[Charles Carpenter]

The ELK mirror problem with Centos/Rocky 8 has been fixed.

 

[Charles Carpenter]

The MeasurementFramework has been updated to fix the docker conflict.
Prometheus system is now working. There is an error in a script due to a mirror problem, but this does not affect the Prometheus install.

The ELK install has a fatal mirror problem that remains to be fixed. I will post here when that is completed.

[Charles Carpenter]

Thanks for bringing this to our attention.

In the above result, setting up Prometheus returned ‘success’: False, ‘msg’: ‘Prometheus playbook install failed’ This means that Grafana, which is part of the Promtheus install was most likely not installed.

The ssh tunnel to trying to connect to Grafana’s port is unable to connect since there is no Grafana running, thus the channel 3: open failed: connect failed: Connection refused error.

The error was caused by an ansible related update which broke the installation process. We have found the problem and will be pushing out a fix today.

 

[Charles Carpenter]

There are a couple of methods to export metrics from your running python code.

For a general overview on writing prometheus exporters see https://prometheus.io/docs/instrumenting/writing_exporters/

Option 1) Create your own exporter in python. This will run a small http server and will allow Prometheus to query your code every x seconds (30 seconds is usually the default). This is best for a consistently running process. Python has a promtheus_client module, pip install prometheus-client that handles most of the work for you. You will need to add a function that will be called whenever a prometheus instance makes the request. See https://prometheus.github.io/client_python/getting-started/three-step-demo/  and  https://pypi.org/project/prometheus-client/  and  https://github.com/prometheus/client_python

You can do a test of the running exporter using curl or wget with the address of the exporter and the path /metrics.

Next configure the prometheus on the meas_node to scrape your newly created exporter. The config file is /opt/fabric_prometheus/prometheus/prometheus_config.yml . ssh to the meas_node and sudo vim /opt/fabric_prometheus/prometheus/prometheus_config.yml Add the new scrape section at the end of the file.

Something like

 

# My Exporter
- job_name: 'my_exporter_name'
  static_configs:
  - targets: ['my-exporter-address:port']

The scrape will default to the /metrics path.

Save the file and

docker restart fabric_prometheus_prometheus

Use the Explore tab in Grafana with the PromQl {job=”my_exporter_name”} to see the metrics.

 

Option 2) Use the node_exporters textfile collector. This is best for sporatic metrics, perhaps a cron job that runs hourly. The collector reads text files found in the /var/lib/node_exporter directory. The files need to be in the format found at https://prometheus.io/docs/instrumenting/exposition_formats/#text-based-format There is a python module for writing out text files See https://prometheus.github.io/client_python/exporting/textfile/ https://github.com/prometheus/node_exporter#textfile-collector

That should get you started. Let me know if you have more questions.
-Charles

 

[Charles Carpenter]

Those use the SI prefixes see https://en.wikipedia.org/wiki/Metric_prefix

So lower case m would be milli or 0.001 or 1/1000 of a packet.

Capital M would be mega or 1,000,000 or a million packet

[Charles Carpenter]

Some very basic metrics will soon be available for public use at KNIT8. These will include the up status of the dataplane switches and their packet counts, but not the other data you are seeking.

[Charles Carpenter]

Hello Acheme,
The MFLib Instrumentize errors are now resolved.  They have been tested on the KNIT6 workshop notebooks found in the default JupyterHub login with jupyter-examples-rel1.5.1

-Charles

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