[pclmulqdq]

Sorry, but "50x larger than RAM" is a pretty small DB - that's an 800 GB database on a machine with 16 GB of RAM. I usually have seen machines with 500-1000x ratios of flash to RAM. "RAM is relatively cheap" is also false when you're storing truly huge amounts of data, which is how the systems you compare yourself to (LevelDB, etc) are usually deployed. Note that RAM is now the single greatest cost when buying servers.

> Now that the total database is 50 times larger than RAM, around half of the key lookups will require a disk I/O.

That is an insanely high cache hit rate, which should have probably set off your "unrepresentative benchmark" detector. I am also a little surprised at the lack of a random writes benchmark. I get that this is marketing material, though.

[hyc_symas]

> I am also a little surprised at the lack of a random writes benchmark.

Eh? This was 20% random writes, 80% random reads. LMDB is for read-heavy workloads.

> That is an insanely high cache hit rate, which should have probably set off your "unrepresentative benchmark" detector.

No, that is normal for a B+tree; the root page and most of the branch pages will always be in cache. This is why you can get excellent efficiency and performance from a DB without tuning to a specific workload.

[pclmulqdq]

> Eh? This was 20% random writes, 80% random reads. LMDB is for read-heavy workloads.

The page says "updates," not "writes." Updates are a constrained form of write where you are writing to an existing key. Updates, importantly, do not affect your index structure, while writes do.

> No, that is normal for a B+tree; the root page and most of the branch pages will always be in cache. This is why you can get excellent efficiency and performance from a DB without tuning to a specific workload.

It is normal for a small B+tree relative to the memory size available on the machine. The "small" was the unrepresentative part of the benchmark, not the "B+tree."

[hyc_symas]

> The page says "updates," not "writes." Updates are a constrained form of write where you are writing to an existing key. Updates, importantly, do not affect your index structure, while writes do.

OK, I see your point. It would only have made things even worse for LevelDB here to do an Add/Delete workload because its garbage compaction passes would have had to do a lot more work.

> It is normal for a small B+tree relative to the memory size available on the machine. The "small" was the unrepresentative part of the benchmark, not the "B+tree."

This was 100 million records, and a 5-level deep tree. To get to 6 levels deep it would be about 10 billion records. Most of the branch pages would still fit in RAM; most queries would require at most 1 more I/O than the 5-level case. The cost is still better than any other approach.