--- title: InfluxQL date and time functions list_title: Date and time functions description: > Use InfluxQL date and time functions to perform time-related operations. menu: influxdb_clustered: name: Date and time parent: influxql-functions weight: 206 --- Use InfluxQL date and time functions to perform time-related operations. - [now()](#now) - [time()](#time) ## now() Returns the current system time (UTC). _Supported only in the [`WHERE` clause](/influxdb/clustered/reference/influxql/where/)._ ```sql now() ``` ## time() Used in the [`GROUP BY` clause](/influxdb/clustered/reference/influxql/group-by/) to group data into time-based intervals, also known as "windows", using the specified interval. Timestamps in the `time` column are updated to the start boundary of the window they're in and grouped by `time`. Windows use preset round-number boundaries based on the specified interval that are independent of time conditions in the [`WHERE` clause](/influxdb/clustered/reference/influxql/where/). This operation can be used to do the following: - Downsample data by aggregating multiple points in each window into a single point per window. - Normalize irregular time series data to occur at regular intervals. _Supported only in the [`GROUP BY` clause](/influxdb/clustered/reference/influxql/group-by/)._ ```sql time(interval[, offset]) ``` #### Arguments - **interval**: Duration literal that specifies the window interval. - **offset**: Duration literal that shifts preset time boundaries forward or backward. Can be positive or negative. _Default is `0s`._ ##### Examples {#time-examples} {{< expand-wrapper >}} {{% expand "Downsample data into time-based intervals" %}} The following example uses the [Bitcoin price sample dataset](/influxdb/clustered/reference/sample-data/#bitcoin-price-data). ```sql SELECT MEAN(price) FROM bitcoin WHERE code = 'GBP' AND time >= '2023-05-01T00:00:00Z' AND time < '2023-05-15T00:00:00Z' GROUP BY time(2d) ``` {{% influxql/table-meta %}} name: bitcoin {{% /influxql/table-meta %}} | time | mean | | :------------------- | -----------------: | | 2023-05-01T00:00:00Z | 23680.120447159094 | | 2023-05-03T00:00:00Z | 24048.71484033149 | | 2023-05-05T00:00:00Z | 24461.9194901099 | | 2023-05-07T00:00:00Z | 23796.43801933702 | | 2023-05-09T00:00:00Z | 23118.709889285707 | | 2023-05-11T00:00:00Z | 22465.008364444446 | | 2023-05-13T00:00:00Z | 22499.464763186803 | {{% /expand %}} {{< /expand-wrapper >}}