Postgres vs MariaDB

Postgres and MariaDB are both open-source relational databases with no licensing fees, but they are not interchangeable. MariaDB is a fork of MySQL, optimized for web application workloads with familiar MySQL compatibility. Postgres is an independent project with a richer type system, stronger SQL standards compliance, and a deeper extension ecosystem.

This post compares them on architecture, SQL capabilities, replication, analytics limits, and when to add a real-time analytical layer on top of either.

Different lineages, different design points

MariaDB began as a fork of MySQL in 2009 when Oracle acquired Sun Microsystems and, with it, MySQL. MariaDB maintains MySQL wire protocol compatibility, meaning applications built for MySQL typically work with MariaDB without code changes. MariaDB's storage engine architecture supports multiple engines per table: InnoDB for transactional workloads, Aria for system tables, and ColumnStore for analytical queries.

Postgres uses a single storage engine (heap-based with MVCC) with a unified query planner. It does not support pluggable storage engines in the same way MariaDB does. Instead, Postgres extends capability through extensions: PostGIS, pgvector, TimescaleDB, Citus, and hundreds of others install as shared libraries that add types, functions, and access methods.

The architectural difference matters for teams choosing a primary database. MariaDB optimizes for drop-in MySQL replacement with optional analytical acceleration via ColumnStore. Postgres optimizes for extensibility and standards-compliant SQL with a path to horizontal scaling via Citus.

Where Postgres excels

Postgres offers capabilities that MariaDB does not match:

Rich type system. Postgres supports arrays, JSONB with GIN indexes, geometric types, network address types, UUID, and custom composite types. JSONB queries with @>, ?, and path operators make Postgres viable as a hybrid relational/document store without a separate MongoDB instance.

SQL standards compliance. Window functions, lateral joins, RETURNING clauses, and full outer joins work as specified in the SQL standard. Postgres's query planner handles complex CTEs and subqueries reliably.

Extension ecosystem. PostGIS for geospatial, pgvector for embeddings, pg_cron for scheduled jobs, and Citus for distributed Postgres are mature, widely deployed extensions.

Concurrent write performance. Postgres MVCC handles concurrent writes from multiple connections without the table-level locking that older MySQL/MariaDB versions used for certain DDL operations.

For handling analytics workloads on Postgres, the database serves moderate analytical needs through materialized views and read replicas. Beyond roughly 50 million rows, aggregation queries start taking 20-30 seconds, which signals the need for an OLAP layer.

Where MariaDB excels

MariaDB retains advantages from its MySQL heritage:

MySQL compatibility. Applications, ORMs, and tools built for MySQL work with MariaDB. Connection strings, SQL dialect, and replication protocols are compatible. Migration from MySQL to MariaDB is typically a configuration change, not a rewrite.

Galera Cluster. MariaDB's multi-master synchronous replication via Galera Cluster provides active-active write capability across nodes with automatic conflict resolution. Postgres synchronous replication is primary-replica, not multi-master.

ColumnStore engine. MariaDB ColumnStore provides columnar storage for analytical queries within the same MariaDB instance. For teams comparing ClickHouse® vs MariaDB ColumnStore, ColumnStore handles moderate analytical workloads but lacks ClickHouse's vectorized execution and sub-second latency at billion-row scale.

Simpler operational model. MariaDB's configuration and tuning parameters are well-documented for web application patterns. Teams with existing MySQL expertise transition to MariaDB without retraining.

Analytics limits on both

Both Postgres and MariaDB are row-oriented OLTP databases. Analytical queries that scan large portions of a table compete with transactional workloads for the same resources.

MariaDB ColumnStore mitigates this for analytical queries routed to ColumnStore tables, but the hybrid row/column architecture adds operational complexity: which tables go in InnoDB vs ColumnStore, how to handle updates on columnar tables, and how to join row-store and column-store tables efficiently.

Postgres has no native columnar engine. Analytical acceleration requires extensions (Citus columnar, TimescaleDB) or an external OLAP database.

For both databases, the pattern that scales is the same: move OLAP workloads off the operational database via CDC into ClickHouse, where columnar storage and vectorized execution handle aggregation queries in milliseconds instead of minutes.

Replication and HA comparison

Postgres's logical replication (available since Postgres 10) enables selective table replication and serves as the foundation for CDC tools like Debezium. MariaDB's binary log replication serves the same CDC purpose with compatible tooling.

JSON and semi-structured data

MariaDB supports JSON columns with JSON functions (JSON_EXTRACT, JSON_CONTAINS, JSON_ARRAYAGG). Postgres JSONB with GIN indexes provides faster containment queries and a richer operator set (@>, ?, #> path operators).

For applications storing mixed relational and document data, Postgres JSONB is the stronger choice. For applications already using MySQL JSON functions, MariaDB maintains compatibility without migration.

Performance characteristics

Postgres and MariaDB both handle OLTP workloads well at moderate scale. Benchmark differences on transactional workloads are typically within 20-30% depending on query patterns, hardware, and tuning.

MariaDB's thread pool plugin (since MariaDB 5.5) improves connection handling under high concurrency by reusing threads instead of creating one thread per connection. Postgres handles concurrency through process-per-connection with PgBouncer pooling.

For write-heavy workloads, both databases use B-tree indexes on primary keys with InnoDB (MariaDB) and heap storage with MVCC (Postgres). Insert throughput on a single node typically reaches 10,000-50,000 rows per second depending on index count and row width.

For read-heavy analytical queries on the same instance, both databases degrade as table size grows. MariaDB ColumnStore provides some relief for analytical queries routed to columnar tables, but ClickHouse delivers significantly faster aggregation queries at billion-row scale compared to ColumnStore's columnar engine.

Adding real-time analytics

The CDC pipeline works identically for Postgres and MariaDB:

-- Postgres: logical replication slot for CDC
SELECT pg_create_logical_replication_slot('tinybird_slot', 'pgoutput');

-- MariaDB: binary log enabled for CDC
-- Debezium MariaDB connector reads binlog events

Changes stream through Kafka into ClickHouse's ReplacingMergeTree tables. Materialized views pre-aggregate metrics. SQL endpoints serve dashboards at sub-second latency.

For scaling Postgres to billions of rows, the operational database handles OLTP while ClickHouse handles the analytical layer. The same architecture applies when MariaDB is the operational source.

Tinybird for Postgres and MariaDB teams

Tinybird is managed ClickHouse with streaming ingestion designed for teams running Postgres or MariaDB as their operational database. The ClickHouse PostgreSQL integration covers Postgres CDC via logical replication. MariaDB CDC via Debezium's binlog connector follows the same pattern into Tinybird datasources.

For real-time data processing, Tinybird's Events API accepts streaming writes alongside CDC ingestion. SQL Pipes define analytical queries as parameterized HTTP endpoints. Your application queries Tinybird for dashboard metrics while Postgres or MariaDB continues handling transactions without analytical load.

Resend, processing 100TB per month on Tinybird, measured 62ms p90 query latency in production without caching. Tinybird is SOC 2 Type II certified. The pipeline from operational database to queryable analytics API takes hours to configure rather than weeks to build.

For teams evaluating the best real-time data analytics tools, pairing either Postgres or MariaDB with Tinybird restores analytical expressiveness without touching the operational write path.

The comparison table below summarizes the decision for teams choosing between Postgres and MariaDB as their primary operational database, with ClickHouse via Tinybird handling the analytical layer regardless of which they pick.

Choosing between them

Postgres is the better choice when you need JSONB, geospatial (PostGIS), vector search (pgvector), complex SQL, or a rich extension ecosystem. MariaDB is the better choice when you need MySQL compatibility, Galera multi-master replication, or a drop-in MySQL replacement with optional ColumnStore analytics.

For real-time analytics at scale, neither database is the answer alone. Both pair with ClickHouse via Tinybird for the analytical layer that operational databases cannot provide.