I have .net core MVC API implementation. In my controller I try to query for 800 records from DB. In result my response body size is abound 6MB. In that case response time is over 6s. My service is in AWS cloud.
I made several tests to make diagnostic of service. In all these scenarios I still ready 800 records from DB. Here is list of my experiments:
Return only 10 records - my response time were under 800ms always and size of response body 20kB.
Return only 100 records - my response time were over 800ms but not timeouts and size of response body 145kB
Try to use my custom json serialization in controller await JsonSerializer.SerializeAsync(HttpContext.Response.Body, limitedResult); - a bit better result but like 10% only
Return only 850 records - my response time were over 6s but and size of response body 6MB
In service I don't have problem with memory or with restart service.
Looks like for Kastrel the problem is to serve large response data.
My objections are connected with buffers I/O which for large response will use disk what can affect performance of AWS docker image.
Question is how to optimize Kastrel to serve large response size?
UPDATE:
I enabled zip compression on server side. My files are compressed quite good because of json format. But result is exact the SAME. Network bandwidth is not a problem. So looks like between my controller and compression is bottleneck. Any suggestion how to configure .net core service to handle large response (>1 MB)?
Since you are already sure that network is not an issue - this mostly points to time being spent in serialization. You can try running the application on local machine and using a profiler such as PerfView to see where the time is spent most for big json.
Related
I have an api query that runs during a post request on one of my views to populate my dashboard page. I know the response size is ~35mb (greater than the 32mb limits set by cloud run). I was wondering how I could by pass this.
My configuration is set via a hypercorn server and serving my django web app as an asgi app. I have 2 minimum instances, 1gb ram, 2 cpus per instance. I have run this docker container locally and can't bypass the amount of data required and also do not want to store the data due to costs. This seems to be the cheapest route. Any pointers or ideas would be helpful. I understand that I can bypass this via http2 end to end solution but I am unable to do so currently. I haven't created any additional hypecorn configurations. Any help appreciated!
The Cloud Run HTTP response limit is 32 MB and cannot be increased.
One suggestion is to compress the response data. Django has compression libraries for Python or just use zlib.
import gzip
data = b"Lots of content to compress"
cdata = gzip.compress(s_in)
# return compressed data in response
Cloud Run supports HTTP/1.1 server side streaming, which has unlimited response size. All you need to do is use chunked transfer encoding.
https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Transfer-Encoding
I am trying to sync 1 million record to ES, and I am doing it using bulk API in batch of 2k.
But after inserting around 25k-32k, elastic search is giving following exception.
Unable to parse response body: org.elasticsearch.ElasticsearchStatusException
ElasticsearchStatusException[Unable to parse response body]; nested: ResponseException[method [POST], host [**********], URI [/_bulk?timeout=1m], status line [HTTP/1.1 403 Request throttled due to too many requests]
403 Request throttled due to too many requests /_bulk]; nested: ResponseException[method [POST], host [************], URI [/_bulk?timeout=1m], status line [HTTP/1.1 403 Request throttled due to too many requests]
403 Request throttled due to too many requests /_bulk];
I am using aws elastic search.
I think, I need to implement wait strategy to handle it, something like keep checking es status and call bulk insert if status all of ES okay.
But not sure how to implement it? Does ES offers anything pre-build for it?
Or Anything better way to handle this?
Thanks in advance.
Update:
I am using AWS elastic search version 6.8
Thanks #dravit for including my previous SO answer in the comment, after following the comments it seems OP wants to improve the performance of bulk indexing and want exponential backoff, which i don't think Elasticsearch provides out of the box.
I see that you are putting a pause of 1 second after every second which will not work in all the cases, and if you have large number of batches and documents to be indexed, for sure it will take a lot of time. There are few more suggestions from my side to improve the performance.
Follow my tips to improve the reindex speed in Elasticsearch and see what all things listed here is applicable and doing them improves speed by what factor.
Find a batching strategy which best suits to your environment, I am not sure but this article from #spinscale who is the developer of java high level rest client might help or you can ask a question on https://discuss.elastic.co/, I remembered he shared a very good batching strategy in one of his webinar but couldn't find the link of it.
Notice various ES metrics apart from bulk threadpool and queue size, and see if your ES still has capacity can you increase the queue size and increase the rate by which you can send requests to ES.
Check the error handling guide here
If you receive persistent 403 Request throttled due to too many requests or 429 Too Many Requests errors, consider scaling vertically. Amazon Elasticsearch Service throttles requests if the payload would cause memory usage to exceed the maximum size of the Java heap.
Scale your application vertically or increase delay between requests.
I have developed a stored process to receive an XML file as a response to a web service call and deployed it as a web service - something similar to the example here.
It is successful and working fine in receiving an XML file which is of ~100KB but failing to receive similar file which is about 3MB. The other system sending the response seems to throw the below error
HTTP Response Code 413 for 'https://mystoredprocessURL'. I understand that this is related payload too large in size.
Could you suggest me how to configure the length of payload size that has to be received so that the stored process can receive a larger file. Tried to research but could not find anything relevant.
my first idea. I hope this helps, but that's just a hint:
SAS SMC-->plug-ins tab-->Application manager-->configuration manager-->
SAS Application infrastucture-->BI web services for java 9.4-->WebServiceMaker
-->Settings tab-->Attachemnt optimized threshold block.
Maybe the default size is 2048.
We are working on setting up an API Management portal for one of our Web API. We are using eventhubs for logging the events and we are transferring the event messages to Azure Blob storage using Azure functions.
We would like to know how can we find the Time taken by API Management portal for providing the response for a message (we are capturing the time taken at the back end api layer but not from the API Management layer).
Regards,
John
The simpler solution is to enable Azure Monitor Diagnostic Logs for the Apimanagement service. You will get raw logs for each request including
durationMs - interval between receiving request line and headers from a client and writing last chunk of response body to a client. All writes and reads include network latency.
BackendTime - time spent waiting on backend response
ClientTime - time spent with client for request and response
CacheTime - time spent on fetching from cache
You can also refer this video.
Not the correct way of doing this, but still get an idea of how much time each request is taking. We can actually use the context variable to set the start time in the inbound policy node and then calculate the end time in the outbound node.
I am working on a mobile app that will broadcast a push message to hundreds of thousands of devices at a time. When each user opens their app from the push message, the app will hit our API for data. The API resource will be identical for each user of this push.
Now let's assume that all 500,000 users open their app at the same time. API Gateway will get 500,000 identical calls.
Because all 500,000 nearly concurrent requests are asking for the same data, I want to cache it. But keep in mind that it takes about 2 seconds to compute the requested value.
What I want to happen
I want API Gateway to see that the data is not in the cache, let the first call through to my backend service while the other requests are held in queue, populate the cache from the first call, and then respond to the other 499,999 requests using the cached data.
What is (seems to be) happening
API Gateway, seeing that there is no cached value, is sending every one of the 500,000 requests to the backend service! So I will be recomputing the value with some complex db query way more times than resources will allow. This happens because the last call comes into API Gateway before the first call has populated the cache.
Is there any way I can get this behavior?
I know that based on my example that perhaps I could prime the cache by invoking the API call myself just before broadcasting the bulk push job, but the actual use-case is slightly more complicated than my simplified example. But rest assured, solving this simplified use-case will solve what I am trying to do.
If you anticipate that kind of burst concurrency, priming the cache yourself is certainly the best option. Have you also considered adding throttling to the stage/method to protect your backend from a large surge in traffic? Clients could be instructed to retry on throttles and they would eventually get a response.
I'll bring your feedback and proposed solution to the team and put it on our backlog.