Package 'paws'

Description

Identity and Access Management Access Analyzer helps you to set, verify, and refine your IAM policies by providing a suite of capabilities. Its features include findings for external and unused access, basic and custom policy checks for validating policies, and policy generation to generate fine-grained policies. To start using IAM Access Analyzer to identify external or unused access, you first need to create an analyzer.

External access analyzers help identify potential risks of accessing resources by enabling you to identify any resource policies that grant access to an external principal. It does this by using logic-based reasoning to analyze resource-based policies in your Amazon Web Services environment. An external principal can be another Amazon Web Services account, a root user, an IAM user or role, a federated user, an Amazon Web Services service, or an anonymous user. You can also use IAM Access Analyzer to preview public and cross-account access to your resources before deploying permissions changes.

Unused access analyzers help identify potential identity access risks by enabling you to identify unused IAM roles, unused access keys, unused console passwords, and IAM principals with unused service and action-level permissions.

Beyond findings, IAM Access Analyzer provides basic and custom policy checks to validate IAM policies before deploying permissions changes. You can use policy generation to refine permissions by attaching a policy generated using access activity logged in CloudTrail logs.

This guide describes the IAM Access Analyzer operations that you can call programmatically. For general information about IAM Access Analyzer, see Identity and Access Management Access Analyzer in the IAM User Guide.

Usage

accessanalyzer(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- accessanalyzer(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- accessanalyzer()
svc$check_access_not_granted(
  access = list(
    list(
      actions = list(
        "s3:PutObject"
      )
    )
  ),
  policyDocument = "{"Version":"2012-10-17","Id":"123","Statement":[{"Sid":...",
  policyType = "RESOURCE_POLICY"
)

## End(Not run)

Description

Operations for Amazon Web Services Account Management

Usage

account(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- account(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- account()
svc$accept_primary_email_update(
  Foo = 123
)

## End(Not run)

Description

Certificate Manager

You can use Certificate Manager (ACM) to manage SSL/TLS certificates for your Amazon Web Services-based websites and applications. For more information about using ACM, see the Certificate Manager User Guide.

Usage

acm(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- acm(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- acm()
svc$add_tags_to_certificate(
  Foo = 123
)

## End(Not run)

Description

This is the Amazon Web Services Private Certificate Authority API Reference. It provides descriptions, syntax, and usage examples for each of the actions and data types involved in creating and managing a private certificate authority (CA) for your organization.

The documentation for each action shows the API request parameters and the JSON response. Alternatively, you can use one of the Amazon Web Services SDKs to access an API that is tailored to the programming language or platform that you prefer. For more information, see Amazon Web Services SDKs.

Each Amazon Web Services Private CA API operation has a quota that determines the number of times the operation can be called per second. Amazon Web Services Private CA throttles API requests at different rates depending on the operation. Throttling means that Amazon Web Services Private CA rejects an otherwise valid request because the request exceeds the operation's quota for the number of requests per second. When a request is throttled, Amazon Web Services Private CA returns a ThrottlingException error. Amazon Web Services Private CA does not guarantee a minimum request rate for APIs.

To see an up-to-date list of your Amazon Web Services Private CA quotas, or to request a quota increase, log into your Amazon Web Services account and visit the Service Quotas console.

Usage

acmpca(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- acmpca(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- acmpca()
svc$create_certificate_authority(
  Foo = 123
)

## End(Not run)

Description

Amazon API Gateway helps developers deliver robust, secure, and scalable mobile and web application back ends. API Gateway allows developers to securely connect mobile and web applications to APIs that run on Lambda, Amazon EC2, or other publicly addressable web services that are hosted outside of AWS.

Usage

apigateway(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- apigateway(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- apigateway()
svc$create_api_key(
  Foo = 123
)

## End(Not run)

Description

The Amazon API Gateway Management API allows you to directly manage runtime aspects of your deployed APIs. To use it, you must explicitly set the SDK's endpoint to point to the endpoint of your deployed API. The endpoint will be of the form https://{api-id}.execute-api.{region}.amazonaws.com/{stage}, or will be the endpoint corresponding to your API's custom domain and base path, if applicable.

Usage

apigatewaymanagementapi(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- apigatewaymanagementapi(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- apigatewaymanagementapi()
svc$delete_connection(
  Foo = 123
)

## End(Not run)

Description

Amazon API Gateway V2

Usage

apigatewayv2(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- apigatewayv2(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- apigatewayv2()
svc$create_api(
  Foo = 123
)

## End(Not run)

Description

Amazon Web Services AppFabric quickly connects software as a service (SaaS) applications across your organization. This allows IT and security teams to easily manage and secure applications using a standard schema, and employees can complete everyday tasks faster using generative artificial intelligence (AI). You can use these APIs to complete AppFabric tasks, such as setting up audit log ingestions or viewing user access. For more information about AppFabric, including the required permissions to use the service, see the Amazon Web Services AppFabric Administration Guide. For more information about using the Command Line Interface (CLI) to manage your AppFabric resources, see the AppFabric section of the CLI Reference.

Usage

appfabric(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- appfabric(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- appfabric()
svc$batch_get_user_access_tasks(
  Foo = 123
)

## End(Not run)

Description

With Application Auto Scaling, you can configure automatic scaling for the following resources:

• Amazon AppStream 2.0 fleets

• Amazon Aurora Replicas

• Amazon Comprehend document classification and entity recognizer endpoints

• Amazon DynamoDB tables and global secondary indexes throughput capacity

• Amazon ECS services

• Amazon ElastiCache for Redis clusters (replication groups)

• Amazon EMR clusters

• Amazon Keyspaces (for Apache Cassandra) tables

• Lambda function provisioned concurrency

• Amazon Managed Streaming for Apache Kafka broker storage

• Amazon Neptune clusters

• Amazon SageMaker endpoint variants

• Amazon SageMaker inference components

• Amazon SageMaker serverless endpoint provisioned concurrency

• Spot Fleets (Amazon EC2)

• Pool of WorkSpaces

• Custom resources provided by your own applications or services

To learn more about Application Auto Scaling, see the Application Auto Scaling User Guide.

API Summary

The Application Auto Scaling service API includes three key sets of actions:

• Register and manage scalable targets - Register Amazon Web Services or custom resources as scalable targets (a resource that Application Auto Scaling can scale), set minimum and maximum capacity limits, and retrieve information on existing scalable targets.

• Configure and manage automatic scaling - Define scaling policies to dynamically scale your resources in response to CloudWatch alarms, schedule one-time or recurring scaling actions, and retrieve your recent scaling activity history.

• Suspend and resume scaling - Temporarily suspend and later resume automatic scaling by calling the register_scalable_target API action for any Application Auto Scaling scalable target. You can suspend and resume (individually or in combination) scale-out activities that are triggered by a scaling policy, scale-in activities that are triggered by a scaling policy, and scheduled scaling.

Usage

applicationautoscaling(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- applicationautoscaling(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- applicationautoscaling()
# This example deletes a scaling policy for the Amazon ECS service called
# web-app, which is running in the default cluster.
svc$delete_scaling_policy(
  PolicyName = "web-app-cpu-lt-25",
  ResourceId = "service/default/web-app",
  ScalableDimension = "ecs:service:DesiredCount",
  ServiceNamespace = "ecs"
)

## End(Not run)

Description

This reference provides descriptions of the AWS Application Cost Profiler API.

The AWS Application Cost Profiler API provides programmatic access to view, create, update, and delete application cost report definitions, as well as to import your usage data into the Application Cost Profiler service.

For more information about using this service, see the AWS Application Cost Profiler User Guide.

Usage

applicationcostprofiler(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- applicationcostprofiler(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- applicationcostprofiler()
svc$delete_report_definition(
  Foo = 123
)

## End(Not run)

Description

Amazon CloudWatch Application Insights is a service that helps you detect common problems with your applications. It enables you to pinpoint the source of issues in your applications (built with technologies such as Microsoft IIS, .NET, and Microsoft SQL Server), by providing key insights into detected problems.

After you onboard your application, CloudWatch Application Insights identifies, recommends, and sets up metrics and logs. It continuously analyzes and correlates your metrics and logs for unusual behavior to surface actionable problems with your application. For example, if your application is slow and unresponsive and leading to HTTP 500 errors in your Application Load Balancer (ALB), Application Insights informs you that a memory pressure problem with your SQL Server database is occurring. It bases this analysis on impactful metrics and log errors.

Usage

applicationinsights(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- applicationinsights(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- applicationinsights()
svc$add_workload(
  Foo = 123
)

## End(Not run)

Description

App Mesh is a service mesh based on the Envoy proxy that makes it easy to monitor and control microservices. App Mesh standardizes how your microservices communicate, giving you end-to-end visibility and helping to ensure high availability for your applications.

App Mesh gives you consistent visibility and network traffic controls for every microservice in an application. You can use App Mesh with Amazon Web Services Fargate, Amazon ECS, Amazon EKS, Kubernetes on Amazon Web Services, and Amazon EC2.

App Mesh supports microservice applications that use service discovery naming for their components. For more information about service discovery on Amazon ECS, see Service Discovery in the Amazon Elastic Container Service Developer Guide. Kubernetes kube-dns and coredns are supported. For more information, see DNS for Services and Pods in the Kubernetes documentation.

Usage

appmesh(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- appmesh(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- appmesh()
svc$create_gateway_route(
  Foo = 123
)

## End(Not run)

Description

Amazon Web Services Service Catalog AppRegistry enables organizations to understand the application context of their Amazon Web Services resources. AppRegistry provides a repository of your applications, their resources, and the application metadata that you use within your enterprise.

Usage

appregistry(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- appregistry(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- appregistry()
svc$associate_attribute_group(
  Foo = 123
)

## End(Not run)

Description

App Runner

App Runner is an application service that provides a fast, simple, and cost-effective way to go directly from an existing container image or source code to a running service in the Amazon Web Services Cloud in seconds. You don't need to learn new technologies, decide which compute service to use, or understand how to provision and configure Amazon Web Services resources.

App Runner connects directly to your container registry or source code repository. It provides an automatic delivery pipeline with fully managed operations, high performance, scalability, and security.

For more information about App Runner, see the App Runner Developer Guide. For release information, see the App Runner Release Notes.

To install the Software Development Kits (SDKs), Integrated Development Environment (IDE) Toolkits, and command line tools that you can use to access the API, see Tools for Amazon Web Services.

Endpoints

For a list of Region-specific endpoints that App Runner supports, see App Runner endpoints and quotas in the Amazon Web Services General Reference.

Usage

apprunner(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- apprunner(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- apprunner()
svc$associate_custom_domain(
  Foo = 123
)

## End(Not run)

Description

Amazon AppStream 2.0

This is the Amazon AppStream 2.0 API Reference. This documentation provides descriptions and syntax for each of the actions and data types in AppStream 2.0. AppStream 2.0 is a fully managed, secure application streaming service that lets you stream desktop applications to users without rewriting applications. AppStream 2.0 manages the AWS resources that are required to host and run your applications, scales automatically, and provides access to your users on demand.

You can call the AppStream 2.0 API operations by using an interface VPC endpoint (interface endpoint). For more information, see Access AppStream 2.0 API Operations and CLI Commands Through an Interface VPC Endpoint in the Amazon AppStream 2.0 Administration Guide.

To learn more about AppStream 2.0, see the following resources:

• Amazon AppStream 2.0 product page

• Amazon AppStream 2.0 documentation

Usage

appstream(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- appstream(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- appstream()
svc$associate_app_block_builder_app_block(
  Foo = 123
)

## End(Not run)

Description

Welcome to the API Reference Guide for zonal shift and zonal autoshift in Amazon Route 53 Application Recovery Controller (Route 53 ARC).

You can start a zonal shift to move traffic for a load balancer resource away from an Availability Zone to help your application recover quickly from an impairment in an Availability Zone. For example, you can recover your application from a developer's bad code deployment or from an Amazon Web Services infrastructure failure in a single Availability Zone.

You can also configure zonal autoshift for supported load balancer resources. Zonal autoshift is a capability in Route 53 ARC where you authorize Amazon Web Services to shift away application resource traffic from an Availability Zone during events, on your behalf, to help reduce your time to recovery. Amazon Web Services starts an autoshift when internal telemetry indicates that there is an Availability Zone impairment that could potentially impact customers.

To help make sure that zonal autoshift is safe for your application, you must also configure practice runs when you enable zonal autoshift for a resource. Practice runs start weekly zonal shifts for a resource, to shift traffic for the resource away from an Availability Zone. Practice runs help you to make sure, on a regular basis, that you have enough capacity in all the Availability Zones in an Amazon Web Services Region for your application to continue to operate normally when traffic for a resource is shifted away from one Availability Zone.

Before you configure practice runs or enable zonal autoshift, we strongly recommend that you prescale your application resource capacity in all Availability Zones in the Region where your application resources are deployed. You should not rely on scaling on demand when an autoshift or practice run starts. Zonal autoshift, including practice runs, works independently, and does not wait for auto scaling actions to complete. Relying on auto scaling, instead of pre-scaling, can result in loss of availability.

If you use auto scaling to handle regular cycles of traffic, we strongly recommend that you configure the minimum capacity of your auto scaling to continue operating normally with the loss of an Availability Zone.

Be aware that Route 53 ARC does not inspect the health of individual resources. Amazon Web Services only starts an autoshift when Amazon Web Services telemetry detects that there is an Availability Zone impairment that could potentially impact customers. In some cases, resources might be shifted away that are not experiencing impact.

For more information about using zonal shift and zonal autoshift, see the Amazon Route 53 Application Recovery Controller Developer Guide.

Usage

arczonalshift(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- arczonalshift(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- arczonalshift()
svc$cancel_zonal_shift(
  Foo = 123
)

## End(Not run)

Description

Amazon Athena is an interactive query service that lets you use standard SQL to analyze data directly in Amazon S3. You can point Athena at your data in Amazon S3 and run ad-hoc queries and get results in seconds. Athena is serverless, so there is no infrastructure to set up or manage. You pay only for the queries you run. Athena scales automatically—executing queries in parallel—so results are fast, even with large datasets and complex queries. For more information, see What is Amazon Athena in the Amazon Athena User Guide.

If you connect to Athena using the JDBC driver, use version 1.1.0 of the driver or later with the Amazon Athena API. Earlier version drivers do not support the API. For more information and to download the driver, see Accessing Amazon Athena with JDBC.

Usage

athena(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- athena(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- athena()
svc$batch_get_named_query(
  Foo = 123
)

## End(Not run)

Description

Welcome to the Audit Manager API reference. This guide is for developers who need detailed information about the Audit Manager API operations, data types, and errors.

Audit Manager is a service that provides automated evidence collection so that you can continually audit your Amazon Web Services usage. You can use it to assess the effectiveness of your controls, manage risk, and simplify compliance.

Audit Manager provides prebuilt frameworks that structure and automate assessments for a given compliance standard. Frameworks include a prebuilt collection of controls with descriptions and testing procedures. These controls are grouped according to the requirements of the specified compliance standard or regulation. You can also customize frameworks and controls to support internal audits with specific requirements.

Use the following links to get started with the Audit Manager API:

• Actions: An alphabetical list of all Audit Manager API operations.

• Data types: An alphabetical list of all Audit Manager data types.

• Common parameters: Parameters that all operations can use.

• Common errors: Client and server errors that all operations can return.

If you're new to Audit Manager, we recommend that you review the Audit Manager User Guide.

Usage

auditmanager(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- auditmanager(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- auditmanager()
svc$associate_assessment_report_evidence_folder(
  Foo = 123
)

## End(Not run)

Description

Amazon Augmented AI (Amazon A2I) adds the benefit of human judgment to any machine learning application. When an AI application can't evaluate data with a high degree of confidence, human reviewers can take over. This human review is called a human review workflow. To create and start a human review workflow, you need three resources: a worker task template, a flow definition, and a human loop.

For information about these resources and prerequisites for using Amazon A2I, see Get Started with Amazon Augmented AI in the Amazon SageMaker Developer Guide.

This API reference includes information about API actions and data types that you can use to interact with Amazon A2I programmatically. Use this guide to:

• Start a human loop with the start_human_loop operation when using Amazon A2I with a custom task type. To learn more about the difference between custom and built-in task types, see Use Task Types . To learn how to start a human loop using this API, see Create and Start a Human Loop for a Custom Task Type in the Amazon SageMaker Developer Guide.

• Manage your human loops. You can list all human loops that you have created, describe individual human loops, and stop and delete human loops. To learn more, see Monitor and Manage Your Human Loop in the Amazon SageMaker Developer Guide.

Amazon A2I integrates APIs from various AWS services to create and start human review workflows for those services. To learn how Amazon A2I uses these APIs, see Use APIs in Amazon A2I in the Amazon SageMaker Developer Guide.

Usage

augmentedairuntime(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- augmentedairuntime(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- augmentedairuntime()
svc$delete_human_loop(
  Foo = 123
)

## End(Not run)

Description

Amazon EC2 Auto Scaling

Amazon EC2 Auto Scaling is designed to automatically launch and terminate EC2 instances based on user-defined scaling policies, scheduled actions, and health checks.

For more information, see the Amazon EC2 Auto Scaling User Guide and the Amazon EC2 Auto Scaling API Reference.

Usage

autoscaling(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- autoscaling(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- autoscaling()
# This example attaches the specified instance to the specified Auto
# Scaling group.
svc$attach_instances(
  AutoScalingGroupName = "my-auto-scaling-group",
  InstanceIds = list(
    "i-93633f9b"
  )
)

## End(Not run)

Description

AWS Auto Scaling

Use AWS Auto Scaling to create scaling plans for your applications to automatically scale your scalable AWS resources.

API Summary

You can use the AWS Auto Scaling service API to accomplish the following tasks:

• Create and manage scaling plans

• Define target tracking scaling policies to dynamically scale your resources based on utilization

• Scale Amazon EC2 Auto Scaling groups using predictive scaling and dynamic scaling to scale your Amazon EC2 capacity faster

• Set minimum and maximum capacity limits

• Retrieve information on existing scaling plans

• Access current forecast data and historical forecast data for up to 56 days previous

To learn more about AWS Auto Scaling, including information about granting IAM users required permissions for AWS Auto Scaling actions, see the AWS Auto Scaling User Guide.

Usage

autoscalingplans(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- autoscalingplans(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- autoscalingplans()
svc$create_scaling_plan(
  Foo = 123
)

## End(Not run)

Description

Backup

Backup is a unified backup service designed to protect Amazon Web Services services and their associated data. Backup simplifies the creation, migration, restoration, and deletion of backups, while also providing reporting and auditing.

Usage

backup(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- backup(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- backup()
svc$cancel_legal_hold(
  Foo = 123
)

## End(Not run)

Description

Backup gateway

Backup gateway connects Backup to your hypervisor, so you can create, store, and restore backups of your virtual machines (VMs) anywhere, whether on-premises or in the VMware Cloud (VMC) on Amazon Web Services.

Add on-premises resources by connecting to a hypervisor through a gateway. Backup will automatically discover the resources in your hypervisor.

Use Backup to assign virtual or on-premises resources to a backup plan, or run on-demand backups. Once you have backed up your resources, you can view them and restore them like any resource supported by Backup.

To download the Amazon Web Services software to get started, navigate to the Backup console, choose Gateways, then choose Create gateway.

Usage

backupgateway(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- backupgateway(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- backupgateway()
svc$associate_gateway_to_server(
  Foo = 123
)

## End(Not run)

Description

Batch

Using Batch, you can run batch computing workloads on the Amazon Web Services Cloud. Batch computing is a common means for developers, scientists, and engineers to access large amounts of compute resources. Batch uses the advantages of the batch computing to remove the undifferentiated heavy lifting of configuring and managing required infrastructure. At the same time, it also adopts a familiar batch computing software approach. You can use Batch to efficiently provision resources, and work toward eliminating capacity constraints, reducing your overall compute costs, and delivering results more quickly.

As a fully managed service, Batch can run batch computing workloads of any scale. Batch automatically provisions compute resources and optimizes workload distribution based on the quantity and scale of your specific workloads. With Batch, there's no need to install or manage batch computing software. This means that you can focus on analyzing results and solving your specific problems instead.

Usage

batch(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- batch(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

Examples

## Not run: 
svc <- batch()
# This example cancels a job with the specified job ID.
svc$cancel_job(
  jobId = "1d828f65-7a4d-42e8-996d-3b900ed59dc4",
  reason = "Cancelling job."
)

## End(Not run)

Description

Describes the API operations for creating, managing, fine-turning, and evaluating Amazon Bedrock models.

Usage

bedrock(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- bedrock(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations