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Configuration generator (a.k.a confGenerator)

confGenerator is a utility to generate from metric definitions provided by users as a set of files, folders and sub-folders the following:

  1. flowlogs2metrics configuration files
  2. Visualization configuration (Grafana)
  3. Metrics documentation

The information from the (human friendly) metric definitions is aggregated and re-structured to generate output configurations and documentation automatically

Usage:

$ ./confgenerator --help
Generate configuration and docs from metric definitions

Usage:
  confgenerator [flags]

Flags:
      --config string                     config file (default is $HOME/.confgen)
      --destConfFile string               destination configuration file (default "flowlogs2metrics.conf.yaml")
      --destGrafanaJsonnetFolder string   destination grafana jsonnet folder
  -h, --help                              help for confgenerator
      --log-level string                  Log level: debug, info, warning, error (default "error")
      --skipWithLabels strings            Skip definitions with Labels
      --srcFolder string                  source folder (default "network_definitions")

Note: The default location for network definitions in flowlogs2metrics is /network_definitions folder

The files and folder structure required as input for confGenerator are:

  1. config.yaml - singleton file holding general information for configuration generation
  2. metric definition files - each file represent one or more metrics including the documentation, flowlogs2metrics pipeline steps and visualization configuration

Note: It is possible to place metric definition files in any sub-folder structure

Note: It is possible to activate ConfGenrator with the default flowlogs2metrics configuration using the command make generate-configuration. This command compiles the code and generates default outputs.

How to write a network definitions

This is easy and quick:

(1) create new definition yaml file or modify existing under the folder network_definitions.
(2) execute

make generate-configuration
make dashboards

(3) For OCP deployment execute

make ocp-deploy

For Kind deployment execute

make local-redeploy

Note: Additional information on usage and deployment can be found in flowlogs2metrics README

Note: learning from examples and existing metric definitions is very-useful

Network definition explained

In this section we explain how network definition are structured. This is useful for development of new network definitions as well as debugging and working with existing network definition.

#fl2m_confgen (1) 
description: (2)
  Network definition description  
details: (3)
  More details  
usage: (4)
  How to use
labels: (5) 
  - label1
  - label2
transform: (6)
  rules: (7)
    - input: inputFieldName (7.1)
      output: outputFieldName (7.2)
      type: operationType (7.3)
      parameters: operationParameters (7.4)
extract: (8)
  type: aggregates (8.1)
  aggregates:
    - name: aggregate_name (8.2)
      by: (8.3)
        - aggregateField1
        - aggregateField2
      operation: operation (8.4)
encode: (9)
  type: prom (9.1)
  prom:
    metrics:
      - name: metricName (9.2)
        type: metricType (9.3)
        valuekey: aggregate_name_value (9.4)
        labels: (9.5)
          - by
          - aggregate
visualization: (10)
  type: grafana
  grafana: 
    - expr: 'fl2m_metricName' (10.1)
      type: graphPanel (10.2)
      dashboard: dashboardName (10.3)
      title:
        grafanaPanelTitle (10.4)

(1) A fixed header #fl2m_confgen that must exist in every network definition file
(2) (3) (4) description, details and usage of the definition file - used for docs
(5) labels - used by visualization and docs

(6) The transform phase defines what network fields to add on top of the original log fields. This is done using multiple rules (7). In each of the rules the input field (7.1) is being evaluated with network operation (7.3) to generate a new field (7.2) with network data. As needed, (7.4) adds additional parameters to the operation.

For additional details on network transform including the list of supported operations, refer to README.md and for additional details on the fields refer to api.md.

(8) Next, the transformed log lines are aggregated using mathematical operation (8.4) based on the by fields (8.3) - this actually moves the data from being log lines into being a metric named (8.2)

For additional details on extract aggregates refer to README.md.

(9) Next, the metrics from (8.2) are sent to prometheus (9.1). Make sure that (9.4) value is set to the metric name from (8.2) with suffix _value. The metric name in prometheus will be called as the value of (9.2) with the prefix from the config.yaml file. The type of the prometheus metric will be (9.3) (e.g. gauge). Prometheus will add labels to the metric based on the (9.5) fields.

(10) next, using grafana to visualize the metric with name from (9.2) including the prefix and using the prometheus expression from (10.1). Grafana will visualize the metric as (10.2) and place the panel inside a dashboard named (10.3) as defined in config.yaml. The title for the panel will be (10.4)

connection_rate_per_dest_subnet.yaml is an example for a network_definition file in which the metric is defined to hold counts for the number of connections per subnet and the visualization is defined to show the top 10 metrics in a graph panel.