Python Elasticsearch Client =========================== Official low-level client for Elasticsearch. Its goal is to provide common ground for all Elasticsearch-related code in Python; because of this it tries to be opinion-free and very extendable. For a more high level client library with more limited scope, have a look at `elasticsearch-dsl`_ - it is a more pythonic library sitting on top of ``elasticsearch-py``. .. _elasticsearch-dsl: https://elasticsearch-dsl.readthedocs.io/ Compatibility ------------- The library is compatible with all Elasticsearch versions since ``0.90.x`` but you **have to use a matching major version**: For **Elasticsearch 7.0** and later, use the major version 7 (``7.x.y``) of the library. For **Elasticsearch 6.0** and later, use the major version 6 (``6.x.y``) of the library. For **Elasticsearch 5.0** and later, use the major version 5 (``5.x.y``) of the library. For **Elasticsearch 2.0** and later, use the major version 2 (``2.x.y``) of the library, and so on. The recommended way to set your requirements in your `setup.py` or `requirements.txt` is:: # Elasticsearch 7.x elasticsearch>=7.0.0,<8.0.0 # Elasticsearch 6.x elasticsearch>=6.0.0,<7.0.0 # Elasticsearch 5.x elasticsearch>=5.0.0,<6.0.0 # Elasticsearch 2.x elasticsearch>=2.0.0,<3.0.0 If you have a need to have multiple versions installed at the same time older versions are also released as ``elasticsearch2``, ``elasticsearch5`` and ``elasticsearch6``. Installation ------------ Install the ``elasticsearch`` package with `pip `_:: pip install elasticsearch Example Usage ------------- :: from datetime import datetime from elasticsearch import Elasticsearch es = Elasticsearch() doc = { 'author': 'kimchy', 'text': 'Elasticsearch: cool. bonsai cool.', 'timestamp': datetime.now(), } res = es.index(index="test-index", id=1, body=doc) print(res['result']) res = es.get(index="test-index", id=1) print(res['_source']) es.indices.refresh(index="test-index") res = es.search(index="test-index", body={"query": {"match_all": {}}}) print("Got %d Hits:" % res['hits']['total']['value']) for hit in res['hits']['hits']: print("%(timestamp)s %(author)s: %(text)s" % hit["_source"]) Features -------- This client was designed as very thin wrapper around Elasticsearch's REST API to allow for maximum flexibility. This means that there are no opinions in this client; it also means that some of the APIs are a little cumbersome to use from Python. We have created some :ref:`helpers` to help with this issue as well as a more high level library (`elasticsearch-dsl`_) on top of this one to provide a more convenient way of working with Elasticsearch. .. _elasticsearch-dsl: https://elasticsearch-dsl.readthedocs.io/ Persistent Connections ~~~~~~~~~~~~~~~~~~~~~~ ``elasticsearch-py`` uses persistent connections inside of individual connection pools (one per each configured or sniffed node). Out of the box you can choose between two ``http`` protocol implementations. See :ref:`transports` for more information. The transport layer will create an instance of the selected connection class per node and keep track of the health of individual nodes - if a node becomes unresponsive (throwing exceptions while connecting to it) it's put on a timeout by the :class:`~elasticsearch.ConnectionPool` class and only returned to the circulation after the timeout is over (or when no live nodes are left). By default nodes are randomized before being passed into the pool and round-robin strategy is used for load balancing. You can customize this behavior by passing parameters to the :ref:`connection_api` (all keyword arguments to the :class:`~elasticsearch.Elasticsearch` class will be passed through). If what you want to accomplish is not supported you should be able to create a subclass of the relevant component and pass it in as a parameter to be used instead of the default implementation. Automatic Retries ~~~~~~~~~~~~~~~~~ If a connection to a node fails due to connection issues (raises :class:`~elasticsearch.ConnectionError`) it is considered in faulty state. It will be placed on hold for ``dead_timeout`` seconds and the request will be retried on another node. If a connection fails multiple times in a row the timeout will get progressively larger to avoid hitting a node that's, by all indication, down. If no live connection is available, the connection that has the smallest timeout will be used. By default retries are not triggered by a timeout (:class:`~elasticsearch.ConnectionTimeout`), set ``retry_on_timeout`` to ``True`` to also retry on timeouts. .. _sniffing: Sniffing ~~~~~~~~ The client can be configured to inspect the cluster state to get a list of nodes upon startup, periodically and/or on failure. See :class:`~elasticsearch.Transport` parameters for details. Some example configurations:: from elasticsearch import Elasticsearch # by default we don't sniff, ever es = Elasticsearch() # you can specify to sniff on startup to inspect the cluster and load # balance across all nodes es = Elasticsearch(["seed1", "seed2"], sniff_on_start=True) # you can also sniff periodically and/or after failure: es = Elasticsearch(["seed1", "seed2"], sniff_on_start=True, sniff_on_connection_fail=True, sniffer_timeout=60) Thread safety ~~~~~~~~~~~~~ The client is thread safe and can be used in a multi threaded environment. Best practice is to create a single global instance of the client and use it throughout your application. If your application is long-running consider turning on :ref:`sniffing` to make sure the client is up to date on the cluster location. By default we allow ``urllib3`` to open up to 10 connections to each node, if your application calls for more parallelism, use the ``maxsize`` parameter to raise the limit:: # allow up to 25 connections to each node es = Elasticsearch(["host1", "host2"], maxsize=25) .. note:: Since we use persistent connections throughout the client it means that the client doesn't tolerate ``fork`` very well. If your application calls for multiple processes make sure you create a fresh client after call to ``fork``. Note that Python's ``multiprocessing`` module uses ``fork`` to create new processes on POSIX systems. SSL and Authentication ~~~~~~~~~~~~~~~~~~~~~~ You can configure the client to use ``SSL`` for connecting to your elasticsearch cluster, including certificate verification and HTTP auth:: from elasticsearch import Elasticsearch # you can use RFC-1738 to specify the url es = Elasticsearch(['https://user:secret@localhost:443']) # ... or specify common parameters as kwargs es = Elasticsearch( ['localhost', 'otherhost'], http_auth=('user', 'secret'), scheme="https", port=443, ) # SSL client authentication using client_cert and client_key from ssl import create_default_context context = create_default_context(cafile="path/to/cert.pem") es = Elasticsearch( ['localhost', 'otherhost'], http_auth=('user', 'secret'), scheme="https", port=443, ssl_context=context, ) .. warning:: ``elasticsearch-py`` doesn't ship with default set of root certificates. To have working SSL certificate validation you need to either specify your own as ``cafile`` or ``capath`` or ``cadata`` or install `certifi`_ which will be picked up automatically. See class :class:`~elasticsearch.Urllib3HttpConnection` for detailed description of the options. .. _certifi: http://certifiio.readthedocs.io/en/latest/ Logging ~~~~~~~ ``elasticsearch-py`` uses the standard `logging library`_ from python to define two loggers: ``elasticsearch`` and ``elasticsearch.trace``. ``elasticsearch`` is used by the client to log standard activity, depending on the log level. ``elasticsearch.trace`` can be used to log requests to the server in the form of ``curl`` commands using pretty-printed json that can then be executed from command line. Because it is designed to be shared (for example to demonstrate an issue) it also just uses ``localhost:9200`` as the address instead of the actual address of the host. If the trace logger has not been configured already it is set to `propagate=False` so it needs to be activated separately. .. _logging library: http://docs.python.org/3.3/library/logging.html Environment considerations -------------------------- When using the client there are several limitations of your environment that could come into play. When using an HTTP load balancer you cannot use the :ref:`sniffing` functionality - the cluster would supply the client with IP addresses to directly connect to the cluster, circumventing the load balancer. Depending on your configuration this might be something you don't want or break completely. In some environments (notably on Google App Engine) your HTTP requests might be restricted so that ``GET`` requests won't accept body. In that case use the ``send_get_body_as`` parameter of :class:`~elasticsearch.Transport` to send all bodies via post:: from elasticsearch import Elasticsearch es = Elasticsearch(send_get_body_as='POST') Compression ~~~~~~~~~~~ When using capacity-constrained networks (low throughput), it may be handy to enable compression. This is especially useful when doing bulk loads or inserting large documents. This will configure compression. :: from elasticsearch import Elasticsearch es = Elasticsearch(hosts, http_compress=True) Running on AWS with IAM ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you want to use this client with IAM based authentication on AWS you can use the `requests-aws4auth`_ package:: from elasticsearch import Elasticsearch, RequestsHttpConnection from requests_aws4auth import AWS4Auth host = 'YOURHOST.us-east-1.es.amazonaws.com' awsauth = AWS4Auth(YOUR_ACCESS_KEY, YOUR_SECRET_KEY, REGION, 'es') es = Elasticsearch( hosts=[{'host': host, 'port': 443}], http_auth=awsauth, use_ssl=True, verify_certs=True, connection_class=RequestsHttpConnection ) print(es.info()) .. _requests-aws4auth: https://pypi.python.org/pypi/requests-aws4auth Customization ------------- Custom serializers ~~~~~~~~~~~~~~~~~~ By default, `JSONSerializer`_ is used to encode all outgoing requests. However, you can implement your own custom serializer:: from elasticsearch.serializer import JSONSerializer class SetEncoder(JSONSerializer): def default(self, obj): if isinstance(obj, set): return list(obj) if isinstance(obj, Something): return 'CustomSomethingRepresentation' return JSONSerializer.default(self, obj) es = Elasticsearch(serializer=SetEncoder()) .. _JSONSerializer: https://github.com/elastic/elasticsearch-py/blob/master/elasticsearch/serializer.py#L24 Contents -------- .. toctree:: :maxdepth: 2 api xpack exceptions connection transports helpers Changelog License ------- Copyright 2018 Elasticsearch Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Indices and tables ------------------ * :ref:`genindex` * :ref:`modindex` * :ref:`search`