prometheus | Deep Learning Library for R | Machine Learning library

If you're using Spring MVC you can define a bean that implements WebMvcTagsProvider to take complete control over the tags that are added to metrics for request-response exchanges. Alternatively, you can define a bean that implements WebMvcTagsContributor to add to the default tags. Similarly, if you're using Spring WebFlux you can define beans that implements WebFluxTagsProvider and WebFluxTagsContributor to take complete control over the tags and contribute additional tags respectively.

I think the answer depends upon what you want to monitor in Spark 2.1.

1. If it is JVM metrics - I don't think you can do that. For the simple reason that you donot know where the JVMs will be created in the Spark cluster. If we knew that it would be impossible to launch multiple JVMs in the same node because each JMX agent would need a port to be assigned dynamically and Prometheus server needs an exact scraping url which would be impossible.

2. If the requirement is to measure business specific metrics using push gateway then yes you can do that because Prometheus server would be scraping a specific scraping url.

Maybe you need to look at a more recent version of Spark3.0 which supports Prometheus. Please follow this link - https://spark.apache.org/docs/latest/monitoring.html

You need to combine the start_http_server function with your monitor_feed functions.

You can either combine everything under a single HTTP server.

Or, as I think you want, you'll need to run 2 HTTP servers, one with each monitor_feed:

So you have already found correct github related issues and asked there also.

• Still seeing "opening storage failed: block dir" on more recent version of prometheus #7090

• does not start up after corrupted meta.json file

And seems currently the only way to fix the problem is to delete 01F6J0P4KBBWVJD2M8B1PE7C5E folder that contains empty meta.json either nothing (link)

It depends on what you used for the $keyFile in your script.

A default name should be part of the /home/pi/.ssh/id_xxx names considered during an SSH session.
But a non-default name would need to be specified in an ~/.ssh/config: double-check if you have one.

Also, in your script, to be sure, don't use ~/.ssh, but /home/$USER/.ssh consistently, to avoid any mistake when the shell substitutes ~.

The app's metrics aren't in Prometheus' YAML-based Exposition format.

Your best bet is to determine whether the app can be configured to export Prometheus metrics (too).

If not, you're going to need either a proxy that sits between your Prometheus server and the app that, when scraped by Prometheus, calls the app's metrics' endpoint and transforms the results into Exposition format.

To my knowledge, there isn't a general-purpose transforming exporter that you can use. But this would be useful. You'd configure it with your endpoints and a transform function and it would do the work for you.

Or, you will need to write your own exporter for the app. But, if the current metric list is sufficient for your needs, that may be too much effort.

Prometheus doesn't implement it's own authentication|authorization.

Your best approach is to secure access to all host(s) running all software and, in this case, to the hosts running these Docker containers and all targets that you scrape etc.

All cloud providers provide ways by which you may limit access to the resources you create with their platforms. You'll want to become familiar with your preferred platforms' mechanisms and you should establish confidence, in part, by proving to yourself that you're able to restrict access to your services when you deploy then to these platforms.

Perhaps start with a simple test web site, secure it, then test that it is secure. Once you're confident in doing this, deploy your Prometheus services.

See this document on Prometheus security

A non-zero for requires that the alert fires for at least two evaluation_intervals to make sure that it covers the requisite amount of time.

Here this is combining with that your time series are sparse, as they only have a data point once an hour so staleness is kicking in. This is unrealistic test data, an interval of 1m would be better.

In combination example the alert won't fire at 63h, but it should fire at 63h1m through 63h5m with a 1m for. A for longer than 5m can never fire given this test data.

There are a couple of important points to understand before getting started:

• When using the Artemis Prometheus Metrics Plugin neither the broker nor the applications "send" metrics to Prometheus. Prometheus itself must retrieve or "scrape" metrics from the broker. This is why the plugin comes with a servlet. The servlet exposes an HTTP endpoint that Prometheus can use to scrape metrics.
• The Artemis Prometheus Metrics Plugin is part of the broker infrastructure. It is not to be deployed as part of an application. The plugin's jar and war files are deployed on the broker and configured in broker.xml and bootstrap.xml respectively.

The Artemis Prometheus Metrics Plugin provides integration with Prometheus using two modules:

• artemis-prometheus-metrics-plugin: This provides the actual implementation of org.apache.activemq.artemis.core.server.metrics.ActiveMQMetricsPlugin and packages it with the Micrometer and Prometheus dependencies in an "uber" jar.

• artemis-prometheus-metrics-plugin-servlet: This provides a war file containing a simple servlet which can be deployed to the broker's embedded web server which then Prometheus can use to scrape metrics.

Once you clone the Artemis Prometheus Metrics Plugin repository simply run mvn install to build these two modules. The output will be in their respective target directories.

After building the modules follow these steps to deploy and configure the Artemis Prometheus Metrics Plugin. If you have some kind of dev-ops group which manages and configures your broker then they would follow these steps.

1. Copy artemis-prometheus-metrics-plugin/target/artemis-prometheus-metrics-plugin-.jar to /lib.

2. Add this to your /etc/broker.xml: