Struct aws_sdk_glue::operation::create_session::CreateSessionInput

#[non_exhaustive]
pub struct CreateSessionInput {

    pub id: Option<String>,
    pub description: Option<String>,
    pub role: Option<String>,
    pub command: Option<SessionCommand>,
    pub timeout: Option<i32>,
    pub idle_timeout: Option<i32>,
    pub default_arguments: Option<HashMap<String, String>>,
    pub connections: Option<ConnectionsList>,
    pub max_capacity: Option<f64>,
    pub number_of_workers: Option<i32>,
    pub worker_type: Option<WorkerType>,
    pub security_configuration: Option<String>,
    pub glue_version: Option<String>,
    pub tags: Option<HashMap<String, String>>,
    pub request_origin: Option<String>,
}

Request to create a new session.

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.

id: Option<String>

The ID of the session request.

description: Option<String>

The description of the session.

role: Option<String>

The IAM Role ARN

command: Option<SessionCommand>

The SessionCommand that runs the job.

timeout: 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.

idle_timeout: 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.

default_arguments: Option<HashMap<String, String>>

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

connections: Option<ConnectionsList>

The number of connections to use for the session.

max_capacity: 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.

number_of_workers: Option<i32>

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

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, 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.

security_configuration: Option<String>

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

glue_version: Option<String>

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

tags: Option<HashMap<String, String>>

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

request_origin: Option<String>

The origin of the request.

Implementations

impl CreateSessionInput

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

The ID of the session request.

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

The description of the session.

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

The IAM Role ARN

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

The SessionCommand that runs the job.

pub fn 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) -> 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) -> Option<&HashMap<String, String>>

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

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

The number of connections to use for the session.

pub fn 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) -> Option<i32>

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

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, 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) -> Option<&str>

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

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

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) -> Option<&HashMap<String, String>>

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

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

The origin of the request.

impl CreateSessionInput

pub fn builder() -> CreateSessionInputBuilder

Creates a new builder-style object to manufacture CreateSessionInput.

Trait Implementations

impl Clone for CreateSessionInput

fn clone(&self) -> CreateSessionInput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateSessionInput

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

Formats the value using the given formatter.

impl PartialEq for CreateSessionInput

fn eq(&self, other: &CreateSessionInput) -> 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 CreateSessionInput