Community Discussions

Hi,I have defined a Data model with two Views, say Employee and Address, both of this do not have any relationship.For instances in Employee and Address view I have kept externalIds same, say emp101, emp102,... for both view instances.EmployeeextrenalId Name Age emp101 Tom 30 emp102 Harry 28  AddressexternalId City Pincode emp101 Pune 123 emp102 Mumbai 456  I am using cognite sdk sync api call to synchronize instances of Employee view.There is one Transformation on Address to update all the pincode values.After running transformation, when sync api is called it always return all the values of Employee instances even if they are not updated. Is there any resolution to this unexpected behavior?  

Is there currently a best practice for adding a geographical location to an object in a data model?I’ve considered simply using a string property to my model containing wkt-formatted strings, or a GeoSpatial feature external id, but none of them seem ideal. I assume there is a size limit on strings - so that wkt might be a bad choice? We’ve previously discussed this for timeseries, where you mentioned that data model geolocation was on the roadmap. Any update on this? 😊 

Hi,I am using Time zone and Calander granularity function (Release timezone and calendar features in DatapointsAPI (beta) by haakonvt · Pull Request #1779 · cognitedata/cognite-sdk-python · GitHub)I am facing an issue wherein I am passing following datadps_lst = client.time_series.data.retrieve_dataframe_in_tz(   external_id=list,   start=datetime(2023, 6, 24, tzinfo=ZoneInfo("America/New_York")),   end=datetime(2024, 6, 27, tzinfo=ZoneInfo("America/New_York")),   aggregates="average",   granularity="1month") For the End date time is given as 27th June 2024; while I am expecting to get a calculation from 1st June- 27th June; I am receiving an output from 1st June- 30th June. the functionality is not considering “end” date while calculating.  

Hi, we are using Cognite PI Extractor and in PI we have time series where there in some cases are only nano seconds between each datapoint. Since CDF use epoch milliseonds how will the extreactor handle such cases? 

Hello,Is there a way to round timestamps of the incoming OPC UA tags to second within the configuration file? I have gone through the documentation but haven't found anything like that. The problem is that for some tags coming from an OPC UA server I make some simple transformations and these tags are coming to CDF with timestamps at ms, whereas the other tags for which I do not apply any transformation on server side, are coming with timestamp at second. The issue I encounter is when I push the data from CDF to PowerBI and pivot the data and I get 2 rows for the same timestamp which PowerBI is displaying at second, 1 row with values for the tags without transformations and the other with the tags transformed which have timestamp in ms on CDF side. I just don't want to apply another query/transformation step to round the timestamp at second on PowerBI side and on CDF this timestamp rounding should be a scheduled transformation running every minute if not every second. Regards,Raluca

Hi Team,I was looking for any limitations to length or any other limitations for Json data type for properties in the data model but was unable to find it in the documentation.Do we have any limitations? If yes, we would want to know. We are planning to use the json data type in the model to store values for an ongoing project as part of our solution.Thanks,Akash@Aditya Kotiyal 

Hi there,Could you confirm if there is a limit on number of tables allowed within single databaseor is there any limit of tables or database per project ?

Hi,Our project uses Cognite Function scheduling to deploy data to CDF, and we struggle to understand what the “Duration” of a Cognite Function actually represents. I have attached a screenshot showing one of our function calls, printing the duration of various function from our source code. As you see the duration of our source code takes 2+ minutes, but the call of the function itself takes less than 2 minutes (start at 7:40 and end at 7:41). Is there a distinction here between what is actually run in the backend, and overhead in terms of instantiating a call to the Cognite Function? Can anyone explain the apparent deviations? Thanks.  

We are ingesting data through OPCUA extractor into timeseries of Cognite.1) How do we associate the OPCUA datapoint to the asset timeseries through the config.yml?2) What is the recommendation if we are ingesting thousands of OPCUA datapoint which will be associate with individual assets, do we write python to link the asset or it can be perform as part of the extractor?  

I have created 1 workflow , in which I am creating dynamic tasks depending on input, it creates batch of ids and create tasks out of it. Below is workflow definitionWorkflowVersionUpsert(   workflow_external_id="test_dynamic-0729",   version="1",   workflow_definition=WorkflowDefinitionUpsert(       description="This workflow has two steps",       tasks=[           WorkflowTask(                external_id="test_sub_tasks",                parameters=FunctionTaskParameters(                    external_id="test_sub_tasks",                    data="${workflow.input}"                ),                retries=1,                timeout=3600,                depends_on=[],                on_failure = "abortWorkflow",            ),           WorkflowTask(                external_id="test_create_sub",                parameters=DynamicTaskParameters(                    tasks="${test_sub_tasks.output.response.tasks}"                ),                name="Dynamic Task",                description="

Is it possible to increase the limit on execution of workflow instances per project or limit on execution workflow instance itself instead of project ? as we require to schedule workflow, on that it depends how much data we get to run workflows instances, so it can be more than 50  

Hello Currently Maintain workorder has been displayed in infield under work order. My understanding is we plan the activities in maintain and the plan get visible in Infield via work order. What is the relationship between activities in maintain as compared to checklist in infield. I am imaging that once we plan activities in maintain, we should be capable to “convert” the activities into checklist in infield?  

Hi,Lately, the “Open in Charts” button in Fusion changed behaviour. It used to open the specific time series in a new chart, while now, the Charts home screen is opened without pointing to the specific time series. Is this intended behaviour? 

I created a process monitoring job several weeks ago and it seems to work as expected showing me the folder I created and alerts were delivered to my inbox. After 2 Recent attempts to create monitoring jobs, the UI stated the job was created but I’m not seeing the new folder nor am I receiving alert emails or seeing indications under the alerts tab in Charts. 

A fictitious example, but one that reproduces my problem, is the following code: for event_list in client.events(chunk_size = 500_000, data_set_ids = ids_sites):        events = event_list.to_pandas() Error message after just over an hour: CogniteAPIError: Unauthorized | code: 401 | X-Request-ID: 9c8702fc-59bf-9f7a-be47-4876d6b433f3 In fact, if I iterate in another way (for example, filtering by date), I can run code for more than 8 hours (for as long as I want, actually, because I re-authenticate between iterations, preventing the token from expiring). However, I don't want to filter by date because it doesn't provide consistent data volume like iterating by chunks.And apparently, I can't authenticate between iterations in the example I provided here, as it seems the chunk persists the initial authentication, which initially makes sense. Does anyone know how to solve this?   

I am analyzing multiple tags with same Engineering Unit. The charts are getting overlapped even after Stacking feature is ON.   

Hi,I need to use a custom python library from a private repository in Cognite Function  I have referred the following document - https://docs.cognite.com/de/cdf/functions/ In requirements.txt have added --index-url with the basic HTTPS authentication referring private repository URL with credentials.However when I am deploying the functions its not deploying and failing with the following error: Function Id 3485252813249865 Note: I have tested this locally its running fineCould you please help with this?

type Relationship @edge @view(space: "space-name", version: "1_1"){    relationName: String!,    relationType: RelationType,    startTime: Timestamp,    endTime: Timestamp} is there any library developed by cognite which will validate above kind of graphQL syntax also is it possible to check the values provided in graphQL ?

Asset which linked to Timeseries or vice versa filtering option is not available in Data Explorer screen in CDFAnd Also, Asset linked to Event or vice versa event linked to Asset Filtering option is not available in Data Explorer screen in CDF.Also, Files to Events or Events to Files etc.

We are using the MQTT hosted extractor in CDF to retrieve data from an MQTT broker hosted on Microsoft Azure Event Grid.One method for authenticating an MQTT client in Azure Event Grid is through thumbprint matching. In this authentication process, a certificate file (.pem), a key file (.key), and a username are verified by the broker. However, in the Cognie hosted extractor, it's currently not possible to provide the username simultaneously with the certificate and key files.If only the key and certificate files are provided, an Implementation Error is encountered as expected.How can I provide username information along with the key files to the hosted extractor? 

HiI am practicing through online help manual through “https://learn.cognite.com/” Where I am successfully logged in to  “cdf-fundamentals” project. Now want to create new data set  as per instruction there is icon on left side to create new data set but I dont find it.Can anyone have idea how to create new data set in “cdf-fundamentals” demo project.  

Hello,Is there a way to use a parameter from OPC-UA Extractor config.yml file in order to make a conversion of some numeric tags (i.e. conversion on the measurement unit from grams to pounds)? As I have not yet found any relatable parameter that would do this and using transformations as the data is pushed in CDF is not efficient on performance as I need to filter out the datapoints already converted.Thanks,Raluca

Hello, I have a SQL table that has 2000 rows total. The SQL DB Extractor 3.0 configured to extract all rows (Select *) and places them in a Raw table.The extractor log shows 2000 rows extracted.2024-08-02 00:00:05.558 UTC [INFO    ] ThreadPoolExecutor-2_0 - No more rows for Caster_LabData_Narrow. 2000 rows extracted in 0.047 seconds2024-08-02 00:00:05.558 UTC [INFO    ] ThreadPoolExecutor-2_0 - Reporting new success run: No more rows for Caster_LabData_Narrow. 2000 rows extracted in 0.047 seconds The Raw table UI shows it has only 1000 rows of data, not the 2000 I am expecting. How can I confirm the exact number of rows in the Raw table? I tried to select all rows from that raw table with a transformation and the preview only showed 1000 rows BUT I can’t tell whether that is a transform preview UI limitation or not.As a test, I tried to select all rows from a different raw table with 1993 rows with a transform and place the results in a 3rd raw table. The resulting 3rd table only shows

The workflow execution id is 1c3ef35b-c66e-4bdc-bf73-09480e291507.The workflow name is yggdrasil_domain_model_containers_transformations_including_raw_transformations. The tasks threw the following error right after the completion of first step, the error information is : 'reasonForIncompletion': 'Something went wrong. Please report this error to support@cognite.com and provide us with the workflow and task identifiers.', Project: Integral-develop  

Hi,We have a large dataset containing more than 10,000 timeseries, and we are working on creating multiple subscriptions to accommodate them but again that can also be having 10k timeseries in sometime. Currently, we are using metadata with a specific property match as one of our filters, given that this metadata could also apply to 10,000 timeseries. we are considering adding a new filter based on createdTime. This would automatically include timeseries added after a specific time to a new subscription, avoiding the need to create new subscriptions manually.Here is the filter we attempted to use:"filter": {  "and": [    {      "equals": {        "property": ["dataSetId"],        "value": "12346576588"      }    },    {      "range": {        "property": ["createdTime"],        "gte": 1719513000000      }    }  ]}We encountered an internal server error (request ID: b45316a2-07ac-9ec8-986f-75bd66d698e4) .Tried with creating createdTime as metadata as well, suspect there might be a limit