Struct aws_sdk_glue::types::builders::JobBuilder

#[non_exhaustive]
pub struct JobBuilder { /* private fields */ }

A builder for Job.

Implementations

impl JobBuilder

pub fn name(self, input: impl Into<String>) -> Self

The name you assign to this job definition.

pub fn set_name(self, input: Option<String>) -> Self

The name you assign to this job definition.

pub fn get_name(&self) -> &Option<String>

The name you assign to this job definition.

pub fn description(self, input: impl Into<String>) -> Self

A description of the job.

pub fn set_description(self, input: Option<String>) -> Self

A description of the job.

pub fn get_description(&self) -> &Option<String>

A description of the job.

pub fn log_uri(self, input: impl Into<String>) -> Self

This field is reserved for future use.

pub fn set_log_uri(self, input: Option<String>) -> Self

This field is reserved for future use.

pub fn get_log_uri(&self) -> &Option<String>

This field is reserved for future use.

pub fn role(self, input: impl Into<String>) -> Self

The name or Amazon Resource Name (ARN) of the IAM role associated with this job.

pub fn set_role(self, input: Option<String>) -> Self

The name or Amazon Resource Name (ARN) of the IAM role associated with this job.

pub fn get_role(&self) -> &Option<String>

The name or Amazon Resource Name (ARN) of the IAM role associated with this job.

pub fn created_on(self, input: DateTime) -> Self

The time and date that this job definition was created.

pub fn set_created_on(self, input: Option<DateTime>) -> Self

The time and date that this job definition was created.

pub fn get_created_on(&self) -> &Option<DateTime>

The time and date that this job definition was created.

pub fn last_modified_on(self, input: DateTime) -> Self

The last point in time when this job definition was modified.

pub fn set_last_modified_on(self, input: Option<DateTime>) -> Self

The last point in time when this job definition was modified.

pub fn get_last_modified_on(&self) -> &Option<DateTime>

The last point in time when this job definition was modified.

pub fn execution_property(self, input: ExecutionProperty) -> Self

An ExecutionProperty specifying the maximum number of concurrent runs allowed for this job.

pub fn set_execution_property(self, input: Option<ExecutionProperty>) -> Self

An ExecutionProperty specifying the maximum number of concurrent runs allowed for this job.

pub fn get_execution_property(&self) -> &Option<ExecutionProperty>

An ExecutionProperty specifying the maximum number of concurrent runs allowed for this job.

pub fn command(self, input: JobCommand) -> Self

The JobCommand that runs this job.

pub fn set_command(self, input: Option<JobCommand>) -> Self

The JobCommand that runs this job.

pub fn get_command(&self) -> &Option<JobCommand>

The JobCommand that runs this job.

pub fn default_arguments( self, k: impl Into<String>, v: impl Into<String> ) -> Self

Adds a key-value pair to default_arguments.

To override the contents of this collection use set_default_arguments.

The default arguments for every run of this job, specified as name-value pairs.

You can specify arguments here that your own job-execution script consumes, as well as arguments that Glue itself consumes.

Job arguments may be logged. Do not pass plaintext secrets as arguments. Retrieve secrets from a Glue Connection, Secrets Manager or other secret management mechanism if you intend to keep them within the Job.

For information about how to specify and consume your own Job arguments, see the Calling Glue APIs in Python topic in the developer guide.

For information about the arguments you can provide to this field when configuring Spark jobs, see the Special Parameters Used by Glue topic in the developer guide.

For information about the arguments you can provide to this field when configuring Ray jobs, see Using job parameters in Ray jobs in the developer guide.

pub fn set_default_arguments( self, input: Option<HashMap<String, String>> ) -> Self

The default arguments for every run of this job, specified as name-value pairs.

You can specify arguments here that your own job-execution script consumes, as well as arguments that Glue itself consumes.

Job arguments may be logged. Do not pass plaintext secrets as arguments. Retrieve secrets from a Glue Connection, Secrets Manager or other secret management mechanism if you intend to keep them within the Job.

For information about how to specify and consume your own Job arguments, see the Calling Glue APIs in Python topic in the developer guide.

For information about the arguments you can provide to this field when configuring Spark jobs, see the Special Parameters Used by Glue topic in the developer guide.

For information about the arguments you can provide to this field when configuring Ray jobs, see Using job parameters in Ray jobs in the developer guide.

pub fn get_default_arguments(&self) -> &Option<HashMap<String, String>>

The default arguments for every run of this job, specified as name-value pairs.

You can specify arguments here that your own job-execution script consumes, as well as arguments that Glue itself consumes.

Job arguments may be logged. Do not pass plaintext secrets as arguments. Retrieve secrets from a Glue Connection, Secrets Manager or other secret management mechanism if you intend to keep them within the Job.

For information about how to specify and consume your own Job arguments, see the Calling Glue APIs in Python topic in the developer guide.

For information about the arguments you can provide to this field when configuring Spark jobs, see the Special Parameters Used by Glue topic in the developer guide.

For information about the arguments you can provide to this field when configuring Ray jobs, see Using job parameters in Ray jobs in the developer guide.

pub fn non_overridable_arguments( self, k: impl Into<String>, v: impl Into<String> ) -> Self

Adds a key-value pair to non_overridable_arguments.

To override the contents of this collection use set_non_overridable_arguments.

Arguments for this job that are not overridden when providing job arguments in a job run, specified as name-value pairs.

pub fn set_non_overridable_arguments( self, input: Option<HashMap<String, String>> ) -> Self

Arguments for this job that are not overridden when providing job arguments in a job run, specified as name-value pairs.

pub fn get_non_overridable_arguments(&self) -> &Option<HashMap<String, String>>

Arguments for this job that are not overridden when providing job arguments in a job run, specified as name-value pairs.

pub fn connections(self, input: ConnectionsList) -> Self

The connections used for this job.

pub fn set_connections(self, input: Option<ConnectionsList>) -> Self

The connections used for this job.

pub fn get_connections(&self) -> &Option<ConnectionsList>

The connections used for this job.

pub fn max_retries(self, input: i32) -> Self

The maximum number of times to retry this job after a JobRun fails.

pub fn set_max_retries(self, input: Option<i32>) -> Self

The maximum number of times to retry this job after a JobRun fails.

pub fn get_max_retries(&self) -> &Option<i32>

The maximum number of times to retry this job after a JobRun fails.

pub fn allocated_capacity(self, input: i32) -> Self

👎Deprecated: This property is deprecated, use MaxCapacity instead.

This field is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) allocated to runs of this job. You can allocate a minimum of 2 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.

pub fn set_allocated_capacity(self, input: Option<i32>) -> Self

👎Deprecated: This property is deprecated, use MaxCapacity instead.

This field is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) allocated to runs of this job. You can allocate a minimum of 2 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.

pub fn get_allocated_capacity(&self) -> &Option<i32>

👎Deprecated: This property is deprecated, use MaxCapacity instead.

This field is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) allocated to runs of this job. You can allocate a minimum of 2 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.

pub fn timeout(self, input: i32) -> Self

The job timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours).

pub fn set_timeout(self, input: Option<i32>) -> Self

The job timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours).

pub fn get_timeout(&self) -> &Option<i32>

The job timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours).

pub fn max_capacity(self, input: f64) -> Self

For Glue version 1.0 or earlier jobs, using the standard worker type, the number of Glue data processing units (DPUs) that can be allocated when this job runs. 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.

For Glue version 2.0 or later jobs, you cannot specify a Maximum capacity. Instead, you should specify a Worker type and the Number of workers.

Do not set MaxCapacity if using WorkerType and NumberOfWorkers.

The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job, an Apache Spark ETL job, or an Apache Spark streaming ETL job:

• When you specify a Python shell job (JobCommand.Name="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.

• When you specify an Apache Spark ETL job (JobCommand.Name="glueetl") or Apache Spark streaming ETL job (JobCommand.Name="gluestreaming"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.

pub fn set_max_capacity(self, input: Option<f64>) -> Self

For Glue version 1.0 or earlier jobs, using the standard worker type, the number of Glue data processing units (DPUs) that can be allocated when this job runs. 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.

For Glue version 2.0 or later jobs, you cannot specify a Maximum capacity. Instead, you should specify a Worker type and the Number of workers.

Do not set MaxCapacity if using WorkerType and NumberOfWorkers.

The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job, an Apache Spark ETL job, or an Apache Spark streaming ETL job:

• When you specify a Python shell job (JobCommand.Name="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.

• When you specify an Apache Spark ETL job (JobCommand.Name="glueetl") or Apache Spark streaming ETL job (JobCommand.Name="gluestreaming"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.

pub fn get_max_capacity(&self) -> &Option<f64>

For Glue version 1.0 or earlier jobs, using the standard worker type, the number of Glue data processing units (DPUs) that can be allocated when this job runs. 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.

For Glue version 2.0 or later jobs, you cannot specify a Maximum capacity. Instead, you should specify a Worker type and the Number of workers.

Do not set MaxCapacity if using WorkerType and NumberOfWorkers.

The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job, an Apache Spark ETL job, or an Apache Spark streaming ETL job:

• When you specify a Python shell job (JobCommand.Name="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.

• When you specify an Apache Spark ETL job (JobCommand.Name="glueetl") or Apache Spark streaming ETL job (JobCommand.Name="gluestreaming"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.

pub fn worker_type(self, input: WorkerType) -> Self

The type of predefined worker that is allocated when a job runs. Accepts a value of G.1X, G.2X, G.4X, G.8X or G.025X for Spark jobs. Accepts the value Z.2X for Ray jobs.

• For the G.1X worker type, each worker maps to 1 DPU (4 vCPUs, 16 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

• For the G.2X worker type, each worker maps to 2 DPU (8 vCPUs, 32 GB of memory) with 128GB disk (approximately 77GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

• For the G.4X worker type, each worker maps to 4 DPU (16 vCPUs, 64 GB of memory) with 256GB disk (approximately 235GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs in the following Amazon Web Services Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm).

• For the G.8X worker type, each worker maps to 8 DPU (32 vCPUs, 128 GB of memory) with 512GB disk (approximately 487GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs, in the same Amazon Web Services Regions as supported for the G.4X worker type.

• For the G.025X worker type, each worker maps to 0.25 DPU (2 vCPUs, 4 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for low volume streaming jobs. This worker type is only available for Glue version 3.0 streaming jobs.

• For the Z.2X worker type, each worker maps to 2 M-DPU (8vCPUs, 64 GB of memory) with 128 GB disk (approximately 120GB free), and provides up to 8 Ray workers based on the autoscaler.

pub fn set_worker_type(self, input: Option<WorkerType>) -> Self

The type of predefined worker that is allocated when a job runs. Accepts a value of G.1X, G.2X, G.4X, G.8X or G.025X for Spark jobs. Accepts the value Z.2X for Ray jobs.

• For the G.1X worker type, each worker maps to 1 DPU (4 vCPUs, 16 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

• For the G.2X worker type, each worker maps to 2 DPU (8 vCPUs, 32 GB of memory) with 128GB disk (approximately 77GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

• For the G.4X worker type, each worker maps to 4 DPU (16 vCPUs, 64 GB of memory) with 256GB disk (approximately 235GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs in the following Amazon Web Services Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm).

• For the G.8X worker type, each worker maps to 8 DPU (32 vCPUs, 128 GB of memory) with 512GB disk (approximately 487GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs, in the same Amazon Web Services Regions as supported for the G.4X worker type.

• For the G.025X worker type, each worker maps to 0.25 DPU (2 vCPUs, 4 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for low volume streaming jobs. This worker type is only available for Glue version 3.0 streaming jobs.

• For the Z.2X worker type, each worker maps to 2 M-DPU (8vCPUs, 64 GB of memory) with 128 GB disk (approximately 120GB free), and provides up to 8 Ray workers based on the autoscaler.

pub fn get_worker_type(&self) -> &Option<WorkerType>

The type of predefined worker that is allocated when a job runs. Accepts a value of G.1X, G.2X, G.4X, G.8X or G.025X for Spark jobs. Accepts the value Z.2X for Ray jobs.

• For the G.1X worker type, each worker maps to 1 DPU (4 vCPUs, 16 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

• For the G.2X worker type, each worker maps to 2 DPU (8 vCPUs, 32 GB of memory) with 128GB disk (approximately 77GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

• For the G.4X worker type, each worker maps to 4 DPU (16 vCPUs, 64 GB of memory) with 256GB disk (approximately 235GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs in the following Amazon Web Services Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm).

• For the G.8X worker type, each worker maps to 8 DPU (32 vCPUs, 128 GB of memory) with 512GB disk (approximately 487GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs, in the same Amazon Web Services Regions as supported for the G.4X worker type.

• For the G.025X worker type, each worker maps to 0.25 DPU (2 vCPUs, 4 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for low volume streaming jobs. This worker type is only available for Glue version 3.0 streaming jobs.

• For the Z.2X worker type, each worker maps to 2 M-DPU (8vCPUs, 64 GB of memory) with 128 GB disk (approximately 120GB free), and provides up to 8 Ray workers based on the autoscaler.

pub fn number_of_workers(self, input: i32) -> Self

The number of workers of a defined workerType that are allocated when a job runs.

pub fn set_number_of_workers(self, input: Option<i32>) -> Self

The number of workers of a defined workerType that are allocated when a job runs.

pub fn get_number_of_workers(&self) -> &Option<i32>

The number of workers of a defined workerType that are allocated when a job runs.

pub fn security_configuration(self, input: impl Into<String>) -> Self

The name of the SecurityConfiguration structure to be used with this job.

pub fn set_security_configuration(self, input: Option<String>) -> Self

The name of the SecurityConfiguration structure to be used with this job.

pub fn get_security_configuration(&self) -> &Option<String>

The name of the SecurityConfiguration structure to be used with this job.

pub fn notification_property(self, input: NotificationProperty) -> Self

Specifies configuration properties of a job notification.

pub fn set_notification_property( self, input: Option<NotificationProperty> ) -> Self

Specifies configuration properties of a job notification.

pub fn get_notification_property(&self) -> &Option<NotificationProperty>

Specifies configuration properties of a job notification.

pub fn glue_version(self, input: impl Into<String>) -> Self

In Spark jobs, GlueVersion determines the versions of Apache Spark and Python that Glue available in a job. The Python version indicates the version supported for jobs of type Spark.

Ray jobs should set GlueVersion to 4.0 or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the Runtime parameter of the Job command.

For more information about the available Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide.

Jobs that are created without specifying a Glue version default to Glue 0.9.

pub fn set_glue_version(self, input: Option<String>) -> Self

In Spark jobs, GlueVersion determines the versions of Apache Spark and Python that Glue available in a job. The Python version indicates the version supported for jobs of type Spark.

Ray jobs should set GlueVersion to 4.0 or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the Runtime parameter of the Job command.

For more information about the available Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide.

Jobs that are created without specifying a Glue version default to Glue 0.9.

pub fn get_glue_version(&self) -> &Option<String>

In Spark jobs, GlueVersion determines the versions of Apache Spark and Python that Glue available in a job. The Python version indicates the version supported for jobs of type Spark.

Ray jobs should set GlueVersion to 4.0 or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the Runtime parameter of the Job command.

For more information about the available Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide.

Jobs that are created without specifying a Glue version default to Glue 0.9.

pub fn code_gen_configuration_nodes( self, k: impl Into<String>, v: CodeGenConfigurationNode ) -> Self

Adds a key-value pair to code_gen_configuration_nodes.

To override the contents of this collection use set_code_gen_configuration_nodes.

The representation of a directed acyclic graph on which both the Glue Studio visual component and Glue Studio code generation is based.

pub fn set_code_gen_configuration_nodes( self, input: Option<HashMap<String, CodeGenConfigurationNode>> ) -> Self

The representation of a directed acyclic graph on which both the Glue Studio visual component and Glue Studio code generation is based.

pub fn get_code_gen_configuration_nodes( &self ) -> &Option<HashMap<String, CodeGenConfigurationNode>>

The representation of a directed acyclic graph on which both the Glue Studio visual component and Glue Studio code generation is based.

pub fn execution_class(self, input: ExecutionClass) -> Self

Indicates whether the job is run with a standard or flexible execution class. The standard execution class is ideal for time-sensitive workloads that require fast job startup and dedicated resources.

The flexible execution class is appropriate for time-insensitive jobs whose start and completion times may vary.

Only jobs with Glue version 3.0 and above and command type glueetl will be allowed to set ExecutionClass to FLEX. The flexible execution class is available for Spark jobs.

pub fn set_execution_class(self, input: Option<ExecutionClass>) -> Self

Indicates whether the job is run with a standard or flexible execution class. The standard execution class is ideal for time-sensitive workloads that require fast job startup and dedicated resources.

The flexible execution class is appropriate for time-insensitive jobs whose start and completion times may vary.

Only jobs with Glue version 3.0 and above and command type glueetl will be allowed to set ExecutionClass to FLEX. The flexible execution class is available for Spark jobs.

pub fn get_execution_class(&self) -> &Option<ExecutionClass>

Indicates whether the job is run with a standard or flexible execution class. The standard execution class is ideal for time-sensitive workloads that require fast job startup and dedicated resources.

The flexible execution class is appropriate for time-insensitive jobs whose start and completion times may vary.

Only jobs with Glue version 3.0 and above and command type glueetl will be allowed to set ExecutionClass to FLEX. The flexible execution class is available for Spark jobs.

pub fn source_control_details(self, input: SourceControlDetails) -> Self

The details for a source control configuration for a job, allowing synchronization of job artifacts to or from a remote repository.

pub fn set_source_control_details( self, input: Option<SourceControlDetails> ) -> Self

The details for a source control configuration for a job, allowing synchronization of job artifacts to or from a remote repository.

pub fn get_source_control_details(&self) -> &Option<SourceControlDetails>

The details for a source control configuration for a job, allowing synchronization of job artifacts to or from a remote repository.

pub fn build(self) -> Job

Consumes the builder and constructs a Job.

Trait Implementations

impl Clone for JobBuilder

fn clone(&self) -> JobBuilder

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for JobBuilder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for JobBuilder

fn default() -> JobBuilder

Returns the “default value” for a type.

impl PartialEq for JobBuilder

fn eq(&self, other: &JobBuilder) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for JobBuilder