Methods to process hierarchical data using ltree in PostgreSQL

In this article, we will learn how to use PostgreSQL's ltree module, which allows data to be stored in a hierarchical tree structure.

What is ltree?

Ltdree is a PostgreSQL module. It implements a data type ltree that represents the tags of data stored in a hierarchical tree structure. A wide range of tools are provided for searching tag trees.

Why choose ltree?

ltree implements a materialized path which is very fast for INSERT/UPDATE/DELETE and faster for SELECT operations
It is usually faster than using recursive CTE or recursive functions that often require recalculating branches
Such as built-in query syntax and operators specifically used for querying and navigation trees
index! ! !

Initial data

First, you should enable extensions in your database. You can do this with the following command:

CREATE EXTENSION ltree;

Let's create a table and add some data to it:

CREATE TABLE comments (user_id integer, description text, path ltree);
INSERT INTO comments (user_id, description, path) VALUES ( 1, md5(random()::text), '0001');
INSERT INTO comments (user_id, description, path) VALUES ( 2, md5(random()::text), '0001.0001.0001');
INSERT INTO comments (user_id, description, path) VALUES ( 2, md5(random()::text), '0001.0001.0001.0001');
INSERT INTO comments (user_id, description, path) VALUES ( 1, md5(random()::text), '0001.0001.0001.0002');
INSERT INTO comments (user_id, description, path) VALUES ( 5, md5(random()::text), '0001.0001.0001.0003');
INSERT INTO comments (user_id, description, path) VALUES ( 6, md5(random()::text), '0001.0002');
INSERT INTO comments (user_id, description, path) VALUES ( 6, md5(random()::text), '0001.0002.0001');
INSERT INTO comments (user_id, description, path) VALUES ( 6, md5(random()::text), '0001.0003');
INSERT INTO comments (user_id, description, path) VALUES ( 8, md5(random()::text), '0001.0003.0001');
INSERT INTO comments (user_id, description, path) VALUES ( 9, md5(random()::text), '0001.0003.0002');
INSERT INTO comments (user_id, description, path) VALUES ( 11, md5(random()::text), '0001.0003.0002.0001');
INSERT INTO comments (user_id, description, path) VALUES ( 2, md5(random()::text), '0001.0003.0002.0002');
INSERT INTO comments (user_id, description, path) VALUES ( 5, md5(random()::text), '0001.0003.0002.0003');
INSERT INTO comments (user_id, description, path) VALUES ( 7, md5(random()::text), '0001.0003.0002.0002.0001');
INSERT INTO comments (user_id, description, path) VALUES ( 20, md5(random()::text), '0001.0003.0002.0002.0002');
INSERT INTO comments (user_id, description, path) VALUES ( 31, md5(random()::text), '0001.0003.0002.0002.0003');
INSERT INTO comments (user_id, description, path) VALUES ( 22, md5(random()::text), '0001.0003.0002.0002.0004');
INSERT INTO comments (user_id, description, path) VALUES ( 34, md5(random()::text), '0001.0003.0002.0002.0005');
INSERT INTO comments (user_id, description, path) VALUES ( 22, md5(random()::text), '0001.0003.0002.0002.0006');

Also, we should add some indexes:

CREATE INDEX path_gist_comments_idx ON comments USING GIST(path);
CREATE INDEX path_comments_idx ON comments USING btree(path);

As you can see, I create comments table with the path field which contains all the paths of the tree of the table. As you can see, for the tree separator, I use 4 numbers and dots.

Let's find the record of path with '0001.0003' in the commenets table:

$ SELECT user_id, path FROM comments WHERE path <@ '0001.0003';
 user_id |   path
---------+--------------------------
  6 | 0001.0003
  8 | 0001.0003.0001
  9 | 0001.0003.0002
  11 | 0001.0003.0002.0001
  2 | 0001.0003.0002.0002
  5 | 0001.0003.0002.0003
  7 | 0001.0003.0002.0002.0001
  20 | 0001.0003.0002.0002.0002
  31 | 0001.0003.0002.0002.0003
  22 | 0001.0003.0002.0002.0004
  34 | 0001.0003.0002.0002.0005
  22 | 0001.0003.0002.0002.0006
(12 rows)

Let's check this SQL via the EXPLAIN command:

$ EXPLAIN ANALYZE SELECT user_id, path FROM comments WHERE path <@ '0001.0003';
            QUERY PLAN
----------------------------------------------------------------------------------------------------
 Seq Scan on comments (cost=0.00..1.24 rows=2 width=38) (actual time=0.013..0.017 rows=12 loops=1)
 Filter: (path <@ '0001.0003'::ltree)
 Rows Removed by Filter: 7
 Total runtime: 0.038 ms
(4 rows)

Let's disable seq scan for testing:

$ SET enable_seqscan=false;
SET
$ EXPLAIN ANALYZE SELECT user_id, path FROM comments WHERE path <@ '0001.0003';
               QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------
 Index Scan using path_gist_comments_idx on comments (cost=0.00..8.29 rows=2 width=38) (actual time=0.023..0.034 rows=12 loops=1)
 Index Cond: (path <@ '0001.0003'::ltree)
 Total runtime: 0.076 ms
(3 rows)

SQL is slow now, but you can see how SQL uses index.
The first SQL statement uses sequence scan because there is not much data in the table.

We can change the implementation method of select "path <@ '0001.0003'":

$ SELECT user_id, path FROM comments WHERE path ~ '0001.0003.*';
user_id |   path
---------+--------------------------
  6 | 0001.0003
  8 | 0001.0003.0001
  9 | 0001.0003.0002
  11 | 0001.0003.0002.0001
  2 | 0001.0003.0002.0002
  5 | 0001.0003.0002.0003
  7 | 0001.0003.0002.0002.0001
  20 | 0001.0003.0002.0002.0002
  31 | 0001.0003.0002.0002.0003
  22 | 0001.0003.0002.0002.0004
  34 | 0001.0003.0002.0002.0005
  22 | 0001.0003.0002.0002.0006
(12 rows)

You should not forget the order of data, as shown in the following example:

$ INSERT INTO comments (user_id, description, path) VALUES ( 9, md5(random()::text), '0001.0003.0001.0001');
$ INSERT INTO comments (user_id, description, path) VALUES ( 9, md5(random()::text), '0001.0003.0001.0002');
$ INSERT INTO comments (user_id, description, path) VALUES ( 9, md5(random()::text), '0001.0003.0001.0003');
$ SELECT user_id, path FROM comments WHERE path ~ '0001.0003.*';
user_id |   path
---------+--------------------------
  6 | 0001.0003
  8 | 0001.0003.0001
  9 | 0001.0003.0002
  11 | 0001.0003.0002.0001
  2 | 0001.0003.0002.0002
  5 | 0001.0003.0002.0003
  7 | 0001.0003.0002.0002.0001
  20 | 0001.0003.0002.0002.0002
  31 | 0001.0003.0002.0002.0003
  22 | 0001.0003.0002.0002.0004
  34 | 0001.0003.0002.0002.0005
  22 | 0001.0003.0002.0002.0006
  9 | 0001.0003.0001.0001
  9 | 0001.0003.0001.0002
  9 | 0001.0003.0001.0003
(15 rows)

Now sort:

$ SELECT user_id, path FROM comments WHERE path ~ '0001.0003.*' ORDER by path;
 user_id |   path
---------+--------------------------
  6 | 0001.0003
  8 | 0001.0003.0001
  9 | 0001.0003.0001.0001
  9 | 0001.0003.0001.0002
  9 | 0001.0003.0001.0003
  9 | 0001.0003.0002
  11 | 0001.0003.0002.0001
  2 | 0001.0003.0002.0002
  7 | 0001.0003.0002.0002.0001
  20 | 0001.0003.0002.0002.0002
  31 | 0001.0003.0002.0002.0003
  22 | 0001.0003.0002.0002.0004
  34 | 0001.0003.0002.0002.0005
  22 | 0001.0003.0002.0002.0006
  5 | 0001.0003.0002.0003
(15 rows)

Several modifiers can be added at the end of the non-asterisk tag of lquery to make it more matched than the exact match:
"@" - case insensitive matching, e.g. a @ matches A
"*" - Match any tags with that prefix, e.g. foo * match foobar
"%" - match words beginning with underscore

$ SELECT user_id, path FROM comments WHERE path ~ '0001.*{1,2}.0001|0002.*' ORDER by path;
 user_id |   path
---------+--------------------------
  2 | 0001.0001.0001
  2 | 0001.0001.0001.0001
  1 | 0001.0001.0001.0002
  5 | 0001.0001.0001.0003
  6 | 0001.0002.0001
  8 | 0001.0003.0001
  9 | 0001.0003.0001.0001
  9 | 0001.0003.0001.0002
  9 | 0001.0003.0001.0003
  9 | 0001.0003.0002
  11 | 0001.0003.0002.0001
  2 | 0001.0003.0002.0002
  7 | 0001.0003.0002.0002.0001
  20 | 0001.0003.0002.0002.0002
  31 | 0001.0003.0002.0002.0003
  22 | 0001.0003.0002.0002.0004
  34 | 0001.0003.0002.0002.0005
  22 | 0001.0003.0002.0002.0006
  5 | 0001.0003.0002.0003
(19 rows)

Let's find all direct children for parent '0001.0003', see below:

$ SELECT user_id, path FROM comments WHERE path ~ '0001.0003.*{1}' ORDER by path;
 user_id |  path
---------+----------------
  8 | 0001.0003.0001
  9 | 0001.0003.0002
(2 rows)

Find all childrens for parent '0001.0003', see below:

$ SELECT user_id, path FROM comments WHERE path ~ '0001.0003.*' ORDER by path;
 user_id |   path
---------+--------------------------
  6 | 0001.0003
  8 | 0001.0003.0001
  9 | 0001.0003.0001.0001
  9 | 0001.0003.0001.0002
  9 | 0001.0003.0001.0003
  9 | 0001.0003.0002
  11 | 0001.0003.0002.0001
  2 | 0001.0003.0002.0002
  7 | 0001.0003.0002.0002.0001
  20 | 0001.0003.0002.0002.0002
  31 | 0001.0003.0002.0002.0003
  22 | 0001.0003.0002.0002.0004
  34 | 0001.0003.0002.0002.0005
  22 | 0001.0003.0002.0002.0006
  5 | 0001.0003.0002.0003
(15 rows)

Find parent for children '0001.0003.0002.0002.0005':

$ SELECT user_id, path FROM comments WHERE path = subpath('0001.0003.0002.0002.0005', 0, -1) ORDER by path;
 user_id |  path
---------+---------------------
  2 | 0001.0003.0002.0002
(1 row)

If your path is not unique, you will get multiple records.

Overview

It can be seen that the materialization path using ltree is very simple. In this article, I do not list all possible uses of ltree. It is not considered a full text search question ltxtquery. But you can do it in the official PostgreSQL documentation (/docs/current/static/Find it in ).

To learn more about PostgreSQL hot news, news and exciting events, please visit the official website of PostgreSQL in China:

To solve more PostgreSQL-related knowledge, technology, and work problems, please visit the official PostgreSQL Q&A community in China:

To download more PostgreSQL-related information, tools, and plug-in questions, please visit the official download website of PostgreSQL in China:

This is the article about using ltree to process hierarchical data in PostgreSQL. For more related PostgreSQL hierarchical data content, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!