Matches or joins data from query results with a table. The table can be provided either as a LookUp file — CSV file or through a limited form of JSON file, uploaded using Lookup Files — or, from LogScale 1.163, as an ad-hoc table Using Ad-hoc Tables.
If you are looking for match expressions, see Match Expressions.
Parameter | Type | Required | Default Value | Description | |
---|---|---|---|---|---|
column | string | optional[a] | field parameter | Which column in the file to use for the match. A single column or an array or columns can be specified. | |
field | string | required | Which field in the event (log line) must match the given column value. A single field or an array or fields can be specified. Field and column must have the same length, are matched in order and must all match. | ||
file [b] | string | required | Specifies the source file (when using Look Up files) or the name of the ad-hoc table. The file name should be specified with .csv or .json suffix. | ||
glob (deprecated) | boolean | optional[a] | false | This parameter is deprecated. Use mode=glob instead. (deprecated in 1.23.0) | |
ignoreCase | boolean | optional[a] | false | If true, ignore case when matching against the CSV data. | |
include | string or array | optional[a] | The columns to include. If no argument is given, include all columns from the corresponding row in the output event. | ||
mode | string | optional[a] | string | The function to use when matching against keys. | |
Valid Values | |||||
cidr | The key is interpreted as a CIDR subnet and the event is matched if the field contains an IP within the subnet. If multiple subnets match, the most specific one is selected or an arbitrary one if there are multiple equally specific subnets. | ||||
glob | The key is interpreted as a globbing pattern with * and
matched accordingly, for example, a CSV key value of
*thisMatch* would match the
field value of
123thisMatch456 . | ||||
string | The matching is done using exact string matching. | ||||
strict | boolean | optional[a] | true | If true (the default) selects only the fields that match a key in the file; if false lets all events through (works like the deprecated lookup() ). | |
[a] Optional parameters use their default value unless explicitly set. |
Hide omitted argument names for this function
Omitted Argument NamesThe argument name for
file
can be omitted; the following forms of this function are equivalent:logscalematch("value",field="value")
and:
logscalematch(file="value",field="value")
These examples show basic structure only.
Hide negatable operation for this function
Negatable Function OperationThis function is negatable, implying the inverse of the result. For example:
logscale!match()
Or:
logscalenot match()
For more information, see Negating the Result of Filter Functions.
When lookup information from files are loaded from a package, the package name should be specified in addition to the filename. For example:
match("falcon/investigate/logoninfo.csv",field="loookupname")
For more information on referring to package resources, see Referencing Package Assets.
The default behavior of this function — when
strict
is set to true —
works like an INNER JOIN
. When
strict
is set to
false
the function enriches
events.
Note
Up until LogScale version 1.140, the file is limited to
1,000,000 rows/lines for exact matching using
mode=string
.
When using mode=glob
, the
underlying CSV is limited to 20,000 rows/lines.
For self-hosted customers, the maximum value for glob matches is
configurable using GLOB_MATCH_LIMIT
.
Using match()
with Lookup Files
When using match()
for joining with Look Up files,
two file formats are supported:
CSV. The recommended format, ensures better performance, allows for additional functionalities (using parameters) and is compatible with related functions, like
readFile()
. See CSV File Formats for more information.JSON. Supports object and array-based formats. See JSON File Formats for more information.
CSV File Formats
For Comma Separated Values (CSV) files, whitespace gets included in
the keys and values. To include the separator
","
in a value, quote using the
"
character. The following file
is a valid CSV file:
userid,name
1,chr
2,krab
"4","p,m"
7,mgr
The first line is interpreted as the column title. When querying, the column in the field should be used to identify which column to match against.
When using match()
with a single column:
match(test.csv, fields=somefield, column=column1)
the last matching row in test.csv
is used.
item | value |
---|---|
field1 | c |
field2 | e |
column3 | f |
item | value |
---|---|
field1 | c |
column2 | e |
column3 | f |
field1 | c |
column2 | d |
column3 | a |
item | value |
---|---|
field1 | c |
column2 | e |
column3 | f |
field1 | c |
column2 | d |
column3 | a |
field1 | c |
column2 | b |
column3 | a |
method | description | url |
---|---|---|
POST | Send Data | /humio/api/v1/ingest/elastic-bulk |
POST | Transfer Data in | /humio/api/v1/ingest/elastic-bulk |
POST | Transfer Data in | /humio/api/v1/ingest/elastic-bulk |
POST | Send Data | /humio/api/v1/ingest/elastic-bulk |
POST | Transfer Data in | /humio/api/v1/ingest/elastic-bulk |
POST | Send Data | /humio/api/v1/ingest/elastic-bulk |
JSON File Formats
For JSON files, two formats are supported:
Object-based, where the lookup field does not have an explicit name
Array-based, where the information is an array of objects
In the Object-based variant, the lookup values are declared as an object with a key and embedded fields, the key field does not have a name.
{
"1": { "name": "chr" },
"2": { "name": "krab" },
"4": { "name": "pmm" },
"7": { "name": "mgr" }
}
When matching against a file in this case, the name of the field in the JSON object does not need to be used; the key for each value is used instead. For example:
groupBy(@timezone)
| count(@timezone)
| match(file="short.json",field=_count)
In the above, the value of _count will be matched, outputting the match value:
_count | name |
---|---|
2 | krab |
In the array-based variant, the lookup values are declared as an array
of objects, you select which field is the key using the
field
parameter in
match()
.
[
{ "userid": "1", "name": "chr" },
{ "userid": "2", "name": "krab" },
{ "userid": "4", "name": "pmm" },
{ "userid": "7", "name": "mgr" }
]
When using this version, the name of the column to be matched must be
specified using the column
argument to match()
:
groupBy(@timezone)
| count(@timezone)
| match(file="long.json",field=_count,column="userid")
This behavior also means that any field in the JSON file can be used as the match value. For example:
...
| match(file="long.json",field=codename,column="name")
This can be useful if you have a JSON file that contains multiple possible lookup values for given records.
For
Important
The match()
does not report an error if the
file format cannot be parsed.
match()
Examples
Matches events for which the id field matches the value of the column in the table "users.csv". Does not add any columns.
match(file="users.csv", column=userid, field=id, include=[])
Matches events for which the id
field is matched case-insensitive by the glob-pattern in the column
userid
in the table
users.csv
, and add all other
columns of the first matching row to those events.
id =~ match(file="users.csv", column=userid, mode=glob, ignoreCase=true)
Let all events pass through, but events for which the
id field matches the value of
the userid column in the table
users.csv
will be enriched with
all other columns of the matching row.
id =~ match(file="users.csv", column=userid, strict=false)
Matches events for which the ip
field matches the CIDR subnet of the
cidr-block column in the table
cidr-file.csv
. Only adds the columns
info and
type from the first matching
row.
match(file="cidr-file.csv", column="cidr-block", field=ip, mode=cidr, include=["info","type"])
column3 | field1 | field2 |
---|---|---|
f | c | e |