kpgaffney

I think the (unsatisfying) answer is "it depends". There's a huge amount of diversity in database workloads, even among the workloads served by SQLite as we mention in the paper.

For read-mostly to read-only OLTP workloads, read latency is the most important factor, so I predict SQLite would have an edge over PostgreSQL due to SQLite's lower complexity and lack of interprocess communication.

For write-heavy OLTP workloads, coordinating concurrent writes becomes important, so I predict PostgreSQL would provide higher throughput than SQLite because PostgreSQL allows more concurrency.

For OLAP workloads, it's less clear. As a client-server database system, PostgreSQL can afford to be more aggressive with memory usage and parallelism. In contrast, SQLite uses memory sparingly and provides minimal intra-query parallelism. If you pressed me to make a prediction, I'd probably say SQLite would generally win for smaller databases. PostgreSQL might be faster for some workloads on larger databases. However, these are just guesses and the only way to be sure is to actually run some benchmarks.

Thanks for the clarification. It's true that transaction latency is limited by the write speed of the storage medium. However, an "average desktop computer" these days has an SSD that can support tens of thousands of transactions per second, depending on the workload.

That's correct, the optimizations from this paper became available in SQLite version 3.38.0.

As we were writing the paper, we did consider implementing hash joins in SQLite. However, we ultimately went with the Bloom filter methods because they resulted in large performance gains for minimal added complexity (2 virtual instructions, a simple data structure, and a small change to the query planner). Hash joins may indeed provide some additional performance gains, but the question (as noted above) is whether they are worth the added complexity.