GoKawiilOne engine, multi-workloads, full durability.
You can explore the full results, methodology, and per-database breakdowns at .
Why we ran these
Database benchmarks are notoriously easy to game, and difficult to get right. Different hardware, different durability settings, different client libraries, a workload that happens to suit one engine's indexing strategy - any of those will tilt the numbers. So we did three things:
Ran every database on the same hardware - an AMD Ryzen Threadripper 9970X (32C/64T), 128 GiB DDR5, NVMe storage, Ubuntu 24.04.
Used the same open-source harness - crud-bench - with each workload translated into each engine's native query language so no database is penalised for an unfamiliar dialect.
Configured every engine for production-grade durability - fsync on, snapshot isolation, no in-memory shortcuts (except where explicitly noted for embedded comparisons).
We also went out of our way to give every database a fair shot. Rather than running each engine on its out-of-the-box defaults, we used optimised configurations across the board - the same kind of tuning a production team would apply before going live. That meant raising connection and worker pool limits to match the 128-client load, sizing buffer pools, page caches, and shared memory to take advantage of the available 128 GiB of RAM, enabling parallel query execution and prepared-statement caching where supported, setting WAL and checkpoint intervals to values recommended by each project's own performance guides, and turning on the indexes and storage engines (InnoDB, WiredTiger, RocksDB-backed stores, etc.) that each database's documentation recommends for OLTP workloads. The goal was to make sure no engine was held back by a conservative default - if a database underperforms here, it isn't because we left it on its laptop-friendly starter config.
Workloads run with 128 clients issuing 48 concurrent queries each, against datasets of a single table with 5 - 15 million rows of mixed-type records (strings, integers, floats, UUIDs, datetimes, booleans, large text fields, geospatial data, and nested objects and arrays).
About last time
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