﻿---
title: Connecting
description: This page contains the information you need to connect the Client with Elasticsearch. Elastic Cloud is the easiest way to get started with Elasticsearch...
url: https://www.elastic.co/elastic/docs-builder/docs/2848/reference/elasticsearch/clients/python/connecting
products:
  - Elasticsearch
  - Elasticsearch Client
  - Elasticsearch Python Client
---

# Connecting
This page contains the information you need to connect the Client with Elasticsearch.

## Connecting to Elastic Cloud

[Elastic Cloud](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/2848/deploy-manage/deploy/elastic-cloud/cloud-hosted) is the easiest way to get started with Elasticsearch. When connecting to Elastic Cloud with the Python Elasticsearch client you should always use the `cloud_id` parameter to connect. You can find this value within the "Manage Deployment" page after you’ve created a cluster (look in the top-left if you’re in Kibana).
We recommend using a Cloud ID whenever possible because your client will be automatically configured for optimal use with Elastic Cloud including HTTPS and HTTP compression.
<tab-set>
  <tab-item title="Standard Python">
    ```python
    import os
    from elasticsearch import Elasticsearch

    # Password for the 'elastic' user generated by Elasticsearch
    ELASTIC_PASSWORD = os.environ['ELASTIC_PASSWORD']

    # Found in the 'Manage Deployment' page
    CLOUD_ID = "deployment-name:dXMtZWFzdDQuZ2Nw..."

    # Create the client instance
    client = Elasticsearch(
        cloud_id=CLOUD_ID,
        basic_auth=("elastic", ELASTIC_PASSWORD)
    )

    # Successful response!
    client.info()
    # {'name': 'instance-0000000000', 'cluster_name': ...}
    ```
  </tab-item>

  <tab-item title="Async Python">
    ```python
    import asyncio
    import os
    from elasticsearch import AsyncElasticsearch

    # Password for the 'elastic' user generated by Elasticsearch
    ELASTIC_PASSWORD = os.environ['ELASTIC_PASSWORD']

    # Found in the 'Manage Deployment' page
    CLOUD_ID = "deployment-name:dXMtZWFzdDQuZ2Nw..."

    async def main():
        # Create the client instance
        client = AsyncElasticsearch(
            cloud_id=CLOUD_ID,
            basic_auth=("elastic", ELASTIC_PASSWORD)
        )

        # Successful response!
        await client.info()
        # {'name': 'instance-0000000000', 'cluster_name': ...}

    asyncio.run(main())
    ```
  </tab-item>
</tab-set>


## Connecting to a self-managed cluster

By default Elasticsearch will start with security features like authentication and TLS enabled. To connect to the Elasticsearch cluster you’ll need to configure the Python Elasticsearch client to use HTTPS with the generated CA certificate in order to make requests successfully.
If you’re getting started with Elasticsearch we recommend reading the documentation on [configuring](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/2848/deploy-manage/deploy/self-managed/configure-elasticsearch) and [starting Elasticsearch](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/2848/deploy-manage/maintenance/start-stop-services/start-stop-elasticsearch) to ensure your cluster is running as expected.
When you start Elasticsearch for the first time you’ll see a distinct block like the one below in the output from Elasticsearch (you may have to scroll up if it’s been a while):
```sh
----------------------------------------------------------------
-> Elasticsearch security features have been automatically configured!
-> Authentication is enabled and cluster connections are encrypted.

->  Password for the elastic user (reset with `bin/elasticsearch-reset-password -u elastic`):
  lhQpLELkjkrawaBoaz0Q

->  HTTP CA certificate SHA-256 fingerprint:
  a52dd93511e8c6045e21f16654b77c9ee0f34aea26d9f40320b531c474676228
...
----------------------------------------------------------------
```

Note down the `elastic` user password and HTTP CA fingerprint for the next sections. In the examples below they will be stored in the variables `ELASTIC_PASSWORD` and `CERT_FINGERPRINT` respectively.
Depending on the circumstances there are two options for verifying the HTTPS connection, either verifying with the CA certificate itself or via the HTTP CA certificate fingerprint.

### Verifying HTTPS with CA certificates

Using the `ca_certs` option is the default way the Python Elasticsearch client verifies an HTTPS connection.
The generated root CA certificate can be found in the `certs` directory in your Elasticsearch config location (`$ES_CONF_PATH/certs/http_ca.crt`). If you’re running Elasticsearch in Docker there is [additional documentation for retrieving the CA certificate](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/2848/deploy-manage/deploy/self-managed/install-elasticsearch-with-docker).
Once you have the `http_ca.crt` file somewhere accessible pass the path to the client via `ca_certs`:
<tab-set>
  <tab-item title="Standard Python">
    ```python
    import os
    from elasticsearch import Elasticsearch

    # Password for the 'elastic' user generated by Elasticsearch
    ELASTIC_PASSWORD = os.environ['ELASTIC_PASSWORD']

    # Create the client instance
    client = Elasticsearch(
        "https://localhost:9200",
        ca_certs="/path/to/http_ca.crt",
        basic_auth=("elastic", ELASTIC_PASSWORD)
    )

    # Successful response!
    client.info()
    # {'name': 'instance-0000000000', 'cluster_name': ...}
    ```
  </tab-item>

  <tab-item title="Async Python">
    ```python
    import asyncio
    import os
    from elasticsearch import AsyncElasticsearch

    # Password for the 'elastic' user generated by Elasticsearch
    ELASTIC_PASSWORD = os.environ['ELASTIC_PASSWORD']

    async def main():
        # Create the client instance
        client = AsyncElasticsearch(
            "https://localhost:9200",
            ca_certs="/path/to/http_ca.crt",
            basic_auth=("elastic", ELASTIC_PASSWORD)
        )

        # Successful response!
        await client.info()
        # {'name': 'instance-0000000000', 'cluster_name': ...}

    asyncio.run(main())
    ```
  </tab-item>
</tab-set>

<note>
  If you don’t specify `ca_certs` or `ssl_assert_fingerprint` then the [certifi package](https://certifiio.readthedocs.io) will be used for `ca_certs` by default if available.
</note>


### Verifying HTTPS with certificate fingerprints (Python 3.10 or later)

<note>
  Using this method **requires using Python 3.10 or later** and isn’t available when using the `aiohttp` HTTP client library so can’t be used with `AsyncElasticsearch`.
</note>

This method of verifying the HTTPS connection takes advantage of the certificate fingerprint value noted down earlier. Take this SHA256 fingerprint value and pass it to the Python Elasticsearch client via `ssl_assert_fingerprint`:
<tab-set>
  <tab-item title="Standard Python">
    ```python
    import os
    from elasticsearch import Elasticsearch

    # Fingerprint either from Elasticsearch startup or above script.
    # Colons and uppercase/lowercase don't matter when using
    # the 'ssl_assert_fingerprint' parameter
    CERT_FINGERPRINT = "A5:2D:D9:35:11:E8:C6:04:5E:21:F1:66:54:B7:7C:9E:E0:F3:4A:EA:26:D9:F4:03:20:B5:31:C4:74:67:62:28"

    # Password for the 'elastic' user generated by Elasticsearch
    ELASTIC_PASSWORD = os.environ['ELASTIC_PASSWORD']

    client = Elasticsearch(
        "https://localhost:9200",
        ssl_assert_fingerprint=CERT_FINGERPRINT,
        basic_auth=("elastic", ELASTIC_PASSWORD)
    )

    # Successful response!
    client.info()
    # {'name': 'instance-0000000000', 'cluster_name': ...}
    ```
  </tab-item>

  <tab-item title="Async Python">
    ```python
    import asyncio
    import os
    from elasticsearch import AsyncElasticsearch

    # Fingerprint either from Elasticsearch startup or above script.
    # Colons and uppercase/lowercase don't matter when using
    # the 'ssl_assert_fingerprint' parameter
    CERT_FINGERPRINT = "A5:2D:D9:35:11:E8:C6:04:5E:21:F1:66:54:B7:7C:9E:E0:F3:4A:EA:26:D9:F4:03:20:B5:31:C4:74:67:62:28"

    # Password for the 'elastic' user generated by Elasticsearch
    ELASTIC_PASSWORD = os.environ['ELASTIC_PASSWORD']

    async def main():
        client = AsyncElasticsearch(
            "https://localhost:9200",
            ssl_assert_fingerprint=CERT_FINGERPRINT,
            basic_auth=("elastic", ELASTIC_PASSWORD)
        )

        # Successful response!
        await client.info()
        # {'name': 'instance-0000000000', 'cluster_name': ...}

    asyncio.run(main())
    ```
  </tab-item>
</tab-set>

The certificate fingerprint can be calculated using `openssl x509` with the certificate file:
```sh
openssl x509 -fingerprint -sha256 -noout -in /path/to/http_ca.crt
```

If you don’t have access to the generated CA file from Elasticsearch you can use the following script to output the root CA fingerprint of the Elasticsearch instance with `openssl s_client`:
```sh
# Replace the values of 'localhost' and '9200' to the
# corresponding host and port values for the cluster.
openssl s_client -connect localhost:9200 -servername localhost -showcerts </dev/null 2>/dev/null \
  | openssl x509 -fingerprint -sha256 -noout -in /dev/stdin
```

The output of `openssl x509` will look something like this:
```sh
SHA256 Fingerprint=A5:2D:D9:35:11:E8:C6:04:5E:21:F1:66:54:B7:7C:9E:E0:F3:4A:EA:26:D9:F4:03:20:B5:31:C4:74:67:62:28
```


## Connecting without security enabled

<warning>
  Running Elasticsearch without security enabled is not recommended.
</warning>

If your cluster is configured with [security explicitly disabled](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/2848/reference/elasticsearch/configuration-reference/security-settings) then you can connect via HTTP:
<tab-set>
  <tab-item title="Standard Python">
    ```python
    from elasticsearch import Elasticsearch

    # Create the client instance
    client = Elasticsearch("http://localhost:9200")

    # Successful response!
    client.info()
    # {'name': 'instance-0000000000', 'cluster_name': ...}
    ```
  </tab-item>

  <tab-item title="Async Python">
    ```python
    import asyncio
    from elasticsearch import AsyncElasticsearch

    async def main():
        # Create the client instance
        client = AsyncElasticsearch("http://localhost:9200")

        # Successful response!
        await client.info()
        # {'name': 'instance-0000000000', 'cluster_name': ...}

    asyncio.run(main())
    ```
  </tab-item>
</tab-set>


## Connecting to multiple nodes

The Python Elasticsearch client supports sending API requests to multiple nodes in the cluster. This means that work will be more evenly spread across the cluster instead of hammering the same node over and over with requests. To configure the client with multiple nodes you can pass a list of URLs, each URL will be used as a separate node in the pool.
<tab-set>
  <tab-item title="Standard Python">
    ```python
    import os
    from elasticsearch import Elasticsearch

    # List of nodes to connect use with different hosts and ports.
    NODES = [
        "https://localhost:9200",
        "https://localhost:9201",
        "https://localhost:9202",
    ]

    # Password for the 'elastic' user generated by Elasticsearch
    ELASTIC_PASSWORD = os.environ['ELASTIC_PASSWORD']

    client = Elasticsearch(
        NODES,
        ca_certs="/path/to/http_ca.crt",
        basic_auth=("elastic", ELASTIC_PASSWORD)
    )
    ```
  </tab-item>

  <tab-item title="Async Python">
    ```python
    import os
    from elasticsearch import AsyncElasticsearch

    # List of nodes to connect use with different hosts and ports.
    NODES = [
        "https://localhost:9200",
        "https://localhost:9201",
        "https://localhost:9202",
    ]

    # Password for the 'elastic' user generated by Elasticsearch
    ELASTIC_PASSWORD = os.environ['ELASTIC_PASSWORD']

    client = AsyncElasticsearch(
        NODES,
        ca_certs="/path/to/http_ca.crt",
        basic_auth=("elastic", ELASTIC_PASSWORD)
    )
    ```
  </tab-item>
</tab-set>

By default nodes are selected using round-robin, but alternate node selection strategies can be configured with `node_selector_class` parameter.
<note>
  If your Elasticsearch cluster is behind a load balancer like when using Elastic Cloud you won’t need to configure multiple nodes. Instead use the load balancer host and port.
</note>


## Authentication

This section contains code snippets to show you how to connect to various Elasticsearch providers. All authentication methods are supported on the client constructor or via the per-request `.options()` method:
<tab-set>
  <tab-item title="Standard Python">
    ```python
    import os
    from elasticsearch import Elasticsearch

    # Authenticate from the constructor
    client = Elasticsearch(
        "https://localhost:9200",
        ca_certs="/path/to/http_ca.crt",
        basic_auth=("username", os.environ["ELASTIC_PASSWORD"])
    )

    # Authenticate via the .options() method:
    client.options(
        basic_auth=("username", os.environ["ELASTIC_PASSWORD"])
    ).indices.get(index="*")

    # You can persist the authenticated client to use
    # later or use for multiple API calls:
    auth_client = client.options(api_key=os.environ["ELASTIC_API_KEY"])
    for i in range(10):
        auth_client.index(
            index="example-index",
            document={"field": i}
        )
    ```
  </tab-item>

  <tab-item title="Async Python">
    ```python
    import asyncio
    import os
    from elasticsearch import AsyncElasticsearch

    async def main():
        # Authenticate from the constructor
        client = AsyncElasticsearch(
            "https://localhost:9200",
            ca_certs="/path/to/http_ca.crt",
            basic_auth=("username", os.environ["ELASTIC_PASSWORD"])
        )

        # Authenticate via the .options() method:
        await client.options(
            basic_auth=("username", os.environ["ELASTIC_PASSWORD"])
        ).indices.get(index="*")

        # You can persist the authenticated client to use
        # later or use for multiple API calls:
        auth_client = client.options(api_key=os.environ["ELASTIC_API_KEY"])
        for i in range(10):
            await auth_client.index(
                index="example-index",
                document={"field": i}
            )

    asyncio.run(main())
    ```
  </tab-item>
</tab-set>


### HTTP Basic authentication (Username and Password)

HTTP Basic authentication uses the `basic_auth` parameter by passing in a username and password within a tuple:
<tab-set>
  <tab-item title="Standard Python">
    ```python
    import os
    from elasticsearch import Elasticsearch

    # Adds the HTTP header 'Authorization: Basic <base64 username:password>'
    client = Elasticsearch(
        "https://localhost:9200",
        ca_certs="/path/to/http_ca.crt",
        basic_auth=("username", os.environ["ELASTIC_PASSWORD"])
    )
    ```
  </tab-item>

  <tab-item title="Async Python">
    ```python
    import os
    from elasticsearch import AsyncElasticsearch

    # Adds the HTTP header 'Authorization: Basic <base64 username:password>'
    client = AsyncElasticsearch(
        "https://localhost:9200",
        ca_certs="/path/to/http_ca.crt",
        basic_auth=("username", os.environ["ELASTIC_PASSWORD"])
    )
    ```
  </tab-item>
</tab-set>


### HTTP Bearer authentication

HTTP Bearer authentication uses the `bearer_auth` parameter by passing the token as a string. This authentication method is used by [Service Account Tokens](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-security-create-service-token) and [Bearer Tokens](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-security-get-token).
<tab-set>
  <tab-item title="Standard Python">
    ```python
    import os
    from elasticsearch import Elasticsearch

    # Adds the HTTP header 'Authorization: Bearer token-value'
    client = Elasticsearch(
        "https://localhost:9200",
        bearer_auth=os.environ["ELASTIC_TOKEN"]
    )
    ```
  </tab-item>

  <tab-item title="Async Python">
    ```python
    import os
    from elasticsearch import AsyncElasticsearch

    # Adds the HTTP header 'Authorization: Bearer token-value'
    client = AsyncElasticsearch(
        "https://localhost:9200",
        bearer_auth=os.environ["ELASTIC_TOKEN"]
    )
    ```
  </tab-item>
</tab-set>


### API Key authentication

You can configure the client to use Elasticsearch's API Key for connecting to your cluster. These can be generated through the [Elasticsearch Create API key API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-security-create-api-key) or [Kibana Stack Management](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/2848/deploy-manage/api-keys/elasticsearch-api-keys#create-api-key).
<tab-set>
  <tab-item title="Standard Python">
    ```python
    from elasticsearch import Elasticsearch

    # Adds the HTTP header 'Authorization: ApiKey <base64 api_key.id:api_key.api_key>'
    client = Elasticsearch(
        "https://localhost:9200",
        ca_certs="/path/to/http_ca.crt",
        api_key=os.environ["ELASTIC_API_KEY"],
    )
    ```
  </tab-item>

  <tab-item title="Async Python">
    ```python
    from elasticsearch import AsyncElasticsearch

    # Adds the HTTP header 'Authorization: ApiKey <base64 api_key.id:api_key.api_key>'
    client = AsyncElasticsearch(
        "https://localhost:9200",
        ca_certs="/path/to/http_ca.crt",
        api_key=os.environ["ELASTIC_API_KEY"],
    )
    ```
  </tab-item>
</tab-set>


## Using the Client in a Function-as-a-Service Environment

This section illustrates the best practices for leveraging the Elasticsearch client in a Function-as-a-Service (FaaS) environment.
The most influential optimization is to initialize the client outside of the function, the global scope.
This practice does not only improve performance but also enables background functionality as – for example – [sniffing](https://www.elastic.co/blog/elasticsearch-sniffing-best-practices-what-when-why-how). The following examples provide a skeleton for the best practices.
<important>
  The async client shouldn’t be used within Function-as-a-Service as a new event loop must be started for each invocation. Instead the synchronous `Elasticsearch` client is recommended.
</important>


### GCP Cloud Functions

```python
import os
from elasticsearch import Elasticsearch

# Client initialization
client = Elasticsearch(
    cloud_id="deployment-name:ABCD...",
    api_key=os.environ["ELASTIC_API_KEY"]
)

def main(request):
    # Use the client
    client.search(index=..., query={"match_all": {}})
```


### AWS Lambda

```python
import os
from elasticsearch import Elasticsearch

# Client initialization
client = Elasticsearch(
    cloud_id="deployment-name:ABCD...",
    api_key=os.environ["ELASTIC_API_KEY"]
)

def main(event, context):
    # Use the client
    client.search(index=..., query={"match_all": {}})
```


### Azure Functions

```python
import azure.functions as func
import os
from elasticsearch import Elasticsearch

# Client initialization
client = Elasticsearch(
    cloud_id="deployment-name:ABCD...",
    api_key=os.environ["ELASTIC_API_KEY"]
)

def main(request: func.HttpRequest) -> func.HttpResponse:
    # Use the client
    client.search(index=..., query={"match_all": {}})
```

Resources used to assess these recommendations:
- [GCP Cloud Functions: Tips & Tricks](https://cloud.google.com/functions/docs/bestpractices/tips#use_global_variables_to_reuse_objects_in_future_invocations)
- [Best practices for working with AWS Lambda functions](https://docs.aws.amazon.com/lambda/latest/dg/best-practices.html)
- [Azure Functions Python developer guide](https://docs.microsoft.com/en-us/azure/azure-functions/functions-reference-python?tabs=azurecli-linux%2Capplication-level#global-variables)
- [AWS Lambda: Comparing the effect of global scope](https://docs.aws.amazon.com/lambda/latest/operatorguide/global-scope.html)