Module aws_sdk_iotanalytics::types
source · Expand description
Data structures used by operation inputs/outputs.
Modules
- Builders
- Error types that AWS IoT Analytics can respond with.
Structs
An activity that adds other attributes based on existing attributes in the message.
Contains informations about errors.
A collection of data from an MQTT topic. Channels archive the raw, unprocessed messages before publishing the data to a pipeline.
The activity that determines the source of the messages to be processed.
Specifies one or more sets of channel messages.
Statistics information about the channel.
Where channel data is stored. You may choose one of
serviceManagedS3
,customerManagedS3
storage. If not specified, the default isserviceManagedS3
. This can't be changed after creation of the channel.Where channel data is stored.
A summary of information about a channel.
Contains information about a column that stores your data.
Information required to run the
containerAction
to produce dataset contents.Used to store channel data in an S3 bucket that you manage. If customer-managed storage is selected, the
retentionPeriod
parameter is ignored. You can't change the choice of S3 storage after the data store is created.Used to store channel data in an S3 bucket that you manage.
S3-customer-managed; When you choose customer-managed storage, the
retentionPeriod
parameter is ignored. You can't change the choice of Amazon S3 storage after your data store is created.Contains information about the data store that you manage.
Information about a dataset.
A
DatasetAction
object that specifies how dataset contents are automatically created.Information about the action that automatically creates the dataset's contents.
The destination to which dataset contents are delivered.
When dataset contents are created, they are delivered to destination specified here.
The state of the dataset contents and the reason they are in this state.
Summary information about dataset contents.
The dataset whose latest contents are used as input to the notebook or application.
The reference to a dataset entry.
A summary of information about a dataset.
The
DatasetTrigger
that specifies when the dataset is automatically updated.Information about a data store.
The datastore activity that specifies where to store the processed data.
Used to store data used by IoT SiteWise in an Amazon S3 bucket that you manage. You can't change the choice of Amazon S3 storage after your data store is created.
Contains information about the data store that you manage, which stores data used by IoT SiteWise.
A single dimension to partition a data store. The dimension must be an
AttributePartition
or aTimestampPartition
.Contains information about the partition dimensions in a data store.
Statistical information about the data store.
Contains information about your data store.
A summary of information about a data store.
Used to limit data to that which has arrived since the last execution of the action.
A structure that contains the configuration information of a delta time session window.
An activity that adds data from the IoT device registry to your message.
An activity that adds information from the IoT Device Shadow service to a message.
The estimated size of the resource.
Contains the configuration information of file formats. IoT Analytics data stores support JSON and Parquet.
An activity that filters a message based on its attributes.
Configuration information for coordination with Glue, a fully managed extract, transform and load (ETL) service.
Configuration information for delivery of dataset contents to IoT Events.
Used to store data used by IoT SiteWise in an Amazon S3 bucket that you manage. You can't change the choice of Amazon S3 storage after your data store is created.
Contains information about the data store that you manage, which stores data used by IoT SiteWise.
Contains the configuration information of the JSON format.
An activity that runs a Lambda function to modify the message.
A structure that contains the name and configuration information of a late data rule.
The information needed to configure a delta time session window.
Information about logging options.
An activity that computes an arithmetic expression using the message's attributes.
Information about a message.
The value of the variable as a structure that specifies an output file URI.
Contains the configuration information of the Parquet format.
A partition dimension defined by an attribute.
Contains information about a pipeline.
An activity that performs a transformation on a message.
A summary of information about a pipeline.
Information that is used to filter message data, to segregate it according to the timeframe in which it arrives.
An activity that removes attributes from a message.
Information about pipeline reprocessing.
The configuration of the resource used to execute the
containerAction
.How long, in days, message data is kept.
Configuration information for delivery of dataset contents to Amazon Simple Storage Service (Amazon S3).
The schedule for when to trigger an update.
Information needed to define a schema.
Used to create a new message using only the specified attributes from the original message.
Used to store channel data in an S3 bucket managed by IoT Analytics. You can't change the choice of S3 storage after the data store is created.
Used to store channel data in an S3 bucket managed by IoT Analytics.
Used to store data in an Amazon S3 bucket managed by IoT Analytics. You can't change the choice of Amazon S3 storage after your data store is created.
Contains information about the data store that is managed by IoT Analytics.
The SQL query to modify the message.
A set of key-value pairs that are used to manage the resource.
A partition dimension defined by a timestamp attribute.
Information about the dataset whose content generation triggers the new dataset content generation.
An instance of a variable to be passed to the
containerAction
execution. Each variable must have a name and a value given by one ofstringValue
,datasetContentVersionValue
, oroutputFileUriValue
.Information about the versioning of dataset contents.
Enums
- When writing a match expression against
ChannelStatus
, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ComputeType
, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
DatasetActionType
, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
DatasetContentState
, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
DatasetStatus
, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
DatastoreStatus
, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. Where data in a data store is stored.. You can choose
serviceManagedS3
storage,customerManagedS3
storage, oriotSiteWiseMultiLayerStorage
storage. The default isserviceManagedS3
. You can't change the choice of Amazon S3 storage after your data store is created.- When writing a match expression against
FileFormatType
, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
LoggingLevel
, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ReprocessingStatus
, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.