Plumber APIs timing - big discrepancies between R Studio Connect and local runs

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1

Hello,

I have a plumber API that takes unexpectedly long to run and I have trouble understanding what the reason for that could be. Let me give you some context:

that API doesn't do any fancy calculations, locally runs in a couple of seconds for json files of 4-5 MB (including importing data). Our expectation was that it would run similarly fast when wrapped in a plumber API, but to our surprise it's quite the contrary.

R Studio Connect API - curl timing results:

time_namelookup:  0.004264
time_connect:         0.004503
time_appconnect:   0.000000
time_pretransfer:    0.004548
time_redirect:         0.000000
time_starttransfer:  0.005525

----------

time_total:  20.593546

Local API - timing of the most important code pieces

Starting server to listen on port 7992

Running the swagger UI at http://127.0.0.1:7992/__swagger__/

Working on it...

Time difference of 3.83 secs <- loading json

Raw file ready to rock...

887

Time difference of 0.81 secs <- data prep

Time difference of 0.10 secs <- variables calculation

Other than that there are no other operations that take time. As you can see the same code runs for over 20 seconds on RSC vs. nearly 5 seconds locally. How come there's such a huge difference between those timings between RSC and when running locally? Is there any way to speed that up?

Thank you!

2

Interesting issue. I wonder if it is specific to your api or if it is a more general problem.

I don’t have any specific advice regarding plumber, but it would be helpful to know

  1. Do other plumber apis have a similar performance issue for you in local vs RSC?
  2. Do you have any similar issues with Shiny apps run locally vs RSC?

I had similar issues for a shiny app once and it turned out to be an issue on my network on how long a specific JavaScript library was taking to load which we diagnosed using the chrome developer tools

Good luck!

3

Not really actually. It's a standard way to retrieve a json request in an API and all our APIs start with retrieving the body that way. I haven't noticed similar behaviour elsewhere, and especially not so apparent.

No, there's no difference.

With regards to your last point - wouldn't the detailed breakdown of timings prove that file transferring etc. takes a minor share of the entire processing time, thus it can't be any issue related to the network or so?

1 Like
4

If there is no issue between any other plumber apis on RSC and running Shiny apps on RSC, that to me suggests it is specific to this one plumbr api - is that a reasonable assumption?

If so, can you profile the underlying R code for the api running directly on RSC vs local to see if there are any difference? Profviz is really useful for this type of analysis: https://rstudio.github.io/profvis/

5

It's a reasonable and logical assumption, however, on the other hand there's nothing in the results of the API that supports that statement. I've also timed that using provis and doing simple time differences (as presented above) and none of the proves there could be such issue. That's why I'm a little bit clueless what the reason for that could be in this case.

6

Sorry to hear that - I am also at a loss at what might be happening. I would suggest reaching out to the rstudio support directly to help diagnose the problem.

7

@konradino I'd like to take a look into this. Are you able to provide a reprex of the problem?

8

@Blair09M - I created a snapshot of the entire project with packrat. What would be the best way of sharing that?

9

@konradino based on the files you shared with me, this is a network issue. Given that the body of the response contains a large (>75 MB) JSON file, network latency is what's causing the discrepancy between a local performance and RStudio Connect performance.

Consider the following script that uses CURL to make a request to both a local endpoint and a remote endpoint hosted on RStudio Connect:

#!/bin/bash
echo 'url,time_namelookup,time_connect,time_appconnect,time_pretransfer,time_redirect,time_starttransfer,time_total,size_upload,speed_upload,size_download,speed_download' > timings.csv

for url in '<local-endpoint>' '<rstudio-connect-endpoint>'
do
        for i in {1..10}
        do
                curl -d "@test.json" -w "%{url_effective},%{time_namelookup},%{time_connect},%{time_appconnect},%{time_pretransfer},%{time_redirect},%{time_starttransfer},%{time_total},%{size_upload},%{speed_upload},%{size_download},%{speed_download}\\n" -o /dev/null -X POST ${url} >> timings.csv
        done
done

This script creates a .csv file containing timing details about each request. Analyzing the results reveals that nearly all of the time discrepancy between the two APIs can be accounted for with download and upload speed.

url size_download speed_download time_total download_time
Local Endpoint 82540139 27285996 3.025613 3.025000
Local Endpoint 82540139 27467600 3.005494 3.005000
Local Endpoint 82540139 27349283 3.018092 3.018000
Local Endpoint 82540139 30401524 2.715642 2.715000
Local Endpoint 82540139 26921115 3.066180 3.066000
Local Endpoint 82540139 27458462 3.006494 3.006000
Local Endpoint 82540139 27313083 3.022872 3.022000
Local Endpoint 82540139 27331171 3.020641 3.020000
Local Endpoint 82540139 28141881 2.933028 2.933000
Local Endpoint 82540139 27550113 2.996060 2.996000
RSC Endpoint 82540139 6719868 12.283238 12.283000
RSC Endpoint 82540139 6846963 12.055490 12.055000
RSC Endpoint 82540139 9036581 9.134867 9.134001
RSC Endpoint 82540139 8575598 9.625306 9.625001
RSC Endpoint 82540139 7688881 10.735571 10.735000
RSC Endpoint 82540139 9607745 8.591900 8.591000
RSC Endpoint 82540139 9847308 8.382065 8.382000
RSC Endpoint 82540139 9207958 8.964228 8.964000
RSC Endpoint 82540139 9427771 8.755820 8.755000
RSC Endpoint 82540139 9588770 8.608930 8.608001

In order to improve performance, these are a couple of options that come to mind:

  1. Use a different serializer
    • Depending on the downstream consumer of this API, you could serialize into something more compressed than JSON in order to cut down on the size of the response.
  2. Create a paginated response
    • Instead of returning everything to the client at once, allow the endpoint to only return a few records at a time, and provide a mechanism for requesting subsequent records.
2 Likes
10

Thank you very much for your detailed answer and taking the time to investigate! I have completely missed the fact that from only 61KB request a 70+ MB response is generated - that's obviously a lot of redundancy that we can cut on in order to speed things up. I checked the results on a real example and the factor is not that large but a real output is often 5-10MB in the end or bigger than that.

I believe the original design was to preserve as much information as possible about what's happening within the API which includes saving all information from the request and adding further to it in the response. I guess it's a good idea for debugging but adds a lot of load to processing the entire request? In general, should I try to keep the response as light as possible?

Apart from that, was there anything else I could do within the API to speed it up? I had an impression that it's quite streamlined already.

A couple more questions:

Use a different serializer

  • Depending on the downstream consumer of this API, you could serialize into something more compressed than JSON in order to cut down on the size of the response.

What would be a better choice in this case?

Create a paginated response

  • Instead of returning everything to the client at once, allow the endpoint to only return a few records at a time, and provide a mechanism for requesting subsequent records.

Not quote sure what you mean. Could you refer me to some resources?

11

The API itself seems streamlined and is responsive, so I don't think there's much else that can be done to improve performance there.

In regards to a different serializer, this really depends on the downstream consumers of the API. If these downstream consumers are other R processes, you could serialize an .rds file instead of raw JSON. Otherwise, you might look into compressing the output using something like gzip. Either of these approaches would require you to create your own serializer. The following example illustrates how you might do this using gzip:

#* @post /predict
#* @serializer contentType list(type="gzip")
function(req, res) {

  # Function logic here

  json_file <- tempfile()
  gz_file <- paste0(json_file, ".gz")
  jsonlite::write_json(df_final, json_file)
  R.utils::gzip(json_file, gz_file)
  readBin(gz_file, "raw", n = file.info(gz_file)$size)
}

Using this type of serialization results in the following:

url size_download speed_download time_total download_time
Local Endpoint 321289 101545 3.164260 3.164006
Local Endpoint 321289 100090 3.210613 3.210001
Local Endpoint 321289 100027 3.212998 3.212023
Local Endpoint 321289 95281 3.372121 3.372015
Local Endpoint 321289 98253 3.270463 3.270017
Local Endpoint 321289 100465 3.198819 3.198019
Local Endpoint 321289 100152 3.208099 3.208014
Local Endpoint 321289 98223 3.271237 3.271016
Local Endpoint 321289 103076 3.117654 3.117011
Local Endpoint 321289 102419 3.137675 3.137006
RSC Endpoint 321289 49307 6.516929 6.516093
RSC Endpoint 321289 67441 4.764885 4.764001
RSC Endpoint 321289 69243 4.640754 4.640021
RSC Endpoint 321289 67257 4.777718 4.777034
RSC Endpoint 321289 71604 4.487497 4.487026
RSC Endpoint 321289 74944 4.287704 4.287054
RSC Endpoint 321289 59531 5.397581 5.397003
RSC Endpoint 321289 67201 4.781622 4.781015
RSC Endpoint 321289 66782 4.811138 4.811012
RSC Endpoint 321289 69830 4.601685 4.601017

Note that in this case the client must be able to handle the .gz file, since it is no longer just vanilla JSON.

Pagination isn't something I've implemented in Plumber, but it should be possible. The idea is that you only return the first n responses to a request, and include some reference in the response that can be used to collect the next n responses. This is most useful if you anticipate that not every request requires the full response immediately. If every request requires the full response, then this approach isn't beneficial.

1 Like
12

@Blair09M - would it also be possible to increase the speed of upload by using custom serializers?

The reason I'm asking is the following: my total API timing is equal to 12 seconds, but when I time its individual elements it sums up to 5 seconds. The output (response) is very small (54KB) and the code inside runs really fast. So most time in the API itself really is consumed in the beginning:

function(req, res) {
payload_json <- req$postBody
payload_ls <- jsonlite::fromJSON(payload_json)

converting the request into something R can digest really blocks my API, but I'm not sure if I can speed it up here? Are there options also for passing data in a compressed way?

I tried the following but it results in an error:

payload_json <- req$postBody
payload_ls <- jsonlite::fromJSON(R.utils::gunzip(payload_json))

simpleError in decompressFile.default(filename = filename, ..., ext = ext, FUN = FUN): No such file:

If that's possible it would also decrease the overall time - the request json had 7MB and only it's upload took the missing 7 seconds from the overall processing time. If we also could nail that it would be awesome!

13

Also lastly, is it possible to dynamically select one serializer or the other? A compressed or a non-compressed json object depending on the request?

14

Also, I ended up experimenting with that gzip solution you posted but something doesn't seem to be working for me. The response of a file.json.gz that I'm getting is:

{"code":30,"error":"We couldn't log you in with the provided credentials. Please ask your RStudio Connect administrator for assistance.","payload":null}

the only difference from the previous json call (except for a different endpoint) is that I add:

-H 'Content-Type: gzip'

in the curl

15

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