Module types

A top-level container for a table. Databases and tables are the fundamental management concepts in Amazon Timestream. All tables in a database are encrypted with the same KMS key.

Dimension

Represents the metadata attributes of the time series. For example, the name and Availability Zone of an EC2 instance or the name of the manufacturer of a wind turbine are dimensions.

DimensionMapping

Endpoint

Represents an available endpoint against which to make API calls against, as well as the TTL for that endpoint.

MagneticStoreRejectedDataLocation

The location to write error reports for records rejected, asynchronously, during magnetic store writes.

MagneticStoreWriteProperties

The set of properties on a table for configuring magnetic store writes.

MeasureValue

Represents the data attribute of the time series. For example, the CPU utilization of an EC2 instance or the RPM of a wind turbine are measures. MeasureValue has both name and value.

MeasureValue is only allowed for type MULTI. Using MULTI type, you can pass multiple data attributes associated with the same time series in a single record

MixedMeasureMapping

MultiMeasureAttributeMapping

MultiMeasureMappings

PartitionKey

An attribute used in partitioning data in a table. A dimension key partitions data using the values of the dimension specified by the dimension-name as partition key, while a measure key partitions data using measure names (values of the 'measure_name' column).

Record

Represents a time-series data point being written into Timestream. Each record contains an array of dimensions. Dimensions represent the metadata attributes of a time-series data point, such as the instance name or Availability Zone of an EC2 instance. A record also contains the measure name, which is the name of the measure being collected (for example, the CPU utilization of an EC2 instance). Additionally, a record contains the measure value and the value type, which is the data type of the measure value. Also, the record contains the timestamp of when the measure was collected and the timestamp unit, which represents the granularity of the timestamp.

Records have a Version field, which is a 64-bit long that you can use for updating data points. Writes of a duplicate record with the same dimension, timestamp, and measure name but different measure value will only succeed if the Version attribute of the record in the write request is higher than that of the existing record. Timestream defaults to a Version of 1 for records without the Version field.

RecordsIngested

Information on the records ingested by this request.

RejectedRecord

Represents records that were not successfully inserted into Timestream due to data validation issues that must be resolved before reinserting time-series data into the system.

ReportConfiguration

Report configuration for a batch load task. This contains details about where error reports are stored.

ReportS3Configuration

RetentionProperties

Retention properties contain the duration for which your time-series data must be stored in the magnetic store and the memory store.

S3Configuration

The configuration that specifies an S3 location.

Schema

A Schema specifies the expected data model of the table.

Table

Represents a database table in Timestream. Tables contain one or more related time series. You can modify the retention duration of the memory store and the magnetic store for a table.

Tag

A tag is a label that you assign to a Timestream database and/or table. Each tag consists of a key and an optional value, both of which you define. With tags, you can categorize databases and/or tables, for example, by purpose, owner, or environment.

Enums§

BatchLoadDataFormat: When writing a match expression against BatchLoadDataFormat, 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.
BatchLoadStatus: When writing a match expression against BatchLoadStatus, 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.
DimensionValueType: When writing a match expression against DimensionValueType, 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.
MeasureValueType: When writing a match expression against MeasureValueType, 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.
PartitionKeyEnforcementLevel: When writing a match expression against PartitionKeyEnforcementLevel, 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.
PartitionKeyType: When writing a match expression against PartitionKeyType, 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.
S3EncryptionOption: When writing a match expression against S3EncryptionOption, 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.
ScalarMeasureValueType: When writing a match expression against ScalarMeasureValueType, 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.
TableStatus: When writing a match expression against TableStatus, 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.
TimeUnit: When writing a match expression against TimeUnit, 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.

Module types

Module types Copy item path

Modules§

Structs§

Enums§

Module types