Generating Depth based tree from Hierarchical Data in MySQL (no CTEs)

You can do it in a single call from php to mysql if you use a stored procedure:

Example calls

mysql> call category_hier(1);

+--------+---------------+---------------+----------------------+-------+
| cat_id | category_name | parent_cat_id | parent_category_name | depth |
+--------+---------------+---------------+----------------------+-------+
|      1 | Location      |          NULL | NULL                 |     0 |
|      3 | USA           |             1 | Location             |     1 |
|      4 | Illinois      |             3 | USA                  |     2 |
|      5 | Chicago       |             3 | USA                  |     2 |
+--------+---------------+---------------+----------------------+-------+
4 rows in set (0.00 sec)


$sql = sprintf("call category_hier(%d)", $id);

Hope this helps 🙂

Full script

Test table structure:

drop table if exists categories;
create table categories
(
cat_id smallint unsigned not null auto_increment primary key,
name varchar(255) not null,
parent_cat_id smallint unsigned null,
key (parent_cat_id)
)
engine = innodb;

Test data:

insert into categories (name, parent_cat_id) values
('Location',null),
   ('USA',1), 
      ('Illinois',2), 
      ('Chicago',2),  
('Color',null), 
   ('Black',3), 
   ('Red',3);

Procedure:

drop procedure if exists category_hier;

delimiter #

create procedure category_hier
(
in p_cat_id smallint unsigned
)
begin

declare v_done tinyint unsigned default 0;
declare v_depth smallint unsigned default 0;

create temporary table hier(
 parent_cat_id smallint unsigned, 
 cat_id smallint unsigned, 
 depth smallint unsigned default 0
)engine = memory;

insert into hier select parent_cat_id, cat_id, v_depth from categories where cat_id = p_cat_id;

/* http://dev.mysql.com/doc/refman/5.0/en/temporary-table-problems.html */

create temporary table tmp engine=memory select * from hier;

while not v_done do

    if exists( select 1 from categories p inner join hier on p.parent_cat_id = hier.cat_id and hier.depth = v_depth) then

        insert into hier 
            select p.parent_cat_id, p.cat_id, v_depth + 1 from categories p 
            inner join tmp on p.parent_cat_id = tmp.cat_id and tmp.depth = v_depth;

        set v_depth = v_depth + 1;          

        truncate table tmp;
        insert into tmp select * from hier where depth = v_depth;

    else
        set v_done = 1;
    end if;

end while;

select 
 p.cat_id,
 p.name as category_name,
 b.cat_id as parent_cat_id,
 b.name as parent_category_name,
 hier.depth
from 
 hier
inner join categories p on hier.cat_id = p.cat_id
left outer join categories b on hier.parent_cat_id = b.cat_id
order by
 hier.depth, hier.cat_id;

drop temporary table if exists hier;
drop temporary table if exists tmp;

end #

Test runs:

delimiter ;

call category_hier(1);

call category_hier(2);

Some performance testing using Yahoo geoplanet places data

drop table if exists geoplanet_places;
create table geoplanet_places
(
woe_id int unsigned not null,
iso_code  varchar(3) not null,
name varchar(255) not null,
lang varchar(8) not null,
place_type varchar(32) not null,
parent_woe_id int unsigned not null,
primary key (woe_id),
key (parent_woe_id)
)
engine=innodb;

mysql> select count(*) from geoplanet_places;
+----------+
| count(*) |
+----------+
|  5653967 |
+----------+

so that’s 5.6 million rows (places) in the table let’s see how the adjacency list implementation/stored procedure called from php handles that.

     1 records fetched with max depth 0 in 0.001921 secs
   250 records fetched with max depth 1 in 0.004883 secs
   515 records fetched with max depth 1 in 0.006552 secs
   822 records fetched with max depth 1 in 0.009568 secs
   918 records fetched with max depth 1 in 0.009689 secs
  1346 records fetched with max depth 1 in 0.040453 secs
  5901 records fetched with max depth 2 in 0.219246 secs
  6817 records fetched with max depth 1 in 0.152841 secs
  8621 records fetched with max depth 3 in 0.096665 secs
 18098 records fetched with max depth 3 in 0.580223 secs
238007 records fetched with max depth 4 in 2.003213 secs

Overall i’m pretty pleased with those cold runtimes as I wouldn’t even begin to consider returning tens of thousands of rows of data to my front end but would rather build the tree dynamically fetching only several levels per call. Oh and just incase you were thinking innodb is slower than myisam – the myisam implementation I tested was twice as slow in all counts.

More stuff here : http://pastie.org/1672733

Hope this helps 🙂