Using the python library requests and uploading larger files I will get the error RemoteDisconnected('Remote end closed connection without response').
However it will work if I change the default User-Agent of the library to something like "Mozilla/5.0".
Does anybody know the reason for this behaviour ?
Edit: Only happens with Property X-Explode-Archive: true
Are there any specific pattern of timeout that you could highlight in this case?
For example: It times out after 60 seconds every time (of that sort)?
I would suggest to check the logs from all the medium configured with the Artifactory instance. Like, Reverse-proxy & the embedded-tomcat too. As the issue is specific to large-sized files, correlate the timeout pattern with the timeouts configured from all the entities which would give us a hint towards this issue.
Related
I am using Celery with Flask after working for a good long while, my celery is showing a celery.backends.rpc.BacklogLimitExceeded error.
My config values are below:
CELERY_BROKER_URL = 'amqp://'
CELERY_TRACK_STARTED = True
CELERY_RESULT_BACKEND = 'rpc'
CELERY_RESULT_PERSISTENT = False
Can anyone explain why the error is appearing and how to resolve it?
I have checked the docs here which doesnt provide any resolution for the issue.
Possibly because your process consuming the results is not keeping up with the process that is producing the results? This can result in a large number of unprocessed results building up - this is the "backlog". When the size of the backlog exceeds an arbitrary limit, BacklogLimitExceeded is raised by celery.
You could try adding more consumers to process the results? Or set a shorter value for the result_expires setting?
The discussion on this closed celery issue may help:
Seems like the database backends would be a much better fit for this purpose.
The amqp/RPC result backends needs to send one message per state update, while for the database based backends (redis, sqla, django, mongodb, cache, etc) every new state update will overwrite the old one.
The "amqp" result backend is not recommended at all since it creates one queue per task, which is required to mimic the database based backends where multiple processes can retrieve the result.
The RPC result backend is preferred for RPC-style calls where only the process that initiated the task can retrieve the result.
But if you want persistent multi-consumer result you should store them in a database.
Using rabbitmq as a broker and redis for results is a great combination, but using an SQL database for results works well too.
My original carbon storage-schema config was set to 10s:1w, 60s:1y and was working fine for months. I've recently updated it to 1s:7d, 10s:30d, 60s,1y. I've resized all my whisper files to reflect the new retention schema using the following bit of bash:
collectd_dir="/opt/graphite/storage/whisper/collectd/"
retention="1s:7d 1m:30d 15m:1y"
find $collectd_dir -type f -name '*.wsp' | parallel whisper-resize.py \
--nobackup {} $retention \;
I've confirmed that they've been updated using whisper-info.py with the correct retention and data points. I've also confirmed that the storage-schema is valid using a storage-schema validation script.
The carbon-cache{1..8}, carbon-relay, carbon-aggregator, and collectd services have been stopped before the whisper resizing, then started once the resizing was complete.
However, when checking in on a Grafana dashboard, I'm seeing empty graphs with correct data points (per sec, but no data) on collectd plugin charts; but with the graphs that are providing data, it's showing data and data points every 10s (old retention), instead of 1s.
The /var/log/carbon/console.log is looking good, and the collectd whisper files all have carbon user access, so no permission denied issues when writing.
When running an ngrep on port 2003 on the graphite host, I'm seeing connections to the relay, along with metrics being sent. Those metrics are then getting relayed to a pool of 8 caches to their pickle port.
Has anyone else experienced similar issues, or can possibly help me diagnose the issue further? Have I missed something here?
So it took me a little while to figure this out. It had nothing to do with the local_settings.py file like some of the old responses, but it had to do with the Interval function in the collectd.conf.
A lot of the older responses mentioned that you needed to include 'Interval 1' inside each Plugin container. I think this would have been great due to the control of each metric. However, that would create config errors in my logs, and break the metric. Setting 'Interval 1' at top level of the config resolved my issues.
Even with debug enabled for RemoteConfig, I still managed to get the following:
Error fetching remote config values Optional(Error Domain=com.google.remoteconfig.ErrorDomain Code=8002 "(null)"
UserInfo={error_throttled_end_time_seconds=1483110267.054194})
Here is my debug code:
let debug = FIRRemoteConfigSettings(developerModeEnabled: true)
FIRRemoteConfig.remoteConfig().configSettings = debug!
Shouldn't the above prevent throttling?
How long will the throttle error remain in effect?
I've experienced the same error due to throttling. I was calling FIRRemoteConfig.remoteConfig().fetchWithExpirationDuration with an expiry that was less than 60 seconds.
To immediately get around this issue during testing, use an alternative device. The throttling occurs against a particular device. e.g. move from your simulator to a device.
The intention is not to have a single client flooding the server with fetch requests every second. Make sensible use of the caching it offers out of the box and fetch only when necessary.
When you receive this error, plug the value of error_throttled_end_time_seconds into an epoch converter (like this one at https://www.epochconverter.com) and it will tell you the time when throttling ends. I've tested this myself, and the throttling remains in effect for 1 hour from the first moment you are throttled. So either wait an hour or try some of the other recommendations given here.
UPDATE: Also, if you continue making config requests and receive the throttle error, the expire timeout does not increase (i.e. "you are not further penalized").
The quick and easy hack to get your app running is to delete the application and reinstall it. Firebase identifies your device as new device on reinstalling.
Hope it helps and save your time.
I installed Symfony3 framework-standard-edition. I'm trying to open the home page(app.php prod) and it is loaded 300-400ms.
This is my profiler information:
also I use php7.
Why it is so long?
You can try to optimize Zend OPCache.
Here are some recommended settings
opcache.revalidate_freq
Basically put, how often (in seconds) should the code cache expire and check if your code has changed. 0 means it checks your PHP code every single request (which adds lots of stat syscalls). Set it to 0 in your development environment. Production doesn't matter because of the next setting.
opcache.validate_timestamps
When this is enabled, PHP will check the file timestamp per your opcache.revalidate_freq value.
When it's disabled, opcache.revaliate_freq is ignored and PHP files are NEVER checked for updated code. So, if you modify your code, the changes won't actually run until you restart or reload PHP (you force a reload with kill -SIGUSR2).
Yes, this is a pain in the ass, but you should use it. Why? While you're updating or deploying code, new code files can get mixed with old ones— the results are unknown. It's unsafe as hell
opcache.max_accelerated_files
Controls how many PHP files, at most, can be held in memory at once. It's important that your project has LESS FILES than whatever you set this at. For a codebase at ~6000 files, I use the prime number 8000 for maxacceleratedfiles.
You can run find . -type f -print | grep php | wc -l to quickly calculate the number of files in your codebase.
opcache.memory_consumption
The default is 64MB. You can use the function opcachegetstatus() to tell how much memory opcache is consuming and if you need to increase the amount.
opcache.interned_strings_buffer
A pretty neat setting with like 0 documentation. PHP uses a technique called string interning to improve performance— so, for example, if you have the string "foobar" 1000 times in your code, internally PHP will store 1 immutable variable for this string and just use a pointer to it for the other 999 times you use it. Cool.
This setting takes it to the next level— instead of having a pool of these immutable string for each SINGLE php-fpm process, this setting shares it across ALL of your php-fpm processes. It saves memory and improves performance, especially in big applications.
The value is set in megabytes, so set it to "16" for 16MB. The default is low, 4MB.
opcache.fast_shutdown
Another interesting setting with no useful documentation. "Allows for faster shutdown".
Oh okay. Like that helps me. What this actually does is provide a faster mechanism for calling the destructors in your code at the end of a single request to speed up the response and recycle php workers so they're ready for the next incoming request faster.
Set it to 1 and turn it on.
opcache=1
opcache.memory_consumption=256
opcache.interned_strings_buffer=16
opcache.max_accelerated_files=8000
opcache.validate_timestamps=0
opcache.revalidate_freq=0
opcache.fast_shutdown=1
I hope it will help improve your performances
[EDIT]
You might also want to look at this answer:
Are Doctrine relations affecting application performance?
TheMrbikus, try some optimization with the following elements:
Use APC
Use Bootstrap files
Reference: http://symfony.com/doc/current/performance.html
Use the OPCache PHP7
Use Apache PHP-FPM.
E-mail sending process, and may slow down during the form rendering operations. Create a blank test Controller.
I have read the example of scrapy-redis but still don't quite understand how to use it.
I have run the spider named dmoz and it works well. But when I start another spider named mycrawler_redis it just got nothing.
Besides I'm quite confused about how the request queue is set. I didn't find any piece of code in the example-project which illustrate the request queue setting.
And if the spiders on different machines want to share the same request queue, how can I get it done? It seems that I should firstly make the slave machine connect to the master machine's redis, but I'm not sure which part to put the relative code in,in the spider.py or I just type it in the command line?
I'm quite new to scrapy-redis and any help would be appreciated !
If the example spider is working and your custom one isn't, there must be something that you have done wrong. Update your question with the code, including all relevant parts, so we can see what went wrong.
Besides I'm quite confused about how the request queue is set. I
didn't find any piece of code in the example-project which illustrate
the request queue setting.
As far as your spider is concerned, this is done by appropriate project settings, for example if you want FIFO:
# Enables scheduling storing requests queue in redis.
SCHEDULER = "scrapy_redis.scheduler.Scheduler"
# Don't cleanup redis queues, allows to pause/resume crawls.
SCHEDULER_PERSIST = True
# Schedule requests using a queue (FIFO).
SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.SpiderQueue'
As far as the implementation goes, queuing is done via RedisSpider which you must inherit from your spider. You can find the code for enqueuing requests here: https://github.com/darkrho/scrapy-redis/blob/a295b1854e3c3d1fddcd02ffd89ff30a6bea776f/scrapy_redis/scheduler.py#L73
As for the connection, you don't need to manually connect to the redis machine, you just specify the host and port information in the settings:
REDIS_HOST = 'localhost'
REDIS_PORT = 6379
And the connection is configured in the ċonnection.py: https://github.com/darkrho/scrapy-redis/blob/a295b1854e3c3d1fddcd02ffd89ff30a6bea776f/scrapy_redis/connection.py
The example of usage can be found in several places: https://github.com/darkrho/scrapy-redis/blob/a295b1854e3c3d1fddcd02ffd89ff30a6bea776f/scrapy_redis/pipelines.py#L17