NAME
DBICx::MaterializedPath - DBIx::Class plugin for automatically tracking
lineage paths in simple data trees.
PREFER
Please see DBIx::Class::Tree::Mobius, DBIx::Class::Graph, and
DBIx::Class::Tree instead of this experimental package.
SYNOPSIS
We need a table, or tables, which represents a tree.
CREATE TABLE tree_data (
id INTEGER PRIMARY KEY NOT NULL,
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 );
__PACKAGE__->load_components(qw(
+DBICx::MaterializedPath
Core
));
# 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"
DESCRIPTION
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—<
http://use.perl.org/~Ovid/journal/39460>—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, <
http://www.dbazine.com/oracle/or-articles/tropashko4>.
CAVEAT
This package requires your table has a single primary key and a method
to look up a parent record by its single primary key.
METHODS
[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.
ancestors
Searches on the materialized path ids excepting the object's own.
This is generally cheap because it uses the path instead of
recursion.
get_root
Returns the root object for a given record.
grandchildren
Return all children and grandchildren.
node_depth
Returns 1 for a record with no parent.
root_node
siblings
max_depth
Set this to assert a maximum tree depth. Default is 500.
set_materialized_path
Probably shouldn't mess with this. It's used by "insert" and
"delete".
OVERRIDDEN METHODS
insert
Sets the materialized path.
update
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–
1
|
3
/ \
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–
2
|
3
/ \
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.
CAVEATS
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.
TO DO
Better documents; obviously.
More tests; what else is new?
One set with nothing changed: use default column names.
One set with everything changed.
CODE REPOSITORY
<
http://github.com/pangyre/p5-dbicx-materializedpath>.
SEE ALSO
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.
AUTHOR
Ashley Pond V �
[email protected] � <
http://pangyresoft.com>.
LICENSE
You may redistribute and modify this software under the same terms as
Perl itself.
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