/** * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. * SPDX-License-Identifier: Apache-2.0. */ #pragma once #include #include #include #include #include #include #include #include #include #include #include #include namespace Aws { namespace Utils { namespace Json { class JsonValue; class JsonView; } // namespace Json } // namespace Utils namespace Glue { namespace Model { /** *

Specifies information used to update an existing job definition. The previous * job definition is completely overwritten by this information.

See * Also:

AWS API * Reference

*/ class JobUpdate { public: AWS_GLUE_API JobUpdate(); AWS_GLUE_API JobUpdate(Aws::Utils::Json::JsonView jsonValue); AWS_GLUE_API JobUpdate& operator=(Aws::Utils::Json::JsonView jsonValue); AWS_GLUE_API Aws::Utils::Json::JsonValue Jsonize() const; /** *

Description of the job being defined.

*/ inline const Aws::String& GetDescription() const{ return m_description; } /** *

Description of the job being defined.

*/ inline bool DescriptionHasBeenSet() const { return m_descriptionHasBeenSet; } /** *

Description of the job being defined.

*/ inline void SetDescription(const Aws::String& value) { m_descriptionHasBeenSet = true; m_description = value; } /** *

Description of the job being defined.

*/ inline void SetDescription(Aws::String&& value) { m_descriptionHasBeenSet = true; m_description = std::move(value); } /** *

Description of the job being defined.

*/ inline void SetDescription(const char* value) { m_descriptionHasBeenSet = true; m_description.assign(value); } /** *

Description of the job being defined.

*/ inline JobUpdate& WithDescription(const Aws::String& value) { SetDescription(value); return *this;} /** *

Description of the job being defined.

*/ inline JobUpdate& WithDescription(Aws::String&& value) { SetDescription(std::move(value)); return *this;} /** *

Description of the job being defined.

*/ inline JobUpdate& WithDescription(const char* value) { SetDescription(value); return *this;} /** *

This field is reserved for future use.

*/ inline const Aws::String& GetLogUri() const{ return m_logUri; } /** *

This field is reserved for future use.

*/ inline bool LogUriHasBeenSet() const { return m_logUriHasBeenSet; } /** *

This field is reserved for future use.

*/ inline void SetLogUri(const Aws::String& value) { m_logUriHasBeenSet = true; m_logUri = value; } /** *

This field is reserved for future use.

*/ inline void SetLogUri(Aws::String&& value) { m_logUriHasBeenSet = true; m_logUri = std::move(value); } /** *

This field is reserved for future use.

*/ inline void SetLogUri(const char* value) { m_logUriHasBeenSet = true; m_logUri.assign(value); } /** *

This field is reserved for future use.

*/ inline JobUpdate& WithLogUri(const Aws::String& value) { SetLogUri(value); return *this;} /** *

This field is reserved for future use.

*/ inline JobUpdate& WithLogUri(Aws::String&& value) { SetLogUri(std::move(value)); return *this;} /** *

This field is reserved for future use.

*/ inline JobUpdate& WithLogUri(const char* value) { SetLogUri(value); return *this;} /** *

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

*/ inline const Aws::String& GetRole() const{ return m_role; } /** *

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

*/ inline bool RoleHasBeenSet() const { return m_roleHasBeenSet; } /** *

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

*/ inline void SetRole(const Aws::String& value) { m_roleHasBeenSet = true; m_role = value; } /** *

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

*/ inline void SetRole(Aws::String&& value) { m_roleHasBeenSet = true; m_role = std::move(value); } /** *

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

*/ inline void SetRole(const char* value) { m_roleHasBeenSet = true; m_role.assign(value); } /** *

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

*/ inline JobUpdate& WithRole(const Aws::String& value) { SetRole(value); return *this;} /** *

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

*/ inline JobUpdate& WithRole(Aws::String&& value) { SetRole(std::move(value)); return *this;} /** *

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

*/ inline JobUpdate& WithRole(const char* value) { SetRole(value); return *this;} /** *

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

*/ inline const ExecutionProperty& GetExecutionProperty() const{ return m_executionProperty; } /** *

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

*/ inline bool ExecutionPropertyHasBeenSet() const { return m_executionPropertyHasBeenSet; } /** *

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

*/ inline void SetExecutionProperty(const ExecutionProperty& value) { m_executionPropertyHasBeenSet = true; m_executionProperty = value; } /** *

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

*/ inline void SetExecutionProperty(ExecutionProperty&& value) { m_executionPropertyHasBeenSet = true; m_executionProperty = std::move(value); } /** *

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

*/ inline JobUpdate& WithExecutionProperty(const ExecutionProperty& value) { SetExecutionProperty(value); return *this;} /** *

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

*/ inline JobUpdate& WithExecutionProperty(ExecutionProperty&& value) { SetExecutionProperty(std::move(value)); return *this;} /** *

The JobCommand that runs this job (required).

*/ inline const JobCommand& GetCommand() const{ return m_command; } /** *

The JobCommand that runs this job (required).

*/ inline bool CommandHasBeenSet() const { return m_commandHasBeenSet; } /** *

The JobCommand that runs this job (required).

*/ inline void SetCommand(const JobCommand& value) { m_commandHasBeenSet = true; m_command = value; } /** *

The JobCommand that runs this job (required).

*/ inline void SetCommand(JobCommand&& value) { m_commandHasBeenSet = true; m_command = std::move(value); } /** *

The JobCommand that runs this job (required).

*/ inline JobUpdate& WithCommand(const JobCommand& value) { SetCommand(value); return *this;} /** *

The JobCommand that runs this job (required).

*/ inline JobUpdate& WithCommand(JobCommand&& value) { SetCommand(std::move(value)); return *this;} /** *

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

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

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

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

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

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

*/ inline const Aws::Map& GetDefaultArguments() const{ return m_defaultArguments; } /** *

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

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

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

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

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

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

*/ inline bool DefaultArgumentsHasBeenSet() const { return m_defaultArgumentsHasBeenSet; } /** *

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

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

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

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

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

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

*/ inline void SetDefaultArguments(const Aws::Map& value) { m_defaultArgumentsHasBeenSet = true; m_defaultArguments = value; } /** *

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

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

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

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

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

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

*/ inline void SetDefaultArguments(Aws::Map&& value) { m_defaultArgumentsHasBeenSet = true; m_defaultArguments = std::move(value); } /** *

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

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

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

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

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

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

*/ inline JobUpdate& WithDefaultArguments(const Aws::Map& value) { SetDefaultArguments(value); return *this;} /** *

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

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

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

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

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

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

*/ inline JobUpdate& WithDefaultArguments(Aws::Map&& value) { SetDefaultArguments(std::move(value)); return *this;} /** *

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

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

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

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

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

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

*/ inline JobUpdate& AddDefaultArguments(const Aws::String& key, const Aws::String& value) { m_defaultArgumentsHasBeenSet = true; m_defaultArguments.emplace(key, value); return *this; } /** *

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

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

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

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

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

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

*/ inline JobUpdate& AddDefaultArguments(Aws::String&& key, const Aws::String& value) { m_defaultArgumentsHasBeenSet = true; m_defaultArguments.emplace(std::move(key), value); return *this; } /** *

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

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

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

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

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

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

*/ inline JobUpdate& AddDefaultArguments(const Aws::String& key, Aws::String&& value) { m_defaultArgumentsHasBeenSet = true; m_defaultArguments.emplace(key, std::move(value)); return *this; } /** *

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

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

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

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

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

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

*/ inline JobUpdate& AddDefaultArguments(Aws::String&& key, Aws::String&& value) { m_defaultArgumentsHasBeenSet = true; m_defaultArguments.emplace(std::move(key), std::move(value)); return *this; } /** *

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

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

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

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

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

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

*/ inline JobUpdate& AddDefaultArguments(const char* key, Aws::String&& value) { m_defaultArgumentsHasBeenSet = true; m_defaultArguments.emplace(key, std::move(value)); return *this; } /** *

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

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

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

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

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

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

*/ inline JobUpdate& AddDefaultArguments(Aws::String&& key, const char* value) { m_defaultArgumentsHasBeenSet = true; m_defaultArguments.emplace(std::move(key), value); return *this; } /** *

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

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

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

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

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

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

*/ inline JobUpdate& AddDefaultArguments(const char* key, const char* value) { m_defaultArgumentsHasBeenSet = true; m_defaultArguments.emplace(key, value); return *this; } /** *

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

*/ inline const Aws::Map& GetNonOverridableArguments() const{ return m_nonOverridableArguments; } /** *

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

*/ inline bool NonOverridableArgumentsHasBeenSet() const { return m_nonOverridableArgumentsHasBeenSet; } /** *

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

*/ inline void SetNonOverridableArguments(const Aws::Map& value) { m_nonOverridableArgumentsHasBeenSet = true; m_nonOverridableArguments = value; } /** *

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

*/ inline void SetNonOverridableArguments(Aws::Map&& value) { m_nonOverridableArgumentsHasBeenSet = true; m_nonOverridableArguments = std::move(value); } /** *

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

*/ inline JobUpdate& WithNonOverridableArguments(const Aws::Map& value) { SetNonOverridableArguments(value); return *this;} /** *

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

*/ inline JobUpdate& WithNonOverridableArguments(Aws::Map&& value) { SetNonOverridableArguments(std::move(value)); return *this;} /** *

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

*/ inline JobUpdate& AddNonOverridableArguments(const Aws::String& key, const Aws::String& value) { m_nonOverridableArgumentsHasBeenSet = true; m_nonOverridableArguments.emplace(key, value); return *this; } /** *

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

*/ inline JobUpdate& AddNonOverridableArguments(Aws::String&& key, const Aws::String& value) { m_nonOverridableArgumentsHasBeenSet = true; m_nonOverridableArguments.emplace(std::move(key), value); return *this; } /** *

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

*/ inline JobUpdate& AddNonOverridableArguments(const Aws::String& key, Aws::String&& value) { m_nonOverridableArgumentsHasBeenSet = true; m_nonOverridableArguments.emplace(key, std::move(value)); return *this; } /** *

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

*/ inline JobUpdate& AddNonOverridableArguments(Aws::String&& key, Aws::String&& value) { m_nonOverridableArgumentsHasBeenSet = true; m_nonOverridableArguments.emplace(std::move(key), std::move(value)); return *this; } /** *

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

*/ inline JobUpdate& AddNonOverridableArguments(const char* key, Aws::String&& value) { m_nonOverridableArgumentsHasBeenSet = true; m_nonOverridableArguments.emplace(key, std::move(value)); return *this; } /** *

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

*/ inline JobUpdate& AddNonOverridableArguments(Aws::String&& key, const char* value) { m_nonOverridableArgumentsHasBeenSet = true; m_nonOverridableArguments.emplace(std::move(key), value); return *this; } /** *

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

*/ inline JobUpdate& AddNonOverridableArguments(const char* key, const char* value) { m_nonOverridableArgumentsHasBeenSet = true; m_nonOverridableArguments.emplace(key, value); return *this; } /** *

The connections used for this job.

*/ inline const ConnectionsList& GetConnections() const{ return m_connections; } /** *

The connections used for this job.

*/ inline bool ConnectionsHasBeenSet() const { return m_connectionsHasBeenSet; } /** *

The connections used for this job.

*/ inline void SetConnections(const ConnectionsList& value) { m_connectionsHasBeenSet = true; m_connections = value; } /** *

The connections used for this job.

*/ inline void SetConnections(ConnectionsList&& value) { m_connectionsHasBeenSet = true; m_connections = std::move(value); } /** *

The connections used for this job.

*/ inline JobUpdate& WithConnections(const ConnectionsList& value) { SetConnections(value); return *this;} /** *

The connections used for this job.

*/ inline JobUpdate& WithConnections(ConnectionsList&& value) { SetConnections(std::move(value)); return *this;} /** *

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

*/ inline int GetMaxRetries() const{ return m_maxRetries; } /** *

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

*/ inline bool MaxRetriesHasBeenSet() const { return m_maxRetriesHasBeenSet; } /** *

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

*/ inline void SetMaxRetries(int value) { m_maxRetriesHasBeenSet = true; m_maxRetries = value; } /** *

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

*/ inline JobUpdate& WithMaxRetries(int value) { SetMaxRetries(value); return *this;} /** *

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

*/ inline int GetTimeout() const{ return m_timeout; } /** *

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

*/ inline bool TimeoutHasBeenSet() const { return m_timeoutHasBeenSet; } /** *

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

*/ inline void SetTimeout(int value) { m_timeoutHasBeenSet = true; m_timeout = value; } /** *

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

*/ inline JobUpdate& WithTimeout(int value) { SetTimeout(value); 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.

*/ inline double GetMaxCapacity() const{ return m_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.

*/ inline bool MaxCapacityHasBeenSet() const { return m_maxCapacityHasBeenSet; } /** *

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.

*/ inline void SetMaxCapacity(double value) { m_maxCapacityHasBeenSet = true; m_maxCapacity = value; } /** *

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.

*/ inline JobUpdate& WithMaxCapacity(double value) { SetMaxCapacity(value); return *this;} /** *

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.

*/ inline const WorkerType& GetWorkerType() const{ return m_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.

*/ inline bool WorkerTypeHasBeenSet() const { return m_workerTypeHasBeenSet; } /** *

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.

*/ inline void SetWorkerType(const WorkerType& value) { m_workerTypeHasBeenSet = true; m_workerType = value; } /** *

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.

*/ inline void SetWorkerType(WorkerType&& value) { m_workerTypeHasBeenSet = true; m_workerType = std::move(value); } /** *

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.

*/ inline JobUpdate& WithWorkerType(const WorkerType& value) { SetWorkerType(value); return *this;} /** *

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.

*/ inline JobUpdate& WithWorkerType(WorkerType&& value) { SetWorkerType(std::move(value)); return *this;} /** *

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

*/ inline int GetNumberOfWorkers() const{ return m_numberOfWorkers; } /** *

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

*/ inline bool NumberOfWorkersHasBeenSet() const { return m_numberOfWorkersHasBeenSet; } /** *

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

*/ inline void SetNumberOfWorkers(int value) { m_numberOfWorkersHasBeenSet = true; m_numberOfWorkers = value; } /** *

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

*/ inline JobUpdate& WithNumberOfWorkers(int value) { SetNumberOfWorkers(value); return *this;} /** *

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

*/ inline const Aws::String& GetSecurityConfiguration() const{ return m_securityConfiguration; } /** *

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

*/ inline bool SecurityConfigurationHasBeenSet() const { return m_securityConfigurationHasBeenSet; } /** *

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

*/ inline void SetSecurityConfiguration(const Aws::String& value) { m_securityConfigurationHasBeenSet = true; m_securityConfiguration = value; } /** *

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

*/ inline void SetSecurityConfiguration(Aws::String&& value) { m_securityConfigurationHasBeenSet = true; m_securityConfiguration = std::move(value); } /** *

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

*/ inline void SetSecurityConfiguration(const char* value) { m_securityConfigurationHasBeenSet = true; m_securityConfiguration.assign(value); } /** *

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

*/ inline JobUpdate& WithSecurityConfiguration(const Aws::String& value) { SetSecurityConfiguration(value); return *this;} /** *

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

*/ inline JobUpdate& WithSecurityConfiguration(Aws::String&& value) { SetSecurityConfiguration(std::move(value)); return *this;} /** *

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

*/ inline JobUpdate& WithSecurityConfiguration(const char* value) { SetSecurityConfiguration(value); return *this;} /** *

Specifies the configuration properties of a job notification.

*/ inline const NotificationProperty& GetNotificationProperty() const{ return m_notificationProperty; } /** *

Specifies the configuration properties of a job notification.

*/ inline bool NotificationPropertyHasBeenSet() const { return m_notificationPropertyHasBeenSet; } /** *

Specifies the configuration properties of a job notification.

*/ inline void SetNotificationProperty(const NotificationProperty& value) { m_notificationPropertyHasBeenSet = true; m_notificationProperty = value; } /** *

Specifies the configuration properties of a job notification.

*/ inline void SetNotificationProperty(NotificationProperty&& value) { m_notificationPropertyHasBeenSet = true; m_notificationProperty = std::move(value); } /** *

Specifies the configuration properties of a job notification.

*/ inline JobUpdate& WithNotificationProperty(const NotificationProperty& value) { SetNotificationProperty(value); return *this;} /** *

Specifies the configuration properties of a job notification.

*/ inline JobUpdate& WithNotificationProperty(NotificationProperty&& value) { SetNotificationProperty(std::move(value)); 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.

*/ inline const Aws::String& GetGlueVersion() const{ return m_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.

*/ inline bool GlueVersionHasBeenSet() const { return m_glueVersionHasBeenSet; } /** *

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.

*/ inline void SetGlueVersion(const Aws::String& value) { m_glueVersionHasBeenSet = true; m_glueVersion = value; } /** *

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.

*/ inline void SetGlueVersion(Aws::String&& value) { m_glueVersionHasBeenSet = true; m_glueVersion = std::move(value); } /** *

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.

*/ inline void SetGlueVersion(const char* value) { m_glueVersionHasBeenSet = true; m_glueVersion.assign(value); } /** *

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.

*/ inline JobUpdate& WithGlueVersion(const Aws::String& value) { SetGlueVersion(value); 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.

*/ inline JobUpdate& WithGlueVersion(Aws::String&& value) { SetGlueVersion(std::move(value)); 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.

*/ inline JobUpdate& WithGlueVersion(const char* value) { SetGlueVersion(value); return *this;} /** *

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

*/ inline const Aws::Map& GetCodeGenConfigurationNodes() const{ return m_codeGenConfigurationNodes; } /** *

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

*/ inline bool CodeGenConfigurationNodesHasBeenSet() const { return m_codeGenConfigurationNodesHasBeenSet; } /** *

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

*/ inline void SetCodeGenConfigurationNodes(const Aws::Map& value) { m_codeGenConfigurationNodesHasBeenSet = true; m_codeGenConfigurationNodes = value; } /** *

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

*/ inline void SetCodeGenConfigurationNodes(Aws::Map&& value) { m_codeGenConfigurationNodesHasBeenSet = true; m_codeGenConfigurationNodes = std::move(value); } /** *

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

*/ inline JobUpdate& WithCodeGenConfigurationNodes(const Aws::Map& value) { SetCodeGenConfigurationNodes(value); return *this;} /** *

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

*/ inline JobUpdate& WithCodeGenConfigurationNodes(Aws::Map&& value) { SetCodeGenConfigurationNodes(std::move(value)); return *this;} /** *

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

*/ inline JobUpdate& AddCodeGenConfigurationNodes(const Aws::String& key, const CodeGenConfigurationNode& value) { m_codeGenConfigurationNodesHasBeenSet = true; m_codeGenConfigurationNodes.emplace(key, value); return *this; } /** *

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

*/ inline JobUpdate& AddCodeGenConfigurationNodes(Aws::String&& key, const CodeGenConfigurationNode& value) { m_codeGenConfigurationNodesHasBeenSet = true; m_codeGenConfigurationNodes.emplace(std::move(key), value); return *this; } /** *

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

*/ inline JobUpdate& AddCodeGenConfigurationNodes(const Aws::String& key, CodeGenConfigurationNode&& value) { m_codeGenConfigurationNodesHasBeenSet = true; m_codeGenConfigurationNodes.emplace(key, std::move(value)); return *this; } /** *

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

*/ inline JobUpdate& AddCodeGenConfigurationNodes(Aws::String&& key, CodeGenConfigurationNode&& value) { m_codeGenConfigurationNodesHasBeenSet = true; m_codeGenConfigurationNodes.emplace(std::move(key), std::move(value)); return *this; } /** *

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

*/ inline JobUpdate& AddCodeGenConfigurationNodes(const char* key, CodeGenConfigurationNode&& value) { m_codeGenConfigurationNodesHasBeenSet = true; m_codeGenConfigurationNodes.emplace(key, std::move(value)); return *this; } /** *

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

*/ inline JobUpdate& AddCodeGenConfigurationNodes(const char* key, const CodeGenConfigurationNode& value) { m_codeGenConfigurationNodesHasBeenSet = true; m_codeGenConfigurationNodes.emplace(key, value); 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.

*/ inline const ExecutionClass& GetExecutionClass() const{ return m_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.

*/ inline bool ExecutionClassHasBeenSet() const { return m_executionClassHasBeenSet; } /** *

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.

*/ inline void SetExecutionClass(const ExecutionClass& value) { m_executionClassHasBeenSet = true; m_executionClass = value; } /** *

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.

*/ inline void SetExecutionClass(ExecutionClass&& value) { m_executionClassHasBeenSet = true; m_executionClass = std::move(value); } /** *

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.

*/ inline JobUpdate& WithExecutionClass(const ExecutionClass& value) { SetExecutionClass(value); 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.

*/ inline JobUpdate& WithExecutionClass(ExecutionClass&& value) { SetExecutionClass(std::move(value)); return *this;} /** *

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

*/ inline const SourceControlDetails& GetSourceControlDetails() const{ return m_sourceControlDetails; } /** *

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

*/ inline bool SourceControlDetailsHasBeenSet() const { return m_sourceControlDetailsHasBeenSet; } /** *

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

*/ inline void SetSourceControlDetails(const SourceControlDetails& value) { m_sourceControlDetailsHasBeenSet = true; m_sourceControlDetails = value; } /** *

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

*/ inline void SetSourceControlDetails(SourceControlDetails&& value) { m_sourceControlDetailsHasBeenSet = true; m_sourceControlDetails = std::move(value); } /** *

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

*/ inline JobUpdate& WithSourceControlDetails(const SourceControlDetails& value) { SetSourceControlDetails(value); return *this;} /** *

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

*/ inline JobUpdate& WithSourceControlDetails(SourceControlDetails&& value) { SetSourceControlDetails(std::move(value)); return *this;} private: Aws::String m_description; bool m_descriptionHasBeenSet = false; Aws::String m_logUri; bool m_logUriHasBeenSet = false; Aws::String m_role; bool m_roleHasBeenSet = false; ExecutionProperty m_executionProperty; bool m_executionPropertyHasBeenSet = false; JobCommand m_command; bool m_commandHasBeenSet = false; Aws::Map m_defaultArguments; bool m_defaultArgumentsHasBeenSet = false; Aws::Map m_nonOverridableArguments; bool m_nonOverridableArgumentsHasBeenSet = false; ConnectionsList m_connections; bool m_connectionsHasBeenSet = false; int m_maxRetries; bool m_maxRetriesHasBeenSet = false; int m_timeout; bool m_timeoutHasBeenSet = false; double m_maxCapacity; bool m_maxCapacityHasBeenSet = false; WorkerType m_workerType; bool m_workerTypeHasBeenSet = false; int m_numberOfWorkers; bool m_numberOfWorkersHasBeenSet = false; Aws::String m_securityConfiguration; bool m_securityConfigurationHasBeenSet = false; NotificationProperty m_notificationProperty; bool m_notificationPropertyHasBeenSet = false; Aws::String m_glueVersion; bool m_glueVersionHasBeenSet = false; Aws::Map m_codeGenConfigurationNodes; bool m_codeGenConfigurationNodesHasBeenSet = false; ExecutionClass m_executionClass; bool m_executionClassHasBeenSet = false; SourceControlDetails m_sourceControlDetails; bool m_sourceControlDetailsHasBeenSet = false; }; } // namespace Model } // namespace Glue } // namespace Aws