Hi @changta, to clarify the limits:

• Call-based limit: Request per second – 5 requests per second
  • What constitutes as request?
    Q: What portion of the above code constitutes a single request?
    A: Any one request to the LSEG servers is considered a request, e.g.:

rd.get_data(
        universe=input_list1[0],  # RIC 'A.N'
        fields=field_list
    )

is only one call, so is

rd.get_data(
        universe=input_list1[0:50],
        fields=field_list
    )

I believe you might possibly hit this limit, because of the 5 requests per second. To make sure that's not the case, please use something like

import time
time.sleep(1)

• Call-based limit: Response volume per minute – 50 MB per minute
  • My resulting output is a txt file with a size of 1.2MB. It takes ~5mins to produce this, I don’t believe this is an issue.

• Datapoints returned per request:
  • I am using get_data
    • No enforced limit for version 1.1.0+
    • Server timeout around 300 seconds.
      Q: What does this mean?
      A: If a request requires LSEG servers to 'work' for more than 300 seconds, it will fail. E.g.: there are calculating services supplied by LSEG APIs (e.g.: Instrument Pricing Analytics) that can take time to run; over 300 seconds is considered 'too much' and we ask that users request calculations for fewer instruments and make repeated requests in order to limit server throughput to ensure server health.

• Daily limits: Requests per day – 10k requests per day
  • Q: What constitutes a request? If I run the above code 10x (with input list each of 10k), does that count as 10 requests or 100,000 requests?
    A: In your code, if the `output` object is 50 items long, then it will send 50 requests, each of "a*b" data items where "a" = number of fields in request and "b" = number of instruments. E.g.: if the first `output` object is 50 items long and `field_list` is 25 items long, then each request will return 25*50 = 1250 data items, and that is only just one request. Same for the second `output` object. So you will send as many requests as there are items in the `output` object.

• Daily limits: Response volume per day – 5GB per day/50 MB per minute
  • Resulting output is 1.2MB – not an issue
  • I believe that, due to the large number of calls, and possibly when debugging too, you might hit this limit... How many times a day do you call this Python code?

Thanks @jonathan.legrand. I see how to display log output for each cell within the notebook; how do I create a log file? I see in the article you noted using the configuration file but I am not familiar with how to make the appropriate modifications. Can you provide detailed steps to enable this so I can produce a log file to share?

Hi @changta ,

The example configuration file can be found here.

You have the option to set initialization parameters of the Refinitiv Data Library in the refinitiv-data.config.json configuration file. This file must be located beside your notebook, in your user folder or in a folder defined by the RD_LIB_CONFIG_PATH environment variable. The RD_LIB_CONFIG_PATH environment variable is the option used by this series of examples. The following code sets this environment variable.

import os
os.environ["RD_LIB_CONFIG_PATH"] = "../../Configuration"

Thank you @raksina.samasiri. @jonathan.legrand is there a way I can send you logs securely? I see the logs include my input, which I would like to remain nonpublic.

Hello @jonathan.legrand - following up to see how I can share logs (securely) for further investigation? Thanks in advance.

Hi @changta ,

You can contact your Refinitiv account representative and send the email of logs to them, then they can forward it to me.