Python elasticsearch.Elasticsearch方法代碼示例

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--es', type=str,
                        help='Root URL to Elasticsearch, ie http://localhost:9200 (defaults to envvar ELYZER_ES_URL or localhost:9200)',
                        action=EnvDefault,
                        required=True,
                        envvar='ELYZER_ES_URL',
                        default='http://localhost:9200')
    parser.add_argument('--index', type=str, action=EnvDefault,
                        required=True, envvar='ELYZER_INDEX',
                        help='Name of the index to find the analyzer, ie tweets (defaults to envvar ELYZER_INDEX)')
    parser.add_argument('--analyzer', type=str, action=EnvDefault, required=True,
                        envvar='ELYZER_ANALYZER',
                        help='Name of the custom analyzer, ie my_text_analyzer (defaults to envvar ELYZER_ANALYZER)')
    parser.add_argument('text', type=str,
                        help='Text to analyze, ie "mary had a little lamb"')
    return vars(parser.parse_args()) 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def main():
    try:
        args = parse_args()
        es = Elasticsearch(args['es'])
        stepWise(es=es,
                 text=args['text'],
                 indexName=args['index'],
                 analyzer=getAnalyzer(indexName=args['index'],
                                      analyzerName=args['analyzer'],
                                      es=es))

    except KeyboardInterrupt:
        print('Interrupted')
    except AnalyzerNotFound as e:
        print(e.error)
    except TransportError as e:
        print("Unexpected Elasticsearch Transport Exception:")
        print(e.error)
        print(e.info) 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def _add_prefix(self, *args, **kwargs):
        if args:
            index = args[0].strip()
        else:
            index = kwargs.get("index", "").strip()
        if index is None or index == "":
            raise NotImplementedError("Elasticsearch index not specified.")

        prefix = "%s_" % self.prefix.strip() if self.prefix and self.prefix.strip() != "" else ""
        ret = []
        for idx in index.split(","):
            ret.append("%s%s" % (prefix, idx))

        index = ",".join(ret)
        if args:
            return index
        else:
            return dict(kwargs, index=index) 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def __init__(self, client, params=None, **kwargs):
        '''
        API for performing easy bulk operations in Elasticsearch.

        :arg client: instance of official Elasticsearch Python client.
        :arg index: Default index for items which don't provide one
        :arg doc_type: Default document type for items which don't provide one
        :arg consistency: Explicit write consistency setting for the operation
        :arg refresh: Refresh the index after performing the operation
        :arg routing: Specific routing value
        :arg replication: Explicitly set the replication type (default: sync)
        :arg timeout: Explicit operation timeout

        .. Note:: all the arguments passed at the time create a new bulk
                  operation can be overridden when
                  :meth:`BulkOperation.execute`: is called.
        '''

        self._client = client
        self._params = params
        self._actions = [] 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def configure(parser: ArgumentParser) -> Callable:
    parser.add_argument(
        '-b', '--brokers', dest='brokers', required=True, type=str,
        help='Kafka brokers to bootstrap from as a comma separated list of <host>:<port>')
    parser.add_argument(
       '-c', '--es-clusters', dest='es_clusters', required=True, type=str,
       help='Elasticsearch servers to bootstrap from as a comma separated list of <host>:<port>')
    parser.add_argument(
        '-t', '--topic', dest='topics', required=True, type=str, nargs='+',
        help='Kafka topic(s) to read indexing requests from. Multiple topics may be provided.')
    parser.add_argument(
        '-g', '--group-id', dest='group_id', type=str, default='TODO',
        help='Kafka consumer group to join')
    parser.add_argument(
        '--prometheus-port', dest='prometheus_port', default=9170, type=int, required=False,
        help='Port to export prometheus metrics over.')
    return main 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def indices_map(clusters: List[Elasticsearch]) -> Mapping[str, Elasticsearch]:
    """Map from addressable index name to elasticsearch client that contains it

    Index names that exist on multiple clusters are treated as existing on
    no clusters. Essentially this only tracks indices that are unique to
    the cluster it lives on.
    """
    indices = cast(Dict[str, Optional[Elasticsearch]], dict())
    for elastic in clusters:
        for index_name, data in elastic.indices.get_alias().items():
            for name in [index_name] + list(data['aliases'].keys()):
                if name not in indices:
                    indices[name] = elastic
                # If an index name exists on multiple clusters we
                # pretend it doesn't exist on any of them.
                elif indices[name] != elastic:
                    indices[name] = None
    return {k: v for k, v in indices.items() if v is not None} 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def __init__(self, client: Elasticsearch):
        super().__init__(client)
        self.cache = CacheClient(client)
        self.store = FeatureStoreClient(client)
        self.feature = FeatureClient(client)
        self.feature_set = FeatureSetClient(client)
        self.model = ModelClient(client)


# Domain objects stored in the plugin. These offer a very simple interface for
# constructing requests and interpreting results of objects stored in the ltr
# plugin.
#
# Note that when encoding these objects to send to the plugin they are almost always
# wrapped in a single-value dict containing the type. So for example to add a feature
# to a feature store:
#
#  feature = StoredFeature('test', ['keywords'], 'mustache', {"match":{"title":"{{keywords}}"}})
#  response = ltr_client.feature.create(feature.name, {'feature': feature.to_dict()}) 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def __init__(self, config=None):

		if isinstance(config, dict) or isinstance(config, OrderedDict):
			self.config = config
		elif isinstance(config, str):
			try:
				self.config = json.load(open(config, "r"))
			except:
				self.config = {}

		self.username = self.config.get("username", "data_security_es_45")
		self.password = self.config.get("password", "Nb6121ca7ffe3")
		es_url = self.config.get("es_url", ['http://zsearch.alipay.com:9999'])

		if isinstance(es_url, list):
			self.es_url = es_url
		else:
			self.es_url = [es_url]

		self.es = Elasticsearch(self.es_url, http_auth=(self.username, self.password)) 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def insert_record_to_ssdeep_index(ssdeep_value, sha256):
    """
    Adds a record to the ssdeep index in elasticsearch
    :param ssdeep_value: The ssdeep hash value of the item
    :param sha256: The sha256 hash value of the item
    """
    chunksize, chunk, double_chunk = ssdeep_value.split(':')
    chunksize = int(chunksize)

    es = elasticsearch.Elasticsearch(['localhost:9200'])

    document = {'chunksize': chunksize, 'chunk': chunk, 'double_chunk': double_chunk, 'ssdeep': ssdeep_value,
                'sha256': sha256}

    es.index('ssdeep-index', 'record', document)
    es.indices.refresh('ssdeep-index') 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def __init__(
        self,
        host="localhost",
        port=443,
        path="",
        scheme="https",
        user=None,
        password=None,
        context=None,
        **kwargs,
    ):
        super().__init__(
            host=host,
            port=port,
            path=path,
            scheme=scheme,
            user=user,
            password=password,
            context=context,
            **kwargs,
        )
        if user and password:
            self.es = Elasticsearch(self.url, http_auth=(user, password), **self.kwargs)
        else:
            self.es = Elasticsearch(self.url, **self.kwargs) 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def __init__(
        self,
        host="localhost",
        port=9200,
        path="",
        scheme="http",
        user=None,
        password=None,
        context=None,
        **kwargs,
    ):
        super().__init__(
            host=host,
            port=port,
            path=path,
            scheme=scheme,
            user=user,
            password=password,
            context=context,
            **kwargs,
        )
        if user and password:
            self.es = Elasticsearch(self.url, http_auth=(user, password), **self.kwargs)
        else:
            self.es = Elasticsearch(self.url, **self.kwargs) 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def run(self):
        with self.input()['Emotion'].open('r') as fopen:
            emotions = json.load(fopen)
        es = Elasticsearch()
        for i in range(0, len(emotions), self.batch_size):
            batch = emotions[i : min(i + self.batch_size, len(emotions))]
            actions = [
                {
                    '_index': self.index,
                    '_type': 'text',
                    '_id': '%d-%s' % (i + j, self.summary),
                    '_source': batch[j],
                }
                for j in range(len(batch))
            ]
            helpers.bulk(es, actions) 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def pull_to_elastic(**kwargs):
    ti = kwargs['ti']
    sentiments = ti.xcom_pull(task_ids = 'push_sentiment', key = 'sentiment')
    es = Elasticsearch()
    for i in range(0, len(sentiments), batch_size):
        batch = sentiments[i : min(i + batch_size, len(sentiments))]
        actions = [
            {
                '_index': 'test_index',
                '_type': 'text',
                '_id': '%d-text' % (j + i),
                '_source': batch[j],
            }
            for j in range(len(batch))
        ]
        helpers.bulk(es, actions) 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def create_elasticsearch_connection(data_storage=None):

        db_conn = Elasticsearch([{'host': data_storage.get("connection_uri"), 'port': 9200}])
        return db_conn 

# 需要導入模塊: import elasticsearch [as 別名]
# 或者: from elasticsearch import Elasticsearch [as 別名]
def get_es_connection():
    """ Try to connect to es """
    hosts = build_es_connection_hosts()
    try:
        return Elasticsearch(hosts)
    except Exception as e:
        logger.warn('Could not contact ElasticSearch with provided configuration.')
        logger.warn(e)
        sys.exit(1)