Hi NovaX! I'm the presenter in the video above. I've read all your comments below, and figured I'd try to give a single overall reply, so here goes. First, it

Hi NovaX! I'm the presenter in the video above. I've read all your comments below, and figured I'd try to give a single overall reply, so here goes. First, it's important to note the difference between Noria (the database) and evmap (the data structure). Noria is the research project I've been working on, and which was published in [OSDI'18](https://www.usenix.org/conference/osdi18/presentation/gjengset https://www.usenix.org/conference/osdi18/presentation/gjengs...). It's primary contribution is partially materialized, dynamic data-flow, something no existing systems provide. We use that to implement materialized views, and find that it gives expressive and high-performance materialized views, beyond what you can currently find in other existing systems. evmap is _not_ novel. Neither the epoch scheme, nor the swapped map. It is simply a neat data-structure that we happen to _use_ in Noria, and which we find gives good performance in the face of highly concurrent reads. We could easily swap out evmap for another concurrent map if we find one that performs better. The reason I built evmap initially was out of sheer laziness. I wanted a data-structure that allowed basically perfectly scaleable reads when writes are infrequent compared to writes, and I didn't want to implement my own hash map, because then you lose out on all the optimizations that more mature implementations carry. evmap ticks both those boxes. It scales pretty much linearly, and can use any map you want internally -- it does _not_ need to have any knowledge of the internals of the map, uses only standard map APIs, and supports things like dynamic map resizing (which many concurrent maps lack) without any special consideration. As for performance, I'm not sure I know where you have those numbers from. The most recent paper I'm aware of with a concurrent key/value store is [FASTER from Microsoft](https://www.microsoft.com/en-us/research/uploads/prod/2018/03/faster-sigmod18.pdf https://www.microsoft.com/en-us/research/uploads/prod/2018/0...), which was published in SIGMOD'18. The best numbers I see reported in Figure 8, with 56 cores, is 100-150M reads/s when there are no writes. That is obviously impressive, and far more than the simple evmap design can provide, but if you look at the competition (like MassTree), those get closer to 50M/s for a read _only_ benchmark, which is pretty close to evmap given the difference in hardware and number of cores. You _are_ right that evmap is slower though, and I can hazard some guesses as to why. First, evmap is a multi-value map, so all operations need to chase one more pointer (to the value list for the key). Second, evmap has essentially half the cache space available to it since the readers sort of end up sharing the cache between the two maps. And third, evmap reads are do the double-epoch-counting trick, which adds two mostly-uncontended-but-not-free increments on the critical path. I haven't looked into the details of what FASTER and MassTree do, but they probably do something clever. And finally, to some of your more specific points of contention: - There are as many cores as threads in the evmap benchmark. - Zipf and uniform having similar performance isn't that odd, because the main cost in that benchmark ends up being the readers needing to re-acquire read access to the cache line for the map pointer, which isn't affected by distirbution. - evmap is indeed not lock-free, but the read path is. The writer does take a lock, but that lock is just used to arrange for _new_ read handles to be added, which should be a rare occurrence. It should generally be uncontended. Phew! Thanks for all your comments and digging -- hopefully this helps answer some of your concerns :)