How does cloud Bigtable’s storage architecture make it ‘naturally fit’ for time-series data?

Question

I don’t understand this claim (GCP cloud Bigtable docs) and the reason behind it.

Storing time-series data in Cloud Bigtable is a natural fit. Cloud Bigtable stores data as unstructured columns in rows; each row has a row key, and row keys are sorted lexicographically.

How does this way of storing make it a natural fit for time series data?

Answer 1

Timeseries is a (Time,Value) key pair. You can put Time into your row key and Value into column. As rows are sorted by key, you can efficiently get values for a given time range as it is a common task when analyzing timeseries.

Answer 2

I think this is covered to some depth in:

https://cloud.google.com/bigtable/docs/schema-design-time-series

Whether that is a "natural fit" or not is a matter of taste or opinion. It does work though, meaning it scales well and can store very large amounts of data.