11 ms·

That's a very good point. I tend to drop down quickly from the ORM to do SQL level optimization on hot spots, and that is the case of the applications where I h

That's a very good point. I tend to drop down quickly from the ORM to do SQL level optimization on hot spots, and that is the case of the applications where I have used this connector. Also, we are talking about entreprise applications with relatively low number of requests, albeit a lot of them resource intensive. In this context, the performance of the connector is not critical. I can tell you that it is good enough that I didn't have to change my usual architecture choices for performance reasons. Your case may be different. edit: changed grammar for clarity

makmanalp 13y ago
This is good to hear. I'm leaning more towards using postgres on my next project, but for the sake of science, let me think it out a bit more: Your average interactive webapp is reasonably query-heavy, some of which is alleviated by reasonable caching. The performance of the connector comes into play when someone hits an uncached page which requires tens of queries. This matters the most when it drastically affects load time. It's really a shitty experience when a user comes to a page, waits more than a second for the page to load. I do find, though, that a large portion of my query overhead comes from round trip time to the db server, and not intermediate processing. So I figure for low to medium traffic sites with reasonable caching (and cache priming when needed), this probably won't be a bit hit, like you say. There is of course the interesting benefit that if you're using a JIT implementation like pypy, then everything within a pure python db adapter is up for auto-optimization. This is interesting because it could effectively provide something close to application-specific hand optimization of the connector, which tends to be a pain in the ass that not many people would bother with.

11 ms·

That's a very good point. I tend to drop down quickly from the ORM to do SQL level optimization on hot spots, and that is the case of the applications where I h

by rodelrod 13y ago

makmanalp 13y ago
This is good to hear. I'm leaning more towards using postgres on my next project, but for the sake of science, let me think it out a bit more: Your average interactive webapp is reasonably query-heavy, some of which is alleviated by reasonable caching. The performance of the connector comes into play when someone hits an uncached page which requires tens of queries. This matters the most when it drastically affects load time. It's really a shitty experience when a user comes to a page, waits more than a second for the page to load. I do find, though, that a large portion of my query overhead comes from round trip time to the db server, and not intermediate processing. So I figure for low to medium traffic sites with reasonable caching (and cache priming when needed), this probably won't be a bit hit, like you say. There is of course the interesting benefit that if you're using a JIT implementation like pypy, then everything within a pure python db adapter is up for auto-optimization. This is interesting because it could effectively provide something close to application-specific hand optimization of the connector, which tends to be a pain in the ass that not many people would bother with.