[[metricset-details]] === Metricset Details This topic provides additional details about creating metricsets. [float] === Adding Special Configuration Options Each metricset can have its own configuration variables defined. To make use of these variables, you must extend the `New` method. For example, let's assume that you want to add a `password` config option to the metricset. You would extend `beat.yml` in the following way: [source,yaml] ---- metricbeat.modules: - module: {module} metricsets: ["{metricset}"] password: "test1234" ---- To read in the new `password` config option, you need to modify the `New` method. First you define a config struct that contains the value types to be read. You can set default values, as needed. Then you pass the config to the `UnpackConfig` method for loading the configuration. Your implementation should look something like this: [source,go] ---- type MetricSet struct { mb.BaseMetricSet password string } func New(base mb.BaseMetricSet) (mb.MetricSet, error) { // Unpack additional configuration options. config := struct { Password string `config:"password"` }{ Password: "", } err := base.Module().UnpackConfig(&config) if err != nil { return nil, err } return &MetricSet{ BaseMetricSet: base, password: config.Password, }, nil } ---- [float] ==== Timeout Connections to Services Each time the `Fetch` method is called, it makes a request to the service, so it's important to handle the connections correctly. We recommended that you set up the connections in the `New` method and persist them in the `MetricSet` object. This allows connections to be reused. One very important point is that connections must respect the timeout variable: `base.Module().Config().Timeout`. If the timeout elapses before the request completes, the request must be ended and an error must be returned to make sure the next request can be started on time. By default the Timeout is set to Period, so one request gets ended before a new request is made. If a request must be ended or has an error, make sure that you return a useful error message. This error message is also sent to Elasticsearch, making it possible to not only fetch metrics from the service, but also report potential problems or errors with the metricset. [float] ==== Data Transformation If the data transformation that has to happen in the `Fetch` method is extensive, we recommend that you create a second file called `data.go` in the same package as the metricset. The `data.go` file should contain a function called `eventMapping(...)`. A separate file is not required, but is currently a best practice because it isolates the functionality of the metricset and `Fetch` method from the data mapping. [float] ==== fields.yml The `fields.yml` file is used for different purposes: * Creates the Elasticsearch template * Creates the Kibana index pattern configuration * Creates the Exported Fields documentation for the metricset To make sure the Elasticsearch template is correct, it's important to keep this file up-to-date with all the changes. There is a `fields.yml` file under `module/{module}/_meta/fields.yml` that contains the general top level structure for all metricsets. Normally you only need to modify the description in this file. Here an example for the `fields.yml` file from the MySQL module. [source,yaml] ---- include::../../metricbeat/module/mysql/_meta/fields.yml[] ---- There is another `fields.yml` file under `module/{module}/{metricset}/_meta/fields.yml` that contains all fields retrieved by the metricset. As field types, each field must have a core data type https://www.elastic.co/guide/en/elasticsearch/reference/master/mapping-types.html#_core_datatypes[supported by elasticsearch]. Here's a very basic example that shows one group from the MySQL `status` metricset: [source,yaml] ---- - name: status type: group description: > `status` contains the metrics that were obtained by the status SQL query. fields: - name: aborted type: group description: > Aborted status fields. fields: - name: clients type: integer description: > The number of connections that were aborted because the client died without closing the connection properly. - name: connects type: integer description: > The number of failed attempts to connect to the MySQL server. ---- As you can see, if there are nested fields, you must use the type `group`. // TODO: Add link to general fields.yml developer guide [float] ==== Testing It's important to also add tests for your metricset. There are three different types of tests that you need for testing a Beat: * unit tests * integration tests * system tests We recommend that you use all three when you create a metricset. Unit tests are written in Go and have no dependencies. Integration tests are also written in Go but require the service from which the module collects metrics to also be running. System tests for Metricbeat also require the service to be running in most cases and are written in Python based on our small Python test framework. We use `virtualenv` to deal with Python dependencies. You can simply run the command `make python-env` and then `. build/python-env/bin/activate` . You should use a combination of the three test types to test your metricsets because each method has advantages and disadvantages. To get started with your own tests, it's best to look at the existing tests. You'll find the unit and integration tests in the `_test.go` files under existing modules and metricsets. The system tests are under `tests/systems`. [float] ===== Adding a Test Environment Integration and system tests need an environment that's running the service. You can create this environment by using Docker and a docker-compose file. If you add a module that requires a service, you must add the service to the virtual environment. To do this, you: * Update the `docker-compose.yml` file with your environment * Update the `docker-entrypoint.sh` script The `docker-compose.yml` file is at the root of Metricbeat. Most services have existing Docker modules and can be added as simply as Redis: [source,yaml] ---- redis: image: redis:3.2.3 ---- To allow the Beat to access your service, make sure that you define the environment variables in the docker-compose file and add the link to the container: [source,yaml] ---- beat: links: - redis environment: - REDIS_HOST=redis - REDIS_PORT=6379 ---- To make sure the service is running before the tests are started, modify the `docker-entrypoint.sh` script to add a check that verifies your service is running. For example, the check for Redis looks like this: [source,shell] ---- waitFor ${REDIS_HOST} ${REDIS_PORT} Redis ---- The environment expects your service to be available as soon as it receives a response from the given address and port. [float] ===== Running the Tests To run all the tests, run `make testsuite`. To only run unit tests, run `make unit-tests` or for integration tests `make integration-tests-environment`. Be aware that a running Docker environment is needed for integration and system tests. Sometimes you may want to run a single integration test, for example, to test a module such as the `apache` module. To do this, you can: . Start the Docker service by running `docker-compose run -p port:port apache`. You can skip this step if, like the `golang` module, your module doesn't need a Docker service. . Run `cd tests/system` to change to the folder that contains the integration tests. . Run `INTEGRATION_TESTS=true nosetests test_apache.py`, remembering to replace `test_apache.py` with your own test file. [float] === Documentation Each module must be documented. The documentation is based on asciidoc and is in the file `module/{module}/_meta/docs.asciidoc` for the module and in `module/{module}/{metricset}/_meta/docs.asciidoc` for the metricset. Basic documentation with the config file and an example output is automatically generated. Use these files to document specific configuration options or usage examples. //// TODO: The following parts should be added as soon as the content exists or the implementation is completed. [float] == Field naming https://github.com/elastic/beats/blob/master/metricbeat/module/doc.go [float] == Dashboards Dashboards are an important part of each metricset. Data gets much more useful when visualized. To create dashboards for the metricset, follow the guide here (link to dashboard guide). ////