I have a python program which query youtube to get the video details. I use the version-3 api. I have multiple processes m and a python pool of 10 processes in each python process.
songs_pool = Pool()
songs_pool =Pool(processes=10)
return_pool = songs_pool.map(getVideo,songs_list)
I get some client errors when the value of m is increased to more than 2 and the pool is increased to >5. I get forbidden errors. When I check the number of requests in the google analytics,it shows that the number of requests are 250 per sec. But according to the documentation the limit is 3000 requests per sec. I dont understand why am I getting the client errors. Can you tell me if there is a way to not get this errors and run the program quicker.
if m = 2 and process = 10 , i get no errors but it takes so much time to complete.
But if I increase them , then I get client errors which are ~ 5% of the total requests.
The per-user-limit is 3000 requests per second from a single IP address, and as soon as you go above that in a given second you'll start getting the forbidden errors. The analytics you see in the developers console will only report your average number of requests over a 5 minute period; therefore, if you had zero requests for 4 minutes, then started running your routine, the console may show only 250 requests per second (as an average) but your app likely is overrunning the limit in a given period of time or two.
It seems that you're handling it in the best way possible if speed is your concern; you'll want to run it fast enough to get a very small number of errors (so you know you're staying up there at your limit). Another option, though, might be to look into using etags; if you find yourself requesting info on the same videos a lot, you can let etags tell you whether or not any info has changed (and if the API responds that nothing has changed, it doesn't count against either your quota or your reqests/sec.)
Related
My moodle site is hosted on AWS Server of 8 GB RAM, i carried out various tests on the server using JMeter (NFT), I have tested from 15 to almost 1000 users, however I am still not getting any error(less than 0.3%). I am using the scripts provided by moodle itself. What could be the issue? Is there any issue with the script? I have attached a screenshot with this which shows the reports of 1000 users test for referenceenter image description here
If you're happy with the amount of errors and response times (maximum response time is more than 1 hour which is kind of too much for me) you can stop here and report the results.
However I doubt that a real user will be happy to wait 1 hour to see the login page so I would rather define some realistic pass/fail criteria, for example would expect the response time to be not more than 5 seconds. In this case you will have > 60% of failures if this is what you're trying to achieve.
You can consider using the following test elements
Set reasonable response timeouts using HTTP Request Defaults:
so if any request will last longer than 5 seconds it will be terminated as failed
Or use Duration Assertion
in this case JMeter will wait for the response and mark it as failed if the response time exceeds the defined duration
I have hosted my node app in Cloud Run and all of my requests served within 300 - 600ms time. But one endpoint that gets data from a 3rd party service so that request takes 1.2s - 2.5s to complete the request.
My doubts regarding this are
Is 1.2s - 2.5s requests suitable for cloud run? Or is there any rule that the requests should be completed within xx ms?
Also see the screenshot, I got a message along with the request in logs "The request caused a new container instance to be started and may thus take longer and use more CPU than a typical request"
What caused a new container instance to be started?
Is there any alternative or work around to handle long requests?
Any advice / suggestions would be greatly appreciated.
Thanks in advance.
I don't think that will be an issue unless you're worried about the cost of the CPU/memory time, which honestly should only matter if you're getting 10k+ requests/day. So, probably doesn't matter and cloud run can handle that just fine (my own app does requests longer than that with no problem)
It's possible that your service was "scaled to zero" meaning that there were no containers left running to serve requests. In that case, it would be necessary to start up a new instance and wait for whatever initializing/startup costs are associated with that process. It's also possible that it was auto-scaled due to all other instances being at their request limits. Make sure that your setting for max concurrent requests per instance is set greater than one - Node/Express can handle multiple requests at once. Plus, you'll only get charged for the total time spend, not per request:
In situations where you get very long (30 seconds, minutes+) operations, it may be a good idea to switch to some different data transfer method. You could use polling, where the client makes a request every 5 seconds and checks if the response is ready. You could also switch to some kind of push-based system like WebSockets, but Cloud Run doesn't have support for that.
TL;DR longer requests (~10-30 seconds) should be fine unless you're worried about the cost of the increased compute time they may occur at scale.
I'm performing batches of 50 notifications requests to facebook graph api using POSTMAN and cURL according to the docs: batch-api-docs
Entire batch completion takes from 7 to 10 seconds with 46 success and 4 errors which is very slow.
Single success request(without batch I mean) takes like 1 second so I expected batch will take similar time but it's not. Does anybody know the reason?
A batch request only reduces the number of HTTP requests you are making. It does not affect anything else that is going on in the background.
Sending a notification to a user and getting a success/error response will simply take a certain amount of time. And multiplying that amount by 1 or by 50 is just going to result in different numbers.
I have been trying to use JMeter to test my server. I have a cloudsearch endpoint on AWS. I have to test if it can scale upto 25000 requests per second without failing. I have tried JMeter with a constant throughput timer with throughput = 1500000 per second and running 1000 threads. I ran it for 10 mins. But When I review the aggregate report it shows an average of only 25 requests per second. How do i get an average of around 25000 requests per second?
Constant Throughput Timer can only pause the threads to reach specified "Target Throughput" value so make sure you provide enough virtual users (threads) to generate desired "requests per minute" value.
You don't have enough Threads to achieve such Requests per second!!!
To get an average (~25000) requests per second, you have to increase the Number of threads.
Remember, The number of threads will impact results if your server faces slowdowns. If so and you don't have enough threads then you will not be injecting the expected load and end up with fewer transactions performed.
You need to increase the number of concurrent users to be at least 25000 (it assumes 1 second response time, if you have 2 seconds response time - you will need 50000)
JMeter default configuration is not suitable for high loads, it is good for tests development and debugging, however when it comes to the real load you need to consider some constraints, i.e.:
Run JMeter test in non-GUI mode
Increase JVM Heap Size and tune other parameters
Disable all listeners during the test
If above tips don't help follow other recommendations from 9 Easy Solutions for a JMeter Load Test “Out of Memory” Failure article or consider Distributed Testing
I am using new relic to get some info on how long response times are taking. I have also been doing load tests using blitz. I can see on new relic that for a lot of the api endpoints it is taking around 300 ms average (which I am totally happy with these are geo spatial queries btw). The only issue is the maximum is 55k ms and some users are complaining of certain things taking a while to load.
How can I make these endpoints more reliable that they will take 300 ms more often than 55k ms?
edit:
The main question is why do these responses sometimes take 55k ms? Is this user connection speed or the code?