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We’re in development of an API retrieving the latest values from timeseries from CDF using the C# SDK. Initially we set the code to look 200 days back (‘200d-ago’). Now, this stopped working all of a sudden this week. My first reaction was to go event further back (‘365d-ago’), but what works is using an even shorter timespan (‘30d-ago’).So question is - are we limited by a quota or have there been changes limiting this functionality lately?
I'm working on identifying the falling and rising edges of the VAL_23-KA-9101-M01:HSI.StatusMotorOn signal. For that and shifting the TS and rest the values, if I get -1 is a falling edge and for 1 it is a rising edge. For the first row it works fine: But I’m also getting a lot of values different than 1 or -1. Especially for more recent years. I guess that the problem is at the aggregating step, since I downloaded the data at a 1m frequency I get not int values for some records. Now, I was trying to download this TS at a 1s frequency, but I get a different number of records depending on the year. E.G. for 2014 I get 34 records for a 10 days time windows with a granularity 1m. If I do the same but for 2020 I get 1411, much more than for 2014, but still a tenth of the expected count. If I change the granularity to 1s, I still get the same number of records, but with more precision in the time stamp: Considering this, is there a recommended granularity to download the data and min
Please add better documentation around the behavior of status code 422 for Create Time Series. Specifically, I’d like these questions answered:When multiple external ids are given and some are duplicate and some are not, will the time series that are not duplicates be inserted and will these created ids be returned in the response? Does this function similar to datapoints where create is actually create or update and the duplicates are updated with the data provided? If not, what is the recommended pattern for create or update of time series entities through the API?I am aware of advice here around “EAFP vs LBYL”, but I am in a case where 98% of the time the time series will not exist and I’d like to optimize for this case. Thanks!
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