Python elasticsearch_dsl.MultiSearch方法代碼示例

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import MultiSearch [as 別名]
def execute_searches(self):
        """Ejecuta la query de todas las series agregadas, e inicializa
        los atributos data y count a partir de las respuestas.
        """

        multi_search = MultiSearch(index=self.index,
                                   doc_type=settings.TS_DOC_TYPE)

        for serie in self.series:
            multi_search = multi_search.add(serie.search)

        responses = multi_search.execute()
        formatter = ResponseFormatter(self.series, responses,
                                      self.args[constants.PARAM_SORT],
                                      self.args[constants.PARAM_PERIODICITY])

        return {
            'data': (formatter.format_response()),
            'count': max([response.hits.total for response in responses])
        } 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import MultiSearch [as 別名]
def _execute_multi_search(self, **kwargs):
        indices = sorted(self._index_searches.keys(), reverse = True) or self._indices

        if self.CACHED_COUNTS_KEY and not self.previous_search_results.get(self.CACHED_COUNTS_KEY):
            self.previous_search_results[self.CACHED_COUNTS_KEY] = {}

        ms = MultiSearch()
        for index_name in indices:
            start_index = 0
            if self.CACHED_COUNTS_KEY:
                if self.previous_search_results[self.CACHED_COUNTS_KEY].get(index_name):
                    index_total = self.previous_search_results[self.CACHED_COUNTS_KEY][index_name]['total']
                    start_index = self.previous_search_results[self.CACHED_COUNTS_KEY][index_name]['loaded']
                    if start_index >= index_total:
                        continue
                else:
                    self.previous_search_results[self.CACHED_COUNTS_KEY][index_name] = {'loaded': 0, 'total': 0}

            searches = self._get_paginated_searches(index_name, start_index=start_index, **kwargs)
            ms = ms.index(index_name.split(','))
            for search in searches:
                ms = ms.add(search)

        responses = self._execute_search(ms)
        parsed_responses = [self._parse_response(response) for response in responses]
        return self._process_multi_search_responses(parsed_responses, **kwargs) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import MultiSearch [as 別名]
def multisearch(*models, **params):
    ms = MultiSearch(using=es.client, index=es.index_name)
    queries = []
    for model in models:
        s = search_for(model, **params)
        ms = ms.add(s._s)
        queries.append(s)
    responses = ms.execute()
    return [
        # _d_ is the only way to access the raw data
        # allowing to rewrap response in a FacetedSearch
        # because default multisearch loose facets
        SearchResult(query, response._d_)
        for query, response in zip(queries, responses)
    ] 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import MultiSearch [as 別名]
def __init__(self):
        self.field_counts = {}
        self.multi_search = MultiSearch() 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import MultiSearch [as 別名]
def _run_multisearch(es, searches):
    """Ejecuta una lista de búsquedas Elasticsearch utilizando la función
    MultiSearch. La cantidad de búsquedas que se envían a la vez es
    configurable vía la variable ES_MULTISEARCH_MAX_LEN.

    Args:
        es (Elasticsearch): Conexión a Elasticsearch.
        searches (list): Lista de elasticsearch_dsl.Search.

    Raises:
        DataConnectionException: Si ocurrió un error al ejecutar las búsquedas.

    Returns:
        list: Lista de respuestas a cada búsqueda.

    """
    step_size = constants.ES_MULTISEARCH_MAX_LEN
    responses = []

    # Partir las búsquedas en varios baches si es necesario.
    for i in range(0, len(searches), step_size):
        end = min(i + step_size, len(searches))
        ms = MultiSearch(using=es)

        for j in range(i, end):
            ms = ms.add(searches[j])

        try:
            responses.extend(ms.execute(raise_on_error=True))
        except elasticsearch.ElasticsearchException as e:
            raise DataConnectionException() from e

    return responses 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import MultiSearch [as 別名]
def get_multi_search(self):
        multi_search = MultiSearch()
        search = self.get_search()
        multi_search = multi_search.add(search)

        if self.args.get(constants.PARAM_AGGREGATIONS) is not None:
            multi_search = self.add_terms_aggregations(multi_search)

        return multi_search 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import MultiSearch [as 別名]
def test_no_querystring_is_valid(self):
        query = FieldSearchQuery(args={})

        with mock.patch.object(MultiSearch, 'execute', return_value=get_mock_search()):
            result = query.execute()

        self.assertFalse(result.get('errors')) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import MultiSearch [as 別名]
def test_query_response_size(self):
        query = FieldSearchQuery(args={'q': 'aceite'})

        with mock.patch.object(MultiSearch, 'execute', return_value=get_mock_search()):
            result = query.execute()

        self.assertEqual(len(result['data']), result['count']) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import MultiSearch [as 別名]
def test_response_params(self):
        limit = '10'
        offset = '15'
        query = FieldSearchQuery(args={'q': 'aceite',
                                       'limit': limit,
                                       'start': offset})

        with mock.patch.object(MultiSearch, 'execute', return_value=get_mock_search()):
            result = query.execute()

        self.assertEqual(result['limit'], int(limit))
        self.assertEqual(result['start'], int(offset)) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import MultiSearch [as 別名]
def run_searches(es, searches):
        """Ejecuta una lista de búsquedas ElasticsearchSearch.

        Para ejecutar las búsquedas, se obtiene un iterador de búsquedas
        elasticsearch_dsl.Search por cada elemento de 'searches'. Utilizando
        los iteradores, se construyen listas de elasticsearch_dsl.Search, que
        son luego ejecutadas utilizando '_run_multisearch'. Después, los
        resultados son devueltos a cada iterador, que pueden o no generar una
        nueva búsqueda elasticsearch_dsl.Search. El proceso se repite hasta que
        todos los iteradores hayan finalizado. Con todo este proceso se logra:

            1) Ejecutar cualquier tipo de búsquedas bajo una mismas interfaz.
            2) Ejecutar búsquedas que requieren distintas cantides de pasos
               bajo una misma interfaz.
            3) Utilizar la funcionalidad de MultiSearch para hacer la menor
               cantidad de consultas posible a Elasticsearch.

        Los resultados de cada búsqueda pueden ser accedidos vía el campo
        '.result' de cada una.

        Args:
            es (Elasticsearch): Conexión a Elasticsearch.
            searches (list): Lista de búsquedas ElasticsearchSearch o
                derivados. La lista puede ser de cualquier largo ya que sus
                contenidos son fraccionados por '_run_multisearch' para evitar
                consultas demasiado extensas a Elasticsearch.

        """
        iterators = [search.search_steps() for search in searches]
        iteration_data = []
        for iterator in iterators:
            search = utils.step_iterator(iterator)

            if search:
                iteration_data.append((iterator, search))

        while iteration_data:
            responses = _run_multisearch(es, [
                search for _, search in iteration_data
            ])

            iterators = (iterator for iterator, _ in iteration_data)
            iteration_data = []

            for iterator, response in zip(iterators, responses):
                search = utils.step_iterator(iterator, response)
                if search:
                    iteration_data.append((iterator, search))