The issue with the Web service returning empty response or "Backend error. 400 Bad Request" has been addressed with the release of Eikon Data APIs library for Python v1.0.1 available on PyPI. This does not mean that with Eikon Data APIs library for Python v1.0.1 one can never receive "Bad request" error or that the service can now provide unlimited data downloads. But one should no longer experience random and frequent empty responses or "Backend error. 400 Bad Request" forcing one to resubmit the same requests in a loop until the data is returned.

@jr123 looks like it returns an empty dict without returning an error, difficult to say without more data. Try re-executing it with either debug set to True, or as a raw_output set to True.

I'm getting the same thing but seems to happen randomly (i.e. running the same code and sometimes it works sometimes it fails). Would be great to understand what it is and why it is happening!

@Zhenya Kovalyov Hello! Same problem with get_data. With debug=True raise Error 408:

This exception means that returned data_dict is empty ('headers' key error is raised)

It reproduced the case :

>>> ek.get_data("PEUP.PA", "ASK", raw_output=True)
{}
>>>

It isn't expected at all and the root cause is on the back-end side.

, @jr123, @oalexandere

Are you still experiencing either the timeout or the empty results issue?

@davidk and @dshi

Just to update you, our development team is looking to implement a fix that would return an error in response to requests similar to the example @davidk provided instead of an empty JSON the Web service returns now. This will make user experience better, as you will see a more meaningful error message compared to what the current KeyError exception provides. We're not however looking to fulfill such enormous requests. If we were to do this, the fulfilment time would be in minutes and the payload would be in Gigabytes. We don't think the bulk download capability is appropriate for an end user terminal product, nor do we think Eikon infrastructure could reliably support this at scale. So, in order to retrieve this data you'll still need to break down the list of RICs into much smaller chunks and use only a small handful of RICs (or perhaps even just one RIC) in each request.

Ok, Thanks @Alex Putkov.

@Alex Putkov.

The problem has apparently gotten worse after the recent DataGrid deployment. Reconstruction below using a simple EPS data request that should generate a payload of under 60 KB:

import eikon as ek

fields = ['TR.PriceTargetMean', 'TR.PriceTargetStdDev', 'TR.PriceTargetNumOfEstimates', 'TR.PriceTargetNumIncEstimates', 'TR.RecMean', 'TR.NumOfRecommendations', 'TR.EPSMean', 'TR.EPSStdDev', 'TR.EPSNumberofEstimates', 'TR.EPSNumIncEstimates', 'TR.EPSPctNumEstRevisingDown(WP=1d)', 'TR.EPSPctNumEstRevisingUp(WP=1d)', 'TR.EPSMean.periodenddate'] 

rics = pd.Index(ek.get_data('0#.SPX', 'TR.RIC')[0]['RIC']).tolist() 

# Returns empty dict
ek.get_data(rics, fields, parameters={'Period':'FY1'}, raw_output=True)

# returns correctly on the first 100 securities, payload is 11,120 bytes

output = ek.get_data(rics[:100], fields, parameters={'Period':'FY1'}, raw_output=True)

I have followed up with support on case [ ref:_00D30602X._5001W1Py1dr:ref ] as well, but thought you and the Data API team should know. These increasing errors are making it unnecessarily difficult to use the Eikon Data API for any but the smallest requests.