Typically you want to gather data about everything in your system.
This generates a lot of datapoints, the majority of which don't change very often over time (if ever).
However, you want fine-grained resolution when they do change. This filter remembers the last value and timestamp that was sent for all of the time series.
If the value doesn't change between sample intervals, it suppresses sending that datapoint.
Once the value does change (or 10 minutes have passed), it sends the last suppressed value and timestamp, plus the current value and timestamp.
In this way all of your graphs and such are correct. Deduplication typically reduces the number of datapoints TSD needs to collect by a large fraction.
This reduces network load and storage in the backend.
Typically you want to gather data about everything in your system. This generates a lot of datapoints, the majority of which don't change very often over time (if ever). However, you want fine-grained resolution when they do change. This filter remembers the last value and timestamp that was sent for all of the time series. If the value doesn't change between sample intervals, it suppresses sending that datapoint. Once the value does change (or 10 minutes have passed), it sends the last suppressed value and timestamp, plus the current value and timestamp. In this way all of your graphs and such are correct. Deduplication typically reduces the number of datapoints TSD needs to collect by a large fraction. This reduces network load and storage in the backend.