Developer Community

Hi there,I’m trying to deploy interactively via browser OAuth using the new App Hosting API, but I’m getting the error below:npx @cognite/dune deploy:interactive🚀 Deploying application via App Hosting API...🔍 Ensuring app exists...✅ App 'bd-dune-poc' already exists📤 Uploading version 0.0.2...❌ Deployment failed: Deployment failed: Upload failed: 502 — [object Object]Do you have any thoughts on what might be causing this?Also, if deploying through Dune is not supported yet, is there another way to deploy using the SDK so that we can make the Cognite-hosted app available?Thanks!

Hi,We're using the Cognite DB extractor (version 3.9.2) with a MongoDB database (Azure Cosmos DB via the MongoDB API) and are trying to set up incremental loading.The documentation for mongodb (https://docs.cognite.com/cdf/integration/guides/extraction/configuration/db#databases.mongodb) doesn't mention the use of start_at and incremental_field but after testing them and looking at the debug logs, we can see the extractor sends the {start_at} placeholder literally without substituting the state value. My questions are:    1. Is start_at substitution supported at all for MongoDB JSON queries, or is it SQL-only?    2. If incremental loading is not supported for MongoDB, is there a recommended workaround?Thanks.

When we’ve tried to update to python sdk v8 we get a 400 validation issue when we try to create a container.We’re currently using v7.92.0, where we have no problems. We’ve tested versions v8.0.7 and v8.2.0.client.config.api_subversion is ‘20230101’ for both 7.92.0 and 8.2.0.Here is an example of a request that fails with: Unexpected field - items[0].properties..constraintState.constraintState seems to be a new property for v8 of the api.ContainerApply( space="Space", external_id="Comp", name="Comp", used_for="node", properties={ "manufacturer_name": ContainerProperty( type=Text(), nullable=True, ) })I notice also that the CDF Jupyter notebook in browser is still on v7.92.0. Is there potentially any other issues preventing an upgrade to v8? Best RegardsDaniel Rasmussen

With Tableau 2026.x introduced the native REST API connector, I wanted to check if anyone has explored using it to connect Cognite Data Fusion (CDF) APIs.Has anyone successfully connected CDF data via the Tableau REST API connector, or evaluated this approach?Any insights, limitations, or best practices would be greatly appreciated. Thanks in advance!

Hello,I am running a function ‘dq_validate_model_integrity’ and ‘dq_validate_broken_references’ which give a 403 unauthorized error for a timeseries intermittently. There has been no change in access or data yet its failing sometimes with a 403 error.Could you please let us know why this issue is coming up frequently?PS: I have attached the screenshots of the passed and failed statuses of both functions with the timestamp as well as the logs of both failed functions. Do let me know if there’s any more information required from my end. Thanks alot :)

Hello,  Is it planned to support RAW querying via CDF Grafana Datasource ?  Thanks ! 

Hello, In our project, we need to use TIMESTAMP_LTZ data type for our timestamps. This data type is only available in Spark starting from 3.4 version. Is it possible to upgrade your version (which is 3.3 I believe) to 3.4 at least please? Thank you

I am new to extending CogniteCore data model to further extend. How to Import CogniteCore datamodel to further extend in a new datamodel space. 

Hello,  Since function deployment using cdf tk is asynchronous, function can fail without any error feedback and stay in failing state until next deployment..Any recommendation to avoid this?Thanks  

Hello, Currently using cdf-tk version 0.5.111. The bySpace param on btree indexes is not yet supported in this version.We are trying to migrate to version 0.7.220. Although, this version sets correctly the bySpace param on indexes it does not seem to resolve correctly the view dependencies in our data model.Error trace:  Deploying 375 views to CDF...WARNING [MEDIUM]: Found a strongly interdependent set of 75 views: sp_dm_dap_knowledge_graph:BHAComponent(version=4.3), sp_dm_dap_knowledge_graph:BHARun(version=4.3), (...)sp_dm_dap_knowledge_graph:WellPath(version=4.3), sp_dm_dap_knowledge_graph:Wellbore(version=4.3) and sp_dm_dap_knowledge_graph:WellboreSection(version=4.3). This might indicate a data model design issue, and the deployment might fail due to API batch size limits.Traceback (most recent call last):  File "pypoetry/virtualenvs/drillx-dwdap-cdf-toolkit-fxS2v_xU-py3.13/bin/cdf-tk", line 8, in <module>    sys.exit(app())             ~~~^^  File "pypoetry/virtualenvs/drill

cdf version is 0.7.236.i cannot find the same quickstart mentioned in the training material. below is the screenshot from my environment.   the instruction mentions two times about “would you like to make changes to the selection”. the action is inconsistent.  The second question should be updated to be the real one.  

We have requirement to update capabilities in existing groups in CDF using Python SDK/API, we can add capabilities while creating group using python SDK, but if we want to updated that created group no provision for that. we tried client.iam.groups.create(group) but it creates new group with same name, in this case how we can update capabilities in group?

I have used the db extractor before, but with version 4.0.0 it now comes with a connect.yml and a config.yml. The documentation still lists one config file to be used. I’m not sure how to use the connection file, which looks like it used to be the cognite portion of the of the config file.I tried combining the two into one file, but when running “ dbextract.exe -c config.yml “ I am getting errors that its not expecting inputs such as version, log-handlers, database, and queries.How am I supposed to use these 2 different files when I run as an executable?Thank you

Hello experts,I'm interested in the following function: the sum of all data points in a time series server-side.I know there are aggregation operations for a specific time interval, but I'm looking for either a cumulative or total sum.I'd like to be able to perform this operation on either a standard time series or a synthetic time series, but unfortunately, I haven't found what I'm looking for yet.Please see below for the operations I am performing; I would like a discrete summation between the multiplication and the output.Does anyone have a server-side solution? Regards,Pierre Rambourg

We have a Cognite Function in which we are retrieving rows from staging tables using below sdk commandclient.raw.rows.retrieve_dataframe(db_name, tbl_name, limit=-1)The problem we are facing is the above command returns an empty Dataframe (0 columns, 0 rows) even though the table exists with data. The code execution does not fail but returns an empty Dataframe.When I manually/locally run the above command on the same database and tables, I get the required data.However, when running inside a CDF function it returns a Dataframe with shape (0,0) 

I posted it in Github, but cross-posting here too.https://github.com/cognitedata/cognite-sdk-python/issues/2525 System information (please complete the following information):OS: Windows11 Python Version: 3.12 SDK Version: 8.Describe the bugThis code previously worked:NodeId.load_if(None)But since v8, I am getting this error:AttributeError: type object 'NodeId' has no attribute 'load_if'. Did you mean: '_load_if'?To ReproduceRunnable code reproducing the error.NodeId.load_if(None)Expected behaviorIn Cognite SDK v7 this returns None.  

Hello experts,I have a question about Cognite Functions. I know there’s a limit on the size of the input data, as discussed here : Cognite Hub and documented there : Cloud provider limitations - Cognite Docs But is there a limit on the size of the function’s output? I have one that works perfectly fine but crashes without any explicit error message when handling a large output payload (>500 KB). I’m on Azure provider.Is there any information on this? Thank you,Best regards,Pierre Rambourg edit : the initial error was on my side, but i’m still interested for output limits !

I'm experiencing an issue with the Workflows editor where the bottom toolbar buttons (zoom in, zoom out, pan to center, auto layout) stop responding in certain conditions.Steps to reproduce:1. Open Workflows in CDF2. Create or edit a workflow3. Open the right panel (e.g., click on a task/agent to configure it)4. Try clicking the zoom in/out/pan buttons in the bottom toolbar5. Observe: Buttons don't respond when right panel is close to the left toolbar6. Maximize browser window or close right panel → buttons work againExpected: Toolbar buttons should work regardless of panel positionsActual: Buttons appear to be blocked, clicks don't register when panels are close together Workaround: Maximize browser window to create space between panels before using toolbar buttonsHas anyone else experienced this? Is there a better workaround or is this a known issue?  

I have 3 Views, ViewA, ViewB & ViewC. View A has direct relations to ViewB & ViewC via properties DefectEventID & AssetId, respectively. Pagination is expected and with Instance.Query end point, looping via cursors, it is not giving results for ViewB  & ViewC, apart from the first 100 records from the first call of instances.query(query). What is the best way to populate all_ViewA_Nodes,all_ViewB_Nodes ,all_ViewC_Nodes  Pagination via multiple cursors from Instance.Query?query = dm.query.Query(            with_={                "ViewA_Nodes": dm.query.NodeResultSetExpression(                    filter=filters.HasData(views=[ViewA]),                    limit=100,                ),                "ViewB_Nodes": dm.query.NodeResultSetExpression(                    from_="ViewA_Nodes",                    through=dm.PropertyId(                        source=ViewA,                        property="DefectEventID"                    ),                    direction="outwards", 

In addition to using config.[env].yaml, is it possible to create a custom file for configuring variables and then reference those placeholders in data models, transformations, workflows, etc ?For instance, I have several common SQL blocks that I would like to share across different transformations. While I could define them as variables in config.[env].yaml, I prefer to keep SQL-related variables separate from others in order to maintain clarity.

Hi Cognite Team,We are currently defining an integration pattern for a partner who will provide data from an OPC UA source into Cognite Data Fusion.Our preference is to use a managed MQTT broker in CDF and avoid deploying any self-hosted MQTT broker or additional bridge components on-prem.Based on the documentation, we understand that the OPC UA Extractor supports an mqtt: output mode, but it also references a requirement for the MQTT-CDF Bridge and a configured broker host/port.To ensure we define the correct and supported architecture, could you please clarify: Is it supported to configure the OPC UA Extractor to publish directly to the Cognite-hosted MQTT broker in CDF? If using the Cognite-managed MQTT broker, is the MQTT-CDF Bridge still required? Is a self-hosted MQTT broker mandatory when using the mqtt: configuration in the OPC UA Extractor? What is the recommended architecture today for a partner delivering OPC UA data into CDF using MQTT? Our intended target architectur

Hello Experts,I’m trying to use advanced filter on timeseries through SDK. I can do what I want except in the following situation: the asset_id property is null. Can anyone help me?I tried :from cognite.client.data_classes.time_series import TimeSeriesPropertyf = filters.And( filters.Equals("data_set_id", 123456), filters.Equals("name", "foobar"), filters.Equals(TimeSeriesProperty.asset_id, ""))[advancedFilter.[2].equals.value A string is not a valid value for the property "assetId"]from cognite.client.data_classes.time_series import TimeSeriesPropertyf = filters.And( filters.Equals("data_set_id", 123456), filters.Not(filters.Exists(TimeSeriesProperty.asset_id)),)-> 0I’m sure there is timeseries where timeseries.asset_id is None.Thanks experts !Regards,Pierre Rambourg

Hello, Is it possible to be alerted by email when a function or a workflow invocation fails? Thank you!

Hello experts,I’m looking to work with data workflows.I’m looking to trigger on node with a specific attribute (direct relation to another view) empty.I’m trying to work with the UI but I struggle with the Data modeling query validation.I don’t understand why the validation fails and what is the expected format for the query : JSON seems to be the expected format as seen here : But documentation uses YAML : https://docs.cognite.com/cdf/dm/dm_concepts/dm_querying#graph-query-examplesI’m a bit lost and I need your help. And my query : { "with":[ { "ts":{ "nodes":{ "filter":{ "exists":{ "property":[ "nameTranslations" ] } } }, "limit":1000 } } ], "select":[ { "ts":{ "sources":[ { "source":{ "type":"view", "sp

How much time does it really take to build a full solution?We deployed a complete solution using Cognite CDF, Cursor, and Anthropic Claude Opus 4.6, powered heavily by Gen AI in just 4 hours.This was not just a prototype screen. It was a fully deployed solution for demo purpose.And here’s the most interesting part:All of the following steps were done using Gen AI:1️⃣ Creation of the data model in Cognite Data Fusion and the Terraform2️⃣ Deployment of the model in Cognite3️⃣ Data generation and ingestion into the Cognite Data Platform4️⃣ Development of a React application on top of Cognite using the Cognite SDK5️⃣ Creation of: Asset Map, KPIs, Production Time Series Dashboardand Well Intervention viewFrom data modeling to frontend application — accelerated with AI.This is not about replacing engineers.It’s about dramatically increasing speed, experimentation, and delivery capacity. The question is no longer “Can we build it?”It’s “How fast can we build it?”Post here!