Normalization Modes
When configuring normalization, you can specify a normalization mode, which dictates the normalization procedure as new data arrives.
Normalization modes
| Mode | Use this mode when |
|---|---|
ALL | The majority of your time series data can be utilized, and you need to use the latest available data. Use this mode when you want to have equal amounts of prenormalized and ingested time series data. This mode uses more resources compared to other modes. |
RECENT | If you are measuring data on a regular schedule, you might not use all of the incoming time series data. This mode does not normalize time series data that has never been normalized before. But for the time series data that has been normalized, continue with normalization as new time series data arrives. |
ON_DEMAND | Use this mode when you are steaming data in real time. If new data has arrived since the last request of the data, (incremental) normalization occurs, and there is a delay in the response. Subsequent requests of normalized data won't experience response delay (provided new data has not arrived). |
AFTER_QUERY | Use this mode when it's more important for you to deliver a fast response than it is for you to have the latest data in the response. Normalization is triggered after the response is delivered. The next access to the same data retrieves the new normalized values |
Configure the normalization mode
To use a different normalization mode, from the Chrome DevTools Console run:
JavaScript
// Use on-demand normalization for this C3 AI environment
NormalizationConfig.inst().setConfigValue('mode', NormalizationMode.ON_DEMAND)
// Check normalization mode for this C3 AI environment
NormalizationConfig.inst().configValue('mode') || 'ON_DEMAND'