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MySQL vs PostgreSQL — Full Comparison Focused on Tree Structures

Hierarchical (tree) data is common in modern systems, appearing in structures such as:

Categories and subcategories
Nested comments
Organizational charts
Chapters → Lessons → Topics
Product attributes
Folder → File systems

MySQL and PostgreSQL handle tree-based data very differently.

Below is a complete comparison.

1. Native Support for Hierarchical Data

PostgreSQL

PostgreSQL provides native, built-in support for hierarchical structures:

LTREE (built-in data type)

Allows storage of paths such as:

root.programming.php.laravel

This makes tree queries extremely fast and efficient.

Recursive CTE (WITH RECURSIVE)

Fully optimized, stable, and high-performance.

MySQL

No native tree data type
MySQL does not provide anything equivalent to LTREE

MySQL 8+ supports Recursive CTE, but:

Performance is slower
Optimizer is less mature
Indexing is less effective
Can hit recursion depth limits

Winner: PostgreSQL

PostgreSQL is designed with hierarchical data in mind.

2. Ways to Represent Tree Structures

Tree structures can be stored using several models.

Here is how each database handles them.

Model 1 — Adjacency List (parent_id)

FeaturePostgreSQLMySQLEasy to implementYesYesQuery childrenEasyEasyQuery ancestorsFast (recursive CTE)Slow (pre–MySQL 8)Query entire treeFastSlowerDepth calculationsRecursive CTERequires manual logic

PostgreSQL performs adjacency list queries much faster due to stronger recursive CTE support.

Model 2 — Nested Set / MPTT

FeaturePostgreSQLMySQLQuery full treeFastFastInsert / move nodesExpensiveExpensiveMaintaining left/right indexesHeavyHeavyIndex performanceStrongGood

Both databases behave similarly, but MySQL may lock tables more aggressively.

Model 3 — Path Enumeration (full path as string)

Example:

/root/programming/php/laravel

FeaturePostgreSQLMySQLString pathsYesYesIndexing pathsGiST/GiN indexesLimitedWildcard searchSupported via LTREE operatorsRequires LIKEQuery descendantsVery fast (path <@ 'x')Slow (LIKE 'x%')

PostgreSQL dominates due to LTREE and specialized indexes.

Model 4 — PostgreSQL LTREE (PostgreSQL only)

MySQL: Not available

PostgreSQL: Full support

Examples:

Ancestor queries

SELECT * FROM categories WHERE path @> 'lms.programming';

Descendant queries

SELECT * FROM categories WHERE path <@ 'lms.programming.php';

Pattern matching

SELECT * FROM categories WHERE path ~ '*.php';

MySQL cannot perform these queries natively.

3. Indexing Differences for Trees

PostgreSQL

Supports advanced index types:

GiST
GiN
SP-GiST

These allow indexing tree paths efficiently.

MySQL

Supports:

BTREE only
Optional HASH (Memory engine only)

BTREE is not optimized for deep hierarchical data.

Winner: PostgreSQL

4. Querying Tree Data

PostgreSQL LTREE example

SELECT name
FROM categories
WHERE path <@ 'lms.programming';

Fast, indexed, and optimized.

PostgreSQL Recursive CTE

WITH RECURSIVE c AS (
    SELECT * FROM categories WHERE id = 10
    UNION ALL
    SELECT child.* FROM categories child
    JOIN c ON child.parent_id = c.id
)
SELECT * FROM c;

MySQL Recursive CTE (MySQL 8+)

WITH RECURSIVE c AS (
    SELECT * FROM categories WHERE id = 10
    UNION ALL
    SELECT child.* FROM categories child
    JOIN c ON child.parent_id = c.id
)
SELECT * FROM c;

Although the syntax appears identical:

MySQL recursive performance:

Slower execution
Higher CPU usage
Harder to optimize
May hit recursion depth limits

PostgreSQL recursive performance:

Highly optimized
Efficient memory handling
Performs well even with large trees

5. Moving Nodes in a Tree

Moving a category involves:

Updating parent_id
Updating the path
Updating all descendant paths

MySQL

Must manually rebuild all descendant paths
No built-in tree functions
No operators to assist with this

PostgreSQL (LTREE)

Rebuilding entire subtree is simple
LTREE operators make updates fast
Identifying affected nodes is trivial

6. Depth and Level Support

PostgreSQL

SELECT nlevel(path) AS depth FROM categories;

Simple, efficient, and built-in.

MySQL

No built-in depth function.

You must count delimiters manually.

7. Searching Trees (Descendants, Ancestors, Siblings)

PostgreSQL

SearchMethodDescendants<@Ancestors@>Pattern matching~WildcardsLTREE patterns

PostgreSQL offers extremely fast and expressive tree search.

MySQL

SearchMethodPerformanceDescendantsLIKE 'path%'SlowAncestorsManual scansSlowPattern matchingLIKELimitedWildcardsLimitedPoor

MySQL search performance is significantly weaker for tree data.

Overall Summary (Tree Handling)

FeaturePostgreSQLMySQLNative tree typeYes (LTREE)NoRecursive CTE performanceExcellentWeakPath indexingGiST/GiNLimitedDescendant queriesVery fastSlowAncestor queriesVery fastSlowPattern searchLTREE operatorsLIKE onlyMoving subtreesEasyComplexDepth calculationBuilt-inManualBest for deep hierarchiesYesNo

Winner for tree structures: PostgreSQL by a large margin