MongoDB Database Extractor

MongoDB Database Extractor

Extract documents from any MongoDB database, local or Atlas, into structured datasets. Connect with a standard URI or host and credentials, pick collections, and export every document as a flat JSON row to CSV, JSON, Excel, or XML.

Moving MongoDB data into analytics tools or spreadsheets usually means writing custom scripts, handling connection pooling, and dealing with BSON type conversions. This Actor connects to your MongoDB instance, reads the documents you specify, and delivers them in a clean, tabular format ready for downstream processing. No custom code, no drivers to install.

Who uses it	What they scrape MongoDB for
Data analysts	Pull production data into a CSV for ad-hoc analysis in Excel or Pandas.
Backend developers	Create regular backups of specific collections to cloud storage.
Data engineers	Feed MongoDB collections into an ETL pipeline or data warehouse.
QA engineers	Export test data snapshots to validate application behavior.

What it does

This Actor connects to a MongoDB database, extracts documents from selected collections, and returns each document as a flat JSON row.

• 🗂️ Selective collection extraction: specify exactly which collections to pull, or exclude noisy ones like logs and temp data.
• 🔐 Full authentication support: username, password, and a separate authentication database for secure deployments.
• 🔒 SSL encryption: toggle SSL on for Atlas and other cloud-hosted MongoDB instances.
• 📏 Document capping: set a maximum number of documents per collection to sample large datasets without a full dump.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

What you can do with MongoDB data

📊 Feed a BI dashboard.

A data analyst runs the Actor weekly on the 'orders' and 'customers' collections to refresh a Tableau extract with the latest sales data.

🗄️ Archive old collections.

A DevOps engineer excludes the active 'sessions' collection and dumps historical 'audit_logs' to JSON files stored in S3 for compliance.

🧪 Seed a staging environment.

A developer caps the 'users' collection at 500 documents to create a sanitized, lightweight dataset for local testing.

🔍 Investigate a data issue.

A support engineer extracts the single 'error_events' collection from the production database to search for patterns in a recent outage.

Why choose this scraper

	What you get
No driver setup	Connect with a standard MongoDB URI or host fields, no npm install required.
Handles BSON types	Dates, ObjectIDs, and binary data are serialized to JSON-safe strings automatically.
Cloud ready	Works with MongoDB Atlas, self-hosted clusters, and Docker instances with SSL support.
Flat row output	Every document becomes one row in a CSV, JSON, Excel, or XML dataset, no nested wrangling.

How it compares

No other Store actor targets MongoDB the same way, so the honest comparison is with the alternatives teams actually weigh.

	MongoDB Database Extractor	Build it in-house	By hand
Setup	Run it now, zero config	Days of engineering	None, but hours per pull
When MongoDB changes	Maintained for you	You fix it	You re-learn the page
Proxies, retries, anti-bot	Built in	Your problem	Browser only
Output	Fixed JSON schema, CSV/Excel export	Whatever you build	Copy-paste
Cost	Pay per result	Engineering time	Analyst hours

Configure the run

Provide a connection string or individual host, port, and credentials, then narrow the extraction by including or excluding specific collections and setting a per-collection document limit. The Input tab lists every parameter.

A first run with the defaults:

{
 "host": "51.222.13.168",
 "port": 27017,
 "database": "myappdb",
 "username": "admin",
 "password": "securepassword123",
 "authSource": "admin",
 "ssl": false,
 "collections": [],
 "excludeCollections": [],
 "maxDocumentsPerCollection": 100
}

Pricing

Pay-per-result: $0.016 per result collected. You pay only for the results written to your dataset.

Results collected	Approximate cost
100 results	$1.60
1,000 results	$16.00
10,000 results	$160.00

New Apify accounts start with $5 in free credit.

Free users

Free-plan runs return up to 10 results as a preview. Upgrade your Apify plan to collect up to 1,000,000 results per run.

Run it

1. Create a free Apify account with $5 in credit.
2. Open the MongoDB Database Extractor.
3. Set your inputs and any filters, then click Start.
4. Export the results as CSV, Excel, JSON, or XML from the Dataset tab.

Run it programmatically through the Apify API (run-sync-get-dataset-items) or the ApifyClient for JavaScript and Python.

Use with AI agents (MCP)

Give an AI agent live access to MongoDB through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

$claude mcp add --transport http apify "https://mcp.apify.com?tools=parseforge/mongodb-database-extractor"

Then prompt it in plain language to run the scraper and read back the results.

Troubleshooting

The run fails with an authentication error.

Check that the username, password, and authentication database fields are correct. If your user is defined in the 'admin' database, make sure 'Authentication Database' is set to 'admin'.

I get a connection timeout.

Verify that your MongoDB host is reachable from the internet. If it is behind a firewall, you will need to whitelist Apify's IP addresses or use a tunnel.

The dataset is empty even though the run succeeded.

Confirm the collection names in 'Collections to Extract' match exactly, including case. Also check that the collections are not listed in 'Collections to Exclude'.

SSL connection fails.

Ensure the 'Use SSL' checkbox is enabled. If your server uses a self-signed certificate, you may need to use a connection string with the tlsAllowInvalidCertificates parameter.

The output contains [Object] instead of real data.

Complex BSON types or deeply nested objects are serialized to JSON strings. If you see an unexpected string, the original field contained a sub-document or array that was flattened.

FAQ

Question	Answer
Can this connect to MongoDB Atlas?	Yes. Use the connection string field with your mongodb+srv URI and enable the SSL toggle. The Actor will connect to your Atlas cluster securely.
What happens to nested documents and arrays?	Nested objects and arrays are stringified into a JSON string within the parent document's row. This keeps the output flat and compatible with CSV and spreadsheet formats.
How do I extract only a few collections, not the whole database?	List the collection names in the 'Collections to Extract' field. The Actor will read only those and skip everything else.
Is my connection string stored securely?	Yes. All input fields, including the connection string and password, are encrypted at rest and only used during the run to establish the database connection.
Can I limit the number of documents pulled?	Yes. Set the 'Max Documents Per Collection' field to a number like 1000, and the Actor will stop reading each collection after reaching that limit.
Does this work with a replica set?	Yes. If your connection string includes a replica set name or multiple hosts, the Actor will use the standard MongoDB driver to connect to the replica set.
What output formats are supported?	You can export the extracted documents to CSV, JSON, Excel, or XML directly from the Apify dataset tab after the run finishes.
Can I run this on a schedule?	Yes. Apify's scheduler lets you run the Actor every hour, day, or week to keep your extracted data up to date automatically.
What if my database requires a different authentication source?	Use the 'Authentication Database' field. Set it to 'admin' or the name of the database where your user credentials are stored.
Does this Actor modify or delete any data?	No. It performs read-only find queries. It will never insert, update, or delete documents in your database.

Related actors

Browse the full ParseForge collection for more scrapers.

🆘 Need help? Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ Disclaimer. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by MongoDB, Inc. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.