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Ashley Pond V — 바보 양키스 > DBICx-MaterializedPath > DBICx::MaterializedPath



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DBICx::MaterializedPath - DBIx::Class plugin for automatically tracking lineage paths in simple data trees.


Please see DBIx::Class::Tree::Mobius, DBIx::Class::Graph, and DBIx::Class::Tree instead of this experimental package.


We need a table, or tables, which represents a tree.

 CREATE TABLE tree_data (
    parent INT(10),
    content TEXT NOT NULL,
    path VARCHAR(255),
    created DATETIME(19) NOT NULL
 CREATE INDEX tree_data_idx_parent ON tree_data (parent);

In your DBIx::Class add this to your components–

 use warnings;
 use strict;
 use parent qw( DBIx::Class );
 # Et cetera.
 __PACKAGE__->parent_column("parent"); # default "parent"
 __PACKAGE__->path_column("path");     # default "materialized_path"
 __PACKAGE__->path_separator(".");     # default "/"
 __PACKAGE__->max_depth(10);           # default "500"


Note, this is an experimental package and not sanctioned by the DBIC core devs.

Uses a column of a table with a tree structure to keep track of lineage. An example lineage showing primary key ids–

 #  1 -> 2 -> 3 -> 10 -> 999 -> 8 -> 42

 my $rec = $result_source->find(999);
 say $rec->parent->id; # prints "10"

It's trivial to find the parent and easy to recurse on the parent to find all ancestors. With a deep tree it becomes somewhat expensive. Take the example above, for example. If you want to get the entire lineage for the record with id "42" you have to do six queries against the database. If you maintain a materialized path you only have to do one.

Consider our record "42" again. With its path 1/2/3/10/999/8/42 we can easily find all its parents–

 my $path = "1/2/3/10/999/8/42";
 my @ancestor_ids = split '/', $path;
 pop @ancestor_ids; # Remove the self id.
 my @ancestors = $result_source
                      ->search({ parent => { -in => \@ancestor_ids },
                               { order_by => \"LENGTH(path)" });

We can thank the great and powerful Ovid's co-worker Mark Morgan——for the sorting solution for ensuring the proper order of ancestors is returned.

See also Trees in SQL: Nested Sets and Materialized Path, Vadim Tropashko,


This package requires your table has a single primary key and a method to look up a parent record by its single primary key.


[path method]

Whatever column you set for your materialized path. In the "SYNOPSIS" code it is set to path to match the sample table definition. The default if you don't set one is materialized_path. This will, of course, cause errors if there is no such column in the table.


Searches on the materialized path ids excepting the object's own. This is generally cheap because it uses the path instead of recursion.


Returns the root object for a given record.


Return all children and grandchildren.


Returns 1 for a record with no parent.


Set this to assert a maximum tree depth. Default is 500.


Probably shouldn't mess with this. It's used by "insert" and "delete".



Sets the materialized path.


Updates which change the parent of a record necessarily cascade through all their children and grandchildren to recompute and set their new materialized paths. E.g., given this tree–

                 / \
               12   8
              /\    /\
             5 13  7  4

You get paths including 1/3/12/13 and 1/3/4. Let's say we change record 3's parent from 1 to 2–

                 / \
               12   8
              /\    /\
             5 13  7  4

The change is simple and it's obvious you have to update record 3 but you just broke the materialized path for records 4, 5, 7, 8, 12, and 13. In a big tree you may have broken hundreds or even thousands of paths with a single parent change. So we have to process all descendants. Our example paths become 2/3/12/13 and 2/3/4. Again, it may seem trivial but it may be expensive depending on the tree's depth and breadth. This simplistic example will require three database reads—children of 3, children of 12, children of 8—and six updates—each of 4, 5, 7, 8, 12, and 13. This doesn't even count the original expense of finding and updating 3 itself. But the point here is that we should have a write seldom, read often situation and this up front expense may save exponentially with regards to ongoing query costs.


If your materialized path column is insufficiently large you're going to have problems. A VARCHAR(255) is only wide enough to support a tree which is 35 nodes deep if the average PK values are integers in the millions. This might be fine for your usage. Just be aware path tracking is not arbitrary, it's limited to the column's width.


Better documents; obviously.
More tests; what else is new?

One set with nothing changed: use default column names.

One set with everything changed.



DBIx::Class::Ordered, DBIx::Class.

WHY NOT DBIx::Class::Ordered?

There are data sets which have implicit, or even tacit, ordering—”position“ in DBIx::Class::Ordered parlance– in the data already. Published articles, for example, will be naturally ordered chronologically. Additional position tracking becomes complex and redundant in this kind of case. You can even run into cases where both types of ordering are necessary like a collection of dictionaries. Each dictionary's terms are ordered alphabetically while each term's definitions would be ordered by a position set at editorial discretion.


Ashley Pond V · ·


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