@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class StartJobRunRequest extends AmazonWebServiceRequest implements Serializable, Cloneable
NOOP| Constructor and Description |
|---|
StartJobRunRequest() |
| Modifier and Type | Method and Description |
|---|---|
StartJobRunRequest |
addArgumentsEntry(String key,
String value)
Add a single Arguments entry
|
StartJobRunRequest |
clearArgumentsEntries()
Removes all the entries added into Arguments.
|
StartJobRunRequest |
clone()
Creates a shallow clone of this object for all fields except the handler context.
|
boolean |
equals(Object obj) |
Integer |
getAllocatedCapacity()
Deprecated.
|
Map<String,String> |
getArguments()
The job arguments associated with this run.
|
String |
getExecutionClass()
Indicates whether the job is run with a standard or flexible execution class.
|
String |
getJobName()
The name of the job definition to use.
|
String |
getJobRunId()
The ID of a previous
JobRun to retry. |
Double |
getMaxCapacity()
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.
|
NotificationProperty |
getNotificationProperty()
Specifies configuration properties of a job run notification.
|
Integer |
getNumberOfWorkers()
The number of workers of a defined
workerType that are allocated when a job runs. |
String |
getSecurityConfiguration()
The name of the
SecurityConfiguration structure to be used with this job run. |
Integer |
getTimeout()
The
JobRun timeout in minutes. |
String |
getWorkerType()
The type of predefined worker that is allocated when a job runs.
|
int |
hashCode() |
void |
setAllocatedCapacity(Integer allocatedCapacity)
Deprecated.
|
void |
setArguments(Map<String,String> arguments)
The job arguments associated with this run.
|
void |
setExecutionClass(String executionClass)
Indicates whether the job is run with a standard or flexible execution class.
|
void |
setJobName(String jobName)
The name of the job definition to use.
|
void |
setJobRunId(String jobRunId)
The ID of a previous
JobRun to retry. |
void |
setMaxCapacity(Double maxCapacity)
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.
|
void |
setNotificationProperty(NotificationProperty notificationProperty)
Specifies configuration properties of a job run notification.
|
void |
setNumberOfWorkers(Integer numberOfWorkers)
The number of workers of a defined
workerType that are allocated when a job runs. |
void |
setSecurityConfiguration(String securityConfiguration)
The name of the
SecurityConfiguration structure to be used with this job run. |
void |
setTimeout(Integer timeout)
The
JobRun timeout in minutes. |
void |
setWorkerType(String workerType)
The type of predefined worker that is allocated when a job runs.
|
String |
toString()
Returns a string representation of this object.
|
StartJobRunRequest |
withAllocatedCapacity(Integer allocatedCapacity)
Deprecated.
|
StartJobRunRequest |
withArguments(Map<String,String> arguments)
The job arguments associated with this run.
|
StartJobRunRequest |
withExecutionClass(ExecutionClass executionClass)
Indicates whether the job is run with a standard or flexible execution class.
|
StartJobRunRequest |
withExecutionClass(String executionClass)
Indicates whether the job is run with a standard or flexible execution class.
|
StartJobRunRequest |
withJobName(String jobName)
The name of the job definition to use.
|
StartJobRunRequest |
withJobRunId(String jobRunId)
The ID of a previous
JobRun to retry. |
StartJobRunRequest |
withMaxCapacity(Double maxCapacity)
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.
|
StartJobRunRequest |
withNotificationProperty(NotificationProperty notificationProperty)
Specifies configuration properties of a job run notification.
|
StartJobRunRequest |
withNumberOfWorkers(Integer numberOfWorkers)
The number of workers of a defined
workerType that are allocated when a job runs. |
StartJobRunRequest |
withSecurityConfiguration(String securityConfiguration)
The name of the
SecurityConfiguration structure to be used with this job run. |
StartJobRunRequest |
withTimeout(Integer timeout)
The
JobRun timeout in minutes. |
StartJobRunRequest |
withWorkerType(String workerType)
The type of predefined worker that is allocated when a job runs.
|
StartJobRunRequest |
withWorkerType(WorkerType workerType)
The type of predefined worker that is allocated when a job runs.
|
addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeoutpublic void setJobName(String jobName)
The name of the job definition to use.
jobName - The name of the job definition to use.public String getJobName()
The name of the job definition to use.
public StartJobRunRequest withJobName(String jobName)
The name of the job definition to use.
jobName - The name of the job definition to use.public void setJobRunId(String jobRunId)
The ID of a previous JobRun to retry.
jobRunId - The ID of a previous JobRun to retry.public String getJobRunId()
The ID of a previous JobRun to retry.
JobRun to retry.public StartJobRunRequest withJobRunId(String jobRunId)
The ID of a previous JobRun to retry.
jobRunId - The ID of a previous JobRun to retry.public Map<String,String> getArguments()
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.
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.
public void setArguments(Map<String,String> 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.
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.
public StartJobRunRequest withArguments(Map<String,String> 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.
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.
public StartJobRunRequest addArgumentsEntry(String key, String value)
public StartJobRunRequest clearArgumentsEntries()
@Deprecated public void setAllocatedCapacity(Integer allocatedCapacity)
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.
allocatedCapacity - 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.
@Deprecated public Integer getAllocatedCapacity()
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.
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.
@Deprecated public StartJobRunRequest withAllocatedCapacity(Integer allocatedCapacity)
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.
allocatedCapacity - 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.
public void setTimeout(Integer timeout)
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.
timeout - 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.
public Integer getTimeout()
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.
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.
public StartJobRunRequest withTimeout(Integer timeout)
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.
timeout - 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.
public void setMaxCapacity(Double maxCapacity)
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.
maxCapacity - 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.
public Double getMaxCapacity()
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.
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.
public StartJobRunRequest withMaxCapacity(Double maxCapacity)
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.
maxCapacity - 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.
public void setSecurityConfiguration(String securityConfiguration)
The name of the SecurityConfiguration structure to be used with this job run.
securityConfiguration - The name of the SecurityConfiguration structure to be used with this job run.public String getSecurityConfiguration()
The name of the SecurityConfiguration structure to be used with this job run.
SecurityConfiguration structure to be used with this job run.public StartJobRunRequest withSecurityConfiguration(String securityConfiguration)
The name of the SecurityConfiguration structure to be used with this job run.
securityConfiguration - The name of the SecurityConfiguration structure to be used with this job run.public void setNotificationProperty(NotificationProperty notificationProperty)
Specifies configuration properties of a job run notification.
notificationProperty - Specifies configuration properties of a job run notification.public NotificationProperty getNotificationProperty()
Specifies configuration properties of a job run notification.
public StartJobRunRequest withNotificationProperty(NotificationProperty notificationProperty)
Specifies configuration properties of a job run notification.
notificationProperty - Specifies configuration properties of a job run notification.public void setWorkerType(String 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.
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.
WorkerTypepublic String getWorkerType()
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.
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.
WorkerTypepublic StartJobRunRequest withWorkerType(String 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.
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.
WorkerTypepublic StartJobRunRequest withWorkerType(WorkerType 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.
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.
WorkerTypepublic void setNumberOfWorkers(Integer numberOfWorkers)
The number of workers of a defined workerType that are allocated when a job runs.
numberOfWorkers - The number of workers of a defined workerType that are allocated when a job runs.public Integer getNumberOfWorkers()
The number of workers of a defined workerType that are allocated when a job runs.
workerType that are allocated when a job runs.public StartJobRunRequest withNumberOfWorkers(Integer numberOfWorkers)
The number of workers of a defined workerType that are allocated when a job runs.
numberOfWorkers - The number of workers of a defined workerType that are allocated when a job runs.public void setExecutionClass(String 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.
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.
ExecutionClasspublic String getExecutionClass()
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.
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.
ExecutionClasspublic StartJobRunRequest withExecutionClass(String 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.
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.
ExecutionClasspublic StartJobRunRequest withExecutionClass(ExecutionClass 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.
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.
ExecutionClasspublic String toString()
toString in class ObjectObject.toString()public StartJobRunRequest clone()
AmazonWebServiceRequestclone in class AmazonWebServiceRequestObject.clone()