Module types

Data structures used by operation inputs/outputs.

Modules

• builders
  Builders
• error
  Error types that Amazon Timestream Query can respond with.

Structs

• ColumnInfo

  Contains the metadata for query results such as the column names, data types, and other attributes.

• Datum

  Datum represents a single data point in a query result.

• DimensionMapping

  This type is used to map column(s) from the query result to a dimension in the destination table.

• Endpoint

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

• ErrorReportConfiguration

  Configuration required for error reporting.

• ErrorReportLocation

  This contains the location of the error report for a single scheduled query call.

• ExecutionStats

  Statistics for a single scheduled query run.

• MixedMeasureMapping

  MixedMeasureMappings are mappings that can be used to ingest data into a mixture of narrow and multi measures in the derived table.

• MultiMeasureAttributeMapping

  Attribute mapping for MULTI value measures.

• MultiMeasureMappings

  Only one of MixedMeasureMappings or MultiMeasureMappings is to be provided. MultiMeasureMappings can be used to ingest data as multi measures in the derived table.

• NotificationConfiguration

  Notification configuration for a scheduled query. A notification is sent by Timestream when a scheduled query is created, its state is updated or when it is deleted.

• ParameterMapping

  Mapping for named parameters.

• QueryInsights

  QueryInsights is a performance tuning feature that helps you optimize your queries, reducing costs and improving performance. With QueryInsights, you can assess the pruning efficiency of your queries and identify areas for improvement to enhance query performance. With QueryInsights, you can also analyze the effectiveness of your queries in terms of temporal and spatial pruning, and identify opportunities to improve performance. Specifically, you can evaluate how well your queries use time-based and partition key-based indexing strategies to optimize data retrieval. To optimize query performance, it's essential that you fine-tune both the temporal and spatial parameters that govern query execution.

  The key metrics provided by QueryInsights are QuerySpatialCoverage and QueryTemporalRange. QuerySpatialCoverage indicates how much of the spatial axis the query scans, with lower values being more efficient. QueryTemporalRange shows the time range scanned, with narrower ranges being more performant.

  Benefits of QueryInsights

  The following are the key benefits of using QueryInsights:

  • Identifying inefficient queries – QueryInsights provides information on the time-based and attribute-based pruning of the tables accessed by the query. This information helps you identify the tables that are sub-optimally accessed.

  • Optimizing your data model and partitioning – You can use the QueryInsights information to access and fine-tune your data model and partitioning strategy.

  • Tuning queries – QueryInsights highlights opportunities to use indexes more effectively.

  The maximum number of Query API requests you're allowed to make with QueryInsights enabled is 1 query per second (QPS). If you exceed this query rate, it might result in throttling.

• QueryInsightsResponse

  Provides various insights and metrics related to the query that you executed.

• QuerySpatialCoverage

  Provides insights into the spatial coverage of the query, including the table with sub-optimal (max) spatial pruning. This information can help you identify areas for improvement in your partitioning strategy to enhance spatial pruning

  For example, you can do the following with the QuerySpatialCoverage information:

  • Add measure_name or use customer-defined partition key (CDPK) predicates.

  • If you've already done the preceding action, remove functions around them or clauses, such as LIKE.

• QuerySpatialCoverageMax

  Provides insights into the table with the most sub-optimal spatial range scanned by your query.

• QueryStatus

  Information about the status of the query, including progress and bytes scanned.

• QueryTemporalRange

  Provides insights into the temporal range of the query, including the table with the largest (max) time range.

• QueryTemporalRangeMax

  Provides insights into the table with the most sub-optimal temporal pruning scanned by your query.

• Row

  Represents a single row in the query results.

• S3Configuration

  Details on S3 location for error reports that result from running a query.

• S3ReportLocation

  S3 report location for the scheduled query run.

• ScheduleConfiguration

  Configuration of the schedule of the query.

• ScheduledQuery

  Scheduled Query

• ScheduledQueryDescription

  Structure that describes scheduled query.

• ScheduledQueryInsights

  Encapsulates settings for enabling QueryInsights on an ExecuteScheduledQueryRequest.

• ScheduledQueryInsightsResponse

  Provides various insights and metrics related to the ExecuteScheduledQueryRequest that was executed.

• ScheduledQueryRunSummary

  Run summary for the scheduled query

• SelectColumn

  Details of the column that is returned by the query.

• SnsConfiguration

  Details on SNS that are required to send the notification.

• 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. Tags enable you to categorize databases and/or tables, for example, by purpose, owner, or environment.

• TargetConfiguration

  Configuration used for writing the output of a query.

• TargetDestination

  Destination details to write data for a target data source. Current supported data source is Timestream.

• TimeSeriesDataPoint

  The timeseries data type represents the values of a measure over time. A time series is an array of rows of timestamps and measure values, with rows sorted in ascending order of time. A TimeSeriesDataPoint is a single data point in the time series. It represents a tuple of (time, measure value) in a time series.

• TimestreamConfiguration

  Configuration to write data into Timestream database and table. This configuration allows the user to map the query result select columns into the destination table columns.

• TimestreamDestination

  Destination for scheduled query.

• Type

  Contains the data type of a column in a query result set. The data type can be scalar or complex. The supported scalar data types are integers, Boolean, string, double, timestamp, date, time, and intervals. The supported complex data types are arrays, rows, and timeseries.

Enums

• 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.
• QueryInsightsMode
  When writing a match expression against QueryInsightsMode, 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.
• QueryPricingModel
  When writing a match expression against QueryPricingModel, 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.
• ScalarType
  When writing a match expression against ScalarType, 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.
• ScheduledQueryInsightsMode
  When writing a match expression against ScheduledQueryInsightsMode, 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.
• ScheduledQueryRunStatus
  When writing a match expression against ScheduledQueryRunStatus, 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.
• ScheduledQueryState
  When writing a match expression against ScheduledQueryState, 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.