Why is KahaDB preferred over JDBC persistence for high throughput?

KahaDB writes persistent messages as sequential appends to local log files and maintains its own lightweight index, so a single store or remove operation is essentially one disk-sequential write plus an in-process index update. JDBC persistence routes every store and remove operation through a relational database driver instead, meaning network round-trips if the database isn't local, SQL parsing, transaction log overhead on the database's own side, and index maintenance on the database's tables - all latency that KahaDB's log-structured design avoids.

JDBC does buy something KahaDB alone doesn't: the ability to point multiple broker instances at the same shared database for certain HA topologies, plus reuse of existing database backup and replication tooling a team already operates. So the trade-off is throughput and latency, where KahaDB wins, versus operational reuse of existing database infrastructure, which is JDBC's niche. For a single high-throughput broker where persistence latency matters most, KahaDB is the default and generally correct choice; JDBC gets chosen for organizational or HA reasons more than raw speed.

There's also an operational dimension to the choice: KahaDB's data lives as opaque log files on local disk, which existing database-centric tooling, like backup jobs, replication, and monitoring dashboards built around SQL, simply can't inspect or manage, whereas JDBC persistence lets a broker's message store be backed up, restored, and monitored using exactly the same tooling a DBA team already runs for the rest of the organization's databases. Teams migrating from a legacy JMS provider that used JDBC persistence sometimes stick with JDBC initially purely to avoid retraining operations staff, even though a later move to KahaDB would likely improve throughput once the team is comfortable managing a new storage format.

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