• Identify the indices in Elasticsearch that you want to migrate.
• Determine the structure of the MongoDB collections where the data will be stored.
• Map the fields from Elasticsearch documents to MongoDB documents.
• Decide on the batch size for data extraction and insertion to avoid memory issues.

• Install a programming language of your choice. Python is commonly used for such scripts, so we’ll use it as an example.
• Install Elasticsearch and MongoDB libraries for Python:

pip install elasticsearch pymongo

Here’s a sample Python script to extract data from an Elasticsearch index:

‍

from elasticsearch import Elasticsearch

# Connect to Elasticsearch

es = Elasticsearch("http://localhost:9200")

# Define the index name

index_name = 'your_index'

# Initialize the search query

search_query = {

    "query": {

        "match_all": {}

    }

}

# Initialize a scroll to keep the search context alive

page = es.search(index=index_name, scroll='2m', size=100, body=search_query)

scroll_id = page['_scroll_id']

# Extract data

documents = []

while True:

    # Get the next page of results

    page = es.scroll(scroll_id=scroll_id, scroll='2m')

    # Check if there are any more documents

    if not page['hits']['hits']:

        break

    # Add documents to our list

    documents.extend(page['hits']['hits'])

# Remember to clear the scroll when you're done

es.clear_scroll(scroll_id=scroll_id)

Depending on the structure of your Elasticsearch documents and your MongoDB schema, you may need to transform the data:

# Transform documents

transformed_documents = []

for doc in documents:

    transformed_doc = {

        'new_field_1': doc['_source']['old_field_1'],

        'new_field_2': doc['_source']['old_field_2'],

        # Add more fields as necessary

    }

    transformed_documents.append(transformed_doc)

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Now, let’s insert the transformed data into MongoDB:

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from pymongo import MongoClient

# Connect to MongoDB

client = MongoClient('mongodb://localhost:27017/')

db = client['your_database']

collection = db['your_collection']

# Insert documents into MongoDB

collection.insert_many(transformed_documents)

• After the migration, verify the integrity of the data in MongoDB.
• Write queries to count documents and check for the presence of specific fields or values.
• Compare counts and samples with the source data in Elasticsearch to ensure consistency.

• Implement error handling in your scripts to manage issues like connection failures, timeouts, and data inconsistencies.
• Log successes and failures of data batches to troubleshoot potential problems.

Once the migration is confirmed to be successful, perform any necessary clean-up:

• Remove temporary files or data used during the migration.
• Close any open connections to Elasticsearch and MongoDB.

Depending on the size of the data, you may need to optimize the scripts:

• Use bulk writes to MongoDB for better performance.
• Adjust the batch size based on system memory and network bandwidth.

• Once the script is tested and optimized, plan the migration in the production environment.
• Ensure you have backups and a rollback plan in case of any issues.