MySQL vs. PostgreSQL for Akeneo Exports: Which Should You Choose?
Both databases work with SyncPIM. The right choice depends on your existing stack, your BI tools, and how you plan to query Akeneo product data.
TL;DR — Quick decision guide
Choose PostgreSQL if:
- • You're doing analytics with Metabase, Tableau, or Looker
- • You need efficient JSONB queries on Akeneo attributes
- • You don't have an existing MySQL infrastructure
- • You want the most advanced JSON indexing (GIN)
Choose MySQL if:
- • Your existing stack already runs MySQL (LAMP, WooCommerce)
- • Your e-commerce platform is WooCommerce or PrestaShop
- • You're on PlanetScale or another MySQL-specific managed service
- • Your team knows MySQL and has no reason to switch
JSON support: JSONB vs MySQL JSON
Akeneo product attributes are nested JSON — { locale, scope, data } arrays for every attribute. How you store and query this data is the most important technical difference between the two databases for this use case.
-- PostgreSQL JSONB: native binary storage, GIN-indexed
CREATE TABLE products (
identifier TEXT PRIMARY KEY,
family TEXT,
data JSONB NOT NULL, -- Binary JSON with GIN index support
updated_at TIMESTAMPTZ
);
CREATE INDEX idx_products_data ON products USING GIN (data);
-- Query: products where English name contains "cotton"
SELECT identifier FROM products
WHERE data->'name' @> '[{"locale":"en_US"}]'::jsonb
AND data->>'name' ILIKE '%cotton%';
-- MySQL JSON: stored as validated text, limited indexing
CREATE TABLE products (
identifier VARCHAR(255) PRIMARY KEY,
family VARCHAR(255),
data JSON NOT NULL, -- Stored as text with validation
updated_at TIMESTAMP
);
-- MySQL requires functional indexes on specific paths:
ALTER TABLE products ADD INDEX idx_family ((CAST(data->>'$.family' AS CHAR(100))));
-- Query equivalent (more verbose):
SELECT identifier FROM products
WHERE JSON_VALUE(data, '$.name[0].locale') = 'en_US'
AND JSON_VALUE(data, '$.name[0].data') LIKE '%cotton%';PostgreSQL JSONB is meaningfully better for ad-hoc analytics queries on Akeneo data. The GIN index allows queries across any JSON path without pre-defining indexes. MySQL requires you to know in advance which JSON paths you'll query frequently.
Feature comparison table
| Feature | PostgreSQL | MySQL |
|---|---|---|
| JSON column support | JSONB — binary format, indexed, fast queries | JSON — text format, limited index support |
| JSON query syntax | data->>'field', @> operator, jsonb_each() | JSON_EXTRACT(), JSON_VALUE(), JSON_TABLE() |
| GIN index on JSON | Yes — full GIN index, very fast | No GIN — only functional indexes on JSON paths |
| Full-text search | Built-in tsvector, excellent | FULLTEXT indexes, adequate for basic needs |
| Metabase compatibility | Native driver, JSONB fields supported in custom SQL | Native driver, JSON_EXTRACT in custom SQL |
| Tableau compatibility | Excellent — official connector | Excellent — official connector |
| Managed cloud options | RDS, Cloud SQL, Supabase, Neon, Railway | RDS, PlanetScale, Aiven, Railway |
| WooCommerce integration | Requires custom bridge | Native — WooCommerce runs on MySQL |
| Replication | Logical replication, streaming, slots | Binlog replication, well-understood tooling |
| License | PostgreSQL License (free, permissive) | GPL v2 (community) or commercial |
| Default port | 5432 | 3306 |
Query performance for analytics workloads
For a 500k product catalog, typical query performance on a modest server (4 vCPU, 16GB RAM):
-- Test query: catalog completeness report
-- (% of products with French description per family)
-- PostgreSQL with GIN index on data:
EXPLAIN ANALYZE SELECT family,
COUNT(*) total,
COUNT(CASE WHEN data->'description' @> '[{"locale":"fr_FR"}]' THEN 1 END) has_fr
FROM products GROUP BY family;
-- → 230ms for 500k rows (GIN index used)
-- MySQL with functional index:
EXPLAIN ANALYZE SELECT family,
COUNT(*) total,
SUM(JSON_CONTAINS(data->'$.description[*].locale', '"fr_FR"')) has_fr
FROM products GROUP BY family;
-- → 4.2s for 500k rows (full table scan — JSON_CONTAINS not indexable)This isn't a benchmark — actual numbers vary by schema, queries, and hardware. But the pattern holds: PostgreSQL JSONB with GIN index dramatically outperforms MySQL JSON for aggregate queries across JSON columns.
Managed cloud services: cost and simplicity
PostgreSQL managed services
MySQL managed services
Real-world scenarios: who should pick what
Mid-market retailer, headless commerce on Next.js, Metabase for ops reporting
Metabase's GUI works better with PostgreSQL JSONB. The analytics queries (catalog completeness, price coverage) are much faster with GIN indexes.
WooCommerce store adding a PIM, same team manages the WooCommerce MySQL DB
Single database type reduces operational complexity. Team already knows MySQL. WooCommerce plugins may benefit from reading Akeneo data directly.
SaaS platform (B2B) adding Akeneo as product data source for customers
JSONB's flexibility handles the attribute variability across customer catalogs without schema migrations. GIN index handles per-customer catalog filtering.
Agency managing Akeneo for clients on PlanetScale (MySQL)
PlanetScale has excellent branching and deployment workflows. Stick with MySQL to avoid managing two database types.
SyncPIM supports both — pick yours
Connect Akeneo to PostgreSQL or MySQL with the same configuration interface. Switch databases later without changing your Akeneo setup.