> The database size for one warehouse is approximately 100 MB (we experiment with five warehouses for a total size of 500MB).
It is not surprising that when your database basically fits in RAM, serializing on one writer is worth doing, because it just plainly reduces contention. You basically gain nothing in a DB engine from multi-writer transactions when this is the case. A large part of a write (the vast majority of write latency) in many systems with a large database comes from reading the index up to the point where you plan to write. If that tree is in RAM, there is no work here, and you instead incur overhead on consistency of that tree by having multiple writers.
I'm not suggesting that these results are useless. They are useful for people whose databases are small because they are meaningfully better than RocksDB/LevelDB which implicitly assume that your database is a *lot* bigger than RAM.
Where are you getting that assumption from? LevelDB was built to be used in Google Chrome, not for multi-TB DBs. RocksDB was optimized specifically for in-memory workloads.
RocksDB is Facebook's offshoot of LevelDB, basically keeping the core architecture of the storage engine (but multithreading it), and is used internally at Facebook as the backing store for many of their database systems. I have never heard from anyone that RocksDB was optimized for in-memory workloads at all, and I think most benchmarks can conclusively say the opposite: both of those DB engines are pretty bad for workloads that fit in memory.
Was a PITA to optimise though; tons of options and little insight into which ones work.
Pretty much all storage libraries written in the past couple of decades are using single writer. Note that single writer doesn't mean single transaction. Merging transactions is easy and highly profitable, after all.