redis vs celery

? reply. You can tell a background worker to send the mail and return to the user much faster. Another thing Python and Go unfortunately have in common is a community (not necessarily core developers) with knee-jerk reactions to any criticism. It also may be that you need to run the task on specialist hardware for some reason. Let us look at the key differences between RabbitMQ vs Redis as below: 1. what all changes i have to do to point my celery backend to redis-sentinel? Much better to use Node.js or Go where the async story is not an afterthought. Unfortunately, PEX files aren't even very common in the Python ecosystem. The kiosk system is human-less thinking, we're not machines operating with pure logistics in mind. If I had said "any remotely personal criticism", like I meant, you'd have an entirely different conclusion, but I guess that doesn't matter when you just want to jump to conclusions. We want to parallelize the processing of that structure, but the costs to pickle it for a multiprocessing approach are much too large. The web request sends a farm message, and a mule picks it up and acts on it. I have checked the pull requests list for existing proposed enhancements. The vast majority of the time, gevent's monkeypatching works without any issues. I've had a lot of issues with systems that start up doing everything synchronously: you'll probably need to refactor it to be asynchronous in emergency mode during a crisis. Volume of this or that is not really an issue: with the exception of chips, these days they hardly prepare anything at all before it’s been ordered, so it doesn’t really matter whether they make a burger or a muffin. The fries are fairly mediocre, but their burgers are pretty fantastic, espesicslky if you’re a fan of animal style. With asyncio, you basically have to rewrite everything from the ground up to always use the new async APIs, and you can't interact with libraries that do sync I/O. You don't need Celery to run a batch job every day for example. You'll also apply the practices of Test-Driven Development with Pytest as you develop a RESTful API. It’s arguable whether either of those are ‘in python’. For another example, in a previous job I worked for a big file sharing service. Python's closest analog is PEX files, but these don't include the runtime and often require you to have the right `.so` files installed on your system, and they also don't work with libraries that assume they are unpacked to the system packages directory or similar. "Best in class" is not a relative term. MVS was the primary innovation, but wanting checksum validation means I have to track all the same data anyway. You can wire all of this stuff up yourself but it's a hugely complicated problem and a massive time sink, but Celery gives you this stuff out of the box. However, from the user perspective it was pretty fast because all we had to do was set the file in the database to "delete in progress" state and tell a background worker to delete the files. Excellent craftmanship of a helpful blog. The docs don't help either, they're plain wrong. Add them to the docker-compose.yml file: Add the task to a new file called tasks.py in "project/server/main": Update the view to connect to Redis, enqueue the task, and respond with the id: Did you notice that we referenced the redis service (from docker-compose.yml) in the REDIS_URL rather than localhost or some other IP? Although this delay chain might be considered worth it you don't want to scale the frontend to multiple workers for some reason e.g. None of those are best in class. The cache is a simple key-value store. https://docs.python.org/3.6/library/multiprocessing.html. You just misconstrued my saying "personal" and clearly meaning "personal criticism" as meaning personal things in general and then criticized me on that straw man. In gevent, you don't use an "async" or "await" syntax. Drive-through is higher priority at most restaurants, because the customer can drive away after ordering but before paying. Good luck. (1) it's undersupported (e.g. As you can see, you've got comments in here even talking about how McDonald's isn't faster with their new system, or how they could have better optimized for customers, links to articles about McDonald's business model etc. Nothing wrong with that, but it's got drawbacks. If I’m worried about catching or spreading the virus, wouldn’t the kiosks be worse? Cool article! The multiprocessing module in the standard library is absolutely a Python-native way to do parallelism: Whoa! The "simpler" Python/Flask solution has an increased complexity when the task at hand is not simple anymore. Queue software is only a good match for the first. It's exactly as useful as "use", only much more pretentious. rapidly. My past few visits (11 to be specific from 11/19 thru 2/20) have yielded 8 minutes of wait time on average. $ pip install Django==2.0 $ pip install Celery==4.1.0 $ pip install redis==2.10.6. I'm just more productive with gevent, personally.). If you're worrying about tweaking Celery for performance, then I suspect your uses may be a bit more complex than uwsgi's mules are designed for though. It has nothing to do with Python, there a plenty of async web python framework. Yep, that about sums it up. The old way was almost better in that it introduced a natural bottleneck so while it took longer to place your order, once you did, the queue in front of you was shorter. If I had the money I'd spent it to open a good old McDonald's I'm sure more people would come. You could get it so the polling is done on the front end and then passes the outcome to the backend but that obviously isn’t a good idea because then the backend is trusting outcome data from the front end. If the subject of the analogy is enough of a hot topic, it will get attention itself. Very reminiscent of the style used by the Head Rush Ajax (. Go's modules provide no additional "understanding" over any of the other Bundler-derived solutions in the world. Very fast. Exact same principle (which I also gave some credit at first), split everything in small chunks so people can go faster.. it all went worse because nobody took responsibility for anything since a single task was now a dozen tiny bits done by a dozen people not really knowing what their bit was for. Yes, you wouldn't do all work in the task queue - commonly, we make some change in the database which can happen pretty fast, and after that transaction commits, we might defer a task that sends a notification, email, whatever. I like minimalism, but sometimes batteries are included for a reason. Will share with devs. Redis is a key-value based storage (REmote DIstributed Storage). Hi, author here! That's why I distinguished McD's out of the comparison with "mid-priced.". RQ (Redis Queue) is another Python library that can help you solve the above problems. We've considered the memory mapped file approach, but it has its own issues. CPU cores are quite powerful. i dont claim to have expertise in your business domain, but you should get the answer here. It gives you concurrency without parrallelism, because Python never did shake the GIL. I think based on McDonald history of optimization they feel like they can solve that supply side issue more easily than the demand side one they were contending with. I wish there was a paragraph up the top that made two points: It’s worth scoping out what your site will need to do up front to some extent. Besides, Go has its own set of problems with parallelism. You don't really _need_ concurrency when you have a machine that can timeshare amongst processes. For example, we have a large data structure that we have to load and process for each request. People are averse to feeling bad, so criticism needs to be extremely subtle in order to not offend. go: tends to wait and implement something once the problem is understood. I was already mad with: "python does not have a go-like concurrency story". S3 bucket, LAN storage). Developers describe Celery as "Distributed task queue". It's not exactly the same of course, but just like goroutines it's pretty easy to just spawn a few jobs off, wait for them to finish, and get the results. Are they not? You were the one associating discussing remotely personal stuff with criticising others, and if that was not bad enough your personal take was that you felt the need to keep criticising others but resorting to subtlety just to keep shoveling criticism without sparking the reactions you're getting for doing the thing you want to do to others. Sure, it helps a lot when you need that, but sometimes you just need a queue. Even if you think you do there's often a much simpler solution that is enough for most needs (Use cron, spawn a process etc). That seems like a good problem cause before they had issues with demand. Passing data back and forth is hard, and you can petty much forget about shared data structures. Saying Five Guys is the worst in a thread that features McDonalds... Five Guys is gourmet compared to McDonalds. But anyway, how is your application supposed to respond after any of those failures? Right now, I'm designing a service that's very similar to OP, with workers waiting for an external API (or APIs) to answer, which can be slow sometimes. Of course if you are stuck with Python it's better than nothing. I used mules in a couple of ways. Can you provide some context for this statement? Kafka - Distributed, fault tolerant, high throughput pub-sub messaging system. Go's track record is not "good" (in that regard I think only Cargo qualifies). Asyncio is a different model, and more irritating for me to work with. Not a data queue model. Even if sending an email synchronously doesn't usually take more than a few milliseconds, you still need to handle cases like servers failing in the middle of the request, temporary upstream unavailability, some expired API, account limits reached, etc... For email, that's why you set a relay within your control, that will accept messages without a fuss and send them around following SMTP conventions. newSingleThreadExecutor(); Where newSingleThreadExecutor method creates an executor that executes a … McD's don't mind if you have to wait, they mind if you leave before you order. You should see: An event handler in project/client/static/main.js is set up that listens for a button click and sends an AJAX POST request to the server with the appropriate task type: 1, 2, or 3. But now your backend has been polling for an update and so you might as well have performed the task in the application because it is still being used up for the duration of the task. This is not trivial stuff..and it shouldn't be trivialised into a go vs python flamewar. And still, since you don't have a central application with a state, you NEED an extra piece to manage the result from the queue. The difference is that it doesn't play well with large parts of the existing Python ecosystem. A full answer might depend on whether you are a customer or a cashier. How does it work on more than one server? The need for multiple servers kinda depends on the application, no? In contrast, Go has built-in package management and gofmt. thats an extraordinary claim which needs evidence. "Busy-looking queue" is a much more frequent problem than "totally-packed-restaurant". If not, no worries. … uWSGI has a built-in celery-ish spooler, cron-ish task scheduler, memcache-ish key-value store, along with. While I agree with the rest of your comment, the sentence "if you’re cloud native maybe you leverage lambdas" made me irrationally angry. Redis - An in-memory database that persists on disk. That problem is all on you. I wasn't even talking about anyone specifically. In that case, it might be valuable to transform CPU waits into IO waits by moving the CPU work to a jobs queue, possibly running its workers on a different set of machines entirely. Redis. I prefer rq - Celery is too complex imho. 1. For example: "leverage" + "use case" = "leverage case". If this one sticks, it's fine. In this article we will demonstrate how to add Celery to a Django application using Redis. I personally don't find it to be a very convincing argument. But funny thing is, I have never seen the first situation on the wild. What is Heroku Redis? I've done (and continue to do) a decent amount of Python. * Distribution: Build a static binary for every target platform and send it to whomever. I may be wrong but here’s one fun example from this comment section that I wanted to “respond” to and demand some clarification on. Most of the time you're i/o bound, or network bound, or storage bound. It's quite a bit more complex and brings in more dependencies than Redis Queue, though. redis celery airflow master-slave sentinel But you haven't to as long as you don't want to. I think my tastebuds must be off because I feel the same way. Yeah, that's exactly why I said "Python just looks worse right now because it's been around longer." The third one is best done synchronous, it doesn't matter the nature of the process or how long it takes. I see your concern is focused on polling case (e.g a chat room). By the end of this post you should be able to: Our goal is to develop a Flask application that works in conjunction with Redis Queue to handle long-running processes outside the normal request/response cycle. I do it when I have to do it. I assume your alternative here is "why not just have a new client tier doing work" which is a reasonable architecture too. You have to set up virtualenvs, not to mention celery and rabbit, and god help you if you're trying to operate it and you forget something or another. Not to mention the case where the mailserver is down or denies service, which will also happen at some point even if you have HA mailserver: be it with AWS emails, mailjet and whatnot. Luckily for me, using uWSGI to deploy anything in any languages: it builds in a nice little spooler that's going to let me spool emails without adding a single new software to my stack: not even having to start another process. There's no middle ground. I only use celery for sending out emails. With a decorator or 2 you can do all of those things on any tasks you want. Putting work into a task queue allows you to do it durably until it can be processed so that it's not lost in some typical "that machine/instance died" scenario. I wrote Python/Flask because the application server model is inherently flawed in Python, while Flask is not asynchronous AFAIK; you MUST use an async model (because Python and multithreading still don't work well together), you can't use threads for long-running tasks. Doing both ops and development I’ve learned to appreciate simple solutions. If you needed a coordination layer or you needed to isolate certain types of traffic then it makes more sense. We don't use containerization or anything, and installing the python system is a nightmare. I wonder how many other people have celery just for email. Why do we need Flask, Celery, and Redis? /plug. RabbitMQ is a message broker. Most of that issues are actually fixed in setuptools if you put all settings in setup.cfg and just call empty setup() in setup.py. The difference is coarse-vs-fine grained parallelism. While GOPATH was certainly idiosyncratic, it generally just worked for me. They are both built on the same technology in the CPython runtime. (which is how I hope you deploy all your production apps). On the other hand, RabbitMQ has been designed as a dedicated message-broker. Really though, I think a lot of people use celery for offloading things like email sending and API calls which, IMHO, isn’t really worth the complexity (especially as SMTP is basically a queue anyway). One thing I would caution against is putting the business logic in the task logic. Python packaging is a mess, but Go doesn't even bother. Besides development, he enjoys building financial models, tech writing, content marketing, and teaching. The few I do I use McDrive because the wait is predictable and shorter. Celery uses the message broker (Redis, RabbitMQ) for storing the tasks, then the workers read off the message broker and execute the stored tasks. Your solution depends on your throughout requirements, the size of your team and their engineering capabilities, what existing solutions you have in place. Please make sure your Redis server is running on a port 6379 or it’ll be showing the port number in the command line when it got started. No languages do this well; Rust and Haskell make it appear easier by making single-threaded code more difficult to write--requiring you to adhere to invariants like functional purity or borrowing. This is revisionist history. It seems to me like you got personally offended, interpreted my comment in the most uncharitable way, and chose to lash out at me instead, and I'm not sure why. Me too! Also, if you do `aiohttp.get("www.example.com/foo.json").json()", you get a TypeError because coroutine has no method '.json()' (you forgot `await`) unless you're using Mypy. Doing things in the background in a simple application, not so much. > Go is absolutely best-in-class if you have typical Python values. Source: I like hamburgers + I geek out over stuff like this. Clone down the base project, and then review the code and project structure: Since we'll need to manage three processes in total (Flask, Redis, worker), we'll use Docker to simplify our workflow by wiring them altogether to run in one terminal window. You could prevent that distraction by using a generic food takeout store and ask people to imagine their favorite. I don't hold that opinion at all. For basic sites, yeah maybe not necessary, but a reliable background processing system has always been a significant accelerator in my projects. As someone who also does technical writing, I agree you should draw from your own experience. Want to follow along? Go has had an equally bad packaging experience. Celery is a viable solution as well. and it has only gotten worse over time. I'm mostly drawing from my own experience and that insight while ordering food inside Mcdo. This is what I have used in the past. It's not just about the time the operation takes, it's about reliability. You're combining multiple problems: maintaining a package for redistribution, and using packages. However, it allows application to respond to 'other' users. Pleasantly surprised I saw this on HN, thanks for posting feross! Performance is pretty close for both. To work with Python packages, you have to pick the right subset of these technologies to work with, and you'll probably have to change course several times because all of them have major hidden pitfalls: * Publish package (including documentation): git tag $VERSION && git push $VERSION, * Add a dependency: add the import declaration in your file and `go build`. Redis is a bit different from the other message brokers. Instead, you'll want to pass these tasks off to a task queue and let a separate worker process deal with it, so you can immediately send a response back to the client. Meanwhile, Python's packaging wars continue to rage on. This means it handles the queue of “messages” between Django and Celery. I've used Python and Go extensively. I fully agree with this assessment, but I don’t see how this puts Python’s story on par with Go’s. Again, I tried to start 400 workers with one core each. I feel even more rushed when people are in line behind me waiting for me to figure out the ui a cashier knows by heart. But as we decided to implement an MVP with a single HTTP request from the client, this whole separation doesn't make any sense, exactly as you noted. Redis is a database that can be used as a message-broker. I use lambdas often and they seem to solve the problems they're meant for well. > Python/Flask has no central "application" concept. gevent is green threads, asyncio is explicit coroutines. > Gevent is like goroutines with GOMAXPROCS=1. That would be useful if you needed a web request to wait for a long running task to finish before sending a response back. Run long-running tasks in the background with a separate worker process.

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