Python elasticsearch_dsl.Search方法代碼示例

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def get_highlights(self, text):
        # query top 10 guesses
        s = Search(index='qb_ir_instance_of')[0:10].query('multi_match', query=text,
                fields=['wiki_content', 'qb_content', 'source_content'])
        s = s.highlight('qb_content').highlight('wiki_content')
        results = list(s.execute())
        guess = results[0] # take the best answer
        _highlights = guess.meta.highlight 
    
        try:
            wiki_content = list(_highlights.wiki_content)
        except AttributeError:
            wiki_content = None
    
        try:
            qb_content = list(_highlights.qb_content)
        except AttributeError:
            qb_content = None

        highlights = {'wiki': wiki_content,
                      'qb': qb_content,
                      'guess': guess.page}
        return highlights 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def build_query(self, level, page, size, limit, keywords=None):
        s = Search(using=self.client, index=self.index_name)
        if level:
            ls = []
            for k in levels:
                if levels[k] <= levels[level]:
                    ls.append(k.lower())
            s = s.query("terms", levelname=ls)
        if page and size:
            s = s[(page - 1) * size:page * size]
        if keywords:
            s = s.query("match", message=keywords)
        if limit:
            now = datetime.now()
            start_time = now - timedelta(days=int(limit))
            s = s.query("range", timestamp={"gte": format_date(start_time), "lte": format_date(now)})
        s = s.sort({"timestamp": {"order": "desc", "unmapped_type": "date"}})
        return s 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def bollinger_band(index='cf_etf_hist_price', start_date='2018-12-26', end_date='2019-03-25', symbol='rfem'):
    ESLowLevelClientByConnection.get_instance()
    search = Search(index=index, using='high_level_client')[0:0]
    search.query = Q(Bool(must=[Range(date={'gte': '2018-12-26', 'lte': '2019-03-25'}), Term(symbol='rfem')]))
    aggs = A(DateHistogram(field='date', interval='1d', format='yyyy-MM-dd', min_doc_count=1))
    aggs_tp = A(ScriptedMetric(init_script='state.totals=[]',
                map_script='state.totals.add((doc.high.value+doc.low.value+doc.close.value)/3)',
                combine_script='double total=0; for (t in state.totals) {total += t} return total',
                reduce_script='double total=0; for (t in states) {total += t} return total'))
    aggs_moving_avg = A(MovingAvg(model='simple', window=20, buckets_path='tp.value'))
    aggs_bbu = A(BucketScript(buckets_path={'SMA':'20_trading_days_moving_avg'}, script='params.SMA + 0.5'))
    aggs_bbl = A(BucketScript(buckets_path={'SMA': '20_trading_days_moving_avg'}, script='params.SMA - 0.5'))
    search.aggs.bucket('Bollinger_band', aggs).metric('tp', aggs_tp).pipeline(
        '20_trading_days_moving_avg', aggs_moving_avg).pipeline('BBU', aggs_bbu).pipeline('BBL', aggs_bbl)
    response = search.execute()
    print(response.to_dict()) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def search(self):
        """
        Construct the Search object.
        """
        s = Search(doc_type=self.doc_types, using=es.client,
                   index=es.index_name)
        # don't return any fields, just the metadata
        s = s.fields([])
        # Sort from parameters
        s = s.sort(*self.sorts)
        # Paginate from parameters
        s = s[self.page_start:self.page_end]
        # Same construction as parent class
        # Allows to give the same signature as simple search
        # ie. Response(data) instead of Response(search, data)
        return s.response_class(partial(SearchResult, self)) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def get_scan_generic_format(client, now, last_fetch_timestamp=None):
    """Gets a scan object in generic format"""
    # if method is simple date - convert the date string to datetime
    es = client.es
    time_field = client.time_field
    fetch_index = client.fetch_index
    if not fetch_index:
        fetch_index = '_all'
    if time_field:
        query = QueryString(query=time_field + ':*')
        range_field = {
            time_field: {'gt': last_fetch_timestamp, 'lte': now}} if last_fetch_timestamp else {
            time_field: {'lte': now}}
        search = Search(using=es, index=fetch_index).filter({'range': range_field}).query(query)
    else:
        search = Search(using=es, index=fetch_index).query(QueryString(query=client.query))
    return search 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def get_scan_insight_format(client, now, last_fetch_timestamp=None, feed_type=None):
    """Gets a scan object in insight format"""
    time_field = client.time_field
    range_field = {
        time_field: {'gt': last_fetch_timestamp, 'lte': now}} if last_fetch_timestamp else {
        time_field: {'lte': now}}
    es = client.es
    query = QueryString(query=time_field + ":*")
    indices = client.fetch_index
    if feed_type == FEED_TYPE_CORTEX_MT:
        indices = '*-shared*'
        tenant_hash = demisto.getIndexHash()
        if tenant_hash:
            # all shared indexes minus this tenant shared
            indices += f',-*{tenant_hash}*-shared*'
    elif not indices:
        indices = '_all'
    search = Search(using=es, index=indices).filter({'range': range_field}).query(query)
    return search 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def es_get_accounts(main_chain='eos', user_id=None, start_vol=None, end_vol=None, from_idx=0, size=100,
                    order='totalEos', fields=None):
    index = '{}_account'.format(main_chain)

    if not fields:
        fields = ['id', 'timestamp', 'updateTimestamp', 'userId', 'totalEos', 'liquidEos', 'stackedEos',
                  'unstackingEos']

    if user_id:
        s = Search(using=es_client, index=index, doc_type='doc') \
            .filter('term', userId=user_id)
    elif start_vol and end_vol:
        range = {order: {'gte': start_vol, 'lt': end_vol}}
        s = Search(using=es_client, index=index, doc_type='doc') \
            .source(include=fields) \
            .filter('range', **range)
        s = s.sort({order: {"order": "desc"}})
    else:
        s = Search(using=es_client, index=index, doc_type='doc').source(include=fields)
        s = s.sort({order: {"order": "desc"}})

    resp = s[from_idx:from_idx + size].execute()

    return es_resp_to_payload(resp) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def es_get_statistic(security_item, the_date=None, start_date=None, end_date=None, level='day',
                     from_idx=0, size=500):
    security_item = to_security_item(security_item)

    index = get_es_statistic_index(security_type=security_item['type'], exchange=security_item['exchange'],
                                   level=level)
    # 單日的日k線直接按id獲取
    if level == 'day' and the_date:
        doc_id = '{}_{}'.format(security_item['id'], to_time_str(the_date))
        return es_client.get_source(index=index, doc_type='doc', id=doc_id)
    elif start_date and end_date:
        s = Search(using=es_client, index=index, doc_type='doc') \
            .filter('term', code=security_item['code']) \
            .filter('range', timestamp={'gte': start_date, 'lte': end_date}) \
            .sort({"timestamp": {"order": "asc"}})

        resp = s[from_idx:from_idx + size].execute()

        return es_resp_to_payload(resp) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def get_trade_history(size=10, from_date='2015-10-10', to_date='now', sort_by='-operation_id_num',
                      search_after=None, base="1.3.0", quote="1.3.121"):

    s = Search(using=es, index="bitshares-*")

    s = s.extra(size=size)
    if search_after and search_after != '':
        s = s.extra(search_after=search_after.split(','))

    q = Q()
    q = q & Q("match", operation_type=4)
    q = q & Q("match", operation_history__op_object__is_maker=True)

    q = q & Q("match", operation_history__op_object__fill_price__base__asset_id=base)
    q = q & Q("match", operation_history__op_object__fill_price__quote__asset_id=quote)

    range_query = Q("range", block_data__block_time={'gte': from_date, 'lte': to_date})
    s.query = q & range_query

    s = s.sort(*sort_by.split(','))
    response = s.execute()
    verify_es_response(response)

    return [hit.to_dict() for hit in response] 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def get_daily_volume(self, from_date, to_date):
        s = Search(using='operations', index="bitshares-*")
        s = s.extra(size=0)
        s = s.query('bool', filter = [
            Q('term', operation_type=4),
            Q('range', block_data__block_time={'gte': from_date, 'lte': to_date}),
            Q('term', operation_history__op_object__fill_price__quote__asset_id__keyword=config.CORE_ASSET_ID)
        ])

        a = A('date_histogram', field='block_data.block_time', interval='1d', format='yyyy-MM-dd') \
                .metric('volume', 'sum', field='operation_history.op_object.fill_price.quote.amount')
        s.aggs.bucket('volume_over_time', a)

        response = s.execute()

        daily_volumes = []
        for daily_volume in response.aggregations.volume_over_time.buckets:
            daily_volumes.append({ 'date': daily_volume.key_as_string, 'volume': daily_volume.volume.value })
        
        return daily_volumes 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def _get_notifications_by_email(self, email):
    search = elasticsearch_dsl.Search(using=self.client, index=self.config['ELASTICSEARCH_INDEX'],
      doc_type=self.config['ELASTICSEARCH_DOCTYPE'])

    query = self.create_query_for_email(search, email)

    # logger.debug("query:\n{!s}", pprint.pformat(query.to_dict()))

    try:
      response = query.execute()
    except elasticsearch.exceptions.ElasticsearchException:
      logger.exception("Exception caught in Elasticsearch query:\n  index: {!r}\n  doc_type: {!r}\n"
                       "  query: {!s}".format(self.config['ELASTICSEARCH_INDEX'],
                         self.config['ELASTICSEARCH_DOCTYPE'], pprint.pformat(query.to_dict())))

    # logger.debug("response:\n{!s}", pprint.pformat(response.to_dict()))

    return response.hits.hits 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def run(self):
        s = Search(using=self.esclient, index=self.esIndex)
        count =0
        while True:
            try:
                #response = s.execute(True)
                response =[]
                for hit in s.scan():
                    response.append(hit)
                if count % 10 == 0:
                    count = 0
                    logging.info("Fetched registered faces from Elastic Search. Number of records found: %d",len(response))
                facematch.update_known_faces(response)
                count = count +1
            except Exception as e:
                logging.exception("Failed to get registered faces from Elastic Search.")
            # Sleep for 60 secs
            time.sleep(60) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def index_search_document(self, *, index: str) -> None:
        """
        Create or replace search document in named index.

        Checks the local cache to see if the document has changed,
        and if not aborts the update, else pushes to ES, and then
        resets the local cache. Cache timeout is set as "cache_expiry"
        in the settings, and defaults to 60s.

        """
        cache_key = self.search_document_cache_key
        new_doc = self.as_search_document(index=index)
        cached_doc = cache.get(cache_key)
        if new_doc == cached_doc:
            logger.debug("Search document for %r is unchanged, ignoring update.", self)
            return
        cache.set(cache_key, new_doc, timeout=get_setting("cache_expiry", 60))
        get_client().index(
            index=index,
            doc_type=self.search_doc_type,
            body=new_doc,
            id=self.pk,  # type: ignore
        ) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def execute(
        cls,
        search: Search,
        search_terms: str = "",
        user: Optional[AbstractBaseUser] = None,
        reference: Optional[str] = "",
        save: bool = True,
    ) -> SearchQuery:
        """Create a new SearchQuery instance and execute a search against ES."""
        warnings.warn(
            "Deprecated - please use `execute_search` function instead.",
            DeprecationWarning,
        )
        return execute_search(
            search, search_terms=search_terms, user=user, reference=reference, save=save
        ) 

# 需要導入模塊: import elasticsearch_dsl [as 別名]
# 或者: from elasticsearch_dsl import Search [as 別名]
def __list_uniques(self, date_range, field_name):
        """Retrieve a list of unique values in a given field within a date range.

        :param date_range:
        :param field_name:
        :return: list  of unique values.
        """
        # Get project list
        s = Search(using=self._es_conn, index=self._es_index)
        s = s.filter('range', **date_range)
        # from:to parameters (=> from: 0, size: 0)
        s = s[0:0]
        s.aggs.bucket('uniques', 'terms', field=field_name, size=1000)
        response = s.execute()
        uniques_list = []
        for item in response.aggregations.uniques.buckets:
            uniques_list.append(item.key)

        return uniques_list