Started using Google Flex Vms recently and in the logs there are multiple requests to /_ah/background that last ~1 hour each time. The only reference to these I could find is this question which mentions they have to do with background threads but I don't believe that's the case here as:
nowhere do we use background threads
that API is deprecated and I'm not even sure we can use it
we do use processes but they're short-lived (nowhere near an hour) and don't print any log messages
Any ideas?
/_ah/background is used in flex for AE API calls that are called outside of an incoming request processing context (like threads, async-io, ...).
Even if you don't do that directly log flushing is still done asynchronously (not part of an income request processing).
This is an implementation details and there is a plan to hide it but still
find a way to show (in log, trace,...) information about these API calls.
FWIW, I thought sharing this picture will provide little insight on the frequency, (possible performance) impact this /_ah/background creates..
is there any workaround to clear this ?
Related
I have to process requests published in a Pub/ Sub Topic by the users of my "service" in a Python application, having a main loop.
Each instance of the service will only be able to process one request a time due to the limitation of the application. Burst of requests (~10 at the beginning, growing to ~ 10^6/7) will mix into mostly total idle time. The cold start time of the application is very high compared to the processing time for a single request.
My code will be a plug-in, which polls the subscription, calls methods in the application based on it and then saves data in Cloud Storage and Big Query.
I have read through the cloud documentation and it seems that Cloud Run is the right solution, together with the synchronous subscription API in Python. Cloud Functions I excluded, because it seems more be suited for asynchronous stuff.
However, I did not fully understand how the auto-scaling works. Basically it would have to do this based on the average processing time for each request and the length of the queue, considering the average start-up time of a container.
Unfortunately I did not find a tutorial or example for such a use-case, especially not really explaining how the auto-scaling happens in detail.
Does anyone have something like that or could one explain me here?
I am running an application on gae flexible with python and flask. I periodically dispatch cloud tasks with a cron job. These basically loop through all users and perform some cluster analysis. The tasks terminate without throwing any kind of error but don't perform all the work (meaning not all users were looped through). It doesn't seem to happen at a consistent time 276.5s - 323.3s nor does it ever stop at the same user. Has anybody experienced anything similar?
My guess is that I am breaching some type of resource limit or timeout somewhere. Things i have thought about or tried:
Cloud tasks should be allowed to run for up to an hour (as per this: https://cloud.google.com/tasks/docs/creating-appengine-handlers)
I increased the timeout of gunicorn workers to be 3600 to reflect this.
I have several workers running.
I tried to find if there are memory spikes or cpu overload but didn't see anything suspicious.
Sorry if I am too vague or am completely missing the point, I am quite confused with this problem. Thank you for any pointers.
Thank you for all the suggestions, I played around with them and have found out the root cause, although by accident reading firestore documentation. I had no indication that this had anything to do with firestore.
From here: https://googleapis.dev/python/firestore/latest/collection.html
I found out that Query.stream() (or Query.get()) has a timeout on the individual documents like so:
Note: The underlying stream of responses will time out after the
max_rpc_timeout_millis value set in the GAPIC client configuration for
the RunQuery API. Snapshots not consumed from the iterator before that
point will be lost.
So what eventually timed out was the query of all users, I came across this by chance, none of the errors I caught pointed me back towards the query. Hope this helps someone in the future!
Other than use Cloud Scheduler, you can inspect the logs to make sure the Tasks ran properly and make sure there's no deadline issues. As application logs are grouped, and after the task itself is executed, it’s sent to Stackdriver. When a task is forcibly terminated, no log may be output. Try catching the Deadline exception so that some log is output and you may see some helpful info to start troubleshooting.
I'm working with Django1.8 and Python2.7.
In a certain part of the project, I open a socket and send some data through it. Due to the way the other end works, I need to leave some time (let's say 10 miliseconds) between each data that I send:
while True:
send(data)
sleep(0.01)
So my question is: is it considered a bad practive to simply use sleep() to create that pause? Is there maybe any other more efficient approach?
UPDATED:
The reason why I need to create that pause is because the other end of the socket is an external service that takes some time to process the chunks of data I send. I should also point out that it doesnt return anything after having received or let alone processed the data. Leaving that brief pause ensures that each chunk of data that I send gets properly processed by the receiver.
EDIT: changed the sleep to 0.01.
Yes, this is bad practice and an anti-pattern. You will tie up the "worker" which is processing this request for an unknown period of time, which will make it unavailable to serve other requests. The classic pattern for web applications is to service a request as-fast-as-possible, as there is generally a fixed or max number of concurrent workers. While this worker is continually sleeping, it's effectively out of the pool. If multiple requests hit this endpoint, multiple workers are tied up, so the rest of your application will experience a bottleneck. Beyond that, you also have potential issues with database locks or race conditions.
The standard approach to handling your situation is to use a task queue like Celery. Your web-application would tell Celery to initiate the task and then quickly finish with the request logic. Celery would then handle communicating with the 3rd party server. Django works with Celery exceptionally well, and there are many tutorials to help you with this.
If you need to provide information to the end-user, then you can generate a unique ID for the task and poll the result backend for an update by having the client refresh the URL every so often. (I think Celery will automatically generate a guid, but I usually specify one.)
Like most things, short answer: it depends.
Slightly longer answer:
If you're running it in an environment where you have many (50+ for example) connections to the webserver, all of which are triggering the sleep code, you're really not going to like the behavior. I would strongly recommend looking at using something like celery/rabbitmq so Django can dump the time delayed part onto something else and then quickly respond with a "task started" message.
If this is production, but you're the only person hitting the webserver, it still isn't great design, but if it works, it's going to be hard to justify the extra complexity of the task queue approach mentioned above.
We have a web service which serves small, arbitrary segments of a fixed inventory of larger MP3 files. The MP3 files are generated on-the-fly by a python application. The model is, make a GET request to a URL specifying which segments you want, get an audio/mpeg stream in response. This is an expensive process.
We're using Nginx as the front-end request handler. Nginx takes care of caching responses for common requests.
We initially tried using Tornado on the back-end to handle requests from Nginx. As you would expect, the blocking MP3 operation kept Tornado from doing its thing (asynchronous I/O). So, we went multithreaded, which solved the blocking problem, and performed quite well. However, it introduced a subtle race condition (under real world load) that we haven't been able to diagnose or reproduce yet. The race condition corrupts our MP3 output.
So we decided to set our application up as a simple WSGI handler behind Apache/mod_wsgi (still w/ Nginx up front). This eliminates the blocking issue and the race condition, but creates a cascading load (i.e. Apache creates too many processses) on the server under real world conditions. We're working on tuning Apache/mod_wsgi right now, but still at a trial-and-error phase. (Update: we've switched back to Tornado. See below.)
Finally, the question: are we missing anything? Is there a better way to serve CPU-expensive resources over HTTP?
Update: Thanks to Graham's informed article, I'm pretty sure this is an Apache tuning problem. In the mean-time, we've gone back to using Tornado and are trying to resolve the data-corruption issue.
For those who were so quick to throw more iron at the problem, Tornado and a bit of multi-threading (despite the data integrity problem introduced by threading) handles the load acceptably on a small (single core) Amazon EC2 instance.
Have you tried Spawning? It is a WSGI server with a flexible assortment of threading modes.
Are you making the mistake of using embedded mode of Apache/mod_wsgi? Read:
http://blog.dscpl.com.au/2009/03/load-spikes-and-excessive-memory-usage.html
Ensure you use daemon mode if using Apache/mod_wsgi.
You might consider a queuing system with AJAX notification methods.
Whenever there is a request for your expensive resource, and that resource needs to be generated, add that request to the queue (if it's not already there). That queuing operation should return an ID of an object that you can query to get its status.
Next you have to write a background service that spins up worker threads. These workers simply dequeue the request, generate the data, then saves the data's location in the request object.
The webpage can make AJAX calls to your server to find out the progress of the generation and to give a link to the file once it's available.
This is how LARGE media sites work - those that have to deal with video in particular. It might be overkill for your MP3 work however.
Alternatively, look into running a couple machines to distribute the load. Your threads on Apache will still block, but atleast you won't consume resources on the web server.
Please define "cascading load", as it has no common meaning.
Your most likely problem is going to be if you're running too many Apache processes.
For a load like this, make sure you're using the prefork mpm, and make sure you're limiting yourself to an appropriate number of processes (no less than one per CPU, no more than two).
It looks like you are doing things right -- just lacking CPU power: can you determine what is the CPU loading in the process of generating these MP3?
I think the next thing you have to do there is to add more hardware to render the MP3's on other machines. Or that or find a way to deliver pre-rendered MP3 (maybe you can cahce some of your media?)
BTW, scaling for the web was the theme of a Keynote lecture by Jacob Kaplan-Moss on PyCon Brasil this year, and it is far from being a closed problem. The stack of technologies one needs to handle is quite impressible - (I could not find an online copy o f the presentation, though - -sorry for that)
My question is: which python framework should I use to build my server?
Notes:
This server talks HTTP with it's clients: GET and POST (via pyAMF)
Clients "submit" "tasks" for processing and, then, sometime later, retrieve the associated "task_result"
submit and retrieve might be separated by days - different HTTP connections
The "task" is a lump of XML describing a problem to be solved, and a "task_result" is a lump of XML describing an answer.
When a server gets a "task", it queues it for processing
The server manages this queue and, when tasks get to the top, organises that they are processed.
the processing is performed by a long running (15 mins?) external program (via subprocess) which is feed the task XML and which produces a "task_result" lump of XML which the server picks up and stores (for later Client retrieval).
it serves a couple of basic HTML pages showing the Queue and processing status (admin purposes only)
I've experimented with twisted.web, using SQLite as the database and threads to handle the long running processes.
But I can't help feeling that I'm missing a simpler solution. Am I? If you were faced with this, what technology mix would you use?
I'd recommend using an existing message queue. There are many to choose from (see below), and they vary in complexity and robustness.
Also, avoid threads: let your processing tasks run in a different process (why do they have to run in the webserver?)
By using an existing message queue, you only need to worry about producing messages (in your webserver) and consuming them (in your long running tasks). As your system grows you'll be able to scale up by just adding webservers and consumers, and worry less about your queuing infrastructure.
Some popular python implementations of message queues:
http://code.google.com/p/stomper/
http://code.google.com/p/pyactivemq/
http://xph.us/software/beanstalkd/
I'd suggest the following. (Since it's what we're doing.)
A simple WSGI server (wsgiref or werkzeug). The HTTP requests coming in will naturally form a queue. No further queueing needed. You get a request, you spawn the subprocess as a child and wait for it to finish. A simple list of children is about all you need.
I used a modification of the main "serve forever" loop in wsgiref to periodically poll all of the children to see how they're doing.
A simple SQLite database can track request status. Even this may be overkill because your XML inputs and results can just lay around in the file system.
That's it. Queueing and threads don't really enter into it. A single long-running external process is too complex to coordinate. It's simplest if each request is a separate, stand-alone, child process.
If you get immense bursts of requests, you might want a simple governor to prevent creating thousands of children. The governor could be a simple queue, built using a list with append() and pop(). Every request goes in, but only requests that fit will in some "max number of children" limit are taken out.
My reaction is to suggest Twisted, but you've already looked at this. Still, I stick by my answer. Without knowing you personal pain-points, I can at least share some things that helped me reduce almost all of the deferred-madness that arises when you have several dependent, blocking actions you need to perform for a client.
Inline callbacks (lightly documented here: http://twistedmatrix.com/documents/8.2.0/api/twisted.internet.defer.html) provide a means to make long chains of deferreds much more readable (to the point of looking like straight-line code). There is an excellent example of the complexity reduction this affords here: http://blog.mekk.waw.pl/archives/14-Twisted-inlineCallbacks-and-deferredGenerator.html
You don't always have to get your bulk processing to integrate nicely with Twisted. Sometimes it is easier to break a large piece of your program off into a stand-alone, easily testable/tweakable/implementable command line tool and have Twisted invoke this tool in another process. Twisted's ProcessProtocol provides a fairly flexible way of launching and interacting with external helper programs. Furthermore, if you suddenly decide you want to cloudify your application, it is not all that big of a deal to use a ProcessProtocol to simply run your bulk processing on a remote server (random EC2 instances perhaps) via ssh, assuming you have the keys setup already.
You can have a look at celery
It seems any python web framework will suit your needs. I work with a similar system on a daily basis and I can tell you, your solution with threads and SQLite for queue storage is about as simple as you're going to get.
Assuming order doesn't matter in your queue, then threads should be acceptable. It's important to make sure you don't create race conditions with your queues or, for example, have two of the same job type running simultaneously. If this is the case, I'd suggest a single threaded application to do the items in the queue one by one.