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.
Install the elasticsearch package with pip:
$ python -m pip install elasticsearchIf your application uses async/await in Python you can install with
the async extra:
$ python -m pip install elasticsearch[async]Read more about how to use asyncio with this project.
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,<3.0.0If you have a need to have multiple versions installed at the same time older
versions are also released as elasticsearch2, elasticsearch5 and elasticsearch6.
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"])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-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.
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.
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)
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.
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.
Cloud ID is an easy way to configure your client to work
with your Elastic Cloud deployment. Combine the cloud_id
with either http_auth or api_key to authenticate
with your Elastic Cloud deployment.
Using cloud_id enables TLS verification and HTTP compression by default
and sets the port to 443 unless otherwise overwritten via the port parameter
or the port value encoded within cloud_id. Using Cloud ID also disables sniffing.
from elasticsearch import Elasticsearch
es = Elasticsearch(
cloud_id="cluster-1:dXMa5Fx...",
http_auth=("elastic", "<password>"),
)You can configure the client to use Elasticsearch's API Key for connecting to your cluster.
Please note this authentication method has been introduced with release of Elasticsearch 6.7.0.
from elasticsearch import Elasticsearch
# you can use the api key tuple es = Elasticsearch(
['node-1', 'node-2', 'node-3'], api_key=('id', 'api_key'),)
# or you pass the base 64 encoded token es = Elasticsearch(
['node-1', 'node-2', 'node-3'], api_key='base64encoded tuple',)
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.
Starting in elasticsearch-py v7.10.0 the library now ships with type hints
and supports basic static type analysis with tools like Mypy and Pyright.
If we write a script that has a type error like using request_timeout with
a str argument instead of float and then run Mypy on the script:
# script.py
from elasticsearch import Elasticsearch
es = Elasticsearch(...)
es.search(
index="test-index",
request_timeout="5" # type error!
)
# $ mypy script.py
# script.py:5: error: Argument "request_timeout" to "search" of "Elasticsearch" has
# incompatible type "str"; expected "Union[int, float, None]"
# Found 1 error in 1 file (checked 1 source file)For now many parameter types for API methods aren't specific to
a type (ie they are of type typing.Any) but in the future
they will be tightened for even better static type checking.
Type hints also allow tools like your IDE to check types and provide better auto-complete functionality.
Warning
The type hints for API methods like search don't match the function signature
that can be found in the source code. Type hints represent optimal usage of the
API methods. Using keyword arguments is highly recommended so all optional parameters
and body are keyword-only in type hints.
JetBrains PyCharm will use the warning Unexpected argument to denote that the
parameter may be keyword-only.
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.
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)Compression is enabled by default when connecting to Elastic Cloud via cloud_id.
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())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())For a more high level client library with more limited scope, have a look at
elasticsearch-dsl - a more pythonic library sitting on top of
elasticsearch-py.
elasticsearch-dsl provides a more convenient and idiomatic way to write and manipulate queries by mirroring the terminology and structure of Elasticsearch JSON DSL while exposing the whole range of the DSL from Python either directly using defined classes or a queryset-like expressions.
It also provides an optional persistence layer for working with documents as Python objects in an ORM-like fashion: defining mappings, retrieving and saving documents, wrapping the document data in user-defined classes.
.. toctree:: :maxdepth: 2 api xpack exceptions async connection transports helpers Changelog
Copyright 2020 Elasticsearch B.V
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.