pulumi-aws-kotlin/com.pulumi.aws.glue.kotlin/MLTransformArgs

MLTransformArgs

data class MLTransformArgs(val description: Output<String>? = null, val glueVersion: Output<String>? = null, val inputRecordTables: Output<List<MLTransformInputRecordTableArgs>>? = null, val maxCapacity: Output<Double>? = null, val maxRetries: Output<Int>? = null, val name: Output<String>? = null, val numberOfWorkers: Output<Int>? = null, val parameters: Output<MLTransformParametersArgs>? = null, val roleArn: Output<String>? = null, val tags: Output<Map<String, String>>? = null, val timeout: Output<Int>? = null, val workerType: Output<String>? = null) : ConvertibleToJava<MLTransformArgs>

Provides a Glue ML Transform resource.

Example Usage

package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.aws.glue.CatalogDatabase;
import com.pulumi.aws.glue.CatalogDatabaseArgs;
import com.pulumi.aws.glue.CatalogTable;
import com.pulumi.aws.glue.CatalogTableArgs;
import com.pulumi.aws.glue.inputs.CatalogTableStorageDescriptorArgs;
import com.pulumi.aws.glue.inputs.CatalogTableStorageDescriptorSerDeInfoArgs;
import com.pulumi.aws.glue.inputs.CatalogTableStorageDescriptorSkewedInfoArgs;
import com.pulumi.aws.glue.inputs.CatalogTablePartitionKeyArgs;
import com.pulumi.aws.glue.MLTransform;
import com.pulumi.aws.glue.MLTransformArgs;
import com.pulumi.aws.glue.inputs.MLTransformInputRecordTableArgs;
import com.pulumi.aws.glue.inputs.MLTransformParametersArgs;
import com.pulumi.aws.glue.inputs.MLTransformParametersFindMatchesParametersArgs;
import com.pulumi.resources.CustomResourceOptions;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }
    public static void stack(Context ctx) {
        var testCatalogDatabase = new CatalogDatabase("testCatalogDatabase", CatalogDatabaseArgs.builder()
            .name("example")
            .build());
        var testCatalogTable = new CatalogTable("testCatalogTable", CatalogTableArgs.builder()
            .name("example")
            .databaseName(testCatalogDatabase.name())
            .owner("my_owner")
            .retention(1)
            .tableType("VIRTUAL_VIEW")
            .viewExpandedText("view_expanded_text_1")
            .viewOriginalText("view_original_text_1")
            .storageDescriptor(CatalogTableStorageDescriptorArgs.builder()
                .bucketColumns("bucket_column_1")
                .compressed(false)
                .inputFormat("SequenceFileInputFormat")
                .location("my_location")
                .numberOfBuckets(1)
                .outputFormat("SequenceFileInputFormat")
                .storedAsSubDirectories(false)
                .parameters(Map.of("param1", "param1_val"))
                .columns(
                    CatalogTableStorageDescriptorColumnArgs.builder()
                        .name("my_column_1")
                        .type("int")
                        .comment("my_column1_comment")
                        .build(),
                    CatalogTableStorageDescriptorColumnArgs.builder()
                        .name("my_column_2")
                        .type("string")
                        .comment("my_column2_comment")
                        .build())
                .serDeInfo(CatalogTableStorageDescriptorSerDeInfoArgs.builder()
                    .name("ser_de_name")
                    .parameters(Map.of("param1", "param_val_1"))
                    .serializationLibrary("org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe")
                    .build())
                .sortColumns(CatalogTableStorageDescriptorSortColumnArgs.builder()
                    .column("my_column_1")
                    .sortOrder(1)
                    .build())
                .skewedInfo(CatalogTableStorageDescriptorSkewedInfoArgs.builder()
                    .skewedColumnNames("my_column_1")
                    .skewedColumnValueLocationMaps(Map.of("my_column_1", "my_column_1_val_loc_map"))
                    .skewedColumnValues("skewed_val_1")
                    .build())
                .build())
            .partitionKeys(
                CatalogTablePartitionKeyArgs.builder()
                    .name("my_column_1")
                    .type("int")
                    .comment("my_column_1_comment")
                    .build(),
                CatalogTablePartitionKeyArgs.builder()
                    .name("my_column_2")
                    .type("string")
                    .comment("my_column_2_comment")
                    .build())
            .parameters(Map.of("param1", "param1_val"))
            .build());
        var testMLTransform = new MLTransform("testMLTransform", MLTransformArgs.builder()
            .roleArn(aws_iam_role.test().arn())
            .inputRecordTables(MLTransformInputRecordTableArgs.builder()
                .databaseName(testCatalogTable.databaseName())
                .tableName(testCatalogTable.name())
                .build())
            .parameters(MLTransformParametersArgs.builder()
                .transformType("FIND_MATCHES")
                .findMatchesParameters(MLTransformParametersFindMatchesParametersArgs.builder()
                    .primaryKeyColumnName("my_column_1")
                    .build())
                .build())
            .build(), CustomResourceOptions.builder()
                .dependsOn(aws_iam_role_policy_attachment.test())
                .build());
    }
}
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Import

Glue ML Transforms can be imported using id, e.g.,

$ pulumi import aws:glue/mLTransform:MLTransform example tfm-c2cafbe83b1c575f49eaca9939220e2fcd58e2d5
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Constructors

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fun MLTransformArgs(description: Output<String>? = null, glueVersion: Output<String>? = null, inputRecordTables: Output<List<MLTransformInputRecordTableArgs>>? = null, maxCapacity: Output<Double>? = null, maxRetries: Output<Int>? = null, name: Output<String>? = null, numberOfWorkers: Output<Int>? = null, parameters: Output<MLTransformParametersArgs>? = null, roleArn: Output<String>? = null, tags: Output<Map<String, String>>? = null, timeout: Output<Int>? = null, workerType: Output<String>? = null)

Functions

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open override fun toJava(): MLTransformArgs

Properties

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val description: Output<String>? = null

Description of the ML Transform.

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val glueVersion: Output<String>? = null

The version of glue to use, for example "1.0". For information about available versions, see the AWS Glue Release Notes.

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val inputRecordTables: Output<List<MLTransformInputRecordTableArgs>>? = null

A list of AWS Glue table definitions used by the transform. see Input Record Tables.

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val maxCapacity: Output<Double>? = null

The number of AWS 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. max_capacity is a mutually exclusive option with number_of_workers and worker_type.

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val maxRetries: Output<Int>? = null

The maximum number of times to retry this ML Transform if it fails.

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val name: Output<String>? = null

The name you assign to this ML Transform. It must be unique in your account.

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val numberOfWorkers: Output<Int>? = null

The number of workers of a defined worker_type that are allocated when an ML Transform runs. Required with worker_type.

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val parameters: Output<MLTransformParametersArgs>? = null

The algorithmic parameters that are specific to the transform type used. Conditionally dependent on the transform type. see Parameters.

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val roleArn: Output<String>? = null

The ARN of the IAM role associated with this ML Transform.

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val tags: Output<Map<String, String>>? = null

Key-value map of resource tags. .If configured with a provider default_tags configuration block present, tags with matching keys will overwrite those defined at the provider-level.

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val timeout: Output<Int>? = null

The ML Transform timeout in minutes. The default is 2880 minutes (48 hours).

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val workerType: Output<String>? = null

The type of predefined worker that is allocated when an ML Transform runs. Accepts a value of Standard, G.1X, or G.2X. Required with number_of_workers.