Struct aws_sdk_glue::types::Job

#[non_exhaustive]
pub struct Job {

    pub name: Option<String>,
    pub description: Option<String>,
    pub log_uri: Option<String>,
    pub role: Option<String>,
    pub created_on: Option<DateTime>,
    pub last_modified_on: Option<DateTime>,
    pub execution_property: Option<ExecutionProperty>,
    pub command: Option<JobCommand>,
    pub default_arguments: Option<HashMap<String, String>>,
    pub non_overridable_arguments: Option<HashMap<String, String>>,
    pub connections: Option<ConnectionsList>,
    pub max_retries: i32,
    pub allocated_capacity: i32,
    pub timeout: Option<i32>,
    pub max_capacity: Option<f64>,
    pub worker_type: Option<WorkerType>,
    pub number_of_workers: Option<i32>,
    pub security_configuration: Option<String>,
    pub notification_property: Option<NotificationProperty>,
    pub glue_version: Option<String>,
    pub code_gen_configuration_nodes: Option<HashMap<String, CodeGenConfigurationNode>>,
    pub execution_class: Option<ExecutionClass>,
    pub source_control_details: Option<SourceControlDetails>,
}

Specifies a job definition.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

name: Option<String>

The name you assign to this job definition.

description: Option<String>

A description of the job.

log_uri: Option<String>

This field is reserved for future use.

role: Option<String>

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

created_on: Option<DateTime>

The time and date that this job definition was created.

last_modified_on: Option<DateTime>

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

execution_property: Option<ExecutionProperty>

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

command: Option<JobCommand>

The JobCommand that runs this job.

default_arguments: Option<HashMap<String, String>>

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

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

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

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

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

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

non_overridable_arguments: Option<HashMap<String, String>>

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

connections: Option<ConnectionsList>

The connections used for this job.

max_retries: i32

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

allocated_capacity: i32

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

This field is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) allocated to runs of this job. You can allocate a minimum of 2 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page.

timeout: Option<i32>

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

max_capacity: Option<f64>

For Glue version 1.0 or earlier jobs, using the standard worker type, the number of Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page.

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

Do not set MaxCapacity if using WorkerType and NumberOfWorkers.

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

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

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

worker_type: Option<WorkerType>

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

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

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

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

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

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

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

number_of_workers: Option<i32>

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

security_configuration: Option<String>

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

notification_property: Option<NotificationProperty>

Specifies configuration properties of a job notification.

glue_version: Option<String>

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

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

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

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

code_gen_configuration_nodes: Option<HashMap<String, CodeGenConfigurationNode>>

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

execution_class: Option<ExecutionClass>

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

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

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

source_control_details: Option<SourceControlDetails>

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

Implementations

impl Job

pub fn name(&self) -> Option<&str>

The name you assign to this job definition.

pub fn description(&self) -> Option<&str>

A description of the job.

pub fn log_uri(&self) -> Option<&str>

This field is reserved for future use.

pub fn role(&self) -> Option<&str>

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

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

The time and date that this job definition was created.

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

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

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

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

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

The JobCommand that runs this job.

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

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

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

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

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

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

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

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

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

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

The connections used for this job.

pub fn max_retries(&self) -> i32

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

pub fn allocated_capacity(&self) -> i32

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

This field is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) allocated to runs of this job. You can allocate a minimum of 2 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page.

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

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

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

For Glue version 1.0 or earlier jobs, using the standard worker type, the number of Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page.

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

Do not set MaxCapacity if using WorkerType and NumberOfWorkers.

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

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

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

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

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

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

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

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

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

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

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

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

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

pub fn security_configuration(&self) -> Option<&str>

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

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

Specifies configuration properties of a job notification.

pub fn glue_version(&self) -> Option<&str>

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

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

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

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

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

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

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

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

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

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

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

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

impl Job

pub fn builder() -> JobBuilder

Creates a new builder-style object to manufacture Job.

Trait Implementations

impl Clone for Job

fn clone(&self) -> Job

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Job

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

Formats the value using the given formatter.

impl PartialEq for Job

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

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

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

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

impl StructuralPartialEq for Job