Python elasticsearch_dsl.Nested方法代碼示例

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Nested [as 別名]
def get_field_sort(self, field_name):
        """
        Given a field name, returns the field name that should be used for sorting. If a mapping defines
        a .raw sub-field, that is used, otherwise the field name itself is used if index=not_analyzed.
        """
        if field_name.endswith('.raw'):
            return field_name
        if field_name in self.sort_fields:
            return self.sort_fields[field_name]
        if field_name in self.document._doc_type.mapping:
            dsl_field = self.document._doc_type.mapping[field_name]
            if isinstance(dsl_field, (dsl.Object, dsl.Nested)):
                return None
            if not isinstance(dsl_field, dsl.String):
                return field_name
            if 'raw' in dsl_field.fields:
                return '%s.raw' % field_name
            elif getattr(dsl_field, 'index', None) == 'not_analyzed':
                return field_name
        return None 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Nested [as 別名]
def test_properties_can_iterate_over_all_the_fields():
    m = mapping.Mapping()
    m.field('f1', 'text', test_attr='f1', fields={'f2': Keyword(test_attr='f2')})
    m.field('f3', Nested(test_attr='f3', properties={
            'f4': Text(test_attr='f4')}))

    assert {'f1', 'f2', 'f3', 'f4'} == {f.test_attr for f in m.properties._collect_fields()} 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Nested [as 別名]
def test_resolve_nested():
    m = mapping.Mapping()
    m.field('n1', 'nested', properties={'n2': Nested(properties={'k1': Keyword()})})
    m.field('k2', 'keyword')

    nested, field = m.resolve_nested('n1.n2.k1')
    assert nested == ['n1', 'n1.n2']
    assert isinstance(field, Keyword)

    nested, field = m.resolve_nested('k2')
    assert nested == []
    assert isinstance(field, Keyword) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Nested [as 別名]
def get_field_highlight(self, field_name):
        if field_name in self.highlight_fields:
            return self.highlight_fields[field_name]
        if field_name in self.document._doc_type.mapping:
            dsl_field = self.document._doc_type.mapping[field_name]
            if isinstance(dsl_field, (dsl.Object, dsl.Nested)):
                return '%s.*' % field_name
            return field_name
        return None 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Nested [as 別名]
def test_mapping_can_collect_all_analyzers_and_normalizers():
    a1 = analysis.analyzer('my_analyzer1',
        tokenizer='keyword',
        filter=['lowercase', analysis.token_filter('my_filter1', 'stop', stopwords=['a', 'b'])],
    )
    a2 = analysis.analyzer('english')
    a3 = analysis.analyzer('unknown_custom')
    a4 = analysis.analyzer('my_analyzer2',
        tokenizer=analysis.tokenizer('trigram', 'nGram', min_gram=3, max_gram=3),
        filter=[analysis.token_filter('my_filter2', 'stop', stopwords=['c', 'd'])],
    )
    a5 = analysis.analyzer('my_analyzer3', tokenizer='keyword')
    n1 = analysis.normalizer('my_normalizer1',
        filter=['lowercase']
    )
    n2 = analysis.normalizer('my_normalizer2',
        filter=['my_filter1', 'my_filter2', analysis.token_filter('my_filter3', 'stop', stopwords=['e', 'f'])]
    )
    n3 = analysis.normalizer('unknown_custom')

    m = mapping.Mapping()
    m.field('title', 'text', analyzer=a1,
        fields={
            'english': Text(analyzer=a2),
            'unknown': Keyword(search_analyzer=a3),
        }
    )
    m.field('comments', Nested(properties={
        'author': Text(analyzer=a4)
    }))
    m.field('normalized_title', 'keyword', normalizer=n1)
    m.field('normalized_comment', 'keyword', normalizer=n2)
    m.field('unknown', 'keyword', normalizer=n3)
    m.meta('_all', analyzer=a5)

    assert {
        'analyzer': {
            'my_analyzer1': {'filter': ['lowercase', 'my_filter1'], 'tokenizer': 'keyword', 'type': 'custom'},
            'my_analyzer2': {'filter': ['my_filter2'], 'tokenizer': 'trigram', 'type': 'custom'},
            'my_analyzer3': {'tokenizer': 'keyword', 'type': 'custom'},
        },
        'normalizer': {
            'my_normalizer1': {'filter': ['lowercase'], 'type': 'custom'},
            'my_normalizer2': {'filter': ['my_filter1', 'my_filter2', 'my_filter3'], 'type': 'custom'},
        },
        'filter': {
            'my_filter1': {'stopwords': ['a', 'b'], 'type': 'stop'},
            'my_filter2': {'stopwords': ['c', 'd'], 'type': 'stop'},
            'my_filter3': {'stopwords': ['e', 'f'], 'type': 'stop'},
        },
        'tokenizer': {
            'trigram': {'max_gram': 3, 'min_gram': 3, 'type': 'nGram'},
        }
    } == m._collect_analysis()

    assert json.loads(json.dumps(m.to_dict())) == m.to_dict()