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When I run the query below, I am getting an error.  If I increase the end time by 1 second I can get a result, but it gives me 2 days and I only want 1.  All my data is consistently daily at midnight, UTC.  Any ideas how to get past this?SELECT  dp.id,  dp.externalId AS `key`,  dp.timestamp,  dp.valueFROM  `_cdf`.`datapoints` dpWHERE  dp.externalId IN (    'f18ae31cbfe544cb7bca08da91e5245a-SuFMiCo0f7c8eca4ce444208853aedd00ded4fb',    '730619216b764ba77bc708da91e5245a-SuFMiCo6bb90df0efbc461d863faedd00df009b'  )  AND dp.timestamp >= TO_TIMESTAMP('2022-09-01T00:00:00Z')  AND dp.timestamp < TO_TIMESTAMP('2022-09-02T00:00:00Z') Gives me a result, but not what I want:  cc @Torgrim Aas  @Sunil Krishnamoorthy 

I have been looking into many work-orders looking for failures or anomalies, but at this point I’m not sure to take the most of the shared information, as example I’ll take the two following events: The work order 6516382362467974 has the following details available:  Description: Exterior leakage - liquid for the main process Shutdown: Revision (full shutdown) Planned Start: 2014-09-11 I also plotted some of the signals from near the planned start, as shown in the below image:  I have a few questions about the first work order:   It seems that the compressor turned off about two weeks before the planned start time (As the green dotted line shows)   is it safe to assume that is discrepancy is because the two-week delay in sharing the data?  Is there a set of signals that I can look into for this work-order, I’m assuming that since there is a leakage, the time series related to lube oil could be affected, or maybe the pressure of the compressor.   The second work order is 54865032

In the TNT application at Statnett we are seeking to collect line and point geospatial features through the FeatureAPI. We have a dataset that has been populated with data about powerlines and substations, and we want to query the client for all of the features in each of these seperately. As there is no list() method in the FeatureAPI, and the search method is limited to 1000 results, we try to do this using the searchStream() method. Our query looks like this:client.geospatial.feature.searchStream('ACLineSegment', { limit: 10000, output: { jsonStreamFormat: 'NEW_LINE_DELIMITED' as const } }); This seems to work fine for the ‘ACLineSegment’ features but when i replace it with ‘Substation’ the call returns an empty string. I’ve tested with the regular search() and this returns features so I would expect the searchStream method to find these as well. Even stranger is that i quickly tested this functionality before the weekend and it seemed to wor

I get the following warning every time I create a CogniteClient: ResourceWarning: unclosed <ssl.SSLSocket fd=5, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=6, laddr=('10.xx.xx.xx', 49572), raddr=('40.xx.xx.xx', 443)> Is this caused by the connection handling in Cognite SDK? I’m using the following Python modules:cognite-sdk-4.11.0cognite-sdk-core-2.56.1

Hi there, I’m new to Cognite in general, but one task that my team is looking into is utilizing Cognite’s SVG files in our LCI and Plant Management applications and I have been looking into it somewhat.Not knowing all the details of the SVG rendition capabilities, I do see that our customer ‘Interactive P&IDs’ dataset in CDF has SVGs renditions without the comments that are in the PDF files (where applicable).Would Cognite be able to include the comments - now or in the future?The reason this is relevant for us is that a number of our doc profiles only have PDF files. Typically old documents on old installations.We’re missing native files (DGN, DWG), thus contractors are updating these drawings using comment features such as clouds, text, lines, etc in Adobe. That information seems to disappear in SVG renditions, but it’s quite critical information, especially in Plant Management systems. Thanks for listening to me!Best regards,Erik

I have found a problem reading timeseries from Cognite with Power BI. The first bit of Power Query has a filter to limit the timeseries retrieved to just the ones I want. The second one is the same, but does not have the filter. What I have found is that adding the filter causes duplicate rows in the table. Once the timeseries rows are duplicated, the timeseries aggregation values are also duplicated. I know that the first query is not the most performant option, but it should still have the correct answer.let Source = Cognite.Contents( #"cogniteContentsParameters", #"cdfEnvironment"), Timeseries_table = Source{[Name="Timeseries",Signature="table"]}[Data], #"Filtered Rows" = Table.SelectRows(Timeseries_table, each ([IsString] = false)), #"Removed Columns" = Table.RemoveColumns(#"Filtered Rows",{"MetaData", "SecurityCategories", "IsStep", "DataSetId", "CreatedTime", "LastUpdatedTime", "Asset", "Latest", "DataPoints", "StringPoints", "Aggregate"}), #"Filtered Rows1" = Tabl

Hi! After switching from experimental to cognite-sdk 3.9.0 I struggle with deployment. 1: Using handleIf i try to deploy using the handle, then the requirements are not handled. The function deploys, but failing at the imports. How should requiremenents be used when deploying like this? Imports are inside handle():def handle(client, data, secrets): from googleapiclient.discovery import build from googleapiclient.errors import HttpErrorCreating function like this:cognite_client.functions.create( name=function_name, external_id=function_ext_id, function_handle=handle)2 - Using a folder.Then I get this error: [WinError 267] The directory name is invalid: 'C:\\Users\\hknap\\AppData\\Local\\Temp\\tmptzkywbdl\\function.zip'If I debug, the folder “tmptzkywbdl” is created, so there should be no permission issues.  3- Manually uploading the zip fileThis works (including the requirements.txt), thanks for adding this possibility. It is just a little extra hassle ;)  would prefer if

Hi!Almost three years ago we made a feature request regarding search. Specifically we want to:Have support for paging. Do grouping on metadata-fields. Define which fields yields relevance to the search-results. Inclusive search-terms using AND instead of OR.I do understand that satisfying the search-requirements of all your users is a daunting task, but perhaps it would be easier to let us do the job instead? I believe it would be very beneficial if we were able to access ElasticSearch directly in some manner.And please keep in mind that the data itself defines relevancy. No two customers are alike and therefore a good generic solution likely doesn’t exist. @Knut Vidvei @Andreea Pastinaru 

In many cases in diagrams including P&ID, there are shortcuts to represent tags, for instance:F2014A/B/C for F2014A , F2014B and F2014C.The contextualization engine in some cases will pick one of the 3 or none at all.Is there a way for the engine to detect trailing slashes and match to multiple tags?See example of patterns below:Example of patterns 

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

I am listing time series for a given asset, and get a lot of results. I need to filter based on the time series external ID, but the only option is by using the “external_id_prefix” argument to the list function. So I build up the prefix left to right. Somewhere in the external id is a parameter that I do not care about, and after comes a new parameter that I want a particular value of. Concrete example of external IDs:IAA_Miros_Weather_Data_WIA_008IAA_Miros_Weather_Data_WIB_008IAA_Miros_Weather_Data_WIC_008IAA_Miros_Weather_Data_WID_008IAA_Miros_Weather_Data_WIE_008I am interested in only getting time series with external IDs that contain “_WI” and that end with “008”. Is there a way to list time series with a wildcard? Something like this:client.time_series.list( asset_ids=[my_asset_id], limit=None, external_id_prefix="IAA_Miros_Weather_Data_WI*_008", partitions=4)I could of course obtain the relevant time series by filtering after the fact. Something like thisresult = cl

Hi Team I am trying to deploy the code into new env facing issue because of black --check.

HiI have retrieved timeseries from an asset in PowerBi. That asset is linked through a relationship column to a group of assets. However the asset and the group it belongs to are connected trough 4 steps:groupwanted <- belongsTo - subgroup<- belongsTo - type of group <- connectsTo - group of a few timeseries assets<- belongsTo - timeseries asset Im wondering how to link the relationships in power bi, and how to orient to the right place. 

Hi Team I am trying to deploy ML Model into the AIR but Creating Env Variable Step is showing the error. Also attached image consisting issue details. Regards.

On serveral occations we have encountered limitations in metadata key length, most frequently when flattening JSON-formatted strings from our event stream data source systems. When the source system presents nested structure of as many as 4 levels we frequently encounter metadata keys that require more than 128 bytes. Up to now we have “solved” the issue by abbreviating the metadatakeys at the price of higher maintenance cost of the code and more importantly, that end-users get the perception that we have transformed the data or even dont understand what it represents. We now consider moving towards a solution where we simply put the entire JSON-formatted string into one single metadata value field, and leave to front-end teams and end users to flatten the structure. We have a similar issue with max number of metadata keys for timesseries (16). Question 1) Could you please suggest other, better options for handling these metadata limitations? Question 2) Will templates come to the resc

Hi everyone, I tried to upload my own dataset(time-series data) into CDF, but I always get error: Request with id 87b235ea-78dd-963a-b343-ff58a0ee084d to https://westeurope-1.cognitedata.com/api/v1/projects/learn/timeseries/data failed with status 400: Timestamp is too high, must be at most 2556143999999 which is 31-12-2050 23:59:59 GMT.I followed with this link:https://learn.cognite.com/path/cognite-data-fusion-fundamentals/working-with-cdf-integrate. I created an assest, 3 time-series. What I want to do is to upload my data to the 3 time-series, and show the variation of my data in CDF. I change the time_stamp of my data into the format of 2022-02-10T17:00:00.000Z, I am not sure if this is necessary but when I preview my datapoints in RAW explorer and CDF automatically change the this format into for example, 164998440000, so I am wondering if this is why I have this error? I also want to ask what is the meaning of span in IFSDB.sensors? It means this time-series is 100 days? or 100

Screenshot for reference.

The curl command suggested here needs quotes around all JSON property names, needs to use " instead of ', and needs trailing commas removed before curl will stop reporting invalid JSON payload. 

More details here: https://github.com/cognitedata/react-auth-wrapper/issues/15Perhaps it could be removed all together?

One thing that would make the finding of the correct data is to label the CDF resources better with labels. https://docs.cognite.com/dev/concepts/resource_types/labels In the contextualization pipeline could things be automatically labeled such as:Document type: P&ID, PDF(Process Flow Diagram), loop diagrams, datasheets, single line. that theses documents are either labeled from the metadata. document ID schema (regex will do it) or from classification algorithm the file, and then gives it a label to the file. that we can see from the label on the resource what it is.The same is for Asset: Is it a Junction box, Transmitter, Skid, Pump, Motor, valve etc etc. Time series: is it a physical measurement, is an inference / calculation / “soft-tag”, is a parameter (like the gain or integration time on PID controller) this could be determent by simple analyzing of the data-point behavior of the time series. cause now when we search for a time series often a time series of a parameter that

I just finished the cognite academy examples on contextualization. I did notice on the PID contextualization example that there were quite a few errors in what seems potentially character recognition pipeline to identify tags in the PDF.The tutorial stated that “these were all good” and “we can accept all”. I suspect such a process would create missing or strange links in the contextualized dataset.Are these known issues?I lack a bit the understanding of the context for the importance of these mispredictions,but I thought to report them anyway just in case.Happy to support you on improving these if they are something that needs improvement.

When I try to access cdf tenant URL (https://slb.fusion.cognite.com/), I get this error.I have been trying to figure this out but nothing so far, kindly suggest where to get these permissions from. 

<<URGENT>>Hi ,We are Unable to deploy Cognite Function. Below is the snippet for the same.Function ID : 269293733668660    @Philippe Bettler  could you please prioritize this issue.  

It would be really nice if we could control these log-messages in a more granular way:  

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!