Config Tokenizer

Tokenizing is the process of transforming input device configuration to a stream of the tokens. Tokenizer accepts raw config and yields lines of parsed tokens. For example, raw config:

interface Fa0/1
  description Some interface
  ip address

converted into:

["interface", "Fa0/1"]
["interface", "Fa0/1", "description", "Some", "interface"]
["interface", "Fa0/1", "ip", "address", "", ""]

Tokenizer must fulfill following requirements:

  • Knows nothing about the meaning of config

  • Low memory usage. Output tokens must be yield whenever ready

  • Backward references should be avoided. Tokenizer should operate current window like a tape. Forward and backward rewinds must be avoided.

  • Output tokens should be grouped and analyzed easy

  • Original context should be preserved whenever possible. See at expanding interface Fa0/1 in following lines

  • Each line of tokens should be further processed independently of each other

It may seems that you need separate tokenizer per each platform. Luckily you are not. Though various configuration format have different meaning, almost all them maintains some code style. Like some languages are indent-based (Python) and some are curly-bracket-based (C, PHP), and some even all-parenthesis (LISP), there are well distinguishable groups of syntaxes. So the real device configurations are groupped in large syntax families with very few exceptions. Usually you can choose one of existing tokenizers and apply some configuration rather than create own tokenizer for a new platform from zero ground.


Builtin tokenizers are collected in the noc.core.confdb.tokenizer package. Tokenizer classes form an hierarchy:

graph TD base --> line line --> context context --> indent line --> curly base --> ini line --> routeros


line(eol='\n', tab_width=0, line_comment=None, inline_comment=None, keep_indent=False, string_quote=None, rewrite=None)

Basic tokenizer, converting line of config into line of tokens, separating by spaces and grouping strings together into single tokens and removing comments. Line tokenizer is suitable when each line of configuration is completely self-sufficient and does not depends on previous or following lines. Though usable by itself, usually used as base class for more advanced tokenizers.

  • eol – End-of-line separator.

  • tab_width – When non-zero replace tabs with tab_width spaces

  • line_comment – When non-empty sets the sequence which starts whole-line comments. I.e. line containing starting spaces followed with line_comment are completely removed from output. (Like ! in Cisco IOS comments)

  • inline_comment – When non-empty sets the sequence which starts inline comments. Unlike the line_comments which cover whole line, inline_comment yields non-empty parts of lines before inline_comments (Like # in Python or // in C).

  • keep_indent – When False removes leading spaces. When True retains leading spaces as single token containing only spaces.

  • string_quote – When non-empty group tokens together when enclosed in string_quote. (Like in Python).

  • rewrite – List of tuples of (compiled regular expression, replacement) to fix input formatting glitches.


context(end_of_context=None, contexts=None, **kwargs)

Descendant of line tokenizer. Adds extra ability to determine and stack current contexts from previous lines and apply current context to each output line of tokens automatically.

Accepts all parameters of line with extra new parameters:

  • end_of_context – When non-empty sets explicit context termination sequence (Like } or end). When explicit context termination token found at the start of the line, current context closed and removed from stack of context and previous context became current

  • contexts – When non-empty sets a list of explicit start of context matching strings. When found from the start of the line the new context is automatically created and pushed to the top of the stack


indent(end_of_context=None, **kwargs)

Descendant of context. Context are detected by start of line indents, like the Python programming language and the IOS-like configs.

Accepts all parameters of line but forcefully sets keep_indent parameter.


curly(start_of_context='{', end_of_context='}', explicit_eol=None, **kwargs)

Descendant of line tokenizer. Adds extra ability to determine and stack current contexts from previous lines and apply current context to each output line of tokens automatically. Context are starting with start_of_context sequence and closed by end_of_context sequence. Unlike context tokenizer context starts and ends are always explicit. Name curly hints to C-style programming languages with their curly braces {} which is good choice for JUNOS-line configs.

  • start_of_context – Explicit start of context sequence (Like {)

  • end_of_context – Explicit end of context sequence (Like })



Basic tokenizer capable of parsing Microsoft Windows INI files. See Python’s ConfigParser module for details



Descendant of line tokenizer adapted to handle MikroTik RouterOS config

Profile Integration


Refer to Profile API

Following profile parameters are responsible for tokenizer configuration:


String containing name of config tokenizer to use. Refer to Tokenizers section for possible values and for recommendations.


Optional dict, containing config tokenizer settings. Refer to Tokenizers section for possible values explanation.

get_config_tokenizer(cls, object):

Classmethod returning actual config tokenizer name and its settings for selected managed object. Returns (config_tokenizer, config_tokenizer_settings) by default. Should be overriden in profile if tokenizer or settings depends on platform or software version.


object – ManagedObject reference


tuple of (config tokenizer name, config tokenizer settings). Must return (None, None) if platform is not supported.

Custom Tokenizer API

Custom tokenizers must be inherited from noc.core.confdb.tokenizer.base.BaseTokenizer class or any of its descendancies. First you must define tokenizer name


Unique name of tokenizer.


class MyTokenizer(BaseTokenizer):
    name = "mytokenizer"

Tokenizer configuration passed as parameters to class constructor.

__init__(self, data, param1=default1, .., paramN=defaultN)
  • data – String containing device configuration

  • param1 – Custom configuration parameter #1 with default value

  • paramN – Custom configuration parameter #N with default value

It is advised to call superclass’ constructor:

class MyTokenizer(BaseTokenizer):
    def __init__(self, data, param1=default1, ...):
        super(MyTokenizer, self).__init__(data)

The actual tokenizer must be implemented in __iter__ method


Iterator yielding tuples of tokens per each line. Tokenizer should analyze variable and call yield operator per each matched line of tokens