Scheduled query patterns
In this section you will find some common patterns of how you can use Amazon Timestream for LiveAnalytics Scheduled Queries to optimize your dashboards to load faster and at reduced costs. The examples below use a DevOps application scenario to illustrate the key concepts which apply to scheduled queries in general, irrespective of the application scenario.
Scheduled Queries in Timestream for LiveAnalytics allow you to express your queries using the full SQL surface area of Timestream for LiveAnalytics. Your query can include one or more source tables, perform aggregations or any other query allowed by Timestream for LiveAnalytics's SQL language, and then materialize the results of the query in another destination table in Timestream for LiveAnalytics. For ease of exposition, this section refers to this target table of a scheduled query as a derived table.
The following are the key points that are covered in this section.
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Using a simple fleet-level aggregate to explain how you can define a scheduled query and understand some basic concepts.
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How you can combine results from the target of a scheduled query (the derived table) with the results from the source table to get the cost and performance benefits of scheduled query.
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What are your trade-offs when configuring the refresh period of the scheduled queries.
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Using scheduled queries for some common scenarios.
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Tracking the last data point from every instance before a specific date.
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Distinct values for a dimension to use for populating variables in a dashboard.
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How you handle late arriving data in the context of scheduled queries.
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How you can use one-off manual executions to handle a variety of scenarios not directly covered by automated triggers for scheduled queries.
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