I am using Twilio to send/receive texts in a Rails 4.2 app. I am sending in bulk, around 1000 at a time, and receiving sporadically.
Currently when I receive a text I save it to the DB (to, from, body) and then pass that record to an ActiveJob worker to process later. For sending messages I currently persist the Twilio params to another DB and pass that record to a different ActiveJob worker. Since I am often doing it in batches I have two workers. The first outgoing message worker sends a single message. The second one queries the DB and finds all the user who should receive the message, creates a DB record for each message that should be sent, and then passes that record to the first outgoing message worker. So the second one basically just creates a bunch of jobs for the first one to process.
Right now I have the workers destroying the records once they finish processing (both incoming and outgoing). I am worried about not persisting things incase the server, redis, or resque go down but I do not know if this is actually a good design pattern. It was suggested to me just to use a vanilla ruby object and pass it's id to the worker but I am not sure how that effects data reliability. So is it over kill to be creating all these DBs and should I just be creating vanilla ruby objects and passing those object's ids to the workers?
Any and all insight is appreciated,
Drew
It seems to me that the approach of sending a minimal amount of data to your jobs is the best approach. Check out the 'Best Practices' section on the sidekiq wiki: https://github.com/mperham/sidekiq/wiki/Best-Practices
What if your queue backs up and that quote object changes in the meantime? Don't save state to Sidekiq, save simple identifiers. Look up the objects once you actually need them in your perform method.
Also in terms of reliability - you should be worried about your job queue going down. It happens. You either design your system to be fault tolerant of a failure or you find a job queue system that has higher reliability guarantees (but even then no queue system can guarantee 100% message deliverability). Sidekiq pro has better reliability guarantees than sidekiq (non-pro), but if you design your jobs with a little bit of forethought, you can create jobs that can scan your database after a crash and re-queue any jobs that may have been lost.
How much work you spend desinging fault tolerant solutions really just depends how critical it is that your information make it from point A to point B :)
Related
In the Sidekiq wiki it talks about the need for jobs to be idempotent and transactional. Conceptually this makes sense to me, and this SO answer has what looks like an effective approach at a small scale. But it's not perfect. Jobs can disappear in the middle of running. We've noticed certain work is incomplete and when we look in the logs they cut short in the middle of the work as if the job just evaporated. Probably due to a server restart or something, but it often doesn't find its way back into the queue. super_fetch tries to address this, but it errs on the side of duplicating jobs. With that we see a lot of jobs that end up running twice simultaneously. Having a database transaction cannot protect us from duplicate work if both transactions start at the same time. We'd need locking to prevent that.
Besides the transaction, though, I haven't been able to figure out a graceful solution when we want to do things in bulk. For example, let's say I need to send out 1000 emails. Options I can think of:
Spawn 1000 jobs, which each individually start a transaction, update a record, and send an email. This seems to be the default, and it is pretty good in terms of idempotency. But it has the side effect of creating a distributed N+1 query, spamming the database and causing user facing slowdowns and timeouts.
Handle all of the emails in one large transaction and accept that emails may be sent more than once, or not at all, depending on the structure. For example:
User.transaction do
users.update_all(email_sent: true)
users.each { |user| UserMailer.notification(user).deliver_now }
end
In the above scenario, if the UserMailer loop halts in the middle due to an error or a server restart, the transaction rolls back and the job goes back into the queue. But any emails that have already been sent can't be recalled, since they're independent of the transaction. So there will be a subset of the emails that get re-sent. Potentially multiple times if there is a code error and the job keeps requeueing.
Handle the emails in small batches of, say, 100, and accept that up to 100 may be sent more than once, or not at all, depending on the structure, as above.
What alternatives am I missing?
One additional problem with any transaction based approach is the risk of deadlocks in PostgreSQL. When a user does something in our system, we may spawn several processes that need to update the record in different ways. In the past the more we've used transactions the more we've had deadlock errors. It's been a couple of years since we went down that path, so maybe more recent versions of PostgreSQL handle deadlock issues better. We tried going one further and locking the record, but then we started getting timeouts on the user side as web processes compete with background jobs for locks.
Is there any systematic way of handling jobs that gracefully copes with these issues? Do I just need to accept the distributed N+1s and layer in more caching to deal with it? Given the fact that we need to use the database to ensure idempotency, it makes me wonder if we should instead be using delayed_job with active_record, since that handles its own locking internally.
This is a really complicated/loaded question, as the architecture really depends on more factors than can be concisely described in simple question/answer formats. However, I can give a general recommendation.
Separate Processing From Delivery
start a transaction, update a record, and send an email
Separate these steps out. Better to avoid doing both a DB update and email send inside a transaction, batched or not.
Do all your logic and record updates inside transactions separately from email sends. Do them individually or in bulk or perhaps even in the original web request if it's fast enough. If you save results to the DB, you can use transactions to rollback failures. If you save results as args to email send jobs, make sure processing entire batch succeeds before enqueing the batch. You have flexibility now b/c it's a pure data transform.
Enqueue email send jobs for each of those data transforms. These jobs must do little to no logic & processing! Keep them dead simple, no DB writes -- all processing should have already been done. Only pass values to an email template and send. This is critical b/c this external effect can't be wrapped in a transaction. Making email send jobs a read-only for your system (it "writes" to email, external to your system) also gives you flexibility -- you can cache, read from replicas, etc.
By doing this, you'll separate the DB load for email processing from email sends, and they are now dealt with separately. Bugs in your email processing won't affect email sends. Email send failures won't affect email processing.
Regarding Row Locking & Deadlocks
There shouldn't be any need to lock rows at all anymore -- the transaction around processing is enough to let the DB engine handle it. There also shouldn't be any deadlocks, since no two jobs are reading and writing the same rows.
Response: Jobs that die in the middle
Say the job is killed just after the transaction completes but before the emails go out.
I've reduced the possibility of that happening as much as possible by processing in a transaction separately from email sending, and making email sending as dead simple as possible. Once the transaction commits, there is no more processing to be done, and the only things left to fail are systems generally outside your control (Redis, Sidekiq, the DB, your hosting service, the internet connection, etc).
Response: Duplicate jobs
Two copies of the same job might get pulled off the queue, both checking some flag before it has been set to "processing"
You're using Sidekiq and not writing your own async job system, so you need to consider job system failures out of your scope. What remains are your job performance characteristics and job system configurations. If you're getting duplicate jobs, my guess is your jobs are taking longer to complete than the configured job timeout. Your job is taking so long that Sidekiq thinks it died (since it hasn't reported back success/fail yet), and then spawns another attempt. Speed up or break up the job so it will succeed or fail within the configured timeout, and this will stop happening (99.99% of the time).
Unlike web requests, there's no human on the other side that will decide whether or not to retry in an async job system. This is why your job performance profile needs to be predictable. Once a system gets large enough, I'd expect completely separate job queues and workers based on differences like:
expected job run time
expected job CPU/mem/disk usage
expected job DB or other I/O usage
job read only? write only? both?
jobs hitting external services
jobs users are actively waiting on
This is a super interesting question but I'm afraid it's nearly impossible to give a "one size fits all" kind of answer that is anything but rather generic. What I can try to answer is your question of individual jobs vs. all jobs at once vs. batching.
In my experience, generally the approach of having a scheduling job that then schedules individual jobs tends to work best. So in a full-blown system I have a schedule defined in clockwork where I schedule a scheduling job which then schedules the individual jobs:
# in config/clock.rb
every(1.day, 'user.usage_report', at: '00:00') do
UserUsageReportSchedulerJob.perform_now
end
# in app/jobs/user_usage_report_scheduler_job.rb
class UserUsageReportSchedulerJob < ApplicationJob
def perform
# need_usage_report is a scope to determine the list of users who need a report.
# This could, of course, also be "all".
User.need_usage_report.each(&UserUsageReportJob.method(:perform_later))
end
end
# in app/jobs/user_usage_report_job.rb
class UserUsageReportJob < ApplicationJob
def perform(user)
# the actual report generation
end
end
If you're worried about concurrency here, tweak Sidekiq's concurrency settings and potentially the connection settings of your PostgreSQL server to allow for the desired level of concurrency. I can say that I've had projects where we've had schedulers that scheduled tens of thousands of individual (small) jobs which Sidekiq then happily took in in batches of 10 or 20 on a low priority queue and processed over a couple of hours with no issues whatsoever for Sidekiq itself, the server, the database etc.
how safe it is to use Resque for long-time-pending tasks in Rails (for example: Resque.enqueue_in(7.days, JobClass)) with resque and resque-scheduler gems? Good to mention that instance is running on Heroku. What is the chance of losing queue?
Semi Persistence
As per my comments, the downside of using Redis (which Resque uses) is the potentiality of downtime.
As the "RAM" of web-apps, Redis stores semi persistent data. This basically means it only retains your data whilst it is operating. Any downtime would lose your queue.
This is the crux of your question -
can you afford to lose your queue?
The answer is only one which you can answer.
There are, however, a number of factors which I would consider:
*Redis isn't a full database... it's a JSON key:value pair store.
In short, this should only be used to give you a simple mechanism to use snippets of data at particular times. We use Redis to store ID's of users on one of our systems, as an example.
This means that if you're storing more data inside Redis than simple ID's or other base data, are you using it correctly?
How Important Is Your Queue?
As with any app, you'll want to retain as much user data as possible.
The difference, however, is how critical the queue is to your system.
I gave the example of a Hotel Reservation system. Perhaps you have "welcome" emails you need to send, "directions" emails to help guests find the hotel, etc. Are these "mission critical"?
I have designed an email marketing system before. The queuing system for that was extensive; but it wasn't the "queue" that was important - it was all the associative data which went with it.
Thus, we saved our data in a datatable and only used Redis to store a pure queue of what was going to be sent, and when. We then ran some scripts every minute (I've forgotten the specifics), which went through the Redis queue.
Bottom line is that I would highly recommend storing the appropriate data in a table, and then only processing the requests as you need them.
The underpin of the queue is that you need to be able to rebuild it if it goes down. If you're storing your values in redis alone, this will prove to be a major bottleneck.
Hitting the DB is well worth the data you'll get from it in the long run.
As part of my app I am sending out an email to many users daily. Depending on their status they will be sent one of five possible types of emails.
The logic that determines which email the user receives is fairly long.
Should I:
1) Should I create a delayed_job for each email
or
2) Send the entire logic (50 lines of Ruby) with the send commands into a single job
What are the pros/cons of either approach?
Further to Sabyasachi Ghosh's answer, here are the differences between DelayedJob and Resque:
DelayedJob relies on the DB
Requires ActiveRecord
Uses Ruby Objects (not just references)
Has much deeper queuing functionality (queue depth etc)
Runs much heavier than resque
Resque relies on Redis
Lightweight
Runs independently of ActiveRecord
Is meant to process references (not entire objects)
Modularity
In answer to your question, I would look at modularity
Rails' is based on the principle of DRY code -- which essentially means you need to be as modular as possible (reusing code wherever you can). This leads to efficiency & simpler development cycles
In light of this, you have to observe your queueing functionality from the perspective of modularity. What does the queuing system actually do?
It queues things
Therefore, you want to include as little code as possible in the queuing system
I would create a redis instance (you can get them on Heroku), and use resque to queue specific information (such as id or email)
This will allow you to use resque to run through the Redis list, sending as many emails as you need
If you have a huge logic i recommed not to put in the delayed job if you need to send a bunch of email. better use resque(https://github.com/resque/resque) or sidekiq(http://sidekiq.org/). as in the time of sending email it delayed job will lock your database so your performance will be low.
If you have small logic and less number of email just go for delayed job for each email. as it is easy to setup and implement.
I think so you need to send each email via delayed job because if anything happens with your delayed job like if your delayed job get crashed or stopped in that case when system will re execute your job it can cause problems so i suggest you to add each email in delayed job.
When a background job starts, it's sent to the back of a queue where a worker handles it; a task clears and the other starts. I think I've got this one right except I don't understand the practical side of it in some cases. Sure, if you're a company sending out 15,000 newsletters once a week using a delayed job makes perfect sense. But when you have an application of even 100 users, in which some task is long enough to need background work (like sending/fetching emails that might take a minute) then each user will have to wait in line while another user gets cleared (in case there's a single worker).
This is the part I'm not sure I'm getting right. I'm talking about the same job, but individually for each user. Does that count as a job per user? If I have 100 users, do I need to keep 100 workers for each one's process to not get tied up?
I've tried using delayed_job to simulate that, and indeed when I sign in with a different account I have to wait until another user's email gets sent until mine is. While the plugin is swift and simple to work with, I think it's not the right approach here.
I've also tried using Ajax, but since it's an HTTP request it ties up the browser in loading mode until it gets a response from the server (even with async: true). Not sure if I ruled this one out too quickly, but I was sortof looking for a more elegant server solution.
Is there a way to achieve a background job like this? (I've heard of different, mostly commercial solutions promising little waiting time, but I'm interested in completely eliminating the queue between users). If not, is there a method to make an ajax request without waiting for a response? I realize my questions are both drastically different but both seem like an appropriate solution to this problem.
Resque is a background processing engine that can support multiple queues.
Ways you could use this:
Group your tasks into queues that make sense on their priority. If you need fast response times, use it in a 'foreground' queue. Slow? (like sending/receiving emails) can be in the 'background' queue
Have one queue per user (you will need to have many many workers for this)
This SO question also gives a way to use delayed_jobs with multiple queues/tables
The purpose of delayed_job and other message queues is to asynchronously process jobs outside of your core application. I always use a queue for sending email since I'm relying on an outside application (sometimes a third-party API like gmail) to send them and I can't guarantee available and operating efficiency.
So for your use case, even with very few users, I highly recommend offloading emails to delayed_job. This will speed up your front end (ajax) and will also give you retries upon failure. You could spin up multiple workers to process the queue, but it shouldn't be necessary with your numbers unless your calls to send mail are taking a really long time (more than a couple seconds?).
And yes in most situations I'd create separate jobs for each user even though the message might be identical. The only time I'd process them all together would be if the email application / API has bulk sending and you can reduce the number of calls significantly by sending a large payload in a few calls.
I'm working on a Rails application that periodically needs to perform large numbers of IO-bound operations. These operations can be performed asynchronously. For example, once per day, for each user, the system needs to query Salesforce.com to fetch the user's current list of accounts (companies) that he's tracking. This results in huge numbers (potentially > 100k) of small queries.
Our current approach is to use ActiveMQ with ActiveMessaging. Each of our users is pushed onto a queue as a different message. Then, the consumer pulls the user off the queue, queries Salesforce.com, and processes the results. But this approach gives us horrible performance. Within a single poller process, we can only process a single user at a time. So, the Salesforce.com queries become serialized. Unless we run literally hundreds of poller processes, we can't come anywhere close to saturating the server running poller.
We're looking at EventMachine as an alternative. It has the advantage of allowing us to kickoff large numbers of Salesforce.com queries concurrently within a single EventMachine process. So, we get great parallelism and utilization of our server.
But there are two problems with EventMachine. 1) We lose the reliable message delivery we had with ActiveMQ/ActiveMessaging. 2) We can't easily restart our EventMachine's periodically to lessen the impact of memory growth. For example, with ActiveMessaging, we have a cron job that restarts the poller once per day, and this can be done without worrying about losing any messages. But with EventMachine, if we restart the process, we could literally lose hundreds of messages that were in progress. The only way I can see around this is to build a persistance/reliable delivery layer on top of EventMachine.
Does anyone have a better approach? What's the best way to reliably execute large numbers of asynchronous IO-bound operations?
I maintain ActiveMessaging, and have been thinking about the issues of a multi-threaded poller also, though not perhaps at the same scale you guys are. I'll give you my thoughts here, but am also happy to discuss further o the active messaging list, or via email if you like.
One trick is that the poller is not the only serialized part of this. STOMP subscriptions, if you do client -> ack in order to prevent losing messages on interrupt, will only get sent a new message on a given connection when the prior message has been ack'd. Basically, you can only have one message being worked on at a time per connection.
So to keep using a broker, the trick will be to have many broker connections/subscriptions open at once. The current poller is pretty heavy for this, as it loads up a whole rails env per poller, and one poller is one connection. But there is nothing magical about the current poller, I could imagine writing a poller as an event machine client that is implemented to create new connections to the broker and get many messages at once.
In my own experiments lately, I have been thinking about using Ruby Enterprise Edition and having a master thread that forks many poller worker threads so as to get the benefit of the reduced memory footprint (much like passenger does), but I think the EM trick could work as well.
I am also an admirer of the Resque project, though I do not know that it would be any better at scaling to many workers - I think the workers might be lighter weight.
http://github.com/defunkt/resque
I've used AMQP with RabbitMQ in a way that would work for you. Since ActiveMQ implements AMQP, I imagine you can use it in a similar way. I have not used ActiveMessaging, which although it seems like an awesome package, I suspect may not be appropriate for this use case.
Here's how you could do it, using AMQP:
Have Rails process send a message saying "get info for user i".
The consumer pulls this off the message queue, making sure to specify that the message requires an 'ack' to be permanently removed from the queue. This means that if the message is not acknowledged as processed, it is returned to the queue for another worker eventually.
The worker then spins off the message into the thousands of small requests to SalesForce.
When all of these requests have successfully returned, another callback should be fired to ack the original message and return a "summary message" that has all the info germane to the original request. The key is using a message queue that lets you acknowledge successful processing of a given message, and making sure to do so only when relevant processing is complete.
Another worker pulls that message off the queue and performs whatever synchronous work is appropriate. Since all the latency-inducing bits have already performed, I imagine this should be fine.
If you're using (C)Ruby, try to never combine synchronous and asynchronous stuff in a single process. A process should either do everything via Eventmachine, with no code blocking, or only talk to an Eventmachine process via a message queue.
Also, writing asynchronous code is incredibly useful, but also difficult to write, difficult to test, and bug-prone. Be careful. Investigate using another language or tool if appropriate.
also checkout "cramp" and "beanstalk"
Someone sent me the following link: http://github.com/mperham/evented/tree/master/qanat/. This is a system that's somewhat similar to ActiveMessaging except that it is built on top of EventMachine. It's almost exactly what we need. The only problem is that it seems to only work with Amazon's queue, not ActiveMQ.