* Contains information about a job run. *
* * @see AWS API Documentation */ @Generated("com.amazonaws:aws-java-sdk-code-generator") public class JobRun implements Serializable, Cloneable, StructuredPojo { /** ** The ID of this job run. *
*/ private String id; /** ** The number of the attempt to run this job. *
*/ private Integer attempt; /** *
* The ID of the previous run of this job. For example, the JobRunId specified in the
* StartJobRun action.
*
* The name of the trigger that started this job run. *
*/ private String triggerName; /** ** The name of the job definition being used in this run. *
*/ private String jobName; /** ** The date and time at which this job run was started. *
*/ private java.util.Date startedOn; /** ** The last time that this job run was modified. *
*/ private java.util.Date lastModifiedOn; /** ** The date and time that this job run completed. *
*/ private java.util.Date completedOn; /** ** The current state of the job run. For more information about the statuses of jobs that have terminated * abnormally, see Glue Job Run * Statuses. *
*/ private String jobRunState; /** ** 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. *
*/ private java.util.Map* An error message associated with this job run. *
*/ private String errorMessage; /** ** A list of predecessors to this job run. *
*/ private java.util.List
* This field is deprecated. Use MaxCapacity instead.
*
* The number of Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; * 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 private Integer allocatedCapacity; /** ** The amount of time (in seconds) that the job run consumed resources. *
*/ private Integer executionTime; /** *
* 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 do not have a timeout. The default for non-streaming jobs is 2,880 minutes (48 hours). *
*/ private Integer timeout; /** ** 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.
*
* 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.
*
* The number of workers of a defined workerType that are allocated when a job runs.
*
* The name of the SecurityConfiguration structure to be used with this job run.
*
* The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using KMS.
* This name can be /aws-glue/jobs/, in which case the default encryption is NONE. If you
* add a role name and SecurityConfiguration name (in other words,
* /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/), then that security configuration is
* used to encrypt the log group.
*
* Specifies configuration properties of a job run notification. *
*/ private NotificationProperty notificationProperty; /** *
* 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. *
*/ private String glueVersion; /** *
* This field populates only for Auto Scaling job runs, and represents the total time each executor ran during the
* lifecycle of a job run in seconds, multiplied by a DPU factor (1 for G.1X, 2 for G.2X,
* or 0.25 for G.025X workers). This value may be different than the
* executionEngineRuntime * MaxCapacity as in the case of Auto Scaling jobs, as the number
* of executors running at a given time may be less than the MaxCapacity. Therefore, it is possible
* that the value of DPUSeconds is less than executionEngineRuntime *
* MaxCapacity.
*
* 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 ID of this job run. *
* * @param id * The ID of this job run. */ public void setId(String id) { this.id = id; } /** ** The ID of this job run. *
* * @return The ID of this job run. */ public String getId() { return this.id; } /** ** The ID of this job run. *
* * @param id * The ID of this job run. * @return Returns a reference to this object so that method calls can be chained together. */ public JobRun withId(String id) { setId(id); return this; } /** ** The number of the attempt to run this job. *
* * @param attempt * The number of the attempt to run this job. */ public void setAttempt(Integer attempt) { this.attempt = attempt; } /** ** The number of the attempt to run this job. *
* * @return The number of the attempt to run this job. */ public Integer getAttempt() { return this.attempt; } /** ** The number of the attempt to run this job. *
* * @param attempt * The number of the attempt to run this job. * @return Returns a reference to this object so that method calls can be chained together. */ public JobRun withAttempt(Integer attempt) { setAttempt(attempt); return this; } /** *
* The ID of the previous run of this job. For example, the JobRunId specified in the
* StartJobRun action.
*
JobRunId specified in the
* StartJobRun action.
*/
public void setPreviousRunId(String previousRunId) {
this.previousRunId = previousRunId;
}
/**
*
* The ID of the previous run of this job. For example, the JobRunId specified in the
* StartJobRun action.
*
JobRunId specified in the
* StartJobRun action.
*/
public String getPreviousRunId() {
return this.previousRunId;
}
/**
*
* The ID of the previous run of this job. For example, the JobRunId specified in the
* StartJobRun action.
*
JobRunId specified in the
* StartJobRun action.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public JobRun withPreviousRunId(String previousRunId) {
setPreviousRunId(previousRunId);
return this;
}
/**
* * The name of the trigger that started this job run. *
* * @param triggerName * The name of the trigger that started this job run. */ public void setTriggerName(String triggerName) { this.triggerName = triggerName; } /** ** The name of the trigger that started this job run. *
* * @return The name of the trigger that started this job run. */ public String getTriggerName() { return this.triggerName; } /** ** The name of the trigger that started this job run. *
* * @param triggerName * The name of the trigger that started this job run. * @return Returns a reference to this object so that method calls can be chained together. */ public JobRun withTriggerName(String triggerName) { setTriggerName(triggerName); return this; } /** ** The name of the job definition being used in this run. *
* * @param jobName * The name of the job definition being used in this run. */ public void setJobName(String jobName) { this.jobName = jobName; } /** ** The name of the job definition being used in this run. *
* * @return The name of the job definition being used in this run. */ public String getJobName() { return this.jobName; } /** ** The name of the job definition being used in this run. *
* * @param jobName * The name of the job definition being used in this run. * @return Returns a reference to this object so that method calls can be chained together. */ public JobRun withJobName(String jobName) { setJobName(jobName); return this; } /** ** The date and time at which this job run was started. *
* * @param startedOn * The date and time at which this job run was started. */ public void setStartedOn(java.util.Date startedOn) { this.startedOn = startedOn; } /** ** The date and time at which this job run was started. *
* * @return The date and time at which this job run was started. */ public java.util.Date getStartedOn() { return this.startedOn; } /** ** The date and time at which this job run was started. *
* * @param startedOn * The date and time at which this job run was started. * @return Returns a reference to this object so that method calls can be chained together. */ public JobRun withStartedOn(java.util.Date startedOn) { setStartedOn(startedOn); return this; } /** ** The last time that this job run was modified. *
* * @param lastModifiedOn * The last time that this job run was modified. */ public void setLastModifiedOn(java.util.Date lastModifiedOn) { this.lastModifiedOn = lastModifiedOn; } /** ** The last time that this job run was modified. *
* * @return The last time that this job run was modified. */ public java.util.Date getLastModifiedOn() { return this.lastModifiedOn; } /** ** The last time that this job run was modified. *
* * @param lastModifiedOn * The last time that this job run was modified. * @return Returns a reference to this object so that method calls can be chained together. */ public JobRun withLastModifiedOn(java.util.Date lastModifiedOn) { setLastModifiedOn(lastModifiedOn); return this; } /** ** The date and time that this job run completed. *
* * @param completedOn * The date and time that this job run completed. */ public void setCompletedOn(java.util.Date completedOn) { this.completedOn = completedOn; } /** ** The date and time that this job run completed. *
* * @return The date and time that this job run completed. */ public java.util.Date getCompletedOn() { return this.completedOn; } /** ** The date and time that this job run completed. *
* * @param completedOn * The date and time that this job run completed. * @return Returns a reference to this object so that method calls can be chained together. */ public JobRun withCompletedOn(java.util.Date completedOn) { setCompletedOn(completedOn); return this; } /** ** The current state of the job run. For more information about the statuses of jobs that have terminated * abnormally, see Glue Job Run * Statuses. *
* * @param jobRunState * The current state of the job run. For more information about the statuses of jobs that have terminated * abnormally, see Glue Job Run * Statuses. * @see JobRunState */ public void setJobRunState(String jobRunState) { this.jobRunState = jobRunState; } /** ** The current state of the job run. For more information about the statuses of jobs that have terminated * abnormally, see Glue Job Run * Statuses. *
* * @return The current state of the job run. For more information about the statuses of jobs that have terminated * abnormally, see Glue Job Run * Statuses. * @see JobRunState */ public String getJobRunState() { return this.jobRunState; } /** ** The current state of the job run. For more information about the statuses of jobs that have terminated * abnormally, see Glue Job Run * Statuses. *
* * @param jobRunState * The current state of the job run. For more information about the statuses of jobs that have terminated * abnormally, see Glue Job Run * Statuses. * @return Returns a reference to this object so that method calls can be chained together. * @see JobRunState */ public JobRun withJobRunState(String jobRunState) { setJobRunState(jobRunState); return this; } /** ** The current state of the job run. For more information about the statuses of jobs that have terminated * abnormally, see Glue Job Run * Statuses. *
* * @param jobRunState * The current state of the job run. For more information about the statuses of jobs that have terminated * abnormally, see Glue Job Run * Statuses. * @return Returns a reference to this object so that method calls can be chained together. * @see JobRunState */ public JobRun withJobRunState(JobRunState jobRunState) { this.jobRunState = jobRunState.toString(); return this; } /** ** 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. *
* * @return 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 java.util.Map
* 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(java.util.Map
* 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public JobRun withArguments(java.util.Map
* An error message associated with this job run.
*
* An error message associated with this job run.
*
* An error message associated with this job run.
*
* A list of predecessors to this job run.
*
* A list of predecessors to this job run.
*
* A list of predecessors to this job run.
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setPredecessorRuns(java.util.Collection)} or {@link #withPredecessorRuns(java.util.Collection)} if you
* want to override the existing values.
*
* A list of predecessors to this job run.
*
* This field is deprecated. Use
* The number of Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated;
* 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.
* MaxCapacity instead.
* The number of Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be * allocated; 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 void setAllocatedCapacity(Integer allocatedCapacity) { this.allocatedCapacity = allocatedCapacity; } /** *
* This field is deprecated. Use MaxCapacity instead.
*
* The number of Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; * 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. *
* * @return This field is deprecated. UseMaxCapacity instead.
* * The number of Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be * allocated; 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() { return this.allocatedCapacity; } /** *
* This field is deprecated. Use MaxCapacity instead.
*
* The number of Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; * 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. *
* * @param allocatedCapacity * This field is deprecated. UseMaxCapacity instead.
* * The number of Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be * allocated; 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. * @return Returns a reference to this object so that method calls can be chained together. */ @Deprecated public JobRun withAllocatedCapacity(Integer allocatedCapacity) { setAllocatedCapacity(allocatedCapacity); return this; } /** *
* The amount of time (in seconds) that the job run consumed resources. *
* * @param executionTime * The amount of time (in seconds) that the job run consumed resources. */ public void setExecutionTime(Integer executionTime) { this.executionTime = executionTime; } /** ** The amount of time (in seconds) that the job run consumed resources. *
* * @return The amount of time (in seconds) that the job run consumed resources. */ public Integer getExecutionTime() { return this.executionTime; } /** ** The amount of time (in seconds) that the job run consumed resources. *
* * @param executionTime * The amount of time (in seconds) that the job run consumed resources. * @return Returns a reference to this object so that method calls can be chained together. */ public JobRun withExecutionTime(Integer executionTime) { setExecutionTime(executionTime); return this; } /** *
* 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 do not have a timeout. The default for non-streaming jobs is 2,880 minutes (48 hours). *
* * @param timeout * TheJobRun 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 do not have a timeout. The default for non-streaming jobs is 2,880 minutes (48 hours). */ public void setTimeout(Integer timeout) { this.timeout = 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 do not have a timeout. The default for non-streaming jobs is 2,880 minutes (48 hours). *
* * @return TheJobRun 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 do not have a timeout. The default for non-streaming jobs is 2,880 minutes (48 hours). */ public Integer getTimeout() { return this.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 do not have a timeout. The default for non-streaming jobs is 2,880 minutes (48 hours). *
* * @param timeout * TheJobRun 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 do not have a timeout. The default for non-streaming jobs is 2,880 minutes (48 hours). * @return Returns a reference to this object so that method calls can be chained together. */ public JobRun withTimeout(Integer timeout) { setTimeout(timeout); return this; } /** *
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* The number of workers of a defined workerType that are allocated when a job runs.
*
workerType that are allocated when a job runs.
*/
public void setNumberOfWorkers(Integer numberOfWorkers) {
this.numberOfWorkers = numberOfWorkers;
}
/**
*
* The number of workers of a defined workerType that are allocated when a job runs.
*
workerType that are allocated when a job runs.
*/
public Integer getNumberOfWorkers() {
return this.numberOfWorkers;
}
/**
*
* The number of workers of a defined workerType that are allocated when a job runs.
*
workerType that are allocated when a job runs.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public JobRun withNumberOfWorkers(Integer numberOfWorkers) {
setNumberOfWorkers(numberOfWorkers);
return this;
}
/**
*
* The name of the SecurityConfiguration structure to be used with this job run.
*
SecurityConfiguration structure to be used with this job run.
*/
public void setSecurityConfiguration(String securityConfiguration) {
this.securityConfiguration = securityConfiguration;
}
/**
*
* The name of the SecurityConfiguration structure to be used with this job run.
*
SecurityConfiguration structure to be used with this job run.
*/
public String getSecurityConfiguration() {
return this.securityConfiguration;
}
/**
*
* The name of the SecurityConfiguration structure to be used with this job run.
*
SecurityConfiguration structure to be used with this job run.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public JobRun withSecurityConfiguration(String securityConfiguration) {
setSecurityConfiguration(securityConfiguration);
return this;
}
/**
*
* The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using KMS.
* This name can be /aws-glue/jobs/, in which case the default encryption is NONE. If you
* add a role name and SecurityConfiguration name (in other words,
* /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/), then that security configuration is
* used to encrypt the log group.
*
/aws-glue/jobs/, in which case the default encryption is
* NONE. If you add a role name and SecurityConfiguration name (in other words,
* /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/), then that security configuration
* is used to encrypt the log group.
*/
public void setLogGroupName(String logGroupName) {
this.logGroupName = logGroupName;
}
/**
*
* The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using KMS.
* This name can be /aws-glue/jobs/, in which case the default encryption is NONE. If you
* add a role name and SecurityConfiguration name (in other words,
* /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/), then that security configuration is
* used to encrypt the log group.
*
/aws-glue/jobs/, in which case the default encryption is
* NONE. If you add a role name and SecurityConfiguration name (in other words,
* /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/), then that security
* configuration is used to encrypt the log group.
*/
public String getLogGroupName() {
return this.logGroupName;
}
/**
*
* The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using KMS.
* This name can be /aws-glue/jobs/, in which case the default encryption is NONE. If you
* add a role name and SecurityConfiguration name (in other words,
* /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/), then that security configuration is
* used to encrypt the log group.
*
/aws-glue/jobs/, in which case the default encryption is
* NONE. If you add a role name and SecurityConfiguration name (in other words,
* /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/), then that security configuration
* is used to encrypt the log group.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public JobRun withLogGroupName(String logGroupName) {
setLogGroupName(logGroupName);
return this;
}
/**
* * Specifies configuration properties of a job run notification. *
* * @param notificationProperty * Specifies configuration properties of a job run notification. */ public void setNotificationProperty(NotificationProperty notificationProperty) { this.notificationProperty = notificationProperty; } /** ** Specifies configuration properties of a job run notification. *
* * @return Specifies configuration properties of a job run notification. */ public NotificationProperty getNotificationProperty() { return this.notificationProperty; } /** ** Specifies configuration properties of a job run notification. *
* * @param notificationProperty * Specifies configuration properties of a job run notification. * @return Returns a reference to this object so that method calls can be chained together. */ public JobRun withNotificationProperty(NotificationProperty notificationProperty) { setNotificationProperty(notificationProperty); return this; } /** *
* 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. *
* * @param glueVersion * 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. */ public void setGlueVersion(String glueVersion) { this.glueVersion = glueVersion; } /** *
* 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. *
* * @return 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. */ public String getGlueVersion() { return this.glueVersion; } /** *
* 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. *
* * @param glueVersion * 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. * @return Returns a reference to this object so that method calls can be chained together. */ public JobRun withGlueVersion(String glueVersion) { setGlueVersion(glueVersion); return this; } /** *
* This field populates only for Auto Scaling job runs, and represents the total time each executor ran during the
* lifecycle of a job run in seconds, multiplied by a DPU factor (1 for G.1X, 2 for G.2X,
* or 0.25 for G.025X workers). This value may be different than the
* executionEngineRuntime * MaxCapacity as in the case of Auto Scaling jobs, as the number
* of executors running at a given time may be less than the MaxCapacity. Therefore, it is possible
* that the value of DPUSeconds is less than executionEngineRuntime *
* MaxCapacity.
*
G.1X, 2 for
* G.2X, or 0.25 for G.025X workers). This value may be different than the
* executionEngineRuntime MaxCapacity as in the case of Auto Scaling jobs, as the
* number of executors running at a given time may be less than the MaxCapacity. Therefore, it
* is possible that the value of DPUSeconds is less than executionEngineRuntime *
* MaxCapacity.
*/
public void setDPUSeconds(Double dPUSeconds) {
this.dPUSeconds = dPUSeconds;
}
/**
*
* This field populates only for Auto Scaling job runs, and represents the total time each executor ran during the
* lifecycle of a job run in seconds, multiplied by a DPU factor (1 for G.1X, 2 for G.2X,
* or 0.25 for G.025X workers). This value may be different than the
* executionEngineRuntime * MaxCapacity as in the case of Auto Scaling jobs, as the number
* of executors running at a given time may be less than the MaxCapacity. Therefore, it is possible
* that the value of DPUSeconds is less than executionEngineRuntime *
* MaxCapacity.
*
G.1X, 2 for
* G.2X, or 0.25 for G.025X workers). This value may be different than the
* executionEngineRuntime MaxCapacity as in the case of Auto Scaling jobs, as the
* number of executors running at a given time may be less than the MaxCapacity. Therefore, it
* is possible that the value of DPUSeconds is less than executionEngineRuntime *
* MaxCapacity.
*/
public Double getDPUSeconds() {
return this.dPUSeconds;
}
/**
*
* This field populates only for Auto Scaling job runs, and represents the total time each executor ran during the
* lifecycle of a job run in seconds, multiplied by a DPU factor (1 for G.1X, 2 for G.2X,
* or 0.25 for G.025X workers). This value may be different than the
* executionEngineRuntime * MaxCapacity as in the case of Auto Scaling jobs, as the number
* of executors running at a given time may be less than the MaxCapacity. Therefore, it is possible
* that the value of DPUSeconds is less than executionEngineRuntime *
* MaxCapacity.
*
G.1X, 2 for
* G.2X, or 0.25 for G.025X workers). This value may be different than the
* executionEngineRuntime MaxCapacity as in the case of Auto Scaling jobs, as the
* number of executors running at a given time may be less than the MaxCapacity. Therefore, it
* is possible that the value of DPUSeconds is less than executionEngineRuntime *
* MaxCapacity.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public JobRun withDPUSeconds(Double dPUSeconds) {
setDPUSeconds(dPUSeconds);
return this;
}
/**
* * 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.
* @see ExecutionClass
*/
public void setExecutionClass(String executionClass) {
this.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.
*
* 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.
* @see ExecutionClass
*/
public String getExecutionClass() {
return this.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.
*
* 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.
* @return Returns a reference to this object so that method calls can be chained together.
* @see ExecutionClass
*/
public JobRun withExecutionClass(String executionClass) {
setExecutionClass(executionClass);
return this;
}
/**
*
* 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.
* @return Returns a reference to this object so that method calls can be chained together.
* @see ExecutionClass
*/
public JobRun withExecutionClass(ExecutionClass executionClass) {
this.executionClass = executionClass.toString();
return this;
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getId() != null)
sb.append("Id: ").append(getId()).append(",");
if (getAttempt() != null)
sb.append("Attempt: ").append(getAttempt()).append(",");
if (getPreviousRunId() != null)
sb.append("PreviousRunId: ").append(getPreviousRunId()).append(",");
if (getTriggerName() != null)
sb.append("TriggerName: ").append(getTriggerName()).append(",");
if (getJobName() != null)
sb.append("JobName: ").append(getJobName()).append(",");
if (getStartedOn() != null)
sb.append("StartedOn: ").append(getStartedOn()).append(",");
if (getLastModifiedOn() != null)
sb.append("LastModifiedOn: ").append(getLastModifiedOn()).append(",");
if (getCompletedOn() != null)
sb.append("CompletedOn: ").append(getCompletedOn()).append(",");
if (getJobRunState() != null)
sb.append("JobRunState: ").append(getJobRunState()).append(",");
if (getArguments() != null)
sb.append("Arguments: ").append(getArguments()).append(",");
if (getErrorMessage() != null)
sb.append("ErrorMessage: ").append(getErrorMessage()).append(",");
if (getPredecessorRuns() != null)
sb.append("PredecessorRuns: ").append(getPredecessorRuns()).append(",");
if (getAllocatedCapacity() != null)
sb.append("AllocatedCapacity: ").append(getAllocatedCapacity()).append(",");
if (getExecutionTime() != null)
sb.append("ExecutionTime: ").append(getExecutionTime()).append(",");
if (getTimeout() != null)
sb.append("Timeout: ").append(getTimeout()).append(",");
if (getMaxCapacity() != null)
sb.append("MaxCapacity: ").append(getMaxCapacity()).append(",");
if (getWorkerType() != null)
sb.append("WorkerType: ").append(getWorkerType()).append(",");
if (getNumberOfWorkers() != null)
sb.append("NumberOfWorkers: ").append(getNumberOfWorkers()).append(",");
if (getSecurityConfiguration() != null)
sb.append("SecurityConfiguration: ").append(getSecurityConfiguration()).append(",");
if (getLogGroupName() != null)
sb.append("LogGroupName: ").append(getLogGroupName()).append(",");
if (getNotificationProperty() != null)
sb.append("NotificationProperty: ").append(getNotificationProperty()).append(",");
if (getGlueVersion() != null)
sb.append("GlueVersion: ").append(getGlueVersion()).append(",");
if (getDPUSeconds() != null)
sb.append("DPUSeconds: ").append(getDPUSeconds()).append(",");
if (getExecutionClass() != null)
sb.append("ExecutionClass: ").append(getExecutionClass());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof JobRun == false)
return false;
JobRun other = (JobRun) obj;
if (other.getId() == null ^ this.getId() == null)
return false;
if (other.getId() != null && other.getId().equals(this.getId()) == false)
return false;
if (other.getAttempt() == null ^ this.getAttempt() == null)
return false;
if (other.getAttempt() != null && other.getAttempt().equals(this.getAttempt()) == false)
return false;
if (other.getPreviousRunId() == null ^ this.getPreviousRunId() == null)
return false;
if (other.getPreviousRunId() != null && other.getPreviousRunId().equals(this.getPreviousRunId()) == false)
return false;
if (other.getTriggerName() == null ^ this.getTriggerName() == null)
return false;
if (other.getTriggerName() != null && other.getTriggerName().equals(this.getTriggerName()) == false)
return false;
if (other.getJobName() == null ^ this.getJobName() == null)
return false;
if (other.getJobName() != null && other.getJobName().equals(this.getJobName()) == false)
return false;
if (other.getStartedOn() == null ^ this.getStartedOn() == null)
return false;
if (other.getStartedOn() != null && other.getStartedOn().equals(this.getStartedOn()) == false)
return false;
if (other.getLastModifiedOn() == null ^ this.getLastModifiedOn() == null)
return false;
if (other.getLastModifiedOn() != null && other.getLastModifiedOn().equals(this.getLastModifiedOn()) == false)
return false;
if (other.getCompletedOn() == null ^ this.getCompletedOn() == null)
return false;
if (other.getCompletedOn() != null && other.getCompletedOn().equals(this.getCompletedOn()) == false)
return false;
if (other.getJobRunState() == null ^ this.getJobRunState() == null)
return false;
if (other.getJobRunState() != null && other.getJobRunState().equals(this.getJobRunState()) == false)
return false;
if (other.getArguments() == null ^ this.getArguments() == null)
return false;
if (other.getArguments() != null && other.getArguments().equals(this.getArguments()) == false)
return false;
if (other.getErrorMessage() == null ^ this.getErrorMessage() == null)
return false;
if (other.getErrorMessage() != null && other.getErrorMessage().equals(this.getErrorMessage()) == false)
return false;
if (other.getPredecessorRuns() == null ^ this.getPredecessorRuns() == null)
return false;
if (other.getPredecessorRuns() != null && other.getPredecessorRuns().equals(this.getPredecessorRuns()) == false)
return false;
if (other.getAllocatedCapacity() == null ^ this.getAllocatedCapacity() == null)
return false;
if (other.getAllocatedCapacity() != null && other.getAllocatedCapacity().equals(this.getAllocatedCapacity()) == false)
return false;
if (other.getExecutionTime() == null ^ this.getExecutionTime() == null)
return false;
if (other.getExecutionTime() != null && other.getExecutionTime().equals(this.getExecutionTime()) == false)
return false;
if (other.getTimeout() == null ^ this.getTimeout() == null)
return false;
if (other.getTimeout() != null && other.getTimeout().equals(this.getTimeout()) == false)
return false;
if (other.getMaxCapacity() == null ^ this.getMaxCapacity() == null)
return false;
if (other.getMaxCapacity() != null && other.getMaxCapacity().equals(this.getMaxCapacity()) == false)
return false;
if (other.getWorkerType() == null ^ this.getWorkerType() == null)
return false;
if (other.getWorkerType() != null && other.getWorkerType().equals(this.getWorkerType()) == false)
return false;
if (other.getNumberOfWorkers() == null ^ this.getNumberOfWorkers() == null)
return false;
if (other.getNumberOfWorkers() != null && other.getNumberOfWorkers().equals(this.getNumberOfWorkers()) == false)
return false;
if (other.getSecurityConfiguration() == null ^ this.getSecurityConfiguration() == null)
return false;
if (other.getSecurityConfiguration() != null && other.getSecurityConfiguration().equals(this.getSecurityConfiguration()) == false)
return false;
if (other.getLogGroupName() == null ^ this.getLogGroupName() == null)
return false;
if (other.getLogGroupName() != null && other.getLogGroupName().equals(this.getLogGroupName()) == false)
return false;
if (other.getNotificationProperty() == null ^ this.getNotificationProperty() == null)
return false;
if (other.getNotificationProperty() != null && other.getNotificationProperty().equals(this.getNotificationProperty()) == false)
return false;
if (other.getGlueVersion() == null ^ this.getGlueVersion() == null)
return false;
if (other.getGlueVersion() != null && other.getGlueVersion().equals(this.getGlueVersion()) == false)
return false;
if (other.getDPUSeconds() == null ^ this.getDPUSeconds() == null)
return false;
if (other.getDPUSeconds() != null && other.getDPUSeconds().equals(this.getDPUSeconds()) == false)
return false;
if (other.getExecutionClass() == null ^ this.getExecutionClass() == null)
return false;
if (other.getExecutionClass() != null && other.getExecutionClass().equals(this.getExecutionClass()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getId() == null) ? 0 : getId().hashCode());
hashCode = prime * hashCode + ((getAttempt() == null) ? 0 : getAttempt().hashCode());
hashCode = prime * hashCode + ((getPreviousRunId() == null) ? 0 : getPreviousRunId().hashCode());
hashCode = prime * hashCode + ((getTriggerName() == null) ? 0 : getTriggerName().hashCode());
hashCode = prime * hashCode + ((getJobName() == null) ? 0 : getJobName().hashCode());
hashCode = prime * hashCode + ((getStartedOn() == null) ? 0 : getStartedOn().hashCode());
hashCode = prime * hashCode + ((getLastModifiedOn() == null) ? 0 : getLastModifiedOn().hashCode());
hashCode = prime * hashCode + ((getCompletedOn() == null) ? 0 : getCompletedOn().hashCode());
hashCode = prime * hashCode + ((getJobRunState() == null) ? 0 : getJobRunState().hashCode());
hashCode = prime * hashCode + ((getArguments() == null) ? 0 : getArguments().hashCode());
hashCode = prime * hashCode + ((getErrorMessage() == null) ? 0 : getErrorMessage().hashCode());
hashCode = prime * hashCode + ((getPredecessorRuns() == null) ? 0 : getPredecessorRuns().hashCode());
hashCode = prime * hashCode + ((getAllocatedCapacity() == null) ? 0 : getAllocatedCapacity().hashCode());
hashCode = prime * hashCode + ((getExecutionTime() == null) ? 0 : getExecutionTime().hashCode());
hashCode = prime * hashCode + ((getTimeout() == null) ? 0 : getTimeout().hashCode());
hashCode = prime * hashCode + ((getMaxCapacity() == null) ? 0 : getMaxCapacity().hashCode());
hashCode = prime * hashCode + ((getWorkerType() == null) ? 0 : getWorkerType().hashCode());
hashCode = prime * hashCode + ((getNumberOfWorkers() == null) ? 0 : getNumberOfWorkers().hashCode());
hashCode = prime * hashCode + ((getSecurityConfiguration() == null) ? 0 : getSecurityConfiguration().hashCode());
hashCode = prime * hashCode + ((getLogGroupName() == null) ? 0 : getLogGroupName().hashCode());
hashCode = prime * hashCode + ((getNotificationProperty() == null) ? 0 : getNotificationProperty().hashCode());
hashCode = prime * hashCode + ((getGlueVersion() == null) ? 0 : getGlueVersion().hashCode());
hashCode = prime * hashCode + ((getDPUSeconds() == null) ? 0 : getDPUSeconds().hashCode());
hashCode = prime * hashCode + ((getExecutionClass() == null) ? 0 : getExecutionClass().hashCode());
return hashCode;
}
@Override
public JobRun clone() {
try {
return (JobRun) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.glue.model.transform.JobRunMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}