#[non_exhaustive]pub struct StartJobRunInputBuilder { /* private fields */ }
Expand description
A builder for StartJobRunInput
.
Implementations§
source§impl StartJobRunInputBuilder
impl StartJobRunInputBuilder
sourcepub fn job_name(self, input: impl Into<String>) -> Self
pub fn job_name(self, input: impl Into<String>) -> Self
The name of the job definition to use.
This field is required.sourcepub fn set_job_name(self, input: Option<String>) -> Self
pub fn set_job_name(self, input: Option<String>) -> Self
The name of the job definition to use.
sourcepub fn get_job_name(&self) -> &Option<String>
pub fn get_job_name(&self) -> &Option<String>
The name of the job definition to use.
sourcepub fn job_run_id(self, input: impl Into<String>) -> Self
pub fn job_run_id(self, input: impl Into<String>) -> Self
The ID of a previous JobRun
to retry.
sourcepub fn set_job_run_id(self, input: Option<String>) -> Self
pub fn set_job_run_id(self, input: Option<String>) -> Self
The ID of a previous JobRun
to retry.
sourcepub fn get_job_run_id(&self) -> &Option<String>
pub fn get_job_run_id(&self) -> &Option<String>
The ID of a previous JobRun
to retry.
sourcepub fn arguments(self, k: impl Into<String>, v: impl Into<String>) -> Self
pub fn arguments(self, k: impl Into<String>, v: impl Into<String>) -> Self
Adds a key-value pair to arguments
.
To override the contents of this collection use set_arguments
.
The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself.
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.
sourcepub fn set_arguments(self, input: Option<HashMap<String, String>>) -> Self
pub fn set_arguments(self, input: Option<HashMap<String, String>>) -> Self
The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself.
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.
sourcepub fn get_arguments(&self) -> &Option<HashMap<String, String>>
pub fn get_arguments(&self) -> &Option<HashMap<String, String>>
The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself.
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.
sourcepub fn allocated_capacity(self, input: i32) -> Self
👎Deprecated: This property is deprecated, use MaxCapacity instead.
pub fn allocated_capacity(self, input: i32) -> Self
This field is deprecated. Use MaxCapacity
instead.
The number of Glue data processing units (DPUs) to allocate to this JobRun. 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.
sourcepub fn set_allocated_capacity(self, input: Option<i32>) -> Self
👎Deprecated: This property is deprecated, use MaxCapacity instead.
pub fn set_allocated_capacity(self, input: Option<i32>) -> Self
This field is deprecated. Use MaxCapacity
instead.
The number of Glue data processing units (DPUs) to allocate to this JobRun. 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.
sourcepub fn get_allocated_capacity(&self) -> &Option<i32>
👎Deprecated: This property is deprecated, use MaxCapacity instead.
pub fn get_allocated_capacity(&self) -> &Option<i32>
This field is deprecated. Use MaxCapacity
instead.
The number of Glue data processing units (DPUs) to allocate to this JobRun. 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.
sourcepub fn timeout(self, input: i32) -> Self
pub fn timeout(self, input: i32) -> Self
The JobRun
timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT
status. This value overrides the timeout value set in the parent job.
Streaming jobs must have timeout values less than 7 days or 10080 minutes. When the value is left blank, the job will be restarted after 7 days based if you have not setup a maintenance window. If you have setup maintenance window, it will be restarted during the maintenance window after 7 days.
sourcepub fn set_timeout(self, input: Option<i32>) -> Self
pub fn set_timeout(self, input: Option<i32>) -> Self
The JobRun
timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT
status. This value overrides the timeout value set in the parent job.
Streaming jobs must have timeout values less than 7 days or 10080 minutes. When the value is left blank, the job will be restarted after 7 days based if you have not setup a maintenance window. If you have setup maintenance window, it will be restarted during the maintenance window after 7 days.
sourcepub fn get_timeout(&self) -> &Option<i32>
pub fn get_timeout(&self) -> &Option<i32>
The JobRun
timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT
status. This value overrides the timeout value set in the parent job.
Streaming jobs must have timeout values less than 7 days or 10080 minutes. When the value is left blank, the job will be restarted after 7 days based if you have not setup a maintenance window. If you have setup maintenance window, it will be restarted during the maintenance window after 7 days.
sourcepub fn max_capacity(self, input: f64) -> Self
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+ 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.
sourcepub fn set_max_capacity(self, input: Option<f64>) -> Self
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+ 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.
sourcepub fn get_max_capacity(&self) -> &Option<f64>
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+ 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.
sourcepub fn security_configuration(self, input: impl Into<String>) -> Self
pub fn security_configuration(self, input: impl Into<String>) -> Self
The name of the SecurityConfiguration
structure to be used with this job run.
sourcepub fn set_security_configuration(self, input: Option<String>) -> Self
pub fn set_security_configuration(self, input: Option<String>) -> Self
The name of the SecurityConfiguration
structure to be used with this job run.
sourcepub fn get_security_configuration(&self) -> &Option<String>
pub fn get_security_configuration(&self) -> &Option<String>
The name of the SecurityConfiguration
structure to be used with this job run.
sourcepub fn notification_property(self, input: NotificationProperty) -> Self
pub fn notification_property(self, input: NotificationProperty) -> Self
Specifies configuration properties of a job run notification.
sourcepub fn set_notification_property(
self,
input: Option<NotificationProperty>,
) -> Self
pub fn set_notification_property( self, input: Option<NotificationProperty>, ) -> Self
Specifies configuration properties of a job run notification.
sourcepub fn get_notification_property(&self) -> &Option<NotificationProperty>
pub fn get_notification_property(&self) -> &Option<NotificationProperty>
Specifies configuration properties of a job run notification.
sourcepub fn worker_type(self, input: WorkerType) -> Self
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 theG.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.
sourcepub fn set_worker_type(self, input: Option<WorkerType>) -> Self
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 theG.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.
sourcepub fn get_worker_type(&self) -> &Option<WorkerType>
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 theG.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.
sourcepub fn number_of_workers(self, input: i32) -> Self
pub fn number_of_workers(self, input: i32) -> Self
The number of workers of a defined workerType
that are allocated when a job runs.
sourcepub fn set_number_of_workers(self, input: Option<i32>) -> Self
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.
sourcepub fn get_number_of_workers(&self) -> &Option<i32>
pub fn get_number_of_workers(&self) -> &Option<i32>
The number of workers of a defined workerType
that are allocated when a job runs.
sourcepub fn execution_class(self, input: ExecutionClass) -> Self
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.
sourcepub fn set_execution_class(self, input: Option<ExecutionClass>) -> Self
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.
sourcepub fn get_execution_class(&self) -> &Option<ExecutionClass>
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.
sourcepub fn build(self) -> Result<StartJobRunInput, BuildError>
pub fn build(self) -> Result<StartJobRunInput, BuildError>
Consumes the builder and constructs a StartJobRunInput
.
source§impl StartJobRunInputBuilder
impl StartJobRunInputBuilder
sourcepub async fn send_with(
self,
client: &Client,
) -> Result<StartJobRunOutput, SdkError<StartJobRunError, HttpResponse>>
pub async fn send_with( self, client: &Client, ) -> Result<StartJobRunOutput, SdkError<StartJobRunError, HttpResponse>>
Sends a request with this input using the given client.
Trait Implementations§
source§impl Clone for StartJobRunInputBuilder
impl Clone for StartJobRunInputBuilder
source§fn clone(&self) -> StartJobRunInputBuilder
fn clone(&self) -> StartJobRunInputBuilder
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moresource§impl Debug for StartJobRunInputBuilder
impl Debug for StartJobRunInputBuilder
source§impl Default for StartJobRunInputBuilder
impl Default for StartJobRunInputBuilder
source§fn default() -> StartJobRunInputBuilder
fn default() -> StartJobRunInputBuilder
source§impl PartialEq for StartJobRunInputBuilder
impl PartialEq for StartJobRunInputBuilder
source§fn eq(&self, other: &StartJobRunInputBuilder) -> bool
fn eq(&self, other: &StartJobRunInputBuilder) -> bool
self
and other
values to be equal, and is used
by ==
.impl StructuralPartialEq for StartJobRunInputBuilder
Auto Trait Implementations§
impl Freeze for StartJobRunInputBuilder
impl RefUnwindSafe for StartJobRunInputBuilder
impl Send for StartJobRunInputBuilder
impl Sync for StartJobRunInputBuilder
impl Unpin for StartJobRunInputBuilder
impl UnwindSafe for StartJobRunInputBuilder
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
source§impl<T> Instrument for T
impl<T> Instrument for T
source§fn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
source§fn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
source§impl<T> IntoEither for T
impl<T> IntoEither for T
source§fn into_either(self, into_left: bool) -> Either<Self, Self>
fn into_either(self, into_left: bool) -> Either<Self, Self>
self
into a Left
variant of Either<Self, Self>
if into_left
is true
.
Converts self
into a Right
variant of Either<Self, Self>
otherwise. Read moresource§fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
self
into a Left
variant of Either<Self, Self>
if into_left(&self)
returns true
.
Converts self
into a Right
variant of Either<Self, Self>
otherwise. Read more