[as a small context provider: I am new to networking and ZERO-MQ, but I did spend quite a bit of time on the guide and examples]
I have the following challenge (done in C++, but irrelevant to the question). I have a single source that generates tasks. I have multiple engines that need to process those tasks, and send back the result.
First attempt:
I created a client with a ZMQ_PUSH socket. The engines have a ZMQ_PULL socket. To get the answers back to the client, I created the reverse: a ZMQ_PUSH on the workers and a ZMQ_PULL on the client. It worked out of the box. Only to find out that after some time the client ran out of memory since I was pushing way more requests than the workers could process. I need some backpressure.
Second attempt:
I added a counter on the client that took care of only pushing when no more than say 1000 tasks were 'in progress'. The out of memory issue was solved, since I was never having more than 1000 'in progress' tasks. But ... some workers were slower than others. Since PUSH/PULL uses fair queueing, the amount of work for that slow worker kept increasing and increasing...until the slowest worker had all 1000 requests queued and the others were starved. I was not using my workers effectively.
Now, what architecture could I use that solves the issue of 'workers with different speed'? Is the 'count the number of in progress tasks' approach a good way of balancing the number of pushed requests? Or is there a way I can PUSH tasks to the workers, and the pushing blocks on a predefined point? Can I do that with HWM?
I am sure this problem is of such a generic nature that I should be able to easily deal with this. Can anyone point me in the right direction?
Thanks!
we used the Paranoid Pirate Protocol http://rfc.zeromq.org/spec:6,
but in case of many very small jobs, where the overhead of communication might be high, a credit-based flow control pattern might be more efficient. http://unprotocols.org/blog:15
in both cases it is necessary for the requester to directly assign jobs to individual workers. this is abstracted away of course and, depending on the use-case, could be made available as a sync call, which returns when all tasks have been processed.
Related
So we have a very huge database which has around 300,000 urls. These urls have to be pinged and get data from.(these urls are radio stations which are playing song. The data is metadata)
Some of them are sometimes inactive and sometimes active.
On any given time, around 80,000 are active. Some respond slow, some respond quickly. I have a server and I am thinking to do this using c++
My goal is to ping and parse(or crawl) them within 1 minute and keep repeating the process because information(the song playing on them) can change over time. ranging from 2-7 minutes mostly. But I am not sure if it is possible.
What should be my approach to do it?
I have thought of creating two programs, one to test if the url is active or not and run it twice a day. And how much time it generally takes to respond. Does it usually respond slow or whether it is responding slower now.
And the other to do the actual crawling where fastest will be crawled first and some dedicated threads for urls which respond faster.
Please i would love more better ideas or better solutions for it. Can any one tell me how to do the maths to find out the number of dedicated threads i should allot to each for getting the results in least number of time
You don't need performance of your CPU (not your bottleneck at the moment), but you need to avoid network layer stall... if the request timeout is 60 seconds, and you have 16 threads, and hit 16 very slow servers (which will time-out eventually), you are generally stalled for 60 seconds and not processing anything more.
So I would start with let's say 500 threads (and like 15-30s timeout, if you know the very slow radios are capable to fit even this), and keep some statistic about their turnaround, and keep adding more working threads dynamically for every original which didn't get response within 2-3 secs. 80000/500 = 160, so each "normally quick" worker thread has then to ping around 160 urls, if each does take 2 seconds, that's still 320 = 5min! So 500 sounds like minimum.
That said, having 500+ threads will somewhat burden CPU and memory (not sure how much, with decent thread/memory model implementation 500 doesn't sounds like much for modern x86 CPU with GB of RAM, even 5000 sounds still reasonable), but I would worry lot more about the network layer and about possible firewalls around, you need server-grade like network for such amount of requests (if I would try something like that from my home, my own router would filter me out with default settings, detecting it as some kind of DoS attack).
So get some statistic how long the request on average take, then take your target time (2-7min), and divide the number of urls by those, like average ping 5s, round time 3min = 300,000/(3*60/5) = 8333.33 threads at least needed. Then you will have to profile your app to verify, that with 8000 threads it will not choke on something else, but it will really handle the task as expected.
(other option is to fire asynchronous http request from single thread, but that sort of creates its own threads for each task any way, so I would rather manage the threads myself, and use synchronous http calls)
And thinking about dynamic grow mechanics... you can keep some counters about how many new requests were added in last second, and how many finished (either responded or failed), and after few seconds of running these should start to form some kind of "throughput" statistic, then if throughput is under desired threshold, you can add more threads.
About active/inactive... keep the response time/last-seen/last-check together with url, and add some further logic to check url only when it makes sense (like not within next 60s, if it did just respond, or check inactive just after 6h from last test). You need also avoid checking the same url in two different threads at the same time, so some central manager code should feed the threads with target (maybe some FIFO thread-safe queue ... actually you can use its size to estimate how well the worker threads are processing it, so you can add more threads when you see the queue is not emptying fast enough = that avoids adding the statistic code to thread themselves).
I am creating a chatbot and need a solution to send messages to the user in the future after a specific delay. I have my system set up with Nginx, Gunicorn and Django. The idea is that if the bot needs to send the user several messages, it can delay each subsequent message by a certain amount of time before it sends it to seem more 'human'.
However, a simple threading.Timer approach won't work because the user might interrupt this process at any moment prompting future messages to be changed, but the timer threads might not be available to be stopped as they are on a different worker. So far I have come across two solutions:
Use threading.Timer blindly to check a to-send list in the database, can create problems with lots of unneeded threads. Also makes the database less clean/organized.
Use celery or some other system to execute these future tasks. Seems like overkill and over-engineering a simple problem. Tasks will always just be delayed function calls. Also a hassle dealing with which messages belong to which conversation.
What would be the best solution for this problem?
Also, a more generic question:
Ideally the best solution would be a framework where I can 'simulate' a new bot for each conversation so it acts as its own entity and holds all the state/message queue information in memory for itself. It would be necessary for this framework to only allocate resources to a bot when it needs to do something based on a preset delay or incoming message. Is there anything that exists like this?
Personally I would use Celery for this; executing delayed function calls is its job. And I don't know why knowing what messages belong where would be more of a problem there than doing it in a thread.
But you might also want to investigate the new Django-Channels work that Andrew Godwin is doing, since that is intended to support async background tasks.
I know this depends on the box hardware, but for example if there are set 100 processes, the default queue is also 100. Does it makes sense to increase PassengerMaxRequestQueueSize to 200 or 300? Probably this depends on free memory. Thoughts?
The best answer will be explaining the setting and probably one or two examples, assuming the server process requests for 2-3 seconds.
Thanks in advance!
Why you should limit queuing
Any requests that aren't immediately handled by an application process, are queued. Queuing is usually is bad: it often means that your server cannot handle the requests quickly enough.
A larger queue means that requests are less likely to be dropped. But this comes with a drawback: during busy times, the larger the queue, the longer your visitors have to wait before they see a response. This causes them to click reload, making the queue even longer (their previous request will stay in the queue; the OS does not know that they've disconnected until it tries to send data back to the visitor), or causes them to leave in frustration.
So having a limit on the queue is a good thing. It limits the impact of the above situation.
You should ensure that requests are queued as little as possible. That could mean:
Making your app faster (if your workload is CPU bound).
Upgrading to faster hardware (if your workload is CPU bound).
Increasing your app's concurrency settings (if your workload is I/O bound), e.g. by increasing the number of processes or threads.
If you cannot prevent requests from being queued, then the next best thing to do is to keep the queue short, and to display a friendly error message upon reaching the queue limit. Something like, "We're sorry, a lot of people are visiting us right now. Please try again later." The documentation for PassengerMaxRequestQueueSize tells you how to do that.
Optimal value for the queue size
It's hard to say what the optimal queue size should be. A good rule of thumb is: set the request queue size to the maximum number of requests you can handle in one second. Depending on your situation you may have to tweak things a little bit.
This rule of thumb comes from the notion of expected burst traffic. How many simultaneous requests do you expect on your server?
Suppose that your queue size is 100, and that for whatever reason you receive 150 requests at the same time. Suppose that your server is fast enough to handle 150 requests in half a second, so you know it's not a performance problem. But if you have a request queue size of 100, then 50 of those requests will be dropped with a "Request queue full" error.
In such a situation, you should set the queue size to the maximum number of concurrent requests that you think you can safely handle without performance issues.
This SO question and the Passenger docs here talk more about working with this. If you want more information about why this is happening on your server you can try running passenger-status (usually you need to run this as root).
If you would like to set a custom error page when visitors see this issue you can use the following (in Apache) to set a custom error page:
PassengerErrorOverride on
ErrorDocument 503 /error503.html
As mentioned by Hongli you can also change the setting PassengerMaxRequestQueueSize to a higher number to queue more requests. You can also set this to 0 and disable it (for most situations this is not an optimal solution however).
For reference, the default error message a visitor to your site will see when bumping against this limit is:
This website is under heavy load
We're sorry, too many people are accessing this website at the same time. We're working on this problem. Please try again later.
i got a very specific question about server programming in UNIX (Debian, kernel 2.6.32). My goal is to learn how to write a server which can handle a huge amount of clients. My target is more than 30 000 concurrent clients (even when my college mentions that 500 000 are possible, which seems QUIIITEEE a huge amount :-)), but i really don't know (even whats possible) and that is why I ask here. So my first question. How many simultaneous clients are possible? Clients can connect whenever they want and get in contact with other clients and form a group (1 group contains a maximum of 12 clients). They can chat with each other, so the TCP/IP package size varies depending on the message sent.
Clients can also send mathematical formulas to the server. The server will solve them and broadcast the answer back to the group. This is a quite heavy operation.
My current approach is to start up the server. Than using fork to create a daemon process. The daemon process binds the socket fd_listen and starts listening. It is a while (1) loop. I use accept() to get incoming calls.
Once a client connects I create a pthread for that client which will run the communication. Clients get added to a group and share some memory together (needed to keep the group running) but still every client is running on a different thread. Getting the access to the memory right was quite a hazzle but works fine now.
In the beginning of the programm i read out the /proc/sys/kernel/threads-max file and according to that i create my threads. The amount of possible threads according to that file is around 5000. Far away from the amount of clients i want to be able to serve.
Another approach i consider is to use select () and create sets. But the access time to find a socket within a set is O(N). This can be quite long if i have more than a couple of thousands clients connected. Please correct me if i am wrong.
Well, i guess i need some ideas :-)
Groetjes
Markus
P.S. i tag it for C++ and C because it applies to both languages.
The best approach as of today is an event loop like libev or libevent.
In most cases you will find that one thread is more than enough, but even if it isn't, you can always have multiple threads with separate loops (at least with libev).
Libev[ent] uses the most efficient polling solution for each OS (and anything is more efficient than select or a thread per socket).
You'll run into a couple of limits:
fd_set size: This is changable at compile time, but has quite a low limit by default, this affects select solutions.
Thread-per-socket will run out of steam far earlier - I suggest putting the longs calculations in separate threads (with pooling if required), but otherwise a single thread approach will probably scale.
To reach 500,000 you'll need a set of machines, and round-robin DNS I suspect.
TCP ports shouldn't be a problem, as long as the server doesn't connection back to the clients. I always seem to forget this, and have to be reminded.
File descriptors themselves shouldn't be too much of a problem, I think, but getting them into your polling solution may be more difficult - certainly you don't want to be passing them in each time.
I think you can use the event model(epoll + worker threads pool) to solve this problem.
first listen and accept in main thread, if the client connects to the server, the main thread distribute the client_fd to one worker thread, and add epoll list, then this worker thread will handle the reqeust from the client.
the number of worker thread can be configured by the problem, and it must be no more the the 5000.
I'm building my first web application after many years of desktop application development (I'm using Django/Python but maybe this is a completely generic question, I'm not sure). So please beware - this may be an ultra-newbie question...
One of my user processes involves heavy processing in the server (i.e. user inputs something, server needs ~10 minutes to process it). On a desktop application, what I would do it throw the user input into a queue protected by a mutex, and have a dedicated background thread running in low priority blocking on the queue using that mutex.
However in the web application everything seems to be oriented towards synchronization with the HTTP requests.
Assuming I will use the database as my queue, what is best practice architecture for running a background process?
There are two schools of thought on this (at least).
Throw the work on a queue and have something else outside your web-stack handle it.
Throw the work on a queue and have something else in your web-stack handle it.
In either case, you create work units in a queue somewhere (e.g. a database table) and let some process take care of them.
I typically work with number 1 where I have a dedicated windows service that takes care of these things. You could also do this with SQL jobs or something similar.
The advantage to item 2 is that you can more easily keep all your code in one place--in the web tier. You'd still need something that triggers the execution (e.g. loading the web page that processes work units with a sufficiently high timeout), but that could be easily accomplished with various mechanisms.
Since:
1) This is a common problem,
2) You're new to your platform
-- I suggest that you look in the contributed libraries for your platform to find a solution to handle the task. In addition to queuing and processing the jobs, you'll also want to consider:
1) status communications between the worker and the web-stack. This will enable web pages that show the percentage complete number for the job, assure the human that the job is progressing, etc.
2) How to ensure that the worker process does not die.
3) If a job has an error, will the worker process automatically retry it periodically?
Will you or an operations person be notified if a job fails?
4) As the number of jobs increase, can additional workers be added to gain parallelism?
Or, even better, can workers be added on other servers?
If you can't find a good solution in Django/Python, you can also consider porting a solution from another platform to yours. I use delayed_job for Ruby on Rails. The worker process is managed by runit.
Regards,
Larry
Speaking generally, I'd look at running background processes on a different server, especially if your web server has any kind of load.
Running long processes in Django: http://iraniweb.com/blog/?p=56