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In Özkan Pakdil’s September 2026 mpazari benchmark, MariaDB 10.3.39 had the lowest average and p95 response times, while MySQL 8.0.42 had the best p99. The results describe one application and test environment—not a universal ranking. PostgreSQL 12’s figures are especially difficult to compare because they came from an earlier run with different SQL on a database cluster shared by several sites.
What did the mpazari benchmark find?
For the main test, MariaDB recorded the best average and p95 latency, but MySQL recorded the best p99. Özkan Pakdil published the results in 2026:
| Database and tested version | Average response time | p95 response time | p99 response time |
|---|---|---|---|
| MariaDB 10.3.39 | 125.52 ms | 129.73 ms | 182.13 ms |
| MySQL 8.0.42 | 132.40 ms | 139.43 ms | 150.07 ms |
| PostgreSQL 12 | 156.97 ms | 171.86 ms | 177.16 ms |
These are response-time figures reported by Pakdil for his 2026 comparison; the PostgreSQL row is contextual rather than a controlled, like-for-like result. The mean and p95 favor MariaDB among the reported numbers, while p99 favors MySQL. Averages alone would hide that difference in tail latency. Read the mpazari benchmark and its test details.
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The test reported 16,025 successful requests for MariaDB, 15,930 for MySQL, and 15,590 for PostgreSQL, with a reported failed-request rate of 0% for all three. Those counts should be read alongside the test differences, not as a clean throughput contest. Peak database RSS was reported as 141 MB for MariaDB, 411 MB for MySQL, and 1.7 GB for PostgreSQL; the PostgreSQL cluster hosted multiple sites, so the 1.7 GB cannot be attributed to this workload alone. (Pakdil, 2026.)
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What workload and environment produced the results?
This was a real Turkish motorcycle-classifieds application rather than a synthetic, general-purpose database test. It used a Spring Boot 4 application built as a Java 25 GraalVM native image, with hand-written SQL. The home page made several small reads for listings, taxonomy, counts, and footer information—roughly five simple database queries per request.
Pakdil reported 31 tables, 20,750 rows in motor_ilanlar, and about 111 MB of data. The server was a Hetzner system with 8 cores and 32 GB RAM running Ubuntu 20.04. This is a small, fully resident dataset on one specified server. It does not tell you which engine will be fastest with a larger-than-memory database, a write-heavy workload, different query shapes, other hardware, or newer engine versions.
How fair is the PostgreSQL comparison?
It is not an engine-pure three-way comparison. Pakdil says the PostgreSQL results came from an earlier live-stack run with different SQL text, and PostgreSQL was running in a cluster shared by several sites. Changes in SQL and shared-server activity can affect latency and resource measurements, so the PostgreSQL figures cannot establish that PostgreSQL is generally slower than either MySQL or MariaDB.
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The MySQL and MariaDB comparison is more directly useful for this particular application, but it is still bounded by the tested versions and workload. MariaDB 10.3.39 is an older generation than MySQL 8.0.42, and the results do not show how a newer MariaDB release would compare.
Did MariaDB use less CPU than MySQL?
In a separate, smaller test with 15 users over two and a half minutes, both databases began equally warm. Pakdil reported average and p95 latency of 125.12 ms and 130.26 ms for MariaDB, compared with 130.25 ms and 136.35 ms for MySQL. Average and peak CPU were 0.2% and 1.0% for MariaDB, versus 6.6% and 9.0% for MySQL.
Pakdil cautions that the request rate was low and the database spent most of the time waiting. These CPU readings describe that test, not a general claim that MySQL is more CPU-intensive or that the difference will matter at higher traffic.
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Does a database benchmark tell you which engine to choose?
Only if the workload resembles yours and the comparison controls the factors that shape the result. Oracle’s MySQL Reference Manual, version 26.7, puts the limitation plainly: “Performance can vary depending on so many different factors that a difference of a few percentage points might not be a decisive victory. The results might shift the opposite way when you test in a different environment.” Oracle MySQL Reference Manual: Measuring Performance (Benchmarking).
Use a benchmark to answer a deployment question, not to crown a database in the abstract. Compare equivalent SQL and configuration, and measure the latency distribution as well as resource use and successful work completed.
Build a representative test
- Replay the application’s actual query mix, including representative reads, writes, joins, and transactions—not only a convenient subset.
- Use realistic data volume and shape. Include the indexes and data distribution the production application depends on.
- Keep engine versions, hardware, operating system, configuration, SQL text, and data-warmth conditions as consistent as possible. Record any differences.
- Test the concurrency and request rate you expect to run. Track completed requests or transactions, errors, CPU, and memory alongside response times.
- Report average latency and tail percentiles such as p95 and p99. A database can lead on the average but trail at p99, as the mpazari figures illustrate.
- Run the test more than once and compare results for consistency before treating small gaps as meaningful.
PostgreSQL’s official pgbench documentation describes a tool that can replay SQL sequences across concurrent sessions, calculate transaction rates, and use custom script files. It recommends runs lasting at least a few minutes and multiple runs to assess reproducibility. PostgreSQL documentation: pgbench.
What about MySQL compatibility when considering MariaDB?
Do not assume that similar origins guarantee drop-in compatibility for your application. A 2025 MariaDB Foundation survey found that over 70% of respondents had encountered no MySQL compatibility issues; other respondents reported issues involving performance, SQL behavior, connectors, and ORM or framework compatibility. That is respondent experience, not a guarantee for a particular system. Check the exact drivers, framework and ORM versions, SQL features, and application behavior you rely on. MariaDB Foundation.
For a migration or new deployment, run integration tests against the target engine and verify application-critical queries and transactions. Benchmarking cannot replace compatibility testing: an engine may perform well on a query replay while differing in behavior your application depends on.
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