Package 'paws.database'

Description

DAX is a managed caching service engineered for Amazon DynamoDB. DAX dramatically speeds up database reads by caching frequently-accessed data from DynamoDB, so applications can access that data with sub-millisecond latency. You can create a DAX cluster easily, using the AWS Management Console. With a few simple modifications to your code, your application can begin taking advantage of the DAX cluster and realize significant improvements in read performance.

Usage

dax(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 <- dax(
  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 <- dax()
svc$create_cluster(
  Foo = 123
)

## End(Not run)

Description

Amazon DocumentDB is a fast, reliable, and fully managed database service. Amazon DocumentDB makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud. With Amazon DocumentDB, you can run the same application code and use the same drivers and tools that you use with MongoDB.

Usage

docdb(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 <- docdb(
  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 <- docdb()
svc$add_source_identifier_to_subscription(
  Foo = 123
)

## End(Not run)

Description

Amazon DocumentDB elastic clusters

Amazon DocumentDB elastic-clusters support workloads with millions of reads/writes per second and petabytes of storage capacity. Amazon DocumentDB elastic clusters also simplify how developers interact with Amazon DocumentDB elastic-clusters by eliminating the need to choose, manage or upgrade instances.

Amazon DocumentDB elastic-clusters were created to:

• provide a solution for customers looking for a database that provides virtually limitless scale with rich query capabilities and MongoDB API compatibility.

• give customers higher connection limits, and to reduce downtime from patching.

• continue investing in a cloud-native, elastic, and class leading architecture for JSON workloads.

Usage

docdbelastic(
  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 <- docdbelastic(
  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 <- docdbelastic()
svc$copy_cluster_snapshot(
  Foo = 123
)

## End(Not run)

Description

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB lets you offload the administrative burdens of operating and scaling a distributed database, so that you don't have to worry about hardware provisioning, setup and configuration, replication, software patching, or cluster scaling.

With DynamoDB, you can create database tables that can store and retrieve any amount of data, and serve any level of request traffic. You can scale up or scale down your tables' throughput capacity without downtime or performance degradation, and use the Amazon Web Services Management Console to monitor resource utilization and performance metrics.

DynamoDB automatically spreads the data and traffic for your tables over a sufficient number of servers to handle your throughput and storage requirements, while maintaining consistent and fast performance. All of your data is stored on solid state disks (SSDs) and automatically replicated across multiple Availability Zones in an Amazon Web Services Region, providing built-in high availability and data durability.

Usage

dynamodb(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 <- dynamodb(
  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 <- dynamodb()
# This example reads multiple items from the Music table using a batch of
# three GetItem requests.  Only the AlbumTitle attribute is returned.
svc$batch_get_item(
  RequestItems = list(
    Music = list(
      Keys = list(
        list(
          Artist = list(
            S = "No One You Know"
          ),
          SongTitle = list(
            S = "Call Me Today"
          )
        ),
        list(
          Artist = list(
            S = "Acme Band"
          ),
          SongTitle = list(
            S = "Happy Day"
          )
        ),
        list(
          Artist = list(
            S = "No One You Know"
          ),
          SongTitle = list(
            S = "Scared of My Shadow"
          )
        )
      ),
      ProjectionExpression = "AlbumTitle"
    )
  )
)

## End(Not run)

Description

Amazon DynamoDB

Amazon DynamoDB Streams provides API actions for accessing streams and processing stream records. To learn more about application development with Streams, see Capturing Table Activity with DynamoDB Streams in the Amazon DynamoDB Developer Guide.

Usage

dynamodbstreams(
  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 <- dynamodbstreams(
  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 <- dynamodbstreams()
# The following example describes a stream with a given stream ARN.
svc$describe_stream(
  StreamArn = "arn:aws:dynamodb:us-west-2:111122223333:table/Forum/stream/2..."
)

## End(Not run)

Description

Amazon ElastiCache is a web service that makes it easier to set up, operate, and scale a distributed cache in the cloud.

With ElastiCache, customers get all of the benefits of a high-performance, in-memory cache with less of the administrative burden involved in launching and managing a distributed cache. The service makes setup, scaling, and cluster failure handling much simpler than in a self-managed cache deployment.

In addition, through integration with Amazon CloudWatch, customers get enhanced visibility into the key performance statistics associated with their cache and can receive alarms if a part of their cache runs hot.

Usage

elasticache(
  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 <- elasticache(
  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 <- elasticache()
svc$add_tags_to_resource(
  Foo = 123
)

## End(Not run)

Description

Amazon Keyspaces (for Apache Cassandra) is a scalable, highly available, and managed Apache Cassandra-compatible database service. Amazon Keyspaces makes it easy to migrate, run, and scale Cassandra workloads in the Amazon Web Services Cloud. With just a few clicks on the Amazon Web Services Management Console or a few lines of code, you can create keyspaces and tables in Amazon Keyspaces, without deploying any infrastructure or installing software.

In addition to supporting Cassandra Query Language (CQL) requests via open-source Cassandra drivers, Amazon Keyspaces supports data definition language (DDL) operations to manage keyspaces and tables using the Amazon Web Services SDK and CLI, as well as infrastructure as code (IaC) services and tools such as CloudFormation and Terraform. This API reference describes the supported DDL operations in detail.

For the list of all supported CQL APIs, see Supported Cassandra APIs, operations, and data types in Amazon Keyspaces in the Amazon Keyspaces Developer Guide.

To learn how Amazon Keyspaces API actions are recorded with CloudTrail, see Amazon Keyspaces information in CloudTrail in the Amazon Keyspaces Developer Guide.

For more information about Amazon Web Services APIs, for example how to implement retry logic or how to sign Amazon Web Services API requests, see Amazon Web Services APIs in the General Reference.

Usage

keyspaces(
  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 <- keyspaces(
  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 <- keyspaces()
svc$create_keyspace(
  Foo = 123
)

## End(Not run)

Description

Lake Formation

Defines the public endpoint for the Lake Formation service.

Usage

lakeformation(
  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 <- lakeformation(
  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 <- lakeformation()
svc$add_lf_tags_to_resource(
  Foo = 123
)

## End(Not run)

Description

MemoryDB is a fully managed, Redis OSS-compatible, in-memory database that delivers ultra-fast performance and Multi-AZ durability for modern applications built using microservices architectures. MemoryDB stores the entire database in-memory, enabling low latency and high throughput data access. It is compatible with Redis OSS, a popular open source data store, enabling you to leverage Redis OSS’ flexible and friendly data structures, APIs, and commands.

Usage

memorydb(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 <- memorydb(
  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 <- memorydb()
svc$batch_update_cluster(
  Foo = 123
)

## End(Not run)

Description

Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security.

This interface reference for Amazon Neptune contains documentation for a programming or command line interface you can use to manage Amazon Neptune. Note that Amazon Neptune is asynchronous, which means that some interfaces might require techniques such as polling or callback functions to determine when a command has been applied. In this reference, the parameter descriptions indicate whether a command is applied immediately, on the next instance reboot, or during the maintenance window. The reference structure is as follows, and we list following some related topics from the user guide.

Usage

neptune(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 <- neptune(
  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 <- neptune()
svc$add_role_to_db_cluster(
  Foo = 123
)

## End(Not run)

Description

Neptune Data API

The Amazon Neptune data API provides SDK support for more than 40 of Neptune's data operations, including data loading, query execution, data inquiry, and machine learning. It supports the Gremlin and openCypher query languages, and is available in all SDK languages. It automatically signs API requests and greatly simplifies integrating Neptune into your applications.

Usage

neptunedata(
  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 <- neptunedata(
  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 <- neptunedata()
svc$cancel_gremlin_query(
  Foo = 123
)

## End(Not run)

Description

The resource management API for Amazon QLDB

Usage

qldb(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 <- qldb(
  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 <- qldb()
svc$cancel_journal_kinesis_stream(
  Foo = 123
)

## End(Not run)

Description

The transactional data APIs for Amazon QLDB

Instead of interacting directly with this API, we recommend using the QLDB driver or the QLDB shell to execute data transactions on a ledger.

• If you are working with an AWS SDK, use the QLDB driver. The driver provides a high-level abstraction layer above this QLDB Session data plane and manages send_command API calls for you. For information and a list of supported programming languages, see Getting started with the driver in the Amazon QLDB Developer Guide.

• If you are working with the AWS Command Line Interface (AWS CLI), use the QLDB shell. The shell is a command line interface that uses the QLDB driver to interact with a ledger. For information, see Accessing Amazon QLDB using the QLDB shell.

Usage

qldbsession(
  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 <- qldbsession(
  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 <- qldbsession()
svc$send_command(
  Foo = 123
)

## End(Not run)

Description

Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the cloud. It provides cost-efficient, resizeable capacity for an industry-standard relational database and manages common database administration tasks, freeing up developers to focus on what makes their applications and businesses unique.

Amazon RDS gives you access to the capabilities of a MySQL, MariaDB, PostgreSQL, Microsoft SQL Server, Oracle, Db2, or Amazon Aurora database server. These capabilities mean that the code, applications, and tools you already use today with your existing databases work with Amazon RDS without modification. Amazon RDS automatically backs up your database and maintains the database software that powers your DB instance. Amazon RDS is flexible: you can scale your DB instance's compute resources and storage capacity to meet your application's demand. As with all Amazon Web Services, there are no up-front investments, and you pay only for the resources you use.

This interface reference for Amazon RDS contains documentation for a programming or command line interface you can use to manage Amazon RDS. Amazon RDS is asynchronous, which means that some interfaces might require techniques such as polling or callback functions to determine when a command has been applied. In this reference, the parameter descriptions indicate whether a command is applied immediately, on the next instance reboot, or during the maintenance window. The reference structure is as follows, and we list following some related topics from the user guide.

Amazon RDS API Reference

• For the alphabetical list of API actions, see API Actions.

• For the alphabetical list of data types, see Data Types.

• For a list of common query parameters, see Common Parameters.

• For descriptions of the error codes, see Common Errors.

Amazon RDS User Guide

• For a summary of the Amazon RDS interfaces, see Available RDS Interfaces.

• For more information about how to use the Query API, see Using the Query API.

Usage

rds(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 <- rds(
  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 <- rds()
svc$add_role_to_db_cluster(
  Foo = 123
)

## End(Not run)

Description

RDS Data API

Amazon RDS provides an HTTP endpoint to run SQL statements on an Amazon Aurora DB cluster. To run these statements, you use the RDS Data API (Data API).

Data API is available with the following types of Aurora databases:

• Aurora PostgreSQL - Serverless v2, Serverless v1, and provisioned

• Aurora MySQL - Serverless v1 only

For more information about the Data API, see Using RDS Data API in the Amazon Aurora User Guide.

Usage

rdsdataservice(
  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 <- rdsdataservice(
  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 <- rdsdataservice()
svc$batch_execute_statement(
  Foo = 123
)

## End(Not run)

Description

Overview

This is an interface reference for Amazon Redshift. It contains documentation for one of the programming or command line interfaces you can use to manage Amazon Redshift clusters. Note that Amazon Redshift is asynchronous, which means that some interfaces may require techniques, such as polling or asynchronous callback handlers, to determine when a command has been applied. In this reference, the parameter descriptions indicate whether a change is applied immediately, on the next instance reboot, or during the next maintenance window. For a summary of the Amazon Redshift cluster management interfaces, go to Using the Amazon Redshift Management Interfaces.

Amazon Redshift manages all the work of setting up, operating, and scaling a data warehouse: provisioning capacity, monitoring and backing up the cluster, and applying patches and upgrades to the Amazon Redshift engine. You can focus on using your data to acquire new insights for your business and customers.

If you are a first-time user of Amazon Redshift, we recommend that you begin by reading the Amazon Redshift Getting Started Guide.

If you are a database developer, the Amazon Redshift Database Developer Guide explains how to design, build, query, and maintain the databases that make up your data warehouse.

Usage

redshift(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 <- redshift(
  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 <- redshift()
svc$accept_reserved_node_exchange(
  Foo = 123
)

## End(Not run)

Description

You can use the Amazon Redshift Data API to run queries on Amazon Redshift tables. You can run SQL statements, which are committed if the statement succeeds.

For more information about the Amazon Redshift Data API and CLI usage examples, see Using the Amazon Redshift Data API in the Amazon Redshift Management Guide.

Usage

redshiftdataapiservice(
  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 <- redshiftdataapiservice(
  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 <- redshiftdataapiservice()
svc$batch_execute_statement(
  Foo = 123
)

## End(Not run)

Description

This is an interface reference for Amazon Redshift Serverless. It contains documentation for one of the programming or command line interfaces you can use to manage Amazon Redshift Serverless.

Amazon Redshift Serverless automatically provisions data warehouse capacity and intelligently scales the underlying resources based on workload demands. Amazon Redshift Serverless adjusts capacity in seconds to deliver consistently high performance and simplified operations for even the most demanding and volatile workloads. Amazon Redshift Serverless lets you focus on using your data to acquire new insights for your business and customers.

To learn more about Amazon Redshift Serverless, see What is Amazon Redshift Serverless.

Usage

redshiftserverless(
  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 <- redshiftserverless(
  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 <- redshiftserverless()
svc$convert_recovery_point_to_snapshot(
  Foo = 123
)

## End(Not run)

Description

Amazon SimpleDB is a web service providing the core database functions of data indexing and querying in the cloud. By offloading the time and effort associated with building and operating a web-scale database, SimpleDB provides developers the freedom to focus on application development.

A traditional, clustered relational database requires a sizable upfront capital outlay, is complex to design, and often requires extensive and repetitive database administration. Amazon SimpleDB is dramatically simpler, requiring no schema, automatically indexing your data and providing a simple API for storage and access. This approach eliminates the administrative burden of data modeling, index maintenance, and performance tuning. Developers gain access to this functionality within Amazon's proven computing environment, are able to scale instantly, and pay only for what they use.

Visit http://aws.amazon.com/simpledb/ for more information.

Usage

simpledb(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 <- simpledb(
  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 <- simpledb()
svc$batch_delete_attributes(
  Foo = 123
)

## End(Not run)

Description

Amazon Timestream Query

Usage

timestreamquery(
  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 <- timestreamquery(
  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 <- timestreamquery()
svc$cancel_query(
  Foo = 123
)

## End(Not run)

Description

Amazon Timestream is a fast, scalable, fully managed time-series database service that makes it easy to store and analyze trillions of time-series data points per day. With Timestream, you can easily store and analyze IoT sensor data to derive insights from your IoT applications. You can analyze industrial telemetry to streamline equipment management and maintenance. You can also store and analyze log data and metrics to improve the performance and availability of your applications.

Timestream is built from the ground up to effectively ingest, process, and store time-series data. It organizes data to optimize query processing. It automatically scales based on the volume of data ingested and on the query volume to ensure you receive optimal performance while inserting and querying data. As your data grows over time, Timestream’s adaptive query processing engine spans across storage tiers to provide fast analysis while reducing costs.

Usage

timestreamwrite(
  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 <- timestreamwrite(
  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 <- timestreamwrite()
svc$create_batch_load_task(
  Foo = 123
)

## End(Not run)