Tags:

• aura-nlp

Categories:

• monitoring

NLP Provisioning metrics

Introduction

In the Aura NLP provisioning component, it is important to know in each moment the quantity of processes restarted in relation with the total processes that, at this moment, work to process the different container. In that way, it could be alerted to an abnormal performance and take measures in this regard.

http_request_duration_seconds

This Prometheus metric is modelled as a summary where its value is the spent time until the remote host responds to an HTTP request.

Note that the value is a float number rounded to its third decimal. It is stored as a Summary in Prometheus.

This metric is intended to store the duration of outgoing requests in seconds.

Labels:

All label values are strings.

• method: HTTP method used by the request being stored (GET, POST, PUT, DELETE, etc.).
• path: HTTP path of the incoming request.
• status_code: the responded HTTP status code (as a string).

Value:

• Request duration in seconds.

nlp_provisioning_killed_processes

This metric is intended to store the number of processes killed in each iteration of the Aura NLP provisioning execution. It is stored as a Gauge in Prometheus.

Value:

• Number worker processes killed in each iteration

nlp_provisioning_alive_processes

This metric is intended to store the number worker processes alive in each iteration of NLP Provisioning. It is stored as a Gauge.

Value:

• Total alive processes.

nlp_provisioning_expected_alive_processes

This metric is intended to store the number of expected alive processes in the NLP Provisioning. It is stored as a Gauge.

Value:

• Set gauge with total alive processes.
• Decrease gauge with finished processes.

nlp_provisioning_container_killed_count

This metric is intended to store the counter of all the processes killed in Aura NLP provisioning. It is stored as a Counter in Prometheus.

Labels:

• container: container URL.

Value:

• Dead process ids (pids).