Python Library for generating docs conforming to common data model .

scribe is a tool to process data collected and conform it to common data model to facilitate data indexing into elasticsearch. Currently scribe only supports data collected through stockpile but it's very easy to integrate other data sources.

It is suggested to use a venv to install and run scribe.

python3 -m venv /path/to/new/virtual/environment
source /path/to/new/virtual/environment/bin/activate
git clone https://github.com/redhat-performance/scribe
pip install -e scribe/

Note: we're creating a python3 venv here as scribe is written in python3 and is currently incompatible with python2

You can easily interact with the utilities through the provided command line options or import the libraries provided by this package to use in your own scripts.

Currently the only functionality provided through command line is scribe. It takes 2 inputs and can be run as follows.

(scribe_venv) [agopi@localhost virtenv3]$ scribe -t stockpile -ip /tmp/stockpile.json

If you're just looking for a way to use scribe to generate documents and consume them, take a look at run_scribe.py (excerpt below):

from transcribe.render import transcribe
for scribe_object in transcribe('/tmp/stockpile.json','stockpile'):
    print(scribe_object)

Please submit a github PR

Scribe package is basically made of two modules:

1. scribes
2. scribe_modules

These 2 modules serve different purpose, scribes are for reading the input data and pre-processing them into a structure that can be used to create scribe_modules

The pre-processed dictionary structure can look like this:

  {
  "scribe_module_1": [
      {
          "host": "localhost",
          "value": "sample_value_1"
      },
      {
          "host": "host1",
          "value": "sample_value_2"
      },
      {
          "host": "host2",
          "value": "sample_value_3"
      }
  ],
  "scribe_module_2": [
      {
          "host": "host2",
          "value": {
              "field1": "sample_filed1_value_3",
              "field2": "sample_field2_value_3"
          }
      },
      {
          "host": "host1",
          "value": [
              {
                  "field1": "sample_filed1_value_1",
                  "field2": "sample_field2_value_1"
              },
              {
                  "field1": "sample_filed1_value_2",
                  "field2": "sample_field2_value_2"
              }
          ]
      }
  ]
  }

Basically the dictionary needs to have first level keys that you've written 'scribe_modules', match the name of the file in scribe_modules/ . The children of each of the module in dictionary should have the 2 keys - 'host' and 'value'. the value for the key 'value' can be either a dictionary or a list of dictionary

Please note that the value for the key 'value' will be the one passed to the scribe_modules while creating the object.

So let's take the simple example of scribe_module_2 for host2, just one object would be created and the value passed would be

  {
  "field1": "sample_filed1_value_3",
  "field2": "sample_field2_value_3"
  }

And like wise for host1, there will be 2 objects created.

for object 1, following value would be passed:

  {
  "field1": "sample_filed1_value_1",
  "field2": "sample_field2_value_1"
  }

for object 2, following value would be passed:

  {
  "field1": "sample_filed1_value_2",
  "field2": "sample_field2_value_2"
  }

While for scribe_module_1 for host1, the value that will be passed would be: "sample_value_2"

Steps to extend scribe to work with a new input-type 'example1' would involve:

1. Creating 'example1.py' in the 'transcribe/scribes/' directory. The sample code would look like:

from . import ScribeBaseClass


class Example1(ScribeBaseClass):

    def example1_build_initial_dict(self):
        output_dict = {}
        Example1_data = load_file(self._path)
        # .... some sort of data manipulation
        # .... to build the output_dict
        return output_dict

    def __init__(self, path=None, source_type=None):
        ScribeBaseClass.__init__(self, source_type=source_type, path=path)
        self._dict = self.example1_build_initial_dict()

    def emit_scribe_dict(self):
        return self._dict

Note the following:

a) from . import ScribeBaseClass needs to be present as we are inheriting from the ScribeBaseClass

b) class Example1(ScribeBaseClass) is where inheritance occurs, ensure that '(ScribeBaseClass)' is present when you write the class definition

c) The first letter in classname must be uppercase that's how factory method is defined.

d) The __init__ function first calls the parent's __init__ function and passes the default arguments which are path and source_type, however more can be added. and they won't be needed to passed on to parent class's __init__ function.

e) emit_scribe_dict is an abstractmethod and thus it needs to be defined in any other class that is written. However the method itself can be changed but it should return the dictionary object as described above.

2. Add the module to choices list in scribe.py at L14, currently it looks like choices=['stockpile'], because at the time of creating this documentation only stockpile data could be transcribed using scribe.

Steps to extend scribe_modules to work with a new module 'scribe_module_1' would involve:

1. Adding a new class 'scribe_module_1.py' to directory 'transcribe/scribe_modules'. It'd probably look something like this:

  from . import ScribeModuleBaseClass


  class Scribe_module_1(ScribeModuleBaseClass):

      # This is the abstract method we have to override
      def parse(self):
          # input data is passed through self._input_dict
          # here is where data manipulation occurs to fit our requirements
          output_dict = {"value": self._input_dict}
          # output data should be passed under the "value" key of a dictionary
          yield output_dict

Note the following important things:

a) from . import ScribeModuleBaseClass needs to be present as we are inheriting from the ScribeModuleBaseClass

b) class Example1(ScribeModuleBaseClass) is where inheritance occurs, ensure that '(ScribeModuleBaseClass)' is present when you write the class.

c) The first letter in classname must be uppercase that's how factory method is defined.

d) setting the new entities should be done inside the parse_ method only, but the user has flexibility of calling another method from either same class or from lib/util.py to do transformation.

2. Add schema for the new class 'example1.yml' to the directory 'transcribe/schema'. Scribe currently uses cerberus to validate the iterable produced by the scribe_modules subclass. Please look at https://docs.python-cerberus.org/en/stable/validation-rules.html for more information on how to write the schema for your class's output.

Note: The name of the yml file should match that of the scribe_modules class that you create it for. Thus, for 'example1' class the file should be named 'example1.yml'

3. When adding a new scribe module for Stockpile please be sure to include an example resulting json with the same name. This will be run in our CI test suite. An easy way to obtain this json is by running the following from your stockpile directory: ansible-playbook -i hosts stockpile.yml --tags=[my_module_tags],dump-facts Then copy the resulting json file to ci/[my_module_name].json

Directory 'transcribe/schema' will essentially contain the data model. Work needs to be done so that these yml files can be used to create templates for elasticsearch. It's on the line of the ViaQ's elasticsearch templates work.

Please refer https://github.com/ViaQ/elasticsearch-templates for more info on how templates can be created.

Do note that, currently ViaQ/elasticsearch-templates doesn't support creating templates from the schema files present in 'transcribe/schema'