To create an instance of the Prometheus provider, edit a YAML file (instance.yml in this example) with the definition of the instance:
Then you can create the instance for the system using the Akamas CLI:
When you create an instance of the Prometheus provider, you should specify some configuration information to allow the provider to extract and process metrics from Prometheus correctly.
You can specify configuration information within the config part of the YAML of the instance definition.
address, a URL or IP identifying the address of the host where Prometheus is installed
port, the port exposed by Prometheus
user, the username for the Prometheus service
password, the user password for the Prometheus service
job, a string to specify the scraping job name. The default is ".*" for all scraping jobs
logLevel, set this to "DETAILED" for some extra logs when searching for metrics (default value is "INFO")
headers, to specify additional custom headers (e.g.: headers: {key: value})
namespace, a string to specify the namespace
duration, integer to determine the duration in seconds for data collection (use a number between 1 and 3600)
enableHttps, boolean to enable HTTPS in Prometheus (since 3.2.6)
ignoreCertificates, boolean to ignore SSL certificates
disableConnectionCheck, boolean to disable initial connection check to Prometheus
The Prometheus provider allows defining additional queries to populate custom metrics or redefine the default ones according to your use case. You can configure additional metrics using the metrics field as shown in the configuration below:
In this example, the telemetry instance will populate cust_metric with the results of the query specified in datasource, maintaining the value of the labels listed under labels.
Please refer to Querying basics | Prometheus for a complete reference of PromQL
Akamas pre-processes the queries before running them, replacing special-purpose placeholders with the fields provided in the components. For example, given the following component definition:
the query sum(jvm_memory_used_bytes{instance=~"$INSTANCE$", job=~"$JOB$"}) will be expanded for this component into sum(jvm_memory_used_bytes{instance=~"service01", job=~"jmx"}). This provides greater flexibility through the templatization of the queries, allowing the same query to select the correct data sources for different components.
The following is the list of available placeholders:
This section reports common use cases addressed by this provider.
To gather kubernetes metrics, the following exporters are required:
kube-state-metrics
cadvisor
As an example, you can define a component with type Kubernetes Container in this way:
Check Java OpenJDK page for a list of all the Java metrics available in Akamas
You can leverage the Prometheus provider to collect Java metrics by using the JMX Exporter. The JMX Exporter is a collector of Java metrics for Prometheus that can be run as an agent for any Java application. Once downloaded, you execute it alongside a Java application with this command:
The command will expose on localhost on port 9100 Java metrics of youJar.jar __ which can be scraped by Prometheus.
config.yaml is a configuration file useful for the activity of this exporter. It is suggested to use this configuration for an optimal experience with the Prometheus provider:
As a next step, add a new scraping target in the configuration of the Prometheus used by the provider:
You can then create a YAML file with the definition of a telemetry instance (prom_instance.yml) of the Prometheus provider:
And you can create the telemetry instance using the Akamas CLI:
Finally, to bind the extracted metrics to the related component, you should add the following field to the properties of the component’s definition:
Check the Linux page for a list of all the system metrics available in Akamas
You can leverage the Prometheus provider to collect system metrics (Linux) by using the Node exporter. The Node exporter is a collector of system metrics for Prometheus that can be run as a standalone executable or a service within a Linux machine to be monitored. Once downloaded, schedule it as a service using, for example, systemd:
Here’s the manifest of the node_exporter service:
The service will expose on localhost on port 9100 system metrics __ which can be scraped by Prometheus.
As a final step, add a new scraping target in the configuration of the Prometheus used by the provider:
You can then create a YAML file with the definition of a telemetry instance (prom_instance.yml) of the Prometheus provider:
And you can create the telemetry instance using the Akamas CLI:
Finally, to bind the extracted metrics to the related component, you should add the following field to the properties of the component’s definition:
| Placeholder | Usage example | Component definition example | Expanded query | Description |
|---|---|---|---|---|
$INSTANCE$, $JOB$
node_load1{instance=~"$INSTANCE$", job=~"$JOB$"}
See Example below
node_load1{instance=~"frontend", job=~"node"}
These placeholders are replaced respectively with the instance and job fields configured in the component’s prometheus configuration.
%FILTERS%
container_memory_usage_bytes{job=~"$JOB$" %FILTERS%}
See Example below
container_memory_usage_bytes{job=~"advisor", name=~"db-.*"}
This placeholder is replaced with a list containing any additional filter in the component’s definition (other than instance and job), where each field is expanded as field_name=~"field_value". This is useful to define additional label matches in the query without the need to hardcode them.
$DURATION$
rate(http_client_requests_seconds_count[$DURATION$])
rate(http_client_requests_seconds_count[30s])
If not set in the component properties, this placeholder is replaced with the duration field configured in the telemety-instance. You should use it with range vectors instead of hardcoding a fixed value.
$NAMESPACE$, $POD$, $CONTAINER$
1e3 * avg(kube_pod_container_resource_limits{resource="cpu", namespace=~"$NAMESPACE$", pod=~"$POD$", container=~"$CONTAINER$" %FILTERS%})
1e3 * avg(kube_pod_container_resource_limits{resource="cpu", namespace=~"boutique", pod=~"adservice.*", container=~"server"})
These placeholders are used within kubernetes environments