跳至主要内容

Building an asset hierarchy with the Cognite core data model

The CogniteAsset concept in the Cognite core data model has properties to represent systems that support industrial functions or processes. You can structure assets hierarchically according to your organization's criteria. For example, an asset can represent a water pump that's part of a larger pump station asset at a clarification plant asset.

CogniteAsset comes with a matching container and view. The container has efficient indexes and constraints and can be used at scale out-of-the-box.

Build an asset hierarchy with CogniteAsset​

We recommend building asset hierarchies starting from a root node and using the parent property to add child assets. Root nodes are nodes with an empty parent property. You can add or change the parent property later to add a node and its children to an existing hierarchy.

When you add or change a node in a hierarchy, a background job—path materializer—recalculates the path from the root node for all nodes affected by the change. The job runs asynchronously, and the time it takes to update all the paths depends on the number of affected nodes.

The example below uses sample data from the Cognite Data Fusion Toolkit. The hierarchy consists of two levels—lift stations and pumps—where pumps are children of a lift station.

To create a generic asset hierarchy with the CogniteAsset view, set the CogniteAsset view as a source during the ingestion process and set the parent property to specify the parent asset or leave it blank for root assets.

Populate data with Transformations​

This example uses Transformations to ingest data from the lift-stations-pumps table from the CDF staging area, RAW.

  1. Upload the data to the staging area, for example, using a script similar to this:

    Uploading collections_pump.csv to staging, RAW

    You can find examples and documentation for Cognite Python SDK here:

    import pandas as pd
    from cognite.client import ClientConfig, CogniteClient

    # client_config = ... set up you authentication here
    client = CogniteClient(client_config)

    # create RAW database and table
    raw_database = client.raw.databases.create("cdm-asset-demo")
    raw_table = client.raw.tables.create(raw_database.name, "lift-stations-pumps")

    # use pandas to read csv file
    pump_data_url = "https://github.com/cognitedata/toolkit/raw/f9c2ce8c4de8928718acc48eb6688338c6b3508f/cognite_toolkit/cognite_modules/examples/example_pump_asset_hierarchy/raw/collections_pump.csv"
    lift_station_pumps_frame = pd.read_csv(pump_data_url)

    # upload dataframe to RAW
    client.raw.rows.insert_dataframe(raw_database.name, raw_table.name, lift_station_pumps_frame)

  2. Create a transformation:

    1. Navigate to Data management > Integrate > Transformations.

    2. Select Create transformation, and enter a unique name and a unique external ID.

    3. Select Next.

    4. Select Cognite core data model under Target data models, then select v1 for Version.

    5. For the Target space, select the space to ingest asset instances into (asset-demo in this example).

    6. Select CogniteAsset as the Target type or relationship.

    7. Select Create or Update as the Action.

    8. Select Create.

      asset transformation

  3. Add and run this SQL specification to create the asset hierarchy:

    -- Add the root manually. The set for lift stations doesn't specify a root asset.
    select
      'lift_pump_stations:root' as externalId,
      'Lift Station Root' as name,
      'Root node to group all lift stations.' as description,
      null as parent

    UNION ALL

    -- select lift station data
    select distinct
      concat('lift_station:', cast(`LiftStationID` as STRING)) as externalId,
      cast(`LiftStationID` as STRING) as name,
      cast(`LocationDescription` as STRING) as description,
      node_reference('asset-demo','lift_pump_stations:root') as parent
    from
      `cdm-asset-demo`.`lift-stations-pumps`
    where isnotnull(nullif(cast(`LiftStationID` as STRING), ' '))

    UNION ALL

    -- select pump data
    select
      concat('pump:', cast(`FacilityID` as STRING)) as externalId,
      cast(`Position` as STRING) as name,
      null as description,
      node_reference('asset-demo', concat('lift_station:', cast(`LiftStationID` as STRING))) as parent
    from
      `cdm-asset-demo`.`lift-stations-pumps`
    where isnotnull(nullif(cast(`LiftStationID` as STRING), ' '));

Build an asset hierarchy and extend CogniteAsset​

Using the generic CogniteAsset in combination with other core data model concepts is often enough to create the necessary data models. If you need to create custom data models, you can extend the Cognite data models and tailor them to your use case, solution, or application.

The example below shows how to extend CogniteAsset to hold more pump data: DesignPointFlowGPM, DesignPointHeadFT, LowHeadFT, and LowHeadFlowGPM.

  1. The first step is to create a container to hold the additional data:

    Pump container definition
    externalId: Pump
    name: Pump
    space: asset-demo
    usedFor: node
    properties:
    DesignPointFlowGPM:
      autoIncrement: false
      defaultValue: null
      description: The flow the pump was designed for, specified in gallons per minute.
      name: DesignPointFlowGPM
      nullable: true
      type:
        list: false
        type: float64
    DesignPointHeadFT:
      autoIncrement: false
      defaultValue: null
      description: The flow head pump was designed for, specified in feet.
      name: DesignPointHeadFT
      nullable: true
      type:
        list: false
        type: float64
    LowHeadFT:
      autoIncrement: false
      defaultValue: null
      description: The low head of the pump, specified in feet.
      name: LowHeadFT
      nullable: true
      type:
        list: false
        type: float64
    LowHeadFlowGPM:
      autoIncrement: false
      defaultValue: null
      description: The low head flow of the pump, specified in gallons per minute.
      name: DesignPointHeadFT
      nullable: true
      type:
        list: false
        type: float64
    constraints:
    requiredAsset:
      constraintType: requires
      require:
        type: container
        space: cdf_cdm
        externalId: CogniteAsset

    The requires constraint ensures that the Pump data is always accompanied with CogniteAsset data.

  2. Next, create two views to present the nodes as lift-station and pump assets. By implementing the CogniteAsset view, you automatically include all the properties defined in that view:

    Pump view definition
    externalId: Pump
    name: Pump
    space: asset-demo
    version: v1
    implements: # <-- Declares that the view implements the CogniteAsset view
      - space: cdf_cdm
        externalId: CogniteAsset
        version: v1
    properties:
      DesignPointFlowGPM:
        container:
          externalId: Pump
          space: asset-demo
          type: container
        containerPropertyIdentifier: DesignPointFlowGPM
        name: DesignPointFlowGPM
      DesignPointHeadFT:
        container:
          externalId: Pump
          space: asset-demo
          type: container
        containerPropertyIdentifier: DesignPointHeadFT
        name: DesignPointHeadFT
      LowHeadFT:
        container:
          externalId: Pump
          space: asset-demo
          type: container
        containerPropertyIdentifier: LowHeadFT
        name: LowHeadFT
      LowHeadFlowGPM:
        container:
          externalId: Pump
          space: asset-demo
          type: container
        containerPropertyIdentifier: LowHeadFlowGPM
        name: LowHeadFlowGPM
    LiftStation view definition
    space: asset-demo
    externalId: LiftStation
    version: v1
    implements: # <-- Declares that the view implements the CogniteAsset view
      - space: cdf_cdm
        externalId: CogniteAsset
        version: v1
    properties:
    filter: # <-- Adds additional filter to only include lift-stations
      prefix:
        property:
          - node
          - externalId
        value: lift_station

    Note the filter property in the LiftStation view. The Pump view includes properties from the Pump container and the core data model containers. The default hasData filter ensures that the view only presents instances that are pumps or have pump data. The LiftStation view has the same fields as the CogniteAsset view. But without the explicit filter, it returns both pumps and lift stations.

    When using explicit filters in views, it's essential to consider query performance. View filters run every time someone uses the view to query data. If necessary, add indexes to avoid degraded performance. The system always indexes the externalId property.

    小心

    View filters don't limit any write operations based on the filter expression. You can ingest an instance through a view without it being returned by the same view.

    When ingesting instance data through a view as a source, the system ensures that all the required fields are populated. When querying, using the hasData filter for a view ensures that the returned results have all the required fields for the view. Even if a view has no required fields, you can use the view to write "blank" values to satisfy the conditions for the hasData filter.

  3. To populate the custom asset hierarchy, create a data model to present the custom views:

    externalId: liftStationsPumps
    name: Lift stations and pumps
    space: asset-demo
    version: v1
    views:
      - externalId: LiftStation
        space: asset-demo
        type: view
        version: v1
      - externalId: Pump
        space: asset-demo
        type: view
        version: v1
    提示

    The below Python script shows how to use the Cognite Python SDK and the .load() function to create data modeling objects from YAML representation. Remember to authenticate the CogniteClient properly.

    Creating custom schema components with Cognite Python SDK.
    Creating custom schema components
    from cognite.client import ClientConfig, CogniteClient
    from cognite.client.data_classes.data_modeling import (
        ContainerApply,
        DataModelApply,
        ViewApply,
    )

    # client_config = ... <-- remember to authenticate.

    client = CogniteClient(client_config)

    pump_container_yaml = """
    externalId: Pump
    name: Pump
    space: asset-demo
    usedFor: node
    properties:
      DesignPointFlowGPM:
        autoIncrement: false
        defaultValue: null
        description: The flow the pump was designed for, specified in gallons per minute.
        name: DesignPointFlowGPM
        nullable: true
        type:
          list: false
          type: float64
      DesignPointHeadFT:
        autoIncrement: false
        defaultValue: null
        description: The flow head pump was designed for, specified in feet.
        name: DesignPointHeadFT
        nullable: true
        type:
          list: false
          type: float64
      LowHeadFT:
        autoIncrement: false
        defaultValue: null
        description: The low head of the pump, specified in feet.
        name: LowHeadFT
        nullable: true
        type:
          list: false
          type: float64
      LowHeadFlowGPM:
        autoIncrement: false
        defaultValue: null
        description: The low head flow of the pump, specified in gallons per minute.
        name: DesignPointHeadFT
        nullable: true
        type:
          list: false
          type: float64
    constraints:
      requiredAsset:
        constraintType: requires
        require:
          type: container
          space: cdf_cdm
          externalId: CogniteAsset
    """

    pump_view_yaml = """
    externalId: Pump
    name: Pump
    space: asset-demo
    version: v1
    implements: # <-- Declares that the view implements the CogniteAsset view
      - space: cdf_cdm
        externalId: CogniteAsset
        version: v1
    properties:
      DesignPointFlowGPM:
        container:
          externalId: Pump
          space: asset-demo
          type: container
        containerPropertyIdentifier: DesignPointFlowGPM
        name: DesignPointFlowGPM
      DesignPointHeadFT:
        container:
          externalId: Pump
          space: asset-demo
          type: container
        containerPropertyIdentifier: DesignPointHeadFT
        name: DesignPointHeadFT
      LowHeadFT:
        container:
          externalId: Pump
          space: asset-demo
          type: container
        containerPropertyIdentifier: LowHeadFT
        name: LowHeadFT
      LowHeadFlowGPM:
        container:
          externalId: Pump
          space: asset-demo
          type: container
        containerPropertyIdentifier: LowHeadFlowGPM
        name: LowHeadFlowGPM
    """

    lift_station_view_yaml = """
    space: asset-demo
    externalId: LiftStation
    version: v1
    implements: # <-- Declares that the view implements the CogniteAsset view
      - space: cdf_cdm
        externalId: CogniteAsset
        version: v1
    filter: # <-- Adds additional filter to only include lift-stations
      prefix:
        property:
          - node
          - externalId
        value: lift_station
    """

    data_model_yaml = """
    externalId: liftStationsPumps
    name: Lift stations and pumps
    space: asset-demo
    version: v1
    views:
      - externalId: LiftStation
        space: asset-demo
        type: view
        version: v1
      - externalId: Pump
        space: asset-demo
        type: view
        version: v1
    """

    pump_container = ContainerApply.load(pump_container_yaml)
    client.data_modeling.containers.apply(pump_container)

    pump_view = ViewApply.load(pump_view_yaml)
    lift_station_view = ViewApply.load(lift_station_view_yaml)
    client.data_modeling.views.apply([pump_view, lift_station_view])

    data_model = DataModelApply.load(data_model_yaml)
    client.data_modeling.data_models.apply(data_model)

  4. In the previous example, we could use the CogniteAsset view to write data for lift stations and pumps. Because Transformations support writing to a single view, you need to create a transformation for each of the LiftStation and Pump views to write to both.

    Follow the steps in the previous example to create transformations for the LiftStation and Pump views. Select Lift stations and pumps as the target data model.

    asset transformation

  5. Add and run these SQL specifications:

    Transformation for Pumps
    select
      concat('pump:', cast(`FacilityID` as STRING)) as externalId,
      cast(`Position` as STRING) as name,
      null as description,
      node_reference('asset-demo', concat('lift_station:', cast(`LiftStationID` as STRING))) as parent,
      `DesignPointHeadFT` as DesignPointHeadFT,
      `DesignPointFlowGPM` as DesignPointFlowGPM,
      `LowHeadFT` as LowHeadFT,
      `LowHeadFlowGPM` as LowHeadFlowGPM
    from
      `cdm-asset-demo`.`lift-stations-pumps`
    where isnotnull(nullif(cast(`LiftStationID` as STRING), ' '));
    Transformation for LiftStations
    -- Add root manual root node as the set does not have a root for lift stations
    select
      'lift_pump_stations:root' as externalId,
      'Lift Station Root' as name,
      'Root node to group all lift stations.' as description,
      null as parent

    UNION ALL

    -- select lift station data
    select distinct
      concat('lift_station:', cast(`LiftStationID` as STRING)) as externalId,
      cast(`LiftStationID` as STRING) as name,
      cast(`LocationDescription` as STRING) as description,
      node_reference('asset-demo','lift_pump_stations:root') as parent
    from
      `cdm-asset-demo`.`lift-stations-pumps`
    where isnotnull(nullif(cast(`LiftStationID` as STRING), ' '))

Query hierarchical data​

The examples below show how to query hierarchical data using Cognite Python SDK.

List by source view​

Views are a specialized representation of data and excellent tools for querying and filtering. For example, you can use the CogniteAsset, LiftStation, and Pump views to fetch nodes:

assets = client.data_modeling.instances.list(sources=ViewId(space="cdf_cdm", external_id="CogniteAsset", version="v1"))
lift_stations = client.data_modeling.instances.list(sources=ViewId(space="asset-demo", external_id="LiftStation", version="v1"))
pumps = client.data_modeling.instances.list(sources=ViewId(space="asset-demo", external_id="Pump", version="v1"))

Setting a view as a source when listing instances, automatically applies a hasData filter to the query. When fetching instances with CogniteAsset as a source, all assets are returned, both lift-stations and pumps. The respective views implement the CogniteAsset view and therefore satisfy the hasData filter.

Aggregate based on a common parent​

Often, you want to find aggregated information about the children of assets. This example finds information on how many pumps there are in a lift station and the average value of the LowHeadFT property for all pumps in a lift station:

from cognite.client.data_classes import aggregations
from cognite.client.data_classes.filters import Equals

# count number of pumps for Riverhouse lift station
count_pumps_for_lift_station = client.data_modeling.instances.aggregate(
    view=ViewId(space="asset-demo", external_id="Pump", version="v1"),
    space="asset-demo",
    filter=Equals(
        property=["asset-demo", "Pump/v1", "parent"],
        value={"space": "asset-demo", "externalId": "lift_station:Riverhouse"},
    ),
    aggregates=aggregations.Count("externalId"),
).value
# average pumpOn value for lift-station someId
average_for_for_lift_station = client.data_modeling.instances.aggregate(
    view=ViewId(space="asset-demo", external_id="Pump", version="v1"),
    space="asset-demo",
    filter=Equals(
        property=["asset-demo", "Pump/v1", "parent"],
        value={"space": "asset-demo", "externalId": "lift_station:Riverhouse"},
    ),
    aggregates=aggregations.Avg("LowHeadFT"),
).value

List all assets in a subtree​

To list all descendants of an asset, use the system-maintained path property of the CogniteAsset view.

from cognite.client.data_classes.data_modeling import NodeId
from cognite.client.data_classes.data_modeling.cdm.v1 import CogniteAsset
from cognite.client.data_classes.filters import Prefix

sub_tree_root = client.data_modeling.instances.retrieve_nodes(
	NodeId("asset-demo", "lift_station:Riverhouse"), node_cls=CogniteAsset
)

sub_tree_nodes = client.data_modeling.instances.list(
	instance_type=CogniteAsset,
	filter=Prefix(property=["cdf_cdm", "CogniteAsset/v1", "path"], value=sub_tree_root.path),
)

The example uses CogniteAsset TypedNode from the core data model definitions, a helper class for de-serialization of data objects.

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