I am using rakismet to check for spam in comments.
Right now, I do it in a before_create callback and I am wondering in a production site, if this is the most efficient way of doing it or should this be done by a background job.
Can you share your experience in terms of how much delay does this add to the responsiveness of your production apps?
I have not used rakisment, but doing any pre-processing on an action will slow it down, and in the situation of your spam filter, it will slow down more and more as more spam indicators are included in the rakismet dictionary.
I would recommend a two step process:
In the before_create, do a minimal spam check to catch very obvious ones. You can search for words ("viagra", "cialis", "debt", etc), as well as check that the submitter isn't submitting many comments very fast. This will be fairly quick, and not slow your app down too much.
In a Delayed Job (One of the more well known background processing libraries for Ruby), run your rakismet checks. These can delete/flag the comments after the fact.
This solution limits blatant spam immediately, and will eventually leverage the capabilities of rakismet to clean up the comments entirely, without causing too much strain or slow down to the system.
One benefit of this approach is that it is extremely easy to scale your Delayed Job processes, but just starting more workers on the same (or different) server(s). This means that your main app won't crawl, as the heavy lifting has been offloaded to multiple instances of the worker process.
Related
We're building a web-app where users will be uploading potentially large files that will need to be processed in the background. The task involves calling 3rd-party APIs so each job can take several hours to complete. We're using DelayedJob to run the background jobs. With every user kicking off a background job, each of which will take a few hours to finish, that will add up to a lot of background jobs every quickly. I am wondering what would be the best way to setup the deployment for this? We're currently hosted on DigitalOcean. I've kicked off 10 DelayedJob workers. Each one (when ideal) takes up 157MB. When actively running it utilizes around 900 MB. Our user-base right now is pretty small so it's not an issue but will be one soon. So on a 4GB droplet, I can probably run like 2 or 3 workers at a time. How should we approach this issue? Should we be looking at using DigitalOcean's API to auto-spin cheap droplets on demand? Should we subscribe to high-memory droplets on a monthly basis instead? If we go with auto-spinning droplets, should we stick with DigitalOcean or would Heroku make more sense? Or is the entire approach wrong and should we be approaching it from an entire different direction? Any help/advice would be very much appreciated.
Thanks!
It sounds like you are limited by memory on the number of workers that you can run on your DigitalOcean host.
If you are worried about scaling, I would focus on making the workers as efficient as possible. Have you done any benchmarking to understanding where the 900MB of memory is being allocated? I'm not sure what the nature of these jobs are, but you mentioned large files. Are you reading the contents of these files into memory, or are you streaming them? Are you using a database with SQL you can tune? Are you making many small API calls when you could be using a batch endpoint? Are you assigning intermediary variables that must then be garbage collected? Can you compress the files before you send them?
Look at the job structure itself. I've found that background jobs work best with many smaller jobs rather than one larger job. This allows execution to happen in parallel, and be more load balanced across all workers. You could even have a job that generates other jobs. If you need a job to orchestrate callbacks when a group of jobs finishes there is a DelayedJobGroup plugin at https://github.com/salsify/delayed_job_groups_plugin that allows you to invoke a final job only after the sibling jobs complete. I would aim for an execution time of a single job to be under 30 seconds. This is arbitrary but it illustrates what I mean by smaller jobs.
Some hosting providers like Amazon provide spot instances where you can pay a lower price on servers that do not have guaranteed availability. These pair well with the many fewer jobs approach I mentioned earlier.
Finally, Ruby might not be the right tool for the job. There are faster languages, and if you are limited by memory, or CPU, you might consider writing these jobs and their workers in another language like Javascript, Go or Rust. These can pair well with a Ruby stack, but offload computationally expensive subroutines to faster languages.
Finally, like many scaling issues, if you have more money than time, you can always throw more hardware at it. At least for a while.
I thing memory and time is more problem for you. you have to use sidekiq gem for this process because it will consume less time and memory consumption for doing the same job,because it uses redis as database which is key value pair db.if the problem continues go with java script.
I am working on an article describing fundamentals of technologies used by scalable systems. I have worked on Erlang before in a self-learning excercise. I have gone through several articles but have not been able to answer the following questions:
What is in the implementation of Erlang that makes it scalable? What makes it able to run concurrent processes more efficiently than technologies like Java?
What is the relation between functional programming and parallelization? With the declarative syntax of Erlang, do we achieve run-time efficiency?
Does process state not make it heavy? If we have thousands of concurrent users and spawn and equal number of processes as gen_server or any other equivalent pattern, each process would maintain a state. With so many processes, will it not be a drain on the RAM?
If a process has to make DB operations and we spawn multiple instances of that process, eventually the DB will become a bottleneck. This happens even if we use traditional models like Apache-PHP. Almost every business application needs DB access. What then do we gain from using Erlang?
How does process restart help? A process crashes when something is wrong in its logic or in the data. OTP allows you to restart a process. If the logic or data does not change, why would the process not crash again and keep crashing always?
Most articles sing praises about Erlang citing its use in Facebook and Whatsapp. I salute Erlang for being scalable, but also want to technically justify its scalability.
Even if I find answers to these queries on an existing link, that will help.
Regards,
Yash
Shortly:
It's unmutable. You have no variables, only terms, tuples and atoms. Program execution can be divided by breakpoint at any place. Fully transactional model.
Processes are even lightweight than .NET threads and isolated.
It's made for communications. Millions of connections? Fully asynchronous? Maximum thread safety? Big cross-platform environment, which built only for one purpose — scale&communicate? It's all Ericsson language — first in this sphere.
You can choose some impersonators like F#, Scala/Akka, Haskell — they are trying to copy features from Erlang, but only Erlang born from and born for only one purpose — telecom.
Answers to other questions you can find on erlang.com and I'm suggesting you to visit handbook. Erlang built for other aims, so it's not for every task, and if you asking about awful things like php, Erlang will not be your language.
I'm no Erlang developer (yet) but from what I have read about it some of the features that makes it very scalable is that Erlang has its own lightweight processes that are using message passing to communicate with each other. Because of this there is no such thing as shared state and locking which is the case when using for example a multi threaded Java application.
Another difference compared to Java is that the Erlang VM does garbage collection on every little process that is running which does not take any time at all compared to Java which does garbage collection only per VM.
If you get problem with bottlenecks from database connection you could start by using a database pooling app running against maybe a replicated PostgreSQL cluster or if you still have bottlenecks use a multi replicated NoSQL setup with Mnesia, Riak or CouchDB.
I think process restarts can be very useful when you are experiencing rare bugs that only appear randomly and only when specific criteria is fulfilled. Bugs that cause the application to crash as soon as you restart the app should optimally be fixed or taken care of with a circuit breaker so that it does not spread further.
Here is one way process restart helps. By not having to deal with all possible error cases. Say you have a program that divides numbers. Some guy enters a zero to divide by. Instead of checking for that possible error (and tons more), just code the "happy case" and let process crash when he enters 3/0. It just restarts, and he can figure out what he did wrong.
You an extend this into an infinite number of situations (attempting to read from a non-existent file because the user misspelled it, etc).
The big reason for process restart being valuable is that not every error happens every time, and checking that it worked is verbose.
Error handling is verbose typically, so writing it interspersed with the logic handling doing a task can make it harder to understand the code. Moving that logic outside of the task allows you to more clearly distinguish between "doing things" code, and "it broke" code. You just let the thing that had a problem fail, and handle it as needed by a supervising party.
Since most errors don't mean that the entire program must stop, only that that particular thing isn't working right, by just restarting the part that broke, you can keep operating in a state of degraded functionality, instead of being down, while you repair the problem.
It should also be noted that the failure recovery is bounded. You have to lay out the limits for how much failure in a certain period of time is too much. If you exceed that limit, the failure propagates to another level of supervision. Each restart includes doing any needed process initialization, which is sometimes enough to fix the problem. For example, in dev, I've accidentally deleted a database file associated with a process. The crashes cascaded up to the level where the file was first created, at which point the problem rectified itself, and everything carried on.
I have a web app which has a single long running task - generating a PDF report. Various graphs are generated, and it takes about 15 sec to process in all. The report is generated by a user.
Processing the report at the time of request currently causes the process to be tied up, and more importantly (given that use of this website is not heavy) sometimes the request times out.
I am therefore redesigning the architecture of this section of the app (Rails 2.3.8). To put this in context, it's unlikely that more than a couple of these reports will be generated per day, and this is an extremely niche application, so significant further scaling isn't a major concern. I do intend to hand off the project in the future though, so stability is.
The most obvious solution I think is to use Spawn to generate a report, and fire a download link to the user in an email once it's complete. Another solution I've looked into is DelayedJob.
Can anyone who's done something similar recommend one approach over another?
delayed_job, or some other queueing mechanism is going to be the easiest thing to set up. With delayed_job you would just enqueue your worker instead of creating the PDF, and a background process on the server would be working from the queue doing whatever work was available. Using spawn to fork your whole process seems a little heavy-handed, and doesn't seem to lend itself well to other minor, but still longer running tasks (like sending emails).
I'm coming from a PHP environment (at least in terms of web dev) and into the beautiful world of Ruby, so I may have some dumb questions. I imagine there are some fundamentally different options available when not using PHP.
In PHP, we use memcache to store alerts we want to display in a bar along the top of the page. When something happens that generates an alert (such as a new blog post being made), a cron script that runs once every 5 minutes or so puts that information into memcache.
Now when a user visits the site, we look in memcache to find any alerts that they haven't already dismissed and we display them.
What I'm guessing I can do differently in Rails, is to by-pass the need for a cron script, and also the need to look in memcache on every request, by using a Singleton and a polling process running in a separate thread to copy from memcache to this singleton. This would, in theory, be more optimized than checking memcache once-per-request and also encapsulate the polling logic into one place, rather than being split between a cron task and the lookup logic.
My question is: are there any caveats to having some sort of runloop in the background while a Rails app is running? I understand the implications of multithreading, from Objective-C/Java, but I'm asking specifically about the Rails (3) environment.
Basically something like:
class SiteAlertsMap < Hash
include Singleton
def initialize
super
begin_polling
end
# ... SNIP, any specific methods etc ...
private
def begin_polling
# Create some other Thread here, which polls at set intervals
end
end
This leads me into a similar question. We push (encrypted) tasks onto an SQS queue, for things related to e-commerce and for long-running background tasks. We don't use cron for this, but rather we have a worker daemon written in PHP, which runs in the background. Right now when we deploy, we have to shut down this worker and start it again from the new code-base. In Rails, could I somehow have this process start and stop with the rails server (unicorn) itself? I don't think that's something I'd running on the main process in a separate thread, since we often want to control it as a process by itself, but it would be nice if it just conveniently ran when the web application was running.
Threading for background processes in ruby would be a terrible mistake, especially since you're using a multi-process server. Using unicorn with say 4 worker processes would mean that you'd be polling from each of them, which is not what you want. Ruby doesn't really have real threads, it has green threads in 1.8 and a global interpreter lock in 1.9 IIRC. Many gems and libraries are also obnoxiously unthreadsafe.
Using memcache is still your best option and, if you have it set up correctly, you should only see it adding a millisecond or two to the request time. Another option which would give you the benefit of persisting these alerts while incurring minimal additional overhead would be to store these alerts in redis. This would better protect you against things like memcache crashing or server reboots.
For the background jobs you should use a similar approach to what you have now, but there are several off the shelf handlers for this like resque, delayed_job, and a few others. If you absolutely have to use SQS as the backend queue, you might be able to find some code to help you, but otherwise you could write it yourself. This still requires the other daemon to be rebooted whenever there is a code change. In practice this isn't a huge concern as best practices dictate using a deployment system like capistrano where a rule can easily be added to bounce the daemon on deploy. I use monit to watch the daemon process, so restarting it is as easy as telling monit to restart it.
In general, Ruby is not like Java/Objective-C when it comes to threads. It follows the more Unix-like model of process based isolation, but the community has come up with best practices and ways to make this less painful than in other languages. Ruby does require a bit more attention to setting up its stack as it is not as simple as enabling mod_php and copying some files around, but once the choices and architecture is understood, it is easier to reason about how your application works. The process model, in my opinion, is much better for web apps as it isolates code and state from the effects of other running operations. The isolation also makes the app easier to work with in a distributed system.
I've been sending emails on my application (ruby 1.8.7, rails 2.3.2) like this
Thread.new{UserMailer.deliver_signup_notification(user)}
Since ruby use green threads, there's any performance advantage doing this, or I can just use
UserMailer.deliver_signup_notification(user)
?
Thanks
Global VM lock will still almost certainly apply while sending that email, meaning no difference.
You should not start threads in a request/response cycle. You should not start threads at all unless you can watch them from create to join, and even then, it is rarely worth the trouble it creates.
Rails is not thread-safe, and is not meant to be from within your controller actions. Only since Rails 2.3 has just dispatching been thread-safe, and only if you turn it on in environment.rb with config.threadsafe!.
This article explains in more detail. If you want to send your message asynchronously use BackgroundRb or its analog.
In general, using green threads to run background tasks asynchronously will mean that your application can respond to the user before the mail is sent. You're not concerned about exploiting multiple CPUs; you're only concerned on off-loading the work onto a background process and returning a web page as soon as possible.
And from examining the Rails documentation, it looks like deliver_signup_notification will block long enough to get the mail queued (although I may be wrong). So using a thread here might make your application seem more responsive, depending on how your mailer is configured.
Unfortunately, it's not clear to me that deliver_signup_notification is necessarily thread-safe. I'd want to read the documentation carefully before relying on that.
Note also that you're making assumptions about the lifetime of a Rails process once a request has been served. Many Rails applications using DRb (or a similar tool) to offload these background tasks onto an entirely separate worker process. The easiest way to do this changes fairly often--see Google for a number of popular libraries.
I have used your exact strategy and our applications are currently running in production (but rails 2.2.2). I've kept a close eye on it and our load has been relatively low (Less than 20 emails sent per day average, with peaks of around 150/day).
So far we have noticed no problems, and this appears to have resolved several performance issues we were having when using Google's mailserver.
If you need something in a hurry then give it a shot, it has been working for us.
They'll be the same as far as I know.