Elasticsearch bulk/batch indexing with python requests module

Elasticsearch bulk/batch indexing with python requests module

Indexing data in Elasticsearch using the Python requests module involves sending bulk/batch requests to the Elasticsearch API. Elasticsearch provides an efficient way to index large amounts of data using bulk API operations. Here's how you can perform bulk indexing using requests in Python:

Prerequisites

Make sure you have Elasticsearch installed and running locally or on a server accessible from your Python environment. Also, ensure you have the requests library installed (pip install requests).

Bulk Indexing with Python requests

1. Prepare Your Data

First, you need to prepare your data in a specific format required by Elasticsearch bulk API. Each action (index, delete, update) is represented by two JSON lines: one metadata line and one data line for each document.

Here is an example of how your data should look:

data_to_index = [ { "index": { "_index": "my_index", "_id": "1" }}, { "field1": "value1", "field2": "value2" }, { "index": { "_index": "my_index", "_id": "2" }}, { "field1": "value3", "field2": "value4" }, # Add more documents as needed ]

2. Construct the Bulk API Request

Use the requests library to construct a POST request to the Elasticsearch bulk API endpoint (/_bulk). Ensure the data is formatted correctly as newline-delimited JSON.

import requests import json # Elasticsearch server configuration elastic_host = 'localhost' elastic_port = 9200 # Elasticsearch bulk API endpoint bulk_api_url = f'http://{elastic_host}:{elastic_port}/_bulk' # Prepare the bulk data bulk_data = '\n'.join(json.dumps(doc) for doc in data_to_index) + '\n' # Send the bulk request headers = {'Content-Type': 'application/json'} response = requests.post(bulk_api_url, headers=headers, data=bulk_data) # Check the response if response.status_code == 200: print("Bulk indexing successful") else: print(f"Bulk indexing failed: {response.text}")

3. Handling Response

  • Response Handling: The response from Elasticsearch will indicate whether the bulk request was successful (200 OK) or if there were any errors. It's important to parse the response to check for any potential issues with indexing.

Notes

  • Ensure that your Elasticsearch index (my_index in the example) exists before attempting to bulk index data into it.
  • Monitor and handle any errors that might occur during bulk indexing, such as mapping conflicts or data format issues.

Summary

Bulk indexing is an efficient way to index large volumes of data into Elasticsearch. By preparing your data in the correct format and using Python's requests module to send bulk requests to the Elasticsearch API, you can optimize indexing performance and handle large datasets effectively. Adjust the code as per your specific Elasticsearch server configuration and data requirements.

Examples

  1. Python Elasticsearch bulk indexing example with requests module

    • Description: Demonstrates how to perform bulk indexing of documents into Elasticsearch using the requests module in Python.
    import requests import json # Elasticsearch endpoint es_url = 'http://localhost:9200/my_index/_bulk' # Sample data to index data = [ { "index": { "_index": "my_index", "_type": "_doc", "_id": "1" } }, { "title": "Document 1", "content": "This is the content of document 1." }, { "index": { "_index": "my_index", "_type": "_doc", "_id": "2" } }, { "title": "Document 2", "content": "This is the content of document 2." } ] # Convert data to newline-delimited JSON newline_data = '\n'.join(json.dumps(item) for item in data) + '\n' # Perform bulk indexing response = requests.post(es_url, headers={'Content-Type': 'application/json'}, data=newline_data) # Check response if response.status_code == 200: print("Documents indexed successfully.") else: print(f"Failed to index documents: {response.content}")

    This code sends a bulk request to Elasticsearch to index multiple documents using the requests module in Python.

  2. Python Elasticsearch bulk indexing from file using requests

    • Description: Illustrates how to read JSON data from a file and perform bulk indexing into Elasticsearch using the requests module.
    import requests import json # Elasticsearch endpoint es_url = 'http://localhost:9200/my_index/_bulk' # Read data from JSON file with open('data.json') as f: data = f.read().strip() # Perform bulk indexing response = requests.post(es_url, headers={'Content-Type': 'application/json'}, data=data) # Check response if response.status_code == 200: print("Bulk indexing successful.") else: print(f"Failed to index documents: {response.content}")

    This code reads JSON data from a file (data.json) and sends it as a bulk request to Elasticsearch for indexing using the requests module.

  3. Python Elasticsearch bulk indexing with error handling

    • Description: Shows how to handle errors and retry logic when performing bulk indexing into Elasticsearch using Python and requests.
    import requests import json import time def bulk_index_documents(documents): es_url = 'http://localhost:9200/my_index/_bulk' newline_data = '\n'.join(json.dumps(item) for item in documents) + '\n' retries = 3 for attempt in range(retries): response = requests.post(es_url, headers={'Content-Type': 'application/json'}, data=newline_data) if response.status_code == 200: print("Bulk indexing successful.") return else: print(f"Failed to index documents (attempt {attempt+1}): {response.content}") time.sleep(5) # Wait before retrying print("Failed after multiple attempts.") # Example usage documents = [ { "index": { "_index": "my_index", "_type": "_doc", "_id": "1" } }, { "title": "Document 1", "content": "This is the content of document 1." }, { "index": { "_index": "my_index", "_type": "_doc", "_id": "2" } }, { "title": "Document 2", "content": "This is the content of document 2." } ] bulk_index_documents(documents)

    This code implements error handling and retry logic for bulk indexing operations in Elasticsearch using Python and the requests module.

  4. Python Elasticsearch bulk indexing with authentication

    • Description: Shows how to perform bulk indexing into Elasticsearch with basic authentication using Python's requests module.
    import requests import json # Elasticsearch endpoint with authentication es_url = 'http://user:password@localhost:9200/my_index/_bulk' # Sample data to index data = [ { "index": { "_index": "my_index", "_type": "_doc", "_id": "1" } }, { "title": "Document 1", "content": "This is the content of document 1." }, { "index": { "_index": "my_index", "_type": "_doc", "_id": "2" } }, { "title": "Document 2", "content": "This is the content of document 2." } ] # Convert data to newline-delimited JSON newline_data = '\n'.join(json.dumps(item) for item in data) + '\n' # Perform bulk indexing with authentication response = requests.post(es_url, headers={'Content-Type': 'application/json'}, data=newline_data) # Check response if response.status_code == 200: print("Documents indexed successfully.") else: print(f"Failed to index documents: {response.content}")

    This code demonstrates how to include basic authentication credentials (user:password) when performing bulk indexing into Elasticsearch using Python and requests.

  5. Python Elasticsearch bulk indexing with retry on failure

    • Description: Illustrates how to retry failed bulk indexing requests in Elasticsearch using Python and requests.
    import requests import json import time def bulk_index_with_retry(documents, retries=3, delay=5): es_url = 'http://localhost:9200/my_index/_bulk' newline_data = '\n'.join(json.dumps(item) for item in documents) + '\n' for attempt in range(retries): response = requests.post(es_url, headers={'Content-Type': 'application/json'}, data=newline_data) if response.status_code == 200: print("Bulk indexing successful.") return else: print(f"Attempt {attempt + 1}: Failed to index documents. Retrying in {delay} seconds.") time.sleep(delay) print("Failed after multiple attempts.") # Example usage documents = [ { "index": { "_index": "my_index", "_type": "_doc", "_id": "1" } }, { "title": "Document 1", "content": "This is the content of document 1." }, { "index": { "_index": "my_index", "_type": "_doc", "_id": "2" } }, { "title": "Document 2", "content": "This is the content of document 2." } ] bulk_index_with_retry(documents)

    This code demonstrates a function (bulk_index_with_retry) that retries failed bulk indexing requests to Elasticsearch with a specified number of attempts and delay.

  6. Python Elasticsearch bulk indexing with batching

    • Description: Shows how to batch large datasets for bulk indexing into Elasticsearch using Python and requests.
    import requests import json # Elasticsearch endpoint es_url = 'http://localhost:9200/my_index/_bulk' def bulk_index_documents(documents, batch_size=100): for i in range(0, len(documents), batch_size): batch = documents[i:i+batch_size] newline_data = '\n'.join(json.dumps(item) for item in batch) + '\n' response = requests.post(es_url, headers={'Content-Type': 'application/json'}, data=newline_data) if response.status_code != 200: print(f"Failed to index batch {i}-{i+batch_size}: {response.content}") return False else: print(f"Indexed batch {i}-{i+batch_size} successfully.") return True # Example usage documents = [ { "index": { "_index": "my_index", "_type": "_doc", "_id": str(i) } } for i in range(1000) ] if bulk_index_documents(documents): print("All documents indexed successfully.") else: print("Bulk indexing failed.")

    This code demonstrates how to batch large datasets (batch_size=100) for bulk indexing into Elasticsearch using Python and requests.

  7. Python Elasticsearch bulk indexing with performance tuning

    • Description: Provides tips and techniques for optimizing performance when performing bulk indexing into Elasticsearch with Python and requests.
    import requests import json import time def bulk_index_documents(documents, batch_size=100, retries=3, delay=5): es_url = 'http://localhost:9200/my_index/_bulk' for i in range(0, len(documents), batch_size): batch = documents[i:i+batch_size] newline_data = '\n'.join(json.dumps(item) for item in batch) + '\n' for attempt in range(retries): response = requests.post(es_url, headers={'Content-Type': 'application/json'}, data=newline_data) if response.status_code == 200: print(f"Indexed batch {i}-{i+batch_size} successfully.") break else: print(f"Attempt {attempt + 1}: Failed to index batch {i}-{i+batch_size}. Retrying in {delay} seconds.") time.sleep(delay) else: print(f"Failed to index batch {i}-{i+batch_size} after {retries} attempts.") # Example usage documents = [ { "index": { "_index": "my_index", "_type": "_doc", "_id": str(i) } } for i in range(1000) ] bulk_index_documents(documents)

    This code includes performance tuning techniques such as batching (batch_size=100) and retry logic (retries=3) for bulk indexing operations in Elasticsearch using Python and requests.

  8. Python Elasticsearch bulk indexing with JSON serialization

    • Description: Demonstrates how to properly serialize Python objects to JSON format for bulk indexing into Elasticsearch using requests.
    import requests import json # Elasticsearch endpoint es_url = 'http://localhost:9200/my_index/_bulk' # Sample Python objects documents = [ { "index": { "_index": "my_index", "_type": "_doc", "_id": "1" } }, { "title": "Document 1", "content": "This is the content of document 1." }, { "index": { "_index": "my_index", "_type": "_doc", "_id": "2" } }, { "title": "Document 2", "content": "This is the content of document 2." } ] # Convert Python objects to JSON json_data = '\n'.join(json.dumps(item) for item in documents) + '\n' # Perform bulk indexing response = requests.post(es_url, headers={'Content-Type': 'application/json'}, data=json_data) # Check response if response.status_code == 200: print("Bulk indexing successful.") else: print(f"Failed to index documents: {response.content}")

    This code demonstrates how to serialize Python objects (documents) to JSON format and perform bulk indexing into Elasticsearch using requests.

  9. Python Elasticsearch bulk indexing with indexing statistics

    • Description: Shows how to collect indexing statistics (success/failure counts) when performing bulk indexing into Elasticsearch using Python and requests.
    import requests import json # Elasticsearch endpoint es_url = 'http://localhost:9200/my_index/_bulk' # Sample data to index data = [ { "index": { "_index": "my_index", "_type": "_doc", "_id": "1" } }, { "title": "Document 1", "content": "This is the content of document 1." }, { "index": { "_index": "my_index", "_type": "_doc", "_id": "2" } }, { "title": "Document 2", "content": "This is the content of document 2." } ] # Convert data to newline-delimited JSON newline_data = '\n'.join(json.dumps(item) for item in data) + '\n' # Perform bulk indexing response = requests.post(es_url, headers={'Content-Type': 'application/json'}, data=newline_data) # Parse response to get indexing statistics if response.status_code == 200: response_data = json.loads(response.content.decode('utf-8')) successes = response_data['items'].count('index') failures = len(data) - successes print(f"Indexed {successes} documents successfully. {failures} documents failed.") else: print(f"Failed to index documents: {response.content}")

    This code demonstrates how to parse the response from Elasticsearch after bulk indexing to collect indexing statistics (success/failure counts).

  10. Python Elasticsearch bulk indexing with Elasticsearch DSL

    • Description: Shows how to use Elasticsearch DSL (Elasticsearch-Py) for bulk indexing operations in Elasticsearch with Python.
    from elasticsearch import Elasticsearch from elasticsearch.helpers import bulk # Elasticsearch connection es = Elasticsearch(['localhost:9200']) # Sample data to index actions = [ { "_index": "my_index", "_type": "_doc", "_id": "1", "_source": { "title": "Document 1", "content": "This is the content of document 1." } }, { "_index": "my_index", "_type": "_doc", "_id": "2", "_source": { "title": "Document 2", "content": "This is the content of document 2." } } ] # Perform bulk indexing success, _ = bulk(es, actions) if success: print("Bulk indexing successful.") else: print("Failed to index documents.")

    This code showcases how to use Elasticsearch DSL (elasticsearch-py) to perform efficient bulk indexing operations (bulk) in Elasticsearch with Python.

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