more performant than....what exactly? If I need to load 1000 rows from a database and splash them on a webpage, will my response time go from the 300ms it takes without asyncio to something "more performant", like 50ms? Answer: no. async only gives you throughput, it has nothing to do with "faster" as far as the Python interpreter / GIL / anything like that. If you aren't actually spanning among dozens/hundreds/thousands of network connections, non-blocking IO isn't buying you much at all over using blocking IO with threads, and of course async / greenlets / threads are not a prerequisite for non-blocking IO in any case (only select() is).
it's nice that uvloop seems to be working on removing the terrible performance latency that out-of-the-box asyncio adds, so that's a reason that asyncio can really be viable as a means of gaining throughput without adding lots of latency you wouldn't get with gevent. But I can do without the enforced async boilerplate. Thanks javascript!
From the last benchmark I ran [1] async IO was insignificantly faster than thread-per-connection blocking IO in terms of latency, and marginally faster only after we hit a large number of clients.
Async IO doesn't necessarily make your code faster, it just makes it difficult to read.
[1] http://byteworm.com/evidence-based-research/2017/03/04/compa...
const tweets = await getTweets(users);
console.log(tweets);
Is async code really harder to read?
Then you'll really be irritated.
yes it does not magically make your fetching 100 rows faster or your pbkdf2()/bcrypt() function. you still need to wait for those.
This type of operation is a given in any production quality webserver, whether it runs with multiple threads and blocking IO or using a non-blocking approach with greenlets. For a web application, this is an implementation detail that should not be explicit within the request handling code (a request handled in the context of a web container after all is a package of data in, a package of data out. no network reading/writing is usually exposed to the web application unless it's trying to expose IO handles to the app, which is unusual). Easy enough with something like Gunicorn.
If you have to do 1000 queries it could, since could async will make it feasible to do them parallel. If it's a single query, maybe async would make it feasible to shard the database.
[1] http://byteworm.com/evidence-based-research/2017/03/04/compa...
Am I wrong?
Potentially, it depends on if you can do other tasks for the same request that don't depend on the data. You might be able to render most of the page for instance. It's not purely about throughput.
Please tell me that 300ms was made up too and that it's not really taking that long.
it seems the main bottleneck when using aiohttp is aiohttp itself, which practically makes the use of uvloop irrelevant
Well, actually, yes. Without async rendering, your webpage is not ready until your 1000 rows of list is placed in Python memory then rendered to HTML as a whole then returned to your browser after like 300ms of server cost.
With async rendering, your webpage's headers and such can be returned immediately, thus your first-byte-to-response time can be done under 50ms, and your page loads by enumerating the rest of 1000 rows and renders the page incrementally.
def on_connection:
send(headers)
send(start of page)
for row in db:
send(row)
send(footer)
The performance comparison is between python managed green threads, and OS managed actual threads. You don't get any new features
It's built by Tom Christie - the original author of Django Rest Foundation.
http://discuss.apistar.org/t/how-does-api-star-fit-in-with-d...
Interestingly it eschews Swagger/OpenAPI in favour of JSON Schema, wonder how that'll pan out; I like the promise of codegen that swagger offers, but haven't found the generated clients to be particularly usable.
I keep hitting a wall with Python when I want to do something like:
1. subscribe to a websocket connection and keep the last received message in state 2. expose an http endpoint to let a client GET that last message.
If you were going to share state in memory between threads, how would you handle the case where the second request goes to a different server or that the process has restarted? You'd need redis anyway, so you might as well just use it in all cases.
http://flask.pocoo.org/docs/0.12/patterns/caching/
I'm not sure how this works with multiple threads though, I imagine you would have to synchronize it yourself.
Enjoy!
While I can write this kind of code, I don't feel like I completely understand some of the concepts.
https://pragprog.com/book/pb7con/seven-concurrency-models-in...
When working with Python and Ruby I find 80ms responses acceptable. In very optimized situations (no framework) this can do down to 20ms.
Now I've used some Haskell, OCaml and Go and I have learned that they can typically respond in <5ms. And that having a framework in place barely increases the response times.
In both cases this includes querying the db several times (db queries usually take less then a millisecond, Redis shall be quite similar to the extend that it does not change outcome).
<5ms makes it possible to not worry about caching (and thus cache invalidation) for a much longer time.
I've come to the conclusion that --considering other languages-- speed is not to be found in Python and Ruby.
Apart from the speed story there's also resource consumption, and in that game it is only compiled languages that truly compete.
Last point: give the point I make above and that nowadays "the web is the UI", I believe that languages for hi-perf application development should: compile to native and compile to JS. Candidates: OCaml/Reason (BuckleScript), Haskell (GHCJS), PureScript (ps-native), [please add if I forgot any]
Wow, never managed to do that. Maybe I have to try it again (last time checked on Django was some years ago).
I'm confused by the relationship between Paxos, the company, and Paxos, the algorithm. Do the authors of Paxos work for Paxos?
Edit:
https://en.m.wikipedia.org/wiki/Paxos_(computer_science)
Ah; both are named for a fictional financial systen
https://magic.io/blog/uvloop-blazing-fast-python-networking/
I would prefer standard benchmarks for this. I hope they submit their framework to TechEnpower benchmarks.
One of the benefits of modern RDBMS is that they make extremely sophisticated use of RAM, and all levels of fast to slow storage below that SSD / RAIDs / slow single spindle.
It is a relative thin layer of rust code between the Redis module interface and SQLite.
At the moment you can simply execute statements but any suggestion and feature request is very welcome.
Yes, it is possible to do join, to use the LIKE operator and pretty much everything that SQLite gives you.
It is a multi-thread module, which means that it does NOT block the main redis thread and perform quite well. On my machine I achieved 50.000 inserts per seconds for the in memory database.
If you have any question feel free to ask here or to open issues and pull request in the main repo.
:)
Proceeds to show an animation of posting a blog post that performs no faster than if it was built using Django.
Might be that the server is insanely slow, but I would have no problems reaching 10k page views per second with some basic PHP and even MariaDB on a low end E3-1230 server. Pretty sure more would be quite easy to...
In async applications event loop is what actually executes your code and performs IO. In essence, event loops are under load all the time.