PostgreSQL vs MySQL for Analytics: Which Is Better for Data Teams?

PostgreSQL is the better analytical database. It has richer SQL (GROUPING SETS, LATERAL joins, recursive CTEs, JSONB with GIN indexes), a mature parallel query engine, and materialized views. MySQL is faster for simple key-value reads and high-throughput OLTP, and it powers an enormous share of the web. But if your job is writing analytical queries against an application database, PostgreSQL gives you more tools and fewer workarounds.

How do PostgreSQL and MySQL compare for analytics?

The gap isn't close on the analytical side. PostgreSQL was designed as an extensible research database. MySQL was designed to serve web pages fast. Both evolved, but the starting assumptions still show.

What does the analytical SQL gap look like in practice?

Here's a concrete example. You want monthly revenue with a running total and month-over-month growth rate.

PostgreSQL:

WITH monthly AS (
  SELECT
    date_trunc('month', created_at) AS month,
    SUM(amount)                     AS revenue
  FROM payments
  WHERE status = 'succeeded'
  GROUP BY 1
)
SELECT
  month,
  revenue,
  SUM(revenue) OVER (ORDER BY month)                     AS running_total,
  ROUND(
    100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
    / NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 1
  )                                                       AS mom_growth_pct
FROM monthly
ORDER BY month;

MySQL 8.0:

WITH monthly AS (
  SELECT
    DATE_FORMAT(created_at, '%Y-%m-01') AS month,
    SUM(amount)                         AS revenue
  FROM payments
  WHERE status = 'succeeded'
  GROUP BY DATE_FORMAT(created_at, '%Y-%m-01')
)
SELECT
  month,
  revenue,
  SUM(revenue) OVER (ORDER BY month)                     AS running_total,
  ROUND(
    100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
    / NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 1
  )                                                       AS mom_growth_pct
FROM monthly
ORDER BY month;

For this query, the syntax is nearly identical -- MySQL 8.0 closed the gap on basic window functions. The difference shows up in the things PostgreSQL has that MySQL doesn't: GROUPING SETS, LATERAL joins, FILTER clauses, and date_trunc (MySQL uses DATE_FORMAT instead). Once your queries get complex -- multi-level rollups, correlated subquery elimination via LATERAL, conditional aggregates -- PostgreSQL lets you express things in one statement that MySQL forces you to break into multiple queries or application logic.

How does the feature gap map out visually?

Analytical SQL Feature Support
==============================
 
Feature                PostgreSQL    MySQL 8.0+
---------------------  ----------    ----------
Window functions       ■■■■■■■■■■    ■■■■■■■□□□
  RANGE frames         ■■■■■■■■■■    ■■■■□□□□□□
  GROUPS frames        ■■■■■■■■■■    □□□□□□□□□□
CTEs (recursive)       ■■■■■■■■■■    ■■■■■■■■□□
LATERAL joins          ■■■■■■■■■■    □□□□□□□□□□
GROUPING SETS          ■■■■■■■■■■    □□□□□□□□□□
ROLLUP / CUBE          ■■■■■■■■■■    ■■■■■■■□□□  (ROLLUP only)
FILTER clause          ■■■■■■■■■■    □□□□□□□□□□
JSONB + GIN indexes    ■■■■■■■■■■    ■■■■■■□□□□  (JSON + functional idx)
Materialized views     ■■■■■■■■■■    □□□□□□□□□□
Parallel query         ■■■■■■■■■■    ■■■□□□□□□□
JIT compilation        ■■■■■■■■■■    □□□□□□□□□□
 
■ = supported/mature   □ = absent or limited

PostgreSQL fills every row. MySQL has gaps exactly where analytical workloads need depth -- multi-dimensional aggregation, query optimization for complex joins, and precomputed result sets.

Which one is faster?

It depends on the query shape.

MySQL's InnoDB engine is heavily optimized for primary-key lookups and simple indexed reads. A SELECT * FROM users WHERE id = 12345 on a 100M row table is marginally faster in MySQL. Insert throughput on simple tables is comparable, with MySQL slightly ahead in high-concurrency OLTP benchmarks.

PostgreSQL wins on complex queries. Its query planner is more sophisticated -- it uses hash joins, merge joins, and nested loop joins and picks between them based on cost estimation. Parallel query spreads large sequential scans and aggregations across multiple CPU cores. JIT compilation (LLVM-based, available since PostgreSQL 11) compiles hot expression evaluation code to machine code at runtime.

For a GROUP BY across 50 million rows with a few JOINs, PostgreSQL will typically finish 2-5x faster than MySQL. The gap widens with query complexity. For a point lookup by primary key, MySQL edges ahead.

The mental model: MySQL is a sprinter on flat ground. PostgreSQL is a distance runner on varied terrain.

How does JSON support compare?

PostgreSQL's JSONB is a binary format stored in a decomposed form. You can index individual keys with GIN indexes, use containment operators (@>), path expressions (jsonb_path_query), and treat JSONB columns almost like first-class relational data.

-- PostgreSQL: find all events where metadata contains a specific key-value
SELECT *
FROM events
WHERE metadata @> '{"source": "stripe", "type": "invoice.paid"}'::jsonb;
-- GIN index on metadata makes this fast at any scale

MySQL's JSON type stores valid JSON documents and supports extraction with -> and ->> operators. Since 8.0 you can create functional indexes on JSON expressions, which helps. But the query patterns are more limited -- no containment operators, no full-path expressions, and the optimizer doesn't handle JSON predicates as naturally.

For data teams querying semi-structured event data or API payloads alongside relational data, PostgreSQL's JSONB is materially better.

What about extensions and the ecosystem?

PostgreSQL's extension model is one of its strongest advantages for data teams:

• PostGIS -- full geospatial database. Nothing else in the relational world matches it.
• TimescaleDB -- time-series on top of Postgres. Continuous aggregates, compression, retention policies.
• Citus -- distributed PostgreSQL for horizontal scaling.
• pg_stat_statements -- built-in query performance monitoring.
• Foreign data wrappers -- query MySQL, MongoDB, S3, or CSV files directly from PostgreSQL.

MySQL's extension model is more limited. Plugins for authentication, storage engines, and audit logging exist, but there's no equivalent to PostGIS, TimescaleDB, or foreign data wrappers. The analytical ecosystem around MySQL is thinner. Its strength is sheer deployed base -- WordPress, LAMP-stack applications, legacy enterprise systems, managed services on every cloud. If you need to run analytics on an existing MySQL database, that's a valid reason to stay.

When does PostgreSQL win?

• You're building a new application and want one database for both OLTP and analytics
• Your queries use window functions, CTEs, JSONB, or geospatial data
• You need materialized views for precomputed dashboards
• You want extensions like TimescaleDB or PostGIS
• Your data team writes complex SQL and values expressiveness

When does MySQL win?

• Your application already runs on MySQL and migration cost is prohibitive
• Your workload is overwhelmingly simple reads and writes (content serving, session stores)
• You're in the WordPress/PHP ecosystem
• You need MySQL-specific replication topologies (group replication, InnoDB Cluster)
• Query complexity is low and you care about raw OLTP throughput

When do you need something purpose-built?

Both PostgreSQL and MySQL are OLTP databases. They store rows, serve transactional workloads, and handle analytical queries as a secondary concern. When analytical queries start competing with your application -- dashboards that slow down your API, GROUP BYs that pin your CPU, reports that need to scan hundreds of millions of rows -- neither database is the right tool anymore.

That's when you look at columnar engines like ClickHouse or embedded analytical databases like DuckDB. Or you keep your application database and put an analytics layer in front of it that handles the query load separately.

Where PostgreSQL and MySQL sit on the OLTP-OLAP spectrum
=========================================================
 
  OLTP                    Mixed                    OLAP
  (transactions)          (both)                   (analytics)
 
  |----|----|----|----|----|----|----|----|----|----|
       MySQL    PostgreSQL                ClickHouse
       InnoDB                             DuckDB
                                          BigQuery
 
  MySQL = optimized here       PostgreSQL = stretches here
  <-- simple reads/writes      complex analytical queries -->

PostgreSQL stretches further toward the analytical end than MySQL does. But neither reaches the territory of a dedicated columnar engine. For most teams under 100M rows, PostgreSQL handles both workloads. Past that, consider a dedicated analytical layer or a tool that queries your application database without putting the load directly on it.

If you're running MongoDB instead of a relational database, the analytical SQL gap is even wider.

FAQ

Can MySQL handle window functions and CTEs now?

Yes. MySQL 8.0 (released 2018) added window functions and CTEs. The implementation works for standard use cases -- running totals, row numbering, recursive hierarchies. But it lacks GROUPS frame type, FILTER clause, GROUPING SETS, and LATERAL joins. If your analytical queries stay simple, MySQL 8.0 is fine. If you need anything beyond basic window functions, you'll hit limits.

Should I migrate from MySQL to PostgreSQL for analytics?

Not automatically. Migration is expensive -- different SQL dialects, different driver behavior, different replication setups, application code changes. If your analytical queries are simple (basic aggregations, a few JOINs, standard window functions), MySQL handles them. Consider migration when you're repeatedly working around MySQL's missing features, or when you're starting a new project and choosing a database. For existing MySQL applications, adding an analytical layer on top (like a SQL editor that connects directly) is often cheaper than migrating.

Is PostgreSQL slower than MySQL for web applications?

For what most web applications do -- read a row by primary key, insert a row, update a few fields -- MySQL is marginally faster. The difference is single-digit percentages in most benchmarks. PostgreSQL's overhead comes from its richer MVCC implementation and more sophisticated query planner (which pays off on complex queries). Both perform well at typical application load. The speed difference matters at extreme scale on simple queries, not at normal traffic.

Can I use both PostgreSQL and MySQL in the same analytics stack?

Yes, and many teams do. Your application runs on MySQL, you replicate to PostgreSQL (or query MySQL directly from your analytics tool), and you write analytical SQL in PostgreSQL's richer dialect. Foreign data wrappers in PostgreSQL can query MySQL directly. Tools like Fastero connect to both and let you query either one without caring about which engine serves which table.

When should I skip both and use a columnar database?

When your analytical queries scan more than 100-500M rows regularly, or when dashboard queries take more than 5-10 seconds on your largest tables. Columnar databases like ClickHouse store data by column rather than by row, which makes aggregations over large datasets 10-100x faster. The trade-off is operational complexity -- you're running a second database. For teams that have outgrown what PostgreSQL can do analytically, see our ClickHouse vs Postgres comparison.

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