


How to connect to AWS OpenSearch or Elasticsearch clusters using python
Connecting to an OpenSearch (ES) service running in AWS using Python is painful. Most examples I find online either don't work or are outdated, leaving me constantly fixing the same issues. To save time and frustration, here’s a collection of working code snippets, up-to-date as of December 2024.
- Connect using the opensearch-py library (OpenSearch ElasticSearch)
-
Connect using the elasticsearch library (ElasticSearch only)
- elasticsearch >= 8
- elasticsearch < 8
Connect using the opensearch-py library (OpenSearch ElasticSearch)
This is my preferred way of connecting to an ES instance managed by AWS. It works for both ElasticSearch and OpenSearch clusters, and the authentication can take advantage of AWS profiles.
Install opensearch-py and boto3 (for authentication):
pip install opensearch-py boto3
At the time of writing, this installs opensearch-py==2.8.0 and boto3==1.35.81.
Now, you can create a client using the following:
import boto3 from opensearchpy import ( AWSV4SignerAuth, OpenSearch, RequestsHttpConnection, ) es_host = "search-my-aws-esdomain-5k2baneoyj4vywjseocultv2au.eu-central-1.es.amazonaws.com" aws_access_key = "AKIAXCUEGTAF3CV7GYKA" aws_secret_key = "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" region = "eu-central-1" # Note: you can also use boto3.Session(profile_name="my-profile") or other ways session = boto3.Session( aws_access_key_id=aws_access_key, aws_secret_access_key=aws_secret_key, region_name=region, ) client = OpenSearch( hosts=[{"host": es_host, "port": 443}], http_auth=AWSV4SignerAuth(session.get_credentials(), region, "es"), connection_class=RequestsHttpConnection, use_ssl=True, )
Note that boto3.Session supports various ways of creating a session: using a profile, environment variables, and more. I will let you check it out!
Once you have it, check the connection using:
client.ping() # should return True client.info() # use this to get a proper error message if ping fails
To check indices:
# List all indices client.cat.indices() client.indices.get("*") # Check the existence of an indice client.indices.exists("my-index")
Connect using the elasticsearch library (ElasticSearch only)
? This only works for ElasticSearch clusters! Connecting to an OpenSearch cluster raises
UnsupportedProductError: The client noticed that the server is not Elasticsearch and we do not support this unknown product
elasticsearch >= 8
Most snippets are still referencing RequestsHttpConnection, a class that was removed in elasticsearch 8.X. If you were googling for the error cannot import name 'RequestsHttpConnection' from 'elasticsearch’, you are at the right place!
Install elasticsearch (this should install elastic-transport as well), and requests_aws4auth . The latter, based on requests, is required to handle authentication to AWS:
pip install elasticsearch requests-aws4auth
At the time of writing, this installs elastic-transport==8.15.1, elasticsearch==8.17.0 and requests-aws4auth==1.3.1.
Now, you can create a client using the following:
from elastic_transport import RequestsHttpNode from elasticsearch import Elasticsearch from requests_aws4auth import AWS4Auth es_endpoint = "search-my-aws-esdomain-5k2baneoyj4vywjseocultv2au.eu-central-1.es.amazonaws.com" aws_access_key = "AKIAXCUEGTAF3CV7GYKA" aws_secret_key = "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" region = "eu-central-1" es = Elasticsearch( f"https://{es_host}", http_auth=AWS4Auth( aws_access_key, aws_secret_key, region, "es", ), verify_certs=True, node_class=RequestsHttpNode, )
Once you have it, check the connection using:
es.ping() # should return True es.info() # use this to get a proper error message if ping fails
elasticsearch < 8
If you are still on an old version of elasticsearch:
pip install "elasticsearch<8" requests-aws4auth
Currently elasticsearch==7.17.12, requests-aws4auth==1.3.1.
Now, you can create a client using the following:
pip install opensearch-py boto3
Check the connection:
import boto3 from opensearchpy import ( AWSV4SignerAuth, OpenSearch, RequestsHttpConnection, ) es_host = "search-my-aws-esdomain-5k2baneoyj4vywjseocultv2au.eu-central-1.es.amazonaws.com" aws_access_key = "AKIAXCUEGTAF3CV7GYKA" aws_secret_key = "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" region = "eu-central-1" # Note: you can also use boto3.Session(profile_name="my-profile") or other ways session = boto3.Session( aws_access_key_id=aws_access_key, aws_secret_access_key=aws_secret_key, region_name=region, ) client = OpenSearch( hosts=[{"host": es_host, "port": 443}], http_auth=AWSV4SignerAuth(session.get_credentials(), region, "es"), connection_class=RequestsHttpConnection, use_ssl=True, )
The above is the detailed content of How to connect to AWS OpenSearch or Elasticsearch clusters using python. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.
