Cost optimization

To optimize the cost of writes, storage, and queries, use the following best practices with Amazon Timestream:

• Batch multiple time series events per write to reduce the number of write requests.

• Consider using Multi-measure records, which allows you to write multiple time-series measures in a single write request and stores your data in a more compact manner. This reduces the number of write requests as well as data storage cost and query cost.

• Use common attributes with batching to batch more time series events per write to further reduce the number of write requests.

• Set the data retention of the memory store to match your application's requirements for processing late-arriving data. Late-arriving data is incoming data with a timestamp earlier than the current time and outside the memory store retention period.

• Set the data retention of the magnetic store to match your long term data storage requirements.

• While writing queries, include only the measure and dimension names essential to query. Adding extraneous columns will increase data scans and therefore will also increase the query cost.

• Where possible, include a time range in the WHERE clause of your query. For example, if you only need the last one hour of data in your dataset, include a time predicate such as time > ago(1h).

• When a query accesses a subset of measures in a table, always include the measure names in the WHERE clause of the query.

• If you've started running a query and realize that the query will not return the results you're looking for, cancel the query to save on cost.