
        ->  Index Scan using ord_item_pk on ord_item c  (cost=0.43..0.84 rows=2 width=18)
              Index Cond: (ord_no = b.ord_no)


We can see that the join order chosen by the planner is :

(employee -> offline_order) -> ord_item 

It seems that the planner made a good decision on the join method and the join order. But let's suppose that the plan is not optimal and you know the right order to join tables efficiently and the better join order is:

employee -> ( offline_order -> ord_item).


In order to produce the better execution plan I used an explicit join.


*SQL statement 2

select a.ename, a.job, b.ord_date, b.ord_status_cd, c.prod_id, c.quantity
  from employee a join (offline_order b join ord_item c on (b.ord_no = c.ord_no))
    on (a.empno = b.empno)
 where a.empno between 1 and 5;


Unlike what I expected the query above produced the same execution plan. PostgreSQL doesn't supply its thinking process, so I can't guarantee the correctness of the description I've give here, but it is probabley quite accurate. In the SQL statement 2, subquery collapse kicked in and the optimizer transformed the SQL statement 2 to the SQL statement 1.

In order to prevent subquery collapse I rewrote the query like the following:


*SQL statement 3

select a.ename, a.job, d.ord_date, d.ord_status_cd, d.prod_id, d.quantity
  from employee a
      ,(select b.empno, b.ord_date, b.ord_status_cd, c.prod_id, c.quantity
          from offline_order b, ord_item c
         where b.ord_no = c.ord_no
         offset 0) d
 where a.empno = d.empno
   and a.empno between 1 and 5;


Hash Join  (cost=49645.45..255746.63 rows=940 width=36)
  Hash Cond: (b.empno = a.empno)
  ->  Hash Join  (cost=49637.00..232006.28 rows=1879589 width=27)
        Hash Cond: (c.ord_no = b.ord_no)
        ->  Seq Scan on ord_item c  (cost=0.00..119133.41 rows=3999941 width=18)
        ->  Hash  (cost=31277.00..31277.00 rows=1000000 width=21)
              ->  Seq Scan on offline_order b  (cost=0.00..31277.00 rows=1000000 width=21)
  ->  Hash  (cost=8.39..8.39 rows=5 width=19)
        ->  Index Scan using employee_pk on employee a  (cost=0.29..8.39 rows=5 width=19)
              Index Cond: ((empno >= '1'::numeric) AND (empno <= '5'::numeric))


The join order, as expected, now is :

employee -> ( offline_order -> ord_item)


You may even want to change the join method. Below are some configuration parameters with which you can influence the join method. But I will not go into details here.

enable_hashjoin

enable_mergejoin

enable_nestloop


With regard to reordering of explict JOINs, there is a parameter which controls the join order to some extent. It is join_collapse_limit. Setting it to 1 prevents the reordering of explicit JOINs. The explicit join order specified in the query will be the actual order in which the tables are joined.


I lowered the parameter join_collapse_limit to 1 and ran the SQL statement 2 again and got this execution plan.


*SQL statement 2

set join_collapse_limit=1; 

select a.ename, a.job, b.ord_date, b.ord_status_cd, c.prod_id, c.quantity
  from employee a join (offline_order b join ord_item c on (b.ord_no = c.ord_no))
    on (a.empno = b.empno)
 where a.empno between 1 and 5;


Gather  (cost=31660.45..133986.49 rows=940 width=36)
  Workers Planned: 2
  ->  Hash Join  (cost=30660.45..132892.49 rows=392 width=36)
        Hash Cond: (b.empno = a.empno)
        ->  Parallel Hash Join  (cost=30652.00..130827.37 rows=783162 width=27)
              Hash Cond: (c.ord_no = b.ord_no)
              ->  Parallel Seq Scan on ord_item c  (cost=0.00..95800.42 rows=1666642 width=18)
              ->  Parallel Hash  (cost=25443.67..25443.67 rows=416667 width=21)
                    ->  Parallel Seq Scan on offline_order b  (cost=0.00..25443.67 rows=416667 width=21)
        ->  Hash  (cost=8.39..8.39 rows=5 width=19)
              ->  Index Scan using employee_pk on employee a  (cost=0.29..8.39 rows=5 width=19)
                    Index Cond: ((empno >= '1'::numeric) AND (empno <= '5'::numeric))


Now the planner produced the join order we wanted to see. But it is slightly different from the plan of the SQL statement 3. The planner launched parallel worker processes. The downside is that you cannot adopt this technique of setting the parameter join_collapse_limit if you are using the connection pool architecture.


This is all about how to force the join order.


Wrap-up

When you want to influence the join order you have two options:

1. Write an SQL statement with an explicit join and set the value of the parameter join_collapse_limit to 1.

2. Use an inline view and prevent subquery collapse with OFFSET clause.


   

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