How we made data aggregation on PostgreSQL better and faster

Re:How we made data aggregation on PostgreSQL better and faster by slt2021 on Tuesday June 21, 2022 @12:48PM (#31826117)

timeseries is usually specific to use cases when you data represents some signal over time, like temperature reading, stock price, etc.

so you need 2 components: timestamp and signal reading, in this case all specific timeseries analytics apply: sliding/tumbling window, avg per window, smoothing, autocorrelation and all other techniques from Digital Signal Processing/timeseries analytics.

Your regular monthly Sales data of ACME Corp by product category and storeId - this is not timeseries, just general BI

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Re:How we made data aggregation on PostgreSQL better and faster by ryanbooz on Tuesday June 21, 2022 @02:58PM (#31827700)

(NB - post author)

Great definition! Having worked for years on both energy and IoT applications, the argument here is that your "monthly sales data" is likely being aggregated from your time-series data (sales transactions over time). If you store the transaction data in a database like TimescaleDB, then continuous aggregates provide the straightforward method for keeping that aggregated, monthly sales data up-to-date. :-D!

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