A time series consists of a sequence of data points connected to a single asset. For example, a water pump asset can have a temperature time series 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 series measuring pressure within the pump, rpm, flow volume, power consumption, and more.Time series store data points as either numbers or strings. This is controlled by the is_string flag on the time series object. Numerical data points can be aggregated before they are returned from a query (e.g., to find the average temperature for a day). String data points, on the other hand, can't be aggregated by CDF but can store arbitrary information like states (e.g., “open”/”closed”) or more complex information (JSON).
Cognite stores discrete data points, but the underlying
process measured by the data points can vary continuously. When interpolating
between data points, we can either assume that each value stays the same until
the 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 average
will 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 a string, 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, 1900 through 23:59:59.999, December 31st, 2099 (in other words, every millisecond in the two centuries from 1900 to but not including 2100). Negative timestamps are used 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 reducing the amount of data transferred. You can aggregate data points by specifying one or more aggregates (e.g., average, minimum, maximum) as well as the time granularity over 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 you ask for daily average temperatures since Monday afternoon last week, the first aggregated data point will contain averages for Monday, the second for Tuesday, etc. Determining aggregate alignment without considering data point timestamps allows CDF to pre-calculate aggregates (e.g., to quickly return daily average temperatures for a year). Consequently, aggregating over 60 minutes can return a different result 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 simply by the non-transactional nature of HTTP. They are maintained in an eventually consistent manner.