Adding Fields

New fields can be created in two ways:

Regular Expression-based Field Extraction

You can extract new fields from your text data using regular expressions and then test their values. This lets you access data that LogScale did not parse when it indexed the data.

For example, if your log entries contain text such as ... disk_free=2000 ..., then you can use a query like the following to find the entries that have less than 1000 free disk space.

logscale
regex("disk_free=(?<space>[0-9]+)")
| space < 1000

The first line uses regular expressions to extract the value after the equals sign and assign it to the field space, and then filter the events where the extracted field is greater than 1000.

The named capturing groups ((?<FIELDNAME>) are used to extract fields in regular expressions. This combines two principles, the usage of grouping in regular expressions using (), and explicit field creation.

The same result can be obtained written using the regex literal syntax:

logscale
@rawstring=/disk_free=(?<space>[0-9]+)/
| space < 1000

You can apply repeat to field extraction to yield one event for each match of the regular expression. This allows processing multiple values for a named field, or a field name that matches a pattern, as in this example:

logscale
regex("value[^=]*=(?<someBar>\\S+)", repeat=true)
| groupby(someBar)

On an input event with a field value of:

accesslog
type=foo value=bar1 valueExtra=bar2 value=bar3

the groupBy() sees all three bar values.

Warning

In order to use field-extraction this way, the regular expression must be a top-level expression, that is, | between bars |. The following does not work

logscale
// DON'T DO THIS - THIS DOES NOT WORK
type=FOO or /disk_free=(?<space>[0-9]+)/
| space < 1000

Note

Since regular expressions require additional processing, it is recommended to do as much simple filtering (text or field matching) as possible earlier in the query chain before applying the regular expression function.

as Parameters

Fields can also be added by functions. Most functions set their result in a field that has the function name prefixed with a _ by default. For example the count() puts its result in a field _count.

Most functions that produce fields have a parameter called as. By setting this parameter you can specify the name of the output field, for example:

logscale
count(as=cnt)

Assigns the result of the count() to the field named cnt (instead of the default _count).

Many functions can be used to generate new fields using the as argument. For example concat():

logscale
concat([aidValue, cidValue], as=checkMe2)

Combines the aidValue and cidValue into a single string.

The format() can be used to format information and values into a new value, for example, formatting two fields with a comma separator for use with a table:

logscale
format(format="%s,%s", field=[a, b], as="combined")
| table(combined)

See also the Assignment Operator for shorthand syntax for assigning results to a field.

Eval Syntax

The function eval() can assign fields while doing numeric computations on the input.

The := syntax is short for eval. Use | between assignments.

logscale
...
| foo := a + b
| bar := a / b
| ...

This is short for the following:

logscale
...
| eval(foo = a + b)
| eval(bar = a / b)
| ...

Assignment Operator

You can use the operator := with functions that take an as parameter. When what is on the right hand side of the assignment is a Function Call, the assignment is rewritten to specify the as= argument which, by convention, is the output field name. For example:

logscale
...
| foo := min(x)
| bar := max(x)
| ...

The above is short for this:

logscale
...
| min(x, as=foo)
| max(x, as=bar)
| ...

Field Operator

The field operator filters a specific field with any function that has the field parameter, so that:

logscale
...
| ip_addr =~ cidr(subnet="127.0.0.1/24")
| ...

Is synonymous with:

logscale
...
| cidr(subnet="127.0.0.1/24", field=ip_addr)
| ...

This works with many functions, including regex() and replace().