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AWS for Query.Farm

aws.glue.get_ml_transforms

Example SQL Queries

SELECT * FROM
aws.glue.get_ml_transforms;

Description

Gets a sortable, filterable list of existing Glue machine learning transforms. Machine learning transforms are a special type of transform that use machine learning to learn the details of the transformation to be performed by learning from examples provided by humans. These transformations are then saved by Glue, and you can retrieve their metadata by calling GetMLTransforms.

Table Definition

Column NameColumn Data Type
filter Input Column

The filter transformation criteria.

STRUCT(
    "name" VARCHAR,
    "transform_type" VARCHAR,
    "status" VARCHAR,
    "glue_version" VARCHAR,
    "created_before" TIMESTAMP_S,
    "created_after" TIMESTAMP_S,
    "last_modified_before" TIMESTAMP_S,
    "last_modified_after" TIMESTAMP_S,
    "schema" STRUCT(
        "name" VARCHAR,
        "data_type" VARCHAR
    )[]
)
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filter.created_after

The time and date after which the transforms were created.

filter.created_before

The time and date before which the transforms were created.

filter.glue_version

This value determines which version of Glue this machine learning transform is compatible with. Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see Glue Versions in the developer guide.

filter.last_modified_after

Filter on transforms last modified after this date.

filter.last_modified_before

Filter on transforms last modified before this date.

filter.name

A unique transform name that is used to filter the machine learning transforms.

filter.schema[]
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filter.schema[].data_type

The type of data in the column.

filter.schema[].name

The name of the column.

filter.status

Filters the list of machine learning transforms by the last known status of the transforms (to indicate whether a transform can be used or not). One of "NOT_READY", "READY", or "DELETING".

filter.transform_type

The type of machine learning transform that is used to filter the machine learning transforms.

max_results Input Column

The maximum number of results to return.

BIGINT
next_token Input Column

A pagination token, if more results are available.

VARCHAR
sort Input Column

The sorting criteria.

STRUCT(
    "column" VARCHAR,
    "sort_direction" VARCHAR
)
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sort.column

The column to be used in the sorting criteria that are associated with the machine learning transform.

sort.sort_direction

The sort direction to be used in the sorting criteria that are associated with the machine learning transform.

_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
transforms

A list of machine learning transforms.

STRUCT(
    "transform_id" VARCHAR,
    "name" VARCHAR,
    "description" VARCHAR,
    "status" VARCHAR,
    "created_on" TIMESTAMP_S,
    "last_modified_on" TIMESTAMP_S,
    "input_record_tables" STRUCT(
        "database_name" VARCHAR,
        "table_name" VARCHAR,
        "catalog_id" VARCHAR,
        "connection_name" VARCHAR,
        "additional_options" MAP(VARCHAR, VARCHAR)
    )[],
    "parameters" STRUCT(
        "transform_type" VARCHAR,
        "find_matches_parameters" STRUCT(
            "primary_key_column_name" VARCHAR,
            "precision_recall_tradeoff" DOUBLE,
            "accuracy_cost_tradeoff" DOUBLE,
            "enforce_provided_labels" BOOLEAN
        )
    ),
    "evaluation_metrics" STRUCT(
        "transform_type" VARCHAR,
        "find_matches_metrics" STRUCT(
            "area_under_pr_curve" DOUBLE,
            "precision" DOUBLE,
            "recall" DOUBLE,
            "f1" DOUBLE,
            "confusion_matrix" STRUCT(
                "num_true_positives" BIGINT,
                "num_false_positives" BIGINT,
                "num_true_negatives" BIGINT,
                "num_false_negatives" BIGINT
            ),
            "column_importances" STRUCT(
                "column_name" VARCHAR,
                "importance" DOUBLE
            )[]
        )
    ),
    "label_count" BIGINT,
    "schema" STRUCT(
        "name" VARCHAR,
        "data_type" VARCHAR
    )[],
    "role" VARCHAR,
    "glue_version" VARCHAR,
    "max_capacity" DOUBLE,
    "worker_type" VARCHAR,
    "number_of_workers" BIGINT,
    "timeout" BIGINT,
    "max_retries" BIGINT,
    "transform_encryption" STRUCT(
        "ml_user_data_encryption" STRUCT(
            "ml_user_data_encryption_mode" VARCHAR,
            "kms_key_id" VARCHAR
        ),
        "task_run_security_configuration_name" VARCHAR
    )
)[]
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transforms[]
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transforms[].created_on

A timestamp. The time and date that this machine learning transform was created.

transforms[].description

A user-defined, long-form description text for the machine learning transform. Descriptions are not guaranteed to be unique and can be changed at any time.

transforms[].evaluation_metrics

An EvaluationMetrics object. Evaluation metrics provide an estimate of the quality of your machine learning transform.

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transforms[].evaluation_metrics.find_matches_metrics

The evaluation metrics for the find matches algorithm.

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transforms[].evaluation_metrics.find_matches_metrics.area_under_pr_curve

The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff.

For more information, see Precision and recall in Wikipedia.

transforms[].evaluation_metrics.find_matches_metrics.column_importances[]
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transforms[].evaluation_metrics.find_matches_metrics.column_importances[].column_name

The name of a column.

transforms[].evaluation_metrics.find_matches_metrics.column_importances[].importance

The column importance score for the column, as a decimal.

transforms[].evaluation_metrics.find_matches_metrics.confusion_matrix

The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making.

For more information, see Confusion matrix in Wikipedia.

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transforms[].evaluation_metrics.find_matches_metrics.confusion_matrix.num_false_negatives

The number of matches in the data that the transform didn't find, in the confusion matrix for your transform.

transforms[].evaluation_metrics.find_matches_metrics.confusion_matrix.num_false_positives

The number of nonmatches in the data that the transform incorrectly classified as a match, in the confusion matrix for your transform.

transforms[].evaluation_metrics.find_matches_metrics.confusion_matrix.num_true_negatives

The number of nonmatches in the data that the transform correctly rejected, in the confusion matrix for your transform.

transforms[].evaluation_metrics.find_matches_metrics.confusion_matrix.num_true_positives

The number of matches in the data that the transform correctly found, in the confusion matrix for your transform.

transforms[].evaluation_metrics.find_matches_metrics.f1

The maximum F1 metric indicates the transform's accuracy between 0 and 1, where 1 is the best accuracy.

For more information, see F1 score in Wikipedia.

transforms[].evaluation_metrics.find_matches_metrics.precision

The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible.

For more information, see Precision and recall in Wikipedia.

transforms[].evaluation_metrics.find_matches_metrics.recall

The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data.

For more information, see Precision and recall in Wikipedia.

transforms[].evaluation_metrics.transform_type

The type of machine learning transform.

transforms[].glue_version

This value determines which version of Glue this machine learning transform is compatible with. Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see Glue Versions in the developer guide.

transforms[].input_record_tables[]
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transforms[].input_record_tables[].additional_options

Additional options for the table. Currently there are two keys supported:

  • pushDownPredicate: to filter on partitions without having to list and read all the files in your dataset.

  • catalogPartitionPredicate: to use server-side partition pruning using partition indexes in the Glue Data Catalog.

transforms[].input_record_tables[].catalog_id

A unique identifier for the Glue Data Catalog.

transforms[].input_record_tables[].connection_name

The name of the connection to the Glue Data Catalog.

transforms[].input_record_tables[].database_name

A database name in the Glue Data Catalog.

transforms[].input_record_tables[].table_name

A table name in the Glue Data Catalog.

transforms[].label_count

A count identifier for the labeling files generated by Glue for this transform. As you create a better transform, you can iteratively download, label, and upload the labeling file.

transforms[].last_modified_on

A timestamp. The last point in time when this machine learning transform was modified.

transforms[].max_capacity

The number of Glue data processing units (DPUs) that are allocated to task runs for this transform. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page.

MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType.

  • If either NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set.

  • If MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set.

  • If WorkerType is set, then NumberOfWorkers is required (and vice versa).

  • MaxCapacity and NumberOfWorkers must both be at least 1.

When the WorkerType field is set to a value other than Standard, the MaxCapacity field is set automatically and becomes read-only.

transforms[].max_retries

The maximum number of times to retry after an MLTaskRun of the machine learning transform fails.

transforms[].name

A user-defined name for the machine learning transform. Names are not guaranteed unique and can be changed at any time.

transforms[].number_of_workers

The number of workers of a defined workerType that are allocated when a task of the transform runs.

If WorkerType is set, then NumberOfWorkers is required (and vice versa).

transforms[].parameters

A TransformParameters object. You can use parameters to tune (customize) the behavior of the machine learning transform by specifying what data it learns from and your preference on various tradeoffs (such as precious vs. recall, or accuracy vs. cost).

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transforms[].parameters.find_matches_parameters

The parameters for the find matches algorithm.

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transforms[].parameters.find_matches_parameters.accuracy_cost_tradeoff

The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy.

Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.

Cost measures how many compute resources, and thus money, are consumed to run the transform.

transforms[].parameters.find_matches_parameters.enforce_provided_labels

The value to switch on or off to force the output to match the provided labels from users. If the value is True, the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False, the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model.

Note that setting this value to true may increase the conflation execution time.

transforms[].parameters.find_matches_parameters.precision_recall_tradeoff

The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision.

The precision metric indicates how often your model is correct when it predicts a match.

The recall metric indicates that for an actual match, how often your model predicts the match.

transforms[].parameters.find_matches_parameters.primary_key_column_name

The name of a column that uniquely identifies rows in the source table. Used to help identify matching records.

transforms[].parameters.transform_type

The type of machine learning transform.

For information about the types of machine learning transforms, see Creating Machine Learning Transforms.

transforms[].role

The name or Amazon Resource Name (ARN) of the IAM role with the required permissions. The required permissions include both Glue service role permissions to Glue resources, and Amazon S3 permissions required by the transform.

  • This role needs Glue service role permissions to allow access to resources in Glue. See Attach a Policy to IAM Users That Access Glue.

  • This role needs permission to your Amazon Simple Storage Service (Amazon S3) sources, targets, temporary directory, scripts, and any libraries used by the task run for this transform.

transforms[].schema[]
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transforms[].schema[].data_type

The type of data in the column.

transforms[].schema[].name

The name of the column.

transforms[].status

The current status of the machine learning transform.

transforms[].timeout

The timeout in minutes of the machine learning transform.

transforms[].transform_encryption

The encryption-at-rest settings of the transform that apply to accessing user data. Machine learning transforms can access user data encrypted in Amazon S3 using KMS.

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transforms[].transform_encryption.ml_user_data_encryption

An MLUserDataEncryption object containing the encryption mode and customer-provided KMS key ID.

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transforms[].transform_encryption.ml_user_data_encryption.kms_key_id

The ID for the customer-provided KMS key.

transforms[].transform_encryption.ml_user_data_encryption.ml_user_data_encryption_mode

The encryption mode applied to user data. Valid values are:

  • DISABLED: encryption is disabled

  • SSEKMS: use of server-side encryption with Key Management Service (SSE-KMS) for user data stored in Amazon S3.

transforms[].transform_encryption.task_run_security_configuration_name

The name of the security configuration.

transforms[].transform_id

The unique transform ID that is generated for the machine learning transform. The ID is guaranteed to be unique and does not change.

transforms[].worker_type

The type of predefined worker that is allocated when a task of this transform runs. Accepts a value of Standard, G.1X, or G.2X.

  • For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker.

  • For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker.

  • For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker.

MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType.

  • If either NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set.

  • If MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set.

  • If WorkerType is set, then NumberOfWorkers is required (and vice versa).

  • MaxCapacity and NumberOfWorkers must both be at least 1.