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Here are the instructions for getting metric data programmatically using curl.
Create a jupyter notebook with the following cells.
# Import MFLib Class
from mflib.mflib import MFLib
slice_name = “<your slice name>”
mf = MFLib(slice_name)# Get the ht_user & ht_password for the slice’s meas_node.
data = {}
# Set the info you want to get.
data[“get”] = [“ht_user”, “ht_password”]
# Call info using data
info_results = mf.info(“prometheus”, data)
print(info_results)Alternatively you can just add the second cell above to the existing prometheus_grafana.ipynb notebook.
Create a tunnel thru the bastion host for port 9090. This is similar to the tunnel needed for accessing grafana but using port 9090:localhost:9090 instead of 10010:localhost:443.
The above cell should print out the meas_node_ip as “Found meas node as meas-node at <your meas_node_ip>”ssh -L 9090:localhost:9090 -F ssh_config -i slice_key ubuntu@<your meas_node ip>
Then make API call using curl or python requests etc…
Here is a simple example using the ht_user and ht_password retrieved above to get the latest up metrics.curl -k -u <ht_user>:<ht_password> https://localhost:9090/api/v1/query\?query\=up
There is a REST API that can be used to access the metrics programmatically in addition to the Grafana views. I will have to add the documentation for that. I’ll post back here once that is done.
The measurement framework sets up its own network to get data from the experimental nodes, therefore experimental networks are mostly unaffected. You could use the metrics, node_network_receive_packets_total and node_network_transmit_packets_total, to see data going in/out of the network interface used for the measurement network to get an idea of the network use.
Most of the Prometheus/Grafana & ELK monitoring processes take place on the measurement node, therefore they have little effect on the experiment nodes.
What is running on the experiment nodes are the node_exporters & file beats.
The node exporter is a binary that only uses resources when it is asked for metrics. see https://github.com/prometheus/node_exporter for information about the resources it uses. You can see what it is doing via top/htop. The node exporter is written in GO and exports some go metrics about its own resource usage. Use the Explore page to search for{ __name__=~”go_.*”, job=”node”}ELK beats are similar but may use more resources since they monitor logs and periodically ship out data. You may also see filebeat, metricbeat and packet beat resource usage using top/htop. I am not sure of the current status of self monitoring of ELK beats.
MFLib currently only performs measurements on a slice.
The Portal, Fabric Portal (fabric-testbed.net) , has information about available resources. Some of that information is available via fablib as KC points to in the previous answer.
Specific infrastructure metrics are available on the infrastructure-metrics.fabric-testbed.net site. These include memory use, cpu load etc… on head and worker nodes. These values are visible using Grafana. There is also a REST API soon to be available to query those values programmatically.
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.
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.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_pythonYou 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-collectorThat should get you started. Let me know if you have more questions.
-CharlesThose 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
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.
