Is there a way to get the request count per session in RavenDB so as to use it for optimization? Like reducing the calls made etc. I know RavenDB limits it to 30 per session. What I would like to know is the count of requests made at any given time. (In code, during run time).
To get the number of requests for a session use session.Advanced.NumberOfRequests.
Related
Background
I have an application that send HTTP request to foreign servers. The application communicating with other services with strict rate limit policy. For example, 5 calls per second. Any call above the allowed rate will get 429 error code.
The application is deployed in the cloud and run by multiple instances. The tasks are coming from shared queue.
The allowed rate limit synced by Redis Rate Limit pattern.
My current implementation
Assuming that the rate limit is 5 per second: I split the time into multiple "window". Each window has maximum rate of 5. Before each call I checking if the counter is less then 5. If yes, fire the request. If no, wait for the next window (after a second).
The problem
In order to sync the application around the Redis, I need to Redis calls: INCR and EXPR. Let's say that each call can take around 250ms to be returned. So we have checking time of ~500ms. Having said that, in some cases you will check for old window because until you will get the answer the current second has been changed. In case that on the next second we will have another 5 quick calls - it will lead to 429 from the server.
Question
As you can see, this pattern not really ensuring that the rate of my application will be up to 5 calls\second.
How do you recommend to do it right?
I am trying to sync oneDrive Files (metadata and permissions) for a domain using MSGraph API using list, children and permission endpoints.
I am using batching for children and permission endpoints, sending 10-20 request urls in single batch requests concurrently for 10 users.
I am getting a lot of 429 errors by doing so. Though, I was also getting 429 errors on making single (non-batched) calls also.
According to the documentation related to throttling, they ask to
1. Reduce the number of operations per request
2. Reduce the frequency of calls.
So, my question is
Does a batch call of 10 get urls, count as 10 different operations and 10 different calls ?
Does a batch call of 10 get urls, count as 10 different operations and
10 different calls ?
Normally, N URLs will be treated as N+1 operations(even more). N operations from the batch URLs and one for the batch URL itself.
Pay attention to the docs:
JSON batching allows you to optimize your application by combining
multiple requests into a single JSON object.
Due to multiple requests have been combined to one request, the server side just need to send back one response too. But the underlying operation for each URL still need to be handle, so the workload on server side is still very high, just may reduce a little.
The answer lies somewhere in between.
Even though the documentation (cannot find the actual page at this moment) says you can combine up to 20 requests, I found out by experimenting that the limit is currently set to 15. So if you reduce the amount off calls in a single batch you should be good to go.
I'm not sure but it might also help to restrict the batches to a single user.
The throttling limit is set to 10000 items per 10 minutes per user resource, see this blog item
I'm testing a web app using jmeter for load test and I getting a hard time on how can I set properly how many threads, ramp-up and loops will I use in order to get a large number of rps. Anyway, I want to check if my server can keep up to 500rps. Does anyone here can help me how can I set it properly. Thanks.
The number of requests per unit of time is called Throughput and mainly depends on two factors:
Number of active threads
Your application response time
The first one is obvious - more threads -> more requests per second. However JMeter will wait for response from the previous thread before starting the next request so application response time matters as well.
So the recommendations are:
Set number of threads in the Thread Group to the number of anticipated users of your system.
Set ramp-up period accordingly to the number of threads so the load will increase (and decrease) gradually, this way you will be able to correlate increasing/decreasing load with the changing response time and throughput
Instead of loops it might be a better idea to set desired test duration using Scheduler section of the Thread Group.
Run your test and observe the actual throughput using i.e. Server Hits Per Second listener or Transactions per second chart of the HTML Reporting Dashboard. If it matches your expectations - you are done, if not - you will need to increase the number of virtual users.
You can use ConcurrencyThreadGroup plugin , Specifically see how to Produce Desired RPS:
Threads pool size can be calculated like RPS * <max response time> / 1000. The more rate desired the more threads you will need. The more response time service have the more threads you will need.
For example, if your service response time may be 2.5sec and target
rps is 1230, you have to have 1230 * 2500 / 1000 = 3075 threads.
Our organisation is currently migrating to Apigee.
I currently have a problem very similar to this one, but due to the fact that I am a Stack Overflow newcomer and have low reputation I couldn't comment on it: Apigee - SpikeArrest behavior
So, in our organisation we have 6 MessageProcessors (MP) and I assume they are working in a strictly round-robin manner.
Please see this config (It is applied to the TARGET ENDPOINT of the ApiProxy):
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<SpikeArrest async="false" continueOnError="false" enabled="true" name="spikearrest-1">
<DisplayName>SpikeArrest-1</DisplayName>
<FaultRules/>
<Properties/>
<Identifier ref="request.header.some-header-name"/>
<MessageWeight ref="request.header.weight"/>
<Rate>3pm</Rate>
</SpikeArrest>
I have a rate of 3pm, which means 1 hit each 20sec, calculated according to ApigeeDoc1.
The problem is that instead of 1 successful hit every 20sec I get 6 successful ones in the range of 20sec and then the SpikeArrest error, meaning it hit once each MP in a round robin manner.
This means I get 6 hit per 20 sec to my api backend instead of the desired 1 hit per 20sec.
Is there any way to sync the spikearrests across the MPs?
ConcurrentRatelimit doesn't seem to help.
SpikeArrest has no ability to be distributed across message processors. It is generally used for stopping large bursts of traffic, not controlling traffic at the levels you are suggesting (3 calls per minute). You generally put it in the Proxy Request Preflow and abort if the traffic is too high.
The closest you can get to 3 per minute using SpikeArrest with your round robin message processors is 1 per minute, which would result in 6 calls per minute. You can only specify SpikeArrests as "n per second" or "n per minute", which does get converted to "1 per 1/n second" or "1 per 1/n minute" as you mentioned above.
Do you really only support one call every 20 seconds on your backend? If you are trying to support one call every 20 seconds per user or app, then I suggest you try to accomplish this using the Quota policy. Quotas can share a counter across all message processors. You could also use quotas with all traffic (instead of per user or per app) by specifying a quota identifier that is a constant. You could allow 3 per minute, but they could all come in at the same time during that minute.
If you are just trying to protect against overtaxing your backend, the ConcurrentRateLimit policy is often used.
The last solution is to implement some custom code.
Update to address further questions:
Restating:
6 message processors handled round robin
want 4 apps to each be allowed 5 calls per second
want the rest of the apps to share 10 calls per second
To get the kind of granularity you are looking for, you'll need to use quotas. Unfortunately you can't set a quota to have a "per second" value on a distributed quota (distributed quota shares the count among message processors rather than having each message processor have its own counter). The best you can do is per minute, which in your case would be 300 calls per minute. Otherwise you can use a non-distributed quota (dividing the quota between the 6 message processors), but the issue you'll have there is that calls that land on some MPs will be rejected while others will be accepted, which can be confusing to your developers.
For distributed quotas you'd set the 300 calls per minute in an API Product (see the docs), and assign that product to your four apps. Then, in your code, if that product is not assigned for the current API call's app, you'd use a quota that is hardcoded to 10 per second (600 per minute) and use a constant identifier rather than the client_id, so that all other traffic uses that quota.
Quotas don't keep you from submitting all your requests nearly simultaneously, and I'm assuming your backend can't handle 1200+ requests all at the same time. You'll need to smooth the traffic using a SpikeArrest policy. You'll want to allow the maximum traffic through the SpikeArrest that your backend can handle. This will help protect against traffic spikes, but you'll probably get some traffic rejected that would normally be allowed by the Quota. The SpikeArrest policy should be checked before the Quota, so that rejected traffic is not counted against the app's quota.
As you can probably see, configuring for situations like yours is more of an art than a science. My suggestion would be to do significant performance/load testing, and tune it until you find the correct values. If you can figure out how to use non-distributed quotas to get acceptable performance and predictability, that will let you work with per second numbers instead of per minute numbers, which will probably make massive spikes less likely.
Good luck!
Unlike Quota limits, the Spike Arrest cannot be synchronized across MP.
But, as you're setting them on a per minute level, you could use Quota Policy instead -- then set it to Distributed and Synchronized and it will coordinate across MP.
Keep in mind there will always be some latency on the synchronization across machines so it will never be a completely precise number.
Using ThreadPoolRuntime, I could get a throughput attiribute that means "The mean number of requests completed per second". It's not what I want. I want to get realtime figure that is not the mean number.
Requests per second is by it's nature an average, so I'm not too sure what you mean by a realtime figure - do you want the number of requests completed in the last second?
The ApplicationRuntimes/[appname]/WorkManagerRuntimes/default/CompletedRequests gives the total number of requests completed for one application, you can use this to calculate an RPS figure over whatever timeframe you want.
Unless this is a custom work manager's thread pool, the number you're going to get back isn't going to be terribly meaningful. And even in the case of a custom thread pool assigned to your particular application component (EJB, WAR file, etc) then the number still isn't likely to mean what you're looking for.
The thread pool is used to perform all work for that component (or in the case of the default thread pool, all work for the server, both internal and client-driven. This means that requests of wildly different 'cost' in terms of CPU and execution time go through the same pool.
What is the problem that you're trying to solve? Is it an understanding of how many requests per second are occurring for particular application components? You might want to look at WLDF as an alternative source for this kind of data, although in either case you'll need to post-process information to get something useful.