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Service Integration with the LDF Monitoring Stack

Premise

As services are implemented in the LDF environment, the LDF monitoring stack will need a way to ask these services about their health and performance related metrics in order to detect issues with the service and to gather data in regards to reporting. The methods used to expose this data need to conform to a set of standards that are compatible with the capabilities of the LDF monitoring framework. A draft of acceptable methods is outlined below.

Method 1: Direct Injection to InfluxDB Endpoint

Streaming metrics straight into an InfluxDB database is an efficient way to inject data into the LDF monitoring framework. The documentation for writing data into InfluxDB is well define and very conventional. NCSA admins working on monitoring are also happy to answer questions about this method and when it comes time for final integration will be available to provision databases and ensure that metrics data is flowing properly into InfluxDB as well as create relevant dashboards for those services.

Method 2: Telegraf

Certain applications have built in support via the Telegraf agent which make deploying monitoring of a service fairly straight forward. One will have to include the telegraf package in the container image with a proper output section format. See the :ref:`example <example01>` below.

Method 3: Prometheus Endpoint

Another option though sometimes less preferred due to a bit high memory footprint is to configure a Prometheus endpoint for the application that can be scraped by the LDF monitoring system. Many applications already have integrations with Prometheus. If an integration is not provided there is documentation on writing a custom integration.

Example: Telegraf Output Configuration

[[outputs.influxdb]]
  database = "$database_name"
  urls = ["https://$influxhostname:8086"]