Cognite Time series API

A time series consists of a sequence of data points connected to asingle asset. For example, a water pump asset can have a temperature timeseries that records a data point in units of °C every second.A single asset can have several time series. The water pump could have additional time seriesmeasuring pressure within the pump, rpm, flow volume, power consumption, and more.Timeseries store data points as either numbers or strings. This is controlled by theis_string flag on the time series object. Numerical data points can be aggregatedbefore they are returned from a query (e.g., to find the average temperature fora day). String data points, on the other hand, can't be aggregated by CDF butcan store arbitrary information like states (e.g., “open”/”closed”) or more complexinformation (JSON).Cognite stores discrete data points, but the underlyingprocess measured by the data points can vary continuously. When interpolatingbetween data points, we can either assume that each value stays the same untilthe next measurement or linearly changes between the two measurements.The `isStep` flag controls this on the time series object. For example,if we estimate the average over a time containing two data points, the averagewill either be close to the first (`isStep`) or close to the mean of the two (not`isStep`).A data point stores a single piece of information, a number or astring, associated with a specific time. Data points are identified by their timestamps,measured in milliseconds since the unix epoch -- 00:00:00.000, January 1st, 1970.The time series service accepts timestamps in the range from 00:00:00.000, January 1st, 1900through 23:59:59.999, December 31st, 2099 (in other words, every millisecond in the twocenturies from 1900 to but not including 2100). Negative timestamps areused to define dates before 1970. Milliseconds is the finest time resolution supported by CDF, i.e.,fractional milliseconds are not supported. Leap seconds are not counted.Numerical data points can be aggregated before they are retrieved from CDF. This allows for faster queries by reducingthe amount of data transferred. You can aggregate data points by specifying oneor more aggregates (e.g., average, minimum, maximum) as well as the time granularityover which the aggregates should be applied (e.g., “1h” for one hour).Aggregates are aligned to the start time modulo the granularity unit. For example, if youask for daily average temperatures since Monday afternoon last week, the firstaggregated data point will contain averages for Monday, the second for Tuesday,etc. Determining aggregate alignment without considering data point timestampsallows CDF to pre-calculate aggregates (e.g., to quickly return daily average temperaturesfor a year). Consequently, aggregating over 60 minutes can return a differentresult than aggregating over 1 hour because the two queries will be aligned differently.Asset references obtained from a time series - through its asset ID - may be invalid simplyby the non-transactional nature of HTTP. They are maintained in an eventually consistentmanner.