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AWS for Query.Farm

aws.forecast.list_explainabilities

Example SQL Queries

SELECT * FROM
aws.forecast.list_explainabilities;

Description

Returns a list of Explainability resources created using the CreateExplainability operation. This operation returns a summary for each Explainability. You can filter the list using an array of Filter objects.

To retrieve the complete set of properties for a particular Explainability resource, use the ARN with the DescribeExplainability operation.

Table Definition

Column NameColumn Data Type
filters Input Column

An array of filters. For each filter, provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the resources that match the statement from the list. The match statement consists of a key and a value.

Filter properties

  • Condition - The condition to apply. Valid values are IS and IS_NOT.

  • Key - The name of the parameter to filter on. Valid values are ResourceArn and Status.

  • Value - The value to match.

STRUCT(
    "key" VARCHAR,
    "value" VARCHAR,
    "condition" VARCHAR
)[]
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filters[]
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filters[].condition

The condition to apply. To include the objects that match the statement, specify IS. To exclude matching objects, specify IS_NOT.

filters[].key

The name of the parameter to filter on.

filters[].value

The value to match.

_aws_profile Input Column

The AWS profile defines the AWS identity used. It can be defined via credentials or by assuming a IAM role.

STRUCT(
    "type" VARCHAR,
    "name" VARCHAR,
    "account_id" VARCHAR,
    "via_profile_name" VARCHAR,
    "assumed_role_arn" VARCHAR,
    "organization" STRUCT(
        "account_name" VARCHAR,
        "id" VARCHAR,
        "tags" STRUCT(
            "key" VARCHAR,
            "value" VARCHAR
        )[],
        "master_account" STRUCT(
            "id" VARCHAR,
            "email" VARCHAR
        ),
        "parents" STRUCT(
            "type" VARCHAR,
            "id" VARCHAR,
            "name" VARCHAR,
            "tags" STRUCT(
                "key" VARCHAR,
                "value" VARCHAR
            )[]
        )[]
    )
)
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_aws_profile.account_id

The AWS account id

_aws_profile.assumed_role_arn

The ARN of the assumed role

_aws_profile.name

The unique name of the profile.

_aws_profile.organization

Information about this profile's membership in the AWS organization.

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_aws_profile.organization.account_name

The name of account speciifed by the organization

_aws_profile.organization.id

The organization id

_aws_profile.organization.master_account
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_aws_profile.organization.master_account.email

The organization master account email address

_aws_profile.organization.master_account.id

The organization master account id

_aws_profile.organization.parents[]
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_aws_profile.organization.parents[].id

The id of the parent

_aws_profile.organization.parents[].name

The name of the parent

_aws_profile.organization.parents[].tags[]
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_aws_profile.organization.parents[].tags[].key
_aws_profile.organization.parents[].tags[].value
_aws_profile.organization.parents[].type

The type of parent can be an organization unit or a root

_aws_profile.organization.tags[]
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_aws_profile.organization.tags[].key
_aws_profile.organization.tags[].value
_aws_profile.type

The type of profile, either 'credentials' or 'assumed_role'

_aws_profile.via_profile_name

This IAM role for this profile is assumed by first utilizing another profile with this name to obtain credentials.

_aws_region Input Column

The AWS region to use.

VARCHAR
creation_time

When the Explainability was created.

TIMESTAMP_S
explainability_arn

The Amazon Resource Name (ARN) of the Explainability.

VARCHAR
explainability_config

The configuration settings that define the granularity of time series and time points for the Explainability.

STRUCT(
    "time_series_granularity" VARCHAR,
    "time_point_granularity" VARCHAR
)
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explainability_config.time_point_granularity

To create an Explainability for all time points in your forecast horizon, use ALL. To create an Explainability for specific time points in your forecast horizon, use SPECIFIC.

Specify time points with the StartDateTime and EndDateTime parameters within the CreateExplainability operation.

explainability_config.time_series_granularity

To create an Explainability for all time series in your datasets, use ALL. To create an Explainability for specific time series in your datasets, use SPECIFIC.

Specify time series by uploading a CSV or Parquet file to an Amazon S3 bucket and set the location within the DataDestination data type.

explainability_name

The name of the Explainability.

VARCHAR
last_modification_time

The last time the resource was modified. The timestamp depends on the status of the job:

  • CREATE_PENDING - The CreationTime.

  • CREATE_IN_PROGRESS - The current timestamp.

  • CREATE_STOPPING - The current timestamp.

  • CREATE_STOPPED - When the job stopped.

  • ACTIVE or CREATE_FAILED - When the job finished or failed.

TIMESTAMP_S
message

Information about any errors that may have occurred during the Explainability creation process.

VARCHAR
resource_arn

The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability.

VARCHAR
status

The status of the Explainability. States include:

  • ACTIVE

  • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

  • CREATE_STOPPING, CREATE_STOPPED

  • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED 

VARCHAR