This guide will walk you through creating a new Filebeat module.
All Filebeat modules currently live in the main Beats repository. To clone the repository and build Filebeat (which you will need for testing), please follow the general instructions in [beats-contributing].
Each Filebeat module is composed of one or more "filesets". We usually create a
module for each service that we support (nginx
for Nginx, mysql
for Mysql,
and so on) and a fileset for each type of log that the service creates. For
example, the Nginx module has access
and error
filesets. You can contribute
a new module (with at least one fileset), or a new fileset for an existing
module.
Note
|
In this guide we use {module} and {fileset} as placeholders for the
module and fileset names. You need to replace these with the actual names you
entered when your created the module and fileset. Only use characters [a-z] and, if required, underscores (_ ). No other characters are allowed.
|
Run the following command in the filebeat
folder:
make create-module MODULE={module}
After running the make create-module
command, you’ll find the module,
along with its generated files, under module/{module}
. This
directory contains the following files:
module/{module}
├── module.yml
└── _meta
└── docs.asciidoc
└── fields.yml
└── kibana
Let’s look at these files one by one.
This file contains list of all the dashboards available for the module and used by export_dashboards.go
script for exporting dashboards.
Each dashboard is defined by an id and the name of json file where the dashboard is saved locally.
At generation new fileset this file will be automatically updated with "default" dashboard settings for new fileset.
Please ensure that this settings are correct.
This file contains module-specific documentation. You should include information about which versions of the service were tested and the variables that are defined in each fileset.
The module level fields.yml
contains descriptions for the module-level fields.
Please review and update the title and the descriptions in this file. The title
is used as a title in the docs, so it’s best to capitalize it.
This folder contains the sample Kibana dashboards for this module. To create
them, you can build them visually in Kibana and then export them with export_dashboards
.
The tool will export all of the dashboard dependencies (visualizations, saved searches) automatically.
You can see various ways of using export_dashboards
at [export-dashboards].
The recommended way to export them is to list your dashboards in your module’s
module.yml
file:
dashboards:
- id: 69f5ae20-eb02-11e7-8f04-beef1daadb05
file: mymodule-overview.json
- id: c0a7ce90-cafe-4242-8647-534bb4c21040
file: mymodule-errors.json
Then run export_dashboards
like this:
$ cd dev-tools/cmd/dashboards
$ make # if export_dashboard is not built yet
$ ./export_dashboards -yml '../../../filebeat/module/{module}/module.yml'
New Filebeat modules might not be compatible with Kibana 5.x. To export dashboards that are compatible with 5.x, run the following command inside the developer virtual environment:
$ cd filebeat
$ make python-env
$ cd module/{module}/
$ python ../../../dev-tools/export_5x_dashboards.py --regex {module} --dir _meta/kibana/5.x
Where the --regex
parameter should match the dashboard you want to export.
Please note that dashboards exported from Kibana 5.x are not compatible with Kibana 6.x.
You can find more details about the process of creating and exporting the Kibana dashboards by reading {beatsdevguide}/new-dashboards.html[this guide].
Run the following command in the filebeat
folder:
make create-fileset MODULE={module} FILESET={fileset}
After running the make create-fileset
command, you’ll find the fileset,
along with its generated files, under module/{module}/{fileset}
. This
directory contains the following files:
module/{module}/{fileset}
├── manifest.yml
├── config
│ └── {fileset}.yml
├── ingest
│ └── pipeline.json
├── _meta
│ └── fields.yml
│ └── kibana
│ └── default
└── test
Let’s look at these files one by one.
The manifest.yml
is the control file for the module, where variables are
defined and the other files are referenced. It is a YAML file, but in many
places in the file, you can use built-in or defined variables by using the
{{.variable}}
syntax.
The var
section of the file defines the fileset variables and their default
values. The module variables can be referenced in other configuration files,
and their value can be overridden at runtime by the Filebeat configuration.
As the fileset creator, you can use any names for the variables you define. Each variable must have a default value. So in it’s simplest form, this is how you can define a new variable:
var:
- name: pipeline
default: with_plugins
Most fileset should have a paths
variable defined, which sets the default
paths where the log files are located:
var:
- name: paths
default:
- /example/test.log*
os.darwin:
- /usr/local/example/test.log*
- /example/test.log*
os.windows:
- c:/programdata/example/logs/test.log*
There’s quite a lot going on in this file, so let’s break it down:
-
The name of the variable is
paths
and the default value is an array with one element:"/example/test.log*"
. -
Note that variable values don’t have to be strings. They can be also numbers, objects, or as shown in this example, arrays.
-
We will use the
paths
variable to set the inputpaths
setting, so "glob" values can be used here. -
Besides the
default
value, the file defines values for particular operating systems: a default for darwin/OS X/macOS systems and a default for Windows systems. These are introduced via theos.darwin
andos.windows
keywords. The values under these keys become the default for the variable, if Filebeat is executed on the respective OS.
Besides the variable definition, the manifest.yml
file also contains
references to the ingest pipeline and input configuration to use (see next
sections):
ingest_pipeline: ingest/pipeline.json
input: config/testfileset.yml
These should point to the respective files from the fileset.
Note that when evaluating the contents of these files, the variables are expanded, which enables you to select one file or the other depending on the value of a variable. For example:
ingest_pipeline: ingest/{{.pipeline}}.json
This example selects the ingest pipeline file based on the value of the
pipeline
variable. For the pipeline
variable shown earlier, the path would
resolve to ingest/with_plugins.json
(assuming the variable value isn’t
overridden at runtime.)
In 6.6 and later, you can specify multiple ingest pipelines.
ingest_pipeline:
- ingest/main.json
- ingest/plain_logs.json
- ingest/json_logs.json
When multiple ingest pipelines are specified the first one in the list is considered to be the entry point pipeline.
One reason for using multiple pipelines might be to send all logs harvested
by this fileset to the entry point pipeline and have it delegate different parts of
the processing to other pipelines. You can read details about setting
this up in the ingest/*.json
section.
The config/
folder contains template files that generate Filebeat input
configurations. The Filebeat inputs are primarily responsible for tailing
files, filtering, and multi-line stitching, so that’s what you configure in the
template files.
A typical example looks like this:
type: log
paths:
{{ range $i, $path := .paths }}
- {{$path}}
{{ end }}
exclude_files: [".gz$"]
You’ll find this example in the template file that gets generated automatically
when you run make create-fileset
. In this example, the paths
variable is
used to construct the paths
list for the input paths
option.
Any template files that you add to the config/
folder need to generate a valid
Filebeat input configuration in YAML format. The options accepted by the
input configuration are documented in the
{filebeat-ref}/configuration-filebeat-options.html[Filebeat Inputs] section of
the Filebeat documentation.
The template files use the templating language defined by the Go standard library.
Here is another example that also configures multiline stitching:
type: log
paths:
{{ range $i, $path := .paths }}
- {{$path}}
{{ end }}
exclude_files: [".gz$"]
multiline:
pattern: "^# User@Host: "
negate: true
match: after
Although you can add multiple configuration files under the config/
folder,
only the file indicated by the manifest.yml
file will be loaded. You can use
variables to dynamically switch between configurations.
The ingest/
folder contains {es} {ref}/ingest.html[ingest pipeline]
configurations. Ingest pipelines are responsible for parsing the log lines and
doing other manipulations on the data.
The files in this folder are JSON or YAML documents representing
{ref}/pipeline.html[pipeline definitions]. Just like with the config/
folder, you can define multiple pipelines, but a single one is loaded at runtime
based on the information from manifest.yml
.
The generator creates a JSON object similar to this one:
{
"description": "Pipeline for parsing {module} {fileset} logs",
"processors": [
],
"on_failure" : [{
"set" : {
"field" : "error.message",
"value" : "{{ _ingest.on_failure_message }}"
}
}]
}
Alternatively, you can use YAML formatted pipelines, which uses a simpler syntax:
description: "Pipeline for parsing {module} {fileset} logs"
processors:
on_failure:
- set:
field: error.message
value: "{{ _ingest.on_failure_message }}"
From here, you would typically add processors to the processors
array to do
the actual parsing. For information about available ingest processors, see the
{ref}/processors.html[processor reference documentation]. In
particular, you will likely find the
{ref}/grok-processor.html[grok processor] to be useful for parsing.
Here is an example for parsing the Nginx access logs.
{
"grok": {
"field": "message",
"patterns":[
"%{IPORHOST:nginx.access.remote_ip} - %{DATA:nginx.access.user_name} \\[%{HTTPDATE:nginx.access.time}\\] \"%{WORD:nginx.access.method} %{DATA:nginx.access.url} HTTP/%{NUMBER:nginx.access.http_version}\" %{NUMBER:nginx.access.response_code} %{NUMBER:nginx.access.body_sent.bytes} \"%{DATA:nginx.access.referrer}\" \"%{DATA:nginx.access.agent}\""
],
"ignore_missing": true
}
}
Note that you should follow the convention of naming of fields prefixed with the
module and fileset name: {module}.{fileset}.field
, e.g.
nginx.access.remote_ip
. Also, please review our [event-conventions].
In 6.6 and later, ingest pipelines can use the
{ref}/conditionals-with-multiple-pipelines.html[pipeline
processor] to delegate
parts of the processings to other pipelines.
This can be useful if you want a fileset to ingest the same logical information presented in different formats, e.g. csv vs. json versions of the same log files. Imagine an entry point ingest pipeline that detects the format of a log entry and then conditionally delegates further processing of that log entry, depending on the format, to another pipeline.
{
"processors": [
{
"grok": {
"field": "message",
"patterns": [
"^%{CHAR:first_char}"
],
"pattern_definitions": {
"CHAR": "."
}
}
},
{
"pipeline": {
"if": "ctx.first_char == '{'",
"name": "{< IngestPipeline "json-log-processing-pipeline" >}" <1>
}
},
{
"pipeline": {
"if": "ctx.first_char != '{'",
"name": "{< IngestPipeline "plain-log-processing-pipeline" >}"
}
}
]
}
-
Use the
IngestPipeline
template function to resolve the name. This function converts the specified name into the fully qualified pipeline ID that is stored in Elasticsearch.
In order for the above pipeline to work, Filebeat must load the entry point pipeline
as well as any sub-pipelines into Elasticsearch. You can tell Filebeat to do
so by specifying all the necessary pipelines for the fileset in its manifest.yml
file. The first pipeline in the list is considered to be the entry point pipeline.
ingest_pipeline:
- ingest/main.json
- ingest/plain_logs.yml
- ingest/json_logs.json
While developing the pipeline definition, we recommend making use of the {ref}/simulate-pipeline-api.html[Simulate Pipeline API] for testing and quick iteration.
By default Filebeat does not update Ingest pipelines if already loaded. If you
want to force updating your pipeline during development, use
./filebeat setup --pipelines
command. This uploads pipelines even if they
are already available on the node.
The fields.yml
file contains the top-level structure for the fields in your
fileset. It is used as the source of truth for:
-
the generated Elasticsearch mapping template
-
the generated Kibana index pattern
-
the generated documentation for the exported fields
Besides the fields.yml
file in the fileset, there is also a fields.yml
file
at the module level, placed under module/{module}/_meta/fields.yml
, which
should contain the fields defined at the module level, and the description of
the module itself. In most cases, you should add the fields at the fileset
level.
After pipeline.json
is created, it is possible to generate a base field.yml
.
make create-fields MODULE={module} FILESET={fileset}
Please, always check the generated file and make sure the fields are correct. You must add field documentation manually.
If the fields are correct, it is time to generate documentation, configuration and Kibana index patterns.
make update
In the test/
directory, you should place sample log files generated by the
service. We have integration tests, automatically executed by CI, that will run
Filebeat on each of the log files under the test/
folder and check that there
are no parsing errors and that all fields are documented.
In addition, assuming you have a test.log
file, you can add a
test.log-expected.json
file in the same directory that contains the expected
documents as they are found via an Elasticsearch search. In this case, the
integration tests will automatically check that the result is the same on each
run.
In order to test the filesets with the sample logs and/or generate the expected output one should run the tests locally for a specific module, using the following procedure under Filebeat directory:
-
Start an Elasticsearch instance locally. For example, using Docker:
docker run \ --name elasticsearch \ -p 9200:9200 -p 9300:9300 \ -e "xpack.security.http.ssl.enabled=false" -e "ELASTIC_PASSWORD=changeme" \ -e "discovery.type=single-node" \ --pull always --rm --detach \ docker.elastic.co/elasticsearch/elasticsearch:master-SNAPSHOT
-
Create an "admin" user on that Elasticsearch instance:
curl -u elastic:changeme \ http://localhost:9200/_security/user/admin \ -X POST -H 'Content-Type: application/json' \ -d '{"password": "changeme", "roles": ["superuser"]}'
-
Create the testing binary:
make filebeat.test
-
Update fields yaml:
make update
-
Create python env:
make python-env
-
Source python env:
source ./build/python-env/bin/activate
-
Run a test, for example to check nginx access log parsing:
INTEGRATION_TESTS=1 BEAT_STRICT_PERMS=false ES_PASS=changeme \ TESTING_FILEBEAT_MODULES=nginx \ pytest tests/system/test_modules.py -v --full-trace
-
Add and remove option env vars as required. Here are some useful ones:
-
TESTING_FILEBEAT_ALLOW_OLDER
: if set to 1, allow connecting older versions of Elasticsearch -
TESTING_FILEBEAT_MODULES
: comma separated list of modules to test. -
TESTING_FILEBEAT_FILESETS
: comma separated list of filesets to test. -
TESTING_FILEBEAT_FILEPATTERN
: glob pattern for log files within the fileset to test. -
GENERATE
: if set to 1, the expected documents will be generated.
-
The filebeat logs are writen to the build
directory. It may be useful to tail them in another terminal using tail -F build/system-tests/run/test_modules.Test.*/output.log
.
For example if there’s a syntax error in an ingest pipeline, the test will probably just hang. The filebeat log output will contain the error message from elasticsearch.