Struct aws_sdk_glue::operation::create_session::builders::CreateSessionFluentBuilder

pub struct CreateSessionFluentBuilder { /* private fields */ }

Fluent builder constructing a request to CreateSession.

Creates a new session.

Implementations

impl CreateSessionFluentBuilder

pub fn as_input(&self) -> &CreateSessionInputBuilder

Access the CreateSession as a reference.

pub async fn send( self ) -> Result<CreateSessionOutput, SdkError<CreateSessionError, HttpResponse>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn customize( self ) -> CustomizableOperation<CreateSessionOutput, CreateSessionError, Self>

Consumes this builder, creating a customizable operation that can be modified before being sent.

pub fn id(self, input: impl Into<String>) -> Self

The ID of the session request.

pub fn set_id(self, input: Option<String>) -> Self

The ID of the session request.

pub fn get_id(&self) -> &Option<String>

The ID of the session request.

pub fn description(self, input: impl Into<String>) -> Self

The description of the session.

pub fn set_description(self, input: Option<String>) -> Self

The description of the session.

pub fn get_description(&self) -> &Option<String>

The description of the session.

pub fn role(self, input: impl Into<String>) -> Self

The IAM Role ARN

pub fn set_role(self, input: Option<String>) -> Self

The IAM Role ARN

pub fn get_role(&self) -> &Option<String>

The IAM Role ARN

pub fn command(self, input: SessionCommand) -> Self

The SessionCommand that runs the job.

pub fn set_command(self, input: Option<SessionCommand>) -> Self

The SessionCommand that runs the job.

pub fn get_command(&self) -> &Option<SessionCommand>

The SessionCommand that runs the job.

pub fn timeout(self, input: i32) -> Self

The number of minutes before session times out. Default for Spark ETL jobs is 48 hours (2880 minutes), the maximum session lifetime for this job type. Consult the documentation for other job types.

pub fn set_timeout(self, input: Option<i32>) -> Self

The number of minutes before session times out. Default for Spark ETL jobs is 48 hours (2880 minutes), the maximum session lifetime for this job type. Consult the documentation for other job types.

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

The number of minutes before session times out. Default for Spark ETL jobs is 48 hours (2880 minutes), the maximum session lifetime for this job type. Consult the documentation for other job types.

pub fn idle_timeout(self, input: i32) -> Self

The number of minutes when idle before session times out. Default for Spark ETL jobs is value of Timeout. Consult the documentation for other job types.

pub fn set_idle_timeout(self, input: Option<i32>) -> Self

The number of minutes when idle before session times out. Default for Spark ETL jobs is value of Timeout. Consult the documentation for other job types.

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

The number of minutes when idle before session times out. Default for Spark ETL jobs is value of Timeout. Consult the documentation for other job types.

pub fn default_arguments( self, k: impl Into<String>, v: impl Into<String> ) -> Self

Adds a key-value pair to DefaultArguments.

To override the contents of this collection use set_default_arguments.

A map array of key-value pairs. Max is 75 pairs.

pub fn set_default_arguments( self, input: Option<HashMap<String, String>> ) -> Self

A map array of key-value pairs. Max is 75 pairs.

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

A map array of key-value pairs. Max is 75 pairs.

pub fn connections(self, input: ConnectionsList) -> Self

The number of connections to use for the session.

pub fn set_connections(self, input: Option<ConnectionsList>) -> Self

The number of connections to use for the session.

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

The number of connections to use for the session.

pub fn max_capacity(self, input: f64) -> Self

The number of Glue data processing units (DPUs) that can be allocated when the job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB memory.

pub fn set_max_capacity(self, input: Option<f64>) -> Self

The number of Glue data processing units (DPUs) that can be allocated when the job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB memory.

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

The number of Glue data processing units (DPUs) that can be allocated when the job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB memory.

pub fn number_of_workers(self, input: i32) -> Self

The number of workers of a defined WorkerType to use for the session.

pub fn set_number_of_workers(self, input: Option<i32>) -> Self

The number of workers of a defined WorkerType to use for the session.

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

The number of workers of a defined WorkerType to use for the session.

pub fn worker_type(self, input: WorkerType) -> Self

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

• 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 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 set_worker_type(self, input: Option<WorkerType>) -> Self

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

• 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 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 get_worker_type(&self) -> &Option<WorkerType>

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

• 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 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 security_configuration(self, input: impl Into<String>) -> Self

The name of the SecurityConfiguration structure to be used with the session

pub fn set_security_configuration(self, input: Option<String>) -> Self

The name of the SecurityConfiguration structure to be used with the session

pub fn get_security_configuration(&self) -> &Option<String>

The name of the SecurityConfiguration structure to be used with the session

pub fn glue_version(self, input: impl Into<String>) -> Self

The Glue version determines the versions of Apache Spark and Python that Glue supports. The GlueVersion must be greater than 2.0.

pub fn set_glue_version(self, input: Option<String>) -> Self

The Glue version determines the versions of Apache Spark and Python that Glue supports. The GlueVersion must be greater than 2.0.

pub fn get_glue_version(&self) -> &Option<String>

The Glue version determines the versions of Apache Spark and Python that Glue supports. The GlueVersion must be greater than 2.0.

pub fn tags(self, k: impl Into<String>, v: impl Into<String>) -> Self

Adds a key-value pair to Tags.

To override the contents of this collection use set_tags.

The map of key value pairs (tags) belonging to the session.

pub fn set_tags(self, input: Option<HashMap<String, String>>) -> Self

The map of key value pairs (tags) belonging to the session.

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

The map of key value pairs (tags) belonging to the session.

pub fn request_origin(self, input: impl Into<String>) -> Self

The origin of the request.

pub fn set_request_origin(self, input: Option<String>) -> Self

The origin of the request.

pub fn get_request_origin(&self) -> &Option<String>

The origin of the request.

Trait Implementations

impl Clone for CreateSessionFluentBuilder

fn clone(&self) -> CreateSessionFluentBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateSessionFluentBuilder

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

Formats the value using the given formatter.