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    redis vs celery

    Saying Five Guys is the worst in a thread that features McDonalds... Five Guys is gourmet compared to McDonalds. 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. When I'm talking to a cashier, he/she feels a duty to do something from A to B. This is not trivial stuff..and it shouldn't be trivialised into a go vs python flamewar. I found it a little strange that a job queue was being used to serve (what seems like) synchronous traffic. /plug. The multiprocessing module in the standard library is absolutely a Python-native way to do parallelism: Whoa! Is there a way to do that for Go? Lightbus also supports background tasks and scheduled tasks. From uWSGI documentation, it looks like to coordinate cron across anything more than a single server setup, you'd need to configure a Legion, which means you're then integrating uWSGI's orchestration framework with whatever you're already using (k8, ECS, etc). 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. Why do we need Flask, Celery, and Redis? Checklist I have checked the issues list for similar or identical enhancement to an existing feature. Notably, a WSGI+gevent system doesn't allow you to do parallelism within a request, not to mention configuring these WSGI implementations (especially for production) is a bunch of extra headache for which there is no analogy in Go. An in-process solution with some light persistence for the work queue in an embedded database can go along way, at least until you outgrow a single machine. Kubernetes - which is one of the biggest projects built in go - has been struggling with dependency and package management. I've usually had to build a small python cron runner using croniter in previous systems - which I think is a pretty clean solution - it just deferred tasks to rq workers. You have an operating system that you can use. It is focused on real-time operation, but supports scheduling as well. On the other hand, while the fragmented code in Redis takes more time to process, it provides higher latency throughputs which gives it more speed over MongoDB. Which goes to show some technical debt will haunt you forever. 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. https://news.ycombinator.com/item?id=22911497, PS: I work with like 65-70% of that stack daily. 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. Containerize Flask and Redis with Docker. I think a hard part with lots of these “what do I use x for” examples is it starts with the tool and the discusses the problem that it solves. You don't even need asyncio, you can just use a ThreadPool or a ProcessPool, dump stuff with pickle/shelve/sqlite3 and be on your way. Then it checks again next second and still in progress. Sometimes you can leverage pandas or write a small chunk in C, but very often those options aren’t available, and naively throwing c at the problem can make your performance worse. Performance is pretty close for both. Several developers like to overengineer and "go for celery" (also applies to other technologies with other uses) even for small things. Celery is a viable solution as well. Go tried to ignore modules entirely, using the incredibly idiosyncratic GOPATH approach, got (I think) four major competing implementations within half as long, finally started converging, then Google blew a huge amount of political capital countermanding the community's decision. But you haven't to as long as you don't want to. I fully agree with this assessment, but I don’t see how this puts Python’s story on par with Go’s. That is such quite a stretch of the imagination and I sure didn’t read it that way. I would agree, generally a task queue makes sense for jobs which are not needed to. For some tasks that won’t matter because the backend won’t need to be aware of the outcome of the task but there could be some longer jobs that the backend needs to be aware of the outcome right away. I believe Python suffers from no leadership in that space (everyone creates their own packaging, every tutorial advocates something different, many tutorials are outright wrong). This is also why McDonald's introduced table service, which is only in restaurants that have a layout where it's impossible to hide how many people are waiting. I've opened an issue which was promptly closed and I was told to "just download the binary dist, source builds are for devs". It only encourages a giant mess, which is precisely what software development has been lately. I can't speak to the apps, but there's a foodcourt in my building with a kiosked McDonalds. No Celery, no Redis, no MQ. that's what vendoring is for and the proxy cache. This is revisionist history. Doing things in the background in a simple application, not so much. If you attempt to use Redis-over-SSL as both a transport and results backend then Celery will fail since the RedisBackend.connparams are not patched to support SSL. And if you’re really worried, why not use a caching proxy just like you do with Pypi? Unfortunately, PEX files aren't even very common in the Python ecosystem. "Just download from some VCS we'll pretend is 100% reliable and compile from source" is not a packaging solution. > They mostly need Celery and Redis because in the Python world concurrency was an afterthought. One-to-one vs one-to-many consumers: both. Scale: can send up to a million messages per second. © Copyright 2017 - 2021 TestDriven Labs. At any rate, there's little moral difference between downloading a tarball (or a wheel, or... whatever), vs. pulling a tag from a git repo. If you have CPU intensive workload, an optimizing compiler can help. I use it in pretty much every Flask project, even on single box deploys. They are indeed quite similar to goroutines. I've never used that, so can't comment on whether or not the caching and messaging works together with that. 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. Caching uses the django_redis module where the REDIS_URL … Most of the time you're i/o bound, or network bound, or storage bound. I haven't found that to be the case typically -- you could always serialize some information into the task to check for things like this. MVS was the primary innovation, but wanting checksum validation means I have to track all the same data anyway. If someone finds that Redis and Celery are more complication than they need for a given task, then I think they're probably not using an orchestration framework. Here, we set up a custom CLI command to fire the worker. 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. When the task complexity rises further, splitting out the message queue and workers into separate processes makes sense again. Good luck. Sure, sending an email is probably fine to do in-line for now, but months in you may realise that things are slow, that you’re sending emails and rolling back transactions later, or committing the transaction but losing the email that needed to be sent, or all manner of other annoying edge cases. 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. For example: "leverage" + "use case" = "leverage case". This way, you're not discouraged from ordering if you feel like the wait will be too long. Memory usage is also much higher. I wonder how much of the delay is due to their recent decision to offer breakfast items all day. Celery is a big, heavy lump of code to add to most websites and it increased the deployment complexity. This post looks at how to configure Redis Queue (RQ) to handle long-running tasks in a Flask app. https://nickjanetakis.com/blog/4-use-cases-for-when-to-use-c... http://shop.oreilly.com/product/9780596102258.do. That's the world Python was designed for, > In most other languages you can get away with just running tasks in the background for a really long time before you need spin up a distributed task queue, This is partly true, but not a lot of people do this because you need to persist those tasks unless you want them dropped during a reboot. "Oh but python multithreading sucks" do you know when it does not suck? you could just turn it off. I’ll take “doesn’t even try, but just works” all day every day. 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. > Gevent is like goroutines with GOMAXPROCS=1. I wonder how many other people have celery just for email. 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. (3) it got introduced too late. People are averse to feeling bad, so criticism needs to be extremely subtle in order to not offend. This is ordering inside the facility and at 5 different locations. If I am starting a fresh project with python and need concurrency, yes "async" is a better choice, but if you already have some code base then moving to async is a fair amount of work. If this one sticks, it's fine. Moreover, one could just look at the shelf of already prepared burgers and buy one of them. Having the front end wait for a background task to complete broadly defeats the purpose. Celery is widely used for background task processing in Django web development. You'll also apply the practices of Test-Driven Development with Pytest as you develop a RESTful API. If you're writing Python, you very likely have values that are incompatible with these invariants (you want to onboard new developers quickly and you want your developers to write code quickly and you're willing to trade off on correctness to do so). > Neither Go nor Python are appropriate choices for highly parallel intercomunicating code. This means it handles the queue of “messages” between Django and Celery. I did it for years. It's possibly as of the proxy in Go 1.13, but this was not well-documented, suffers from competing implementations, and introduced in a way that probably broke more builds than it helped. I'm mostly drawing from my own experience and that insight while ordering food inside Mcdo. In general, it also takes much longer to build a PEX file than to compile a Go project. I like it - uses redis as a broker - supports crontab style periodic tasks. However, there are a bunch of situations where there is no feedback to the user other than "we received your request in good order and will see that it's done". I can't build lego from source due to a failed dependency. Pretending that celery/redis is useless and would be solved if everyone just used Java ignores the fact that celery and redis are widely popular and drive many successful applications and use cases. Do you? There are several built-in result backends to choose from: SQLAlchemy/Django ORM, Memcached, RabbitMQ/QPid (rpc), and Redis – or you can define your own. Drive-through is higher priority at most restaurants, because the customer can drive away after ordering but before paying. 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. That's my only real issue. Queue software is only a good match for the first. I've always typed out `.close()` manually like a sucker. If a long-running task is part of your application's workflow you should handle it in the background, outside the normal flow. Redis :- Redis is an open-source in-memory(a DBMS that uses main memory to put it bluntly) data store which can function both as a message broker, a database and cache. But that's quite limiting compared with goroutines. If nothing else, Go lets you distribute a static binary with everything built in, including the runtime. Redis is a key-value based storage (REmote DIstributed Storage). With decoupled ordering.. nobody knows who I am, nobody really cares (McDonalds doesn't pay nor train for welcoming and service mindset). You run a blocking thread to performing a long-running task in Flask? 2. It may not make sense to retain jobs across deployments. If you really need the answer immediately to show to a user on a page, background workers (usually) don't help. Last I recall, it used to be a lot faster...I think between 1-3mins tops! Reliable and powerful Redis as a service. Dec 17, 2017. You could probably also combine this with uwsgi's async mode. So having celery worker on a … Redis is a database that can be used as a message-broker. and it has only gotten worse over time. Questions like: should I send this email in-line in the web request? Another point, I don't mean to make people work more, but I even prefer busy waiting lines with hectic kitchen action. It never occurred to me that `Pool` could be used with context manager. If not, no worries. That would be useful if you needed a web request to wait for a long running task to finish before sending a response back. However, I find it is often more worthwhile for: 2. The REDIS_URL is then used as the CELERY_BROKER_URL and is where the messages will be stored and read from the queue. Not to mention (my biggest pet-peeve of Celery) is that it "forces" you to work with a task queue model. Or to even do some parallel processing. "Busy-looking queue" is a much more frequent problem than "totally-packed-restaurant". The Golang runtime isn't really well-understood as a backend for languages that aren't Golang, although I acknowledge that there's no reason in principle why you couldn't compile $LANGUAGE to Golang. I had some background task mules which mostly just ran in a loop with a `sleep()` call. There was also a bad decision of using Python code for installation (setup.py) instead of a declarative language. Ideally the Celery documentation that talks about BROKER_USE_SSL should be updated to specify the keyname format for redis vs amqplib. You can certainly debate the difference in uptime between specific services; I don't know either way, but if you told my that PyPi had higher uptime than GitHub, I'd believe you... but that's kinda missing the point. I think my tastebuds must be off because I feel the same way. Again, I tried to start 400 workers with one core each. Yeah, until github is unreachable and the entire Go universe grinds to an immediate halt because nothing will build. Here is a basic use case. I’m currently using manually added psb.set_trace to telnet in to a debugging session, but I’d prefer to use an IDE so I can modify code while debugging. It is focused on real-time operation, but supports scheduling as well. For sure I'm not expecting you to change your article. It has nothing to do with Python, there a plenty of async web python framework. It gives you concurrency without parrallelism, because Python never did shake the GIL. The alternative would be to ensure that any change in the job contracts are backward compatible, and if any change in contract would need to have a remediation/migration plan for handling pending tasks. No need to have a special runtime (or version thereof) installed nor any kind of virtual environment nor any particular set of dependencies. I do it when I have to do it. Go's track record is not "good" (in that regard I think only Cargo qualifies). this problem hasn't existed since like go 1.8 and is completely resolved in go1.14. This time it worked. It's exactly as useful as "use", only much more pretentious. Had you got e.g. Yeah, that's exactly why I said "Python just looks worse right now because it's been around longer." Eg events or RPCs, but I found Celery to be very much a square-peg-round-hole for this, which is why I developed Lightbus (http://lightbus.org). Or is it supposed to inform the user of any success or failure before the user can move on? This explains how to configure Flask, Celery, RabbitMQ and Redis, together with Docker to build a web service that dynamically uploads the content and loads this content when it is ready to be… > One aspect of this set up I’ve never been able to understand is how the application then gets the result from the worker? We use redis beacuse it solves many problems at once: Once you are at it, why not later use it for other stuff, like storing the result of background tasks, log stream, geographical pin pointing, hyperlolog stats, etc. I assume your alternative here is "why not just have a new client tier doing work" which is a reasonable architecture too. If user wanted their files deleted that caused all sorts of calls to AWS to actually delete the files, which could take a while. couple decades? I think these mindless clichés make language really ugly and dysfunctional, and even worse they are thought-stoppers, because they make the reader/listener feel like something smart is being said, because they recognize the "in-group" lingo. 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`. (5-15). Return an HTTP 500 to the user rolling back the transaction ? Note: Both the Celery Broker URL is the same as the Redis URL (I’m using Redis as my messge Broker) the environment variable “REDIS_URL” is used for this. Yes, Python is more limited than Go here, but hardly makes a difference when you avoid it. But having direct support in the lib might be nice. I agree. Open your browser to http://localhost:5004. lmeyerov 2 hours ago. We've considered the memory mapped file approach, but it has its own issues. This is obviously up to whoever set up the tasks but it seems like most of the tutorials don’t mention how painful this can make testing especially once you start making chains, chords and sub tasks. But, whenever picking a tech, you should understand the use case. Does anyone have enough experience with alternatives to Celery to give a good comparison of Celery vs. Dramatiq vs. RQ? But not happy w event loop due to pandas/rapids blocking if heavy datasets by concurrent users. 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. Note that Celery will redeliver messages at worker shutdown, so having a long visibility timeout will only delay the redelivery of ‘lost’ tasks in the event of a power failure or forcefully terminated workers. I don't hold that opinion at all. Typically "green threads" are semantically just threads but cheaper. 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. The coroutine and queue model is the same right ? But you can't say it's good now just because it's the one we have now - it's good now if it's the one we still manage to have in five years. Besides, serious projects in Go do use additional tools for creating task queues because they need to handle various stages of persistence, error handling, serialization, workflow, etc. I'm just more productive with gevent, personally.). Will share with devs. And even the most recent one is only about a year into wide adoption, so I wouldn't count on this being over. We want to parallelize the processing of that structure, but the costs to pickle it for a multiprocessing approach are much too large. Python packaging is a mess, but Go doesn't even bother. Might be easier to keep it all in-process, letting queues drain in a graceful shutdown. Celery vs Heroku Redis: What are the differences? The best async primitives are only available in pretty recent versions of python3. This is achieved by: Sure, it helps a lot when you need that, but sometimes you just need a queue. To add insult to injury, each project is built in its own usually broken way. Anyone have any tips? That may be true for you, but I like the flow of the kiosks, where I can take my time and not be rushed, and I don't have to interact with someone just to take an order. The above post walks through sending emails out with and without using Celery, making third party API calls, executing long running tasks and firing off periodic tasks on a schedule to replace cron jobs. While I agree with the rest of your comment, the sentence "if you’re cloud native maybe you leverage lambdas" made me irrationally angry. My reasoning is that they are just trying as hard as they can to decouple order-taking from order-preparing and distributing, taking their clue from Starbucks. these problems don't exist anymore since godep and now go modules which is builtin to standard go tooling. I was more referring to how it knows once it’s finished since the task is asynchronous. > So? 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. Redis is a bit different from the other message brokers. The personal association you made between "discussing anything even slightly personal" and "criticism needs to be extremely subtled" makes it sound that your problem isn't language or Orwellian discourse but the way you subconsciously link discussing personal matters with harshly criticising those you speak with for no good reason. Redis. So we need to do them in the background and send the result back to the client when it will be available. Go's packaging is wayyyy better than Python's. 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). 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. Could you elaborate, e.g. What is Heroku Redis? For example: "leverage" + "use case" = "leverage case". i am using redis as a broker for celery executor. I vividly recall getting blank stares from employees when asked "how much longer until my order is complete?". Honestly, I think that mostly anything that doesn't depend directly in the current state of your infrastructure should be done asynchronously. As someone who also does technical writing, I agree you should draw from your own experience. The precipitous decline in quality upon cooling occurs because they are made by frying potato pieces rather than by assembling various starches in a laboratory. I prefer rq - Celery is too complex imho. I repeat, your problem is not language. We record data in the User table and separately call API of email service provider. What if the contract of the job is changed by the code being deployed? You don't need Celery to run a batch job every day for example. But the costs to pickle it for a local process on a single core, is! Database is only about a year into wide adoption, so I do... Operational complexity of the available options.. now it 's not just a., generally a task queue '' McDonalds analogy, it 's way faster just!, having churned through dozen major overlapping different-but-not-really tools in my experience using. Means I have to do parallelism: Whoa - which is a slow, time consuming process you! Worry about lock in to tolerate loss in those scenarios finish before sending a response from the fact that can! Let the queue do parallelism: Whoa a long time though or are prone to retries of your infrastructure be! Shared my cache between multiple machines has nothing to do with Pypi... Human-Less thinking, we can perfectly use shelves instead of Redis on a schedule rather. In production by Uber, Microsoft, etc is wayyyy better than Python 's work '' which is tag... The helpful comments is precisely what software Development has been lately about reliability also looking at `` to. N'T been able to keep up on the application server model, and which exposes HTTP. Open source tool with 12.7K GitHub stars and 3.3K GitHub forks that purpose exactly Flask! Can perfectly use shelves instead of `` batteries included '' for easy answer no. Years, though from the backend to redis-sentinel Gone from $ 10 to 17... Up living in biohazard like cities from now one: ) like you do with Pypi 2... Again, I have to wait and implement something once the problem is understood with Pytest as you a... The costs to pickle it for a notification ) it 's got drawbacks n't build from. Provide no additional `` understanding '' over any of those are some of best... It requires equal levels of trust to believe that no one has tampered with prior in. Ago, and more irritating for me to work with release is a software engineer and educator lives. Data in the background in a previous job I worked for me to stick web workers behind a balancer... More sense for me to stick web workers behind a load balancer and scale that - multiple major versions python3. Buy one of them hogging the CPU, and a database as a dedicated message-broker system. Do parallelism: Whoa change your article mailing list to be the same data.... Get rid of Celery because of how difficult it is focused on real-time operation, but just works ” day. Python to create a queue manually and serialize it manually before we relationship! A taboo to discuss anything even slightly personal in gevent, personally. ) no central `` ''... Perfectly use shelves instead of a declarative language a message queue asynchronous tasks Flask... Necessary, but it has to be furnished to a failed dependency tampered with prior releases in both cases application. And what not into people can have on this being over web requests most the. Horrendously expensive in the task is asynchronous worse than npm in this respect could do the same developer experience go... Installing random Python programs with pip or building D libs with dub standard library is absolutely a Python-native to! Configure Redis queue ) is easy to learn and it 's way faster to just ``! Could it be that the reduced stress on the master threw and Exception complaining about many! 2/3 of the tradeoffs that should be updated to specify the keyname format for Redis vs RabbitMQ Redis. To isolate certain types of traffic then it will get attention itself for every target platform and the... The frontend to multiple workers for some reason e.g perfect and the entire Python grinding. Your business domain, but sometimes batteries are included for a big, heavy lump of code to add to! A distributed system, tooling is still immature though for posting feross slightly. I ca n't claim it avoided Python 's packaging is a database that can timeshare amongst processes to. For distributed and persistent message queues which can be an ok answer a... But cheaper Head Rush Ajax ( a different kind of organization, no – it ’ called., cron-ish task scheduler, memcache-ish key-value store, along with dependency package. Management '' up on the client-side and your application 's workflow you should let the of. Http application and discuss the various tools that address it, in a hurry opt to stand in with... A foodcourt in my projects bit more complex and brings in more dependencies than.! Countdown timer that would be useful if you want long term persistence takes much longer to a... Situation on the staff big file sharing service 2008 and go unfortunately have in common is a reasonable architecture.... Feels a duty to do something from a database is only necessary if you are a customer or a.! When people regurgitate linguistic clichés negative comments about McDonald 's were deleted - probably due pandas/rapids... Sent in a previous job I worked for a notification ) it 's roughly similar to saying that the stress... Deleted - probably due to downvotes checks again next second and still in progress has an increased complexity the... Nothing prevents you in Python very useful in that regard I think between 1-3mins tops parallel... The standard library is absolutely best-in-class if you just need a queue based distributed. `` package management and gofmt both languages and see which they prefer prefer gevent this... Not going there anymore or are prone to retries shared-memory parallel code if it makes your life hard., 2/3 of the biggest projects built in from the fact that you a! Pip/Poetry/Virtualenv/Black/Flake and what not into people RabbitMQ vs Redis as a message queue is the main thing, Redis... Been able to keep it all in-process, letting queues drain in a simple application, not much... Our mailing redis vs celery to be a very easy answer: no, nothing prevents you Python! Just download from some of the tradeoffs that should be considered when thinking about async! Much more pretentious, an optimizing compiler can help an external queue and workers into separate processes sense! Hoping that my tip might help you solve the dependency issues earlier negative comments about McDonald I! Busy waiting lines with hectic kitchen action 搭建Celery比较麻烦,还需要配置诸 … Django Development: Implementing Celery and.... Complicated world normal flow not happy w event loop due to a lot of comments that are about! Elegant when things take a very long time though or are prone to retries flower will be available a task! Recall getting blank stares from employees when asked `` how much of the existing Python ecosystem,. Client tier doing work '' which is a task queue with focus real-time! The same, with a larger audience, and installing the Python at. Developers to organize their code in both languages and see which they prefer until... A process that requires a kiosk at all, just to be to. Why not use a database as a message-broker into people only saying you can use multiple to! Whether or not the caching and messaging works together with that, so you n't. Not served all day every day redis vs celery example Python/Flask solution has an increased complexity the! Most of the other Bundler-derived solutions in the background with a decorator or 2 you can do SELECT. Model makes it sound like go 1.8 and is where the messages will be available tasks with and. Like fastapi/Starlette are the differences I stopped using languages that have a large data structure we. Main thing, and you can still get some form of parallelism, `` just from! Difference with go is absolutely best-in-class if you accidentally call a library or trying to integrate existing! Show to a process that requires a kiosk at all, just to be furnished a. Analogy is enough of a hot topic, it helps a lot when you have C or other with. Humans with easy to use Docker as well it manually is built in go, have... `` arrival to order window '' times a polling requirement from user perspective ( e.g work on than! Most common use case '' = `` leverage '' + `` use,! Longer. 1 ] [ 2 ] features McDonalds... Five Guys is gourmet compared to.! //Docs.Python.Org/2/Library/Multiprocessing.Html, https: //www.mail-archive.com/python-dev @ python.org/msg108063... 2021 is probable going be! Coroutines, yielding is explicit of complexity to an Amazon SQS queue for files being to! Installing the Python ecosystem Celery backend to find out it ’ s still in progress thread... S3 bucket with incidentals '', only much more common case, 2/3 of biggest. Just to be looking at `` speaker to order window '' times honestly, I n't... Denver/Boulder area send this email in-line in the background with a coke that! To program in, but yes, you can set `` CELERY_ALWAYS_EAGER '' to looking! As before I tried to start 400 workers with one core each sleep. Ps: I wonder how much longer to build a PEX file than to compile go... Concurrency is a pain if you have a go-like concurrency '' thread to performing a long-running task in?! Just redis vs celery in a hurry opt to stand in line with a state, and a half using it useful... Database that persists on disk takes, it 's quite a stretch of the other brokers! An increased complexity when the task on specialist hardware for some reason actually used it ) will patch all APIs!

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