Earlier quoted context omitted.
unless you're using MyISAM or something like that, all your queries are in transactions. edit: also, I'd challenge you to prove that for a web request that needs to make ten read queries to a relational database, from Python, that you can get better performance by opening up ten separate database connections (or from a pool) and running one query in each, bundled into the async construct of your choice and then mergi…
It's not uncommon to have ~20 pooled connections lying around. Maybe it's not that frequently used in Python or PHP, but in various other platforms, that's just the normal case. At least in Java, C#, Golang. And even psycopg2 offers a Pooling Abstraction (I guess it's not used in Django, but SQLAlchemy offers that aswell) But of course running a blocking driver atop a non-blocking framework does not give the best per…
OK but you're doing....500 req/s let's say, so, if base latency is 50ms, you're going to have at least 25 requests in play at once, so that's 500 database connections. That's one worker process. If your site is using....two app servers, or your web service has multiple worker processes, or etc., now you have 1000, 1500, etc. database connections in play at capacity. This is a lot. Not to mention you'd better be using a middleware connection pool if you have that many connections per process to at least reduce the DB connection use for processes that aren't at capacity.
On MySQL, each DB connection is a thread (MariaDB has a new thread pooling option also), so after all the trouble we've gone to not use threads, we are stuck with them anyway. On Postgresql, each DB connection is a fork(), and they also use a lot of memory. In both of these cases, we have to be mindful of having too many connections in play for the DB servers to perform well. We're purposely using many, many more DB connections than we need on the client side to try to grab at some fleeting performance gain by stacking small queries among several transactions/connections per request which is not how these databases were designed to be used (a DB like Redis, sure, but an RDBMS, not so much), and on the client side, I still argue that the overhead of all the async primitives is going to be in a very tight race to not be ultimately slower than running the queries in serial (plus the code is much more complicated), and throughput across many requests is reduced using this approach. Marginal / fleeting gains on the client vs. huge price to pay on the server + code complexity + ACID is gone makes this a pretty tough value proposition.
Postgresql wiki at https://wiki.postgresql.org/wiki/Number_Of_Database_Connecti...: "You can generally improve both latency and throughput by limiting the number of database connections with active transactions to match the available number of resources, and queuing any requests to start a new database transaction which come in while at the limit. ". Which means stuffing a load of connections per request means you're limiting the throughput of your applications....and throughput is the reason we'd want to use non-blocking IO in the first place.
> However just challenging it without proof is not really that useful.
this is all about a commonly made assertion (async == speed) that is never shown to be true and I only ask for proof of that assertion. Or maybe if blog posts like this one could be a little more specific in their language, which would go a long way towards bringing people back into reality.