Scheduled query concepts - Amazon Timestream

Scheduled query concepts

Query string - This is the query whose result you are pre-computing and storing in another Timestream for LiveAnalytics table. You can define a scheduled query using the full SQL surface area of Timestream for LiveAnalytics, which provides you the flexibility of writing queries with common table expressions, nested queries, window functions, or any kind of aggregate and scalar functions that are supported by Timestream for LiveAnalytics query language.

Schedule expression - Allows you to specify when your scheduled query instances are run. You can specify the expressions using a cron expression (such as run at 8 AM UTC every day) or rate expression (such as run every 10 minutes).

Target configuration - Allows you to specify how you map the result of a scheduled query into the destination table where the results of this scheduled query will be stored.

Notification configuration -Timestream for LiveAnalytics automatically runs instances of a scheduled query based on your schedule expression. You receive a notification for every such query run on an SNS topic that you configure when you create a scheduled query. This notification specifies whether the instance was successfully run or encountered any errors. In addition, it provides information such as the bytes metered, data written to the target table, next invocation time, and so on.

The following is an example of this kind of notification message.

{ "type":"AUTO_TRIGGER_SUCCESS", "arn":"arn:aws:timestream:us-east-1:123456789012:scheduled-query/ PT1mPerMinutePerRegionMeasureCount-9376096f7309", "nextInvocationEpochSecond":1637302500, "scheduledQueryRunSummary": { "invocationEpochSecond":1637302440, "triggerTimeMillis":1637302445697, "runStatus":"AUTO_TRIGGER_SUCCESS", "executionStats": { "executionTimeInMillis":21669, "dataWrites":36864, "bytesMetered":13547036820, "recordsIngested":1200, "queryResultRows":1200 } } }

In this notification message, bytesMetered is the bytes that the query scanned on the source table, and dataWrites is the bytes written to the target table.


If you are consuming these notifications programmatically, be aware that new fields could be added to the notification message in the future.

Error report location - Scheduled queries asynchronously run and store data in the target table. If an instance encounters any errors (for example, invalid data which could not be stored), the records that encountered errors are written to an error report in the error report location you specify at creation of a scheduled query. You specify the S3 bucket and prefix for the location. Timestream for LiveAnalytics appends the scheduled query name and invocation time to this prefix to help you identify the errors associated with a specific instance of a scheduled query.

Tagging - You can optionally specify tags that you can associate with a scheduled query. For more details, see Tagging Timestream for LiveAnalytics Resources.


In the following example, you compute a simple aggregate using a scheduled query:

SELECT region, bin(time, 1m) as minute, SUM(CASE WHEN measure_name = 'metrics' THEN 20 ELSE 5 END) as numDataPoints FROM raw_data.devops WHERE time BETWEEN @scheduled_runtime - 10m AND @scheduled_runtime + 1m GROUP BY bin(time, 1m), region

@scheduled_runtime parameter - In this example, you will notice the query accepting a special named parameter @scheduled_runtime. This is a special parameter (of type Timestamp) that the service sets when invoking a specific instance of a scheduled query so that you can deterministically control the time range for which a specific instance of a scheduled query analyzes the data in the source table. You can use @scheduled_runtime in your query in any location where a Timestamp type is expected.

Consider an example where you set a schedule expression: cron(0/5 * * * ? *) where the scheduled query will run at minute 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55 of every hour. For the instance that is triggered at 2021-12-01 00:05:00, the @scheduled_runtime parameter is initialized to this value, such that the instance at this time operates on data in the range 2021-11-30 23:55:00 to 2021-12-01 00:06:00.

Instances with overlapping time ranges - As you will see in this example, two subsequent instances of a scheduled query can overlap in their time ranges. This is something you can control based on your requirements, the time predicates you specify, and the schedule expression. In this case, this overlap allows these computations to update the aggregates based on any data whose arrival was slightly delayed, up to 10 minutes in this example. The query run triggered at 2021-12-01 00:00:00 will cover the time range 2021-11-30 23:50:00 to 2021-12-30 00:01:00 and the query run triggered at 2021-12-01 00:05:00 will cover the range 2021-11-30 23:55:00 to 2021-12-01 00:06:00.

To ensure correctness and to make sure that the aggregates stored in the target table match the aggregates computed from the source table, Timestream for LiveAnalytics ensures that the computation at 2021-12-01 00:05:00 will be performed only after the computation at 2021-12-01 00:00:00 has completed. The results of the latter computations can update any previously materialized aggregate if a newer value is generated. Internally, Timestream for LiveAnalytics uses record versions where records generated by latter instances of a scheduled query will be assigned a higher version number. Therefore, the aggregates computed by the invocation at 2021-12-01 00:05:00 can update the aggregates computed by the invocation at 2021-12-01 00:00:00, assuming newer data is available on the source table.

Automatic triggers vs. manual triggers - After a scheduled query is created, Timestream for LiveAnalytics will automatically run the instances based on the specified schedule. Such automated triggers are managed entirely by the service.

However, there might be scenarios where you might want to manually initiate some instances of a scheduled query. Examples include if a specific instance failed in a query run, if there was late-arriving data or updates in the source table after the automated schedule run, or if you want to update the target table for time ranges that are not covered by automated query runs (for example, for time ranges before creation of a scheduled query).

You can use the ExecuteScheduledQuery API to manually initiate a specific instance of a scheduled query by passing the InvocationTime parameter, which is a value used for the @scheduled_runtime parameter. The following are a few important considerations when using the ExecuteScheduledQuery API:

  • If you are triggering multiple of these invocations, you need to make sure that these invocations do not generate results in overlapping time ranges. If you cannot ensure non-overlapping time ranges, then make sure that these query runs are initiated sequentially one after the other. If you concurrently initiate multiple query runs that overlap in their time ranges, then you can see trigger failures where you might see version conflicts in the error reports for these query runs.

  • You can initiate the invocations with any timestamp value for @scheduled_runtime. So it is your responsibility to appropriately set the values so the appropriate time ranges are updated in the target table corresponding to the ranges where data was updated in the source table.