Reduce takes one or more time series returned from a query or an expression and turns each series into a single number. floorįloor rounds the number down to the nearest integer value. ceilĬeil rounds the number up to the nearest integer value. (This function should probably take an argument so it can add precision to the rounded value). The inf, infn, nan, and null functions all return a single value of the name.

If the value is less than 0, NaN is returned. Log returns the natural logarithm of of its argument which can be a number or a series. Is_number takes a number or a series and returns 1 for all real number values and 0 for other values (which are null, Inf+, Inf-, and NaN). Is_null takes a number or a series and returns 1 for null values and 0 for other values. This function exists because NaN is not equal to NaN. Is_nan takes a number or a series and returns 1 for NaN values and 0 for other values. NoteIf you need to specifically check for negative infinity for example, you can do a comparison like $A = infn(). Is_inf takes a number or a series and returns 1 for Inf values (negative or positive) and 0 for other values. absĪbs returns the absolute value of its argument which can be a number or a series. When it is a series, the operation of performed for the value of each point in the series. When functions can take either numbers or series, than the same type as the argument will be returned. While most functions exist in the own expression operations, the math operation does have some functions similar to math operators or symbols. The relational and logical operators return 0 for false 1 for true. Currently, if within a variable such as $A there are different tag keys for each item, the join behavior is undefined.The string columns become labels and the number column the corresponding value.

The example above will produce a number that works with expressions. So for example if using a data source that returns multiple series on one frame in the table view, you might notice it looks different when executed with expressions.Ĭurrently, the only non-time series format (number) is supported when you’re using data frames and you have a table response that returns a data frame with no time, string columns, and one number column: Loc When it does this, it restructures data to be either one time series or one number per data frame. In the future we intend to add an assertion of the query return type (number or time series) data so expressions can handle errors better.ĭata source queries, when used with expressions, are executed by the expression engine. The data is generally assumed to be labeled time series data. Server-side expressions only support data source queries for backend data sources. Each collection is a set, where each item in the set is uniquely identified by its dimensions which are stored as labels or key-value pairs.

A collection of numbers, where each number is an item.Įach collection is returned from a single data source query or expression and represented by the RefID.

To reference the output of an individual expression or a data source query in another expression, this identifier is used as a variable. Each individual expression or query is represented by a variable that is a named identifier known as its RefID (e.g., the default letter A or B). For example, a query that returns multiple series, where each series is identified by labels or tags.Īn individual expression takes one or more queries or other expressions as input and adds data to the result. They also operate on multiple-dimensional data. Copying data from storage to the Grafana server for processing is inefficient, so expressions are targeted at lightweight data processing.Įxpressions work with data source queries that return time series or number data. For example: const query = Character.NoteWhen possible, you should do data processing inside the data source. In older content this parameter is sometimes called query or conditions. The first parameter to Model.find(), Query#find(), Model.findOne(), etc.

Mongodb json query syntax