Data Model

Bro/Zeek log data model.

class zlogging.model.Model(*args, **kwargs)

Bases: object

Log data model.

Variables

• __fields__ (OrderedDict mapping str and BaseType) – Fields of the data model.

• __record_fields__ (OrderedDict mapping str and RecordType) – Fields of record data type in the data model.

• __empty_field__ (bytes) – Placeholder for empty field.

• __unset_field__ (bytes) – Placeholder for unset field.

• __set_separator__ (bytes) – Separator for set/vector fields.

Warns

BroDeprecationWarning – Use of bro_* type annotations.

Raises

• ModelValueError – In case of inconsistency between field data types, or values of unset_field, empty_field and set_separator.

• ModelTypeError – Wrong parameters when initialisation.

Note

Customise the Model.__post_init__ method in your subclassed data model to implement your own ideas.

Example

Define a custom log data model using the prefines Bro/Zeek data types, or subclasses of BaseType:

class MyLog(Model):
    field_one = StringType()
    field_two = SetType(element_type=PortType)

Or you may use type annotations as PEP 484 introduced when declaring data models. All available type hints can be found in zlogging.typing:

class MyLog(Model):
    field_one: zeek_string
    field_two: zeek_set[zeek_port]

However, when mixing annotations and direct assignments, annotations will take proceedings, i.e. the Model class shall process first annotations then assignments. Should there be any conflicts, ModelError will be raised.

See also

See expand_typing() for more information about processing the fields.

property fields

fields of the data model

Type

OrderedDict mapping str and BaseType

Return type

OrderedDict[str, BaseType]

property unset_field

placeholder for empty field

Type

bytes

Return type

bytes

property empty_field

placeholder for unset field

Type

bytes

Return type

bytes

property set_separator

separator for set/vector fields

Type

bytes

Return type

bytes

__post_init__()

Post-processing customisation.

Return type

None

__call__(format)

Serialise data model with given format.

Parameters

format (str) – Serialisation format.

Return type

Any

Returns

The serialised data.

Raises

ModelFormatError – If format is not supproted, i.e. Mode.to{format}() does not exist.

tojson()

Serialise data model as JSON log format.

Return type

OrderedDict[str, Any]

Returns

An OrderedDict mapping each field and serialised JSON serialisable data.

toascii()

Serialise data model as ASCII log format.

Return type

OrderedDict[str, str]

Returns

An OrderedDict mapping each field and serialised text data.

asdict(dict_factory=None)

Convert data model as a dictionary mapping field names to field values.

Parameters

dict_factory (Optional[Type[dict]]) – If given, dict_factory will be used instead of built-in dict.

Return type

Dict[str, Any]

Returns

A dictionary mapping field names to field values.

astuple(tuple_factory=None)

Convert data model as a tuple of field values.

Parameters

tuple_factory (Optional[Type[tuple]]) – If given, tuple_factory will be used instead of built-in namedtuple.

Return type

Tuple[Any, ..]

Returns

A tuple of field values.

zlogging.model.new_model(name, **fields)

Create a data model dynamically with the appropriate fields.

Parameters

• name (str) – data model name

• **fields – defined fields of the data model

• fields (Any) –

Returns

created data model

Return type

Model

Examples

Typically, we define a data model by subclassing the Model class, as following:

class MyLog(Model):
    field_one = StringType()
    field_two = SetType(element_type=PortType)

when defining dynamically with new_model(), the definition above can be rewrote to:

MyLog = new_model('MyLog', field_one=StringType(), field_two=SetType(element_type=PortType))