Whether you’re migrating databases, sharing data for analysis, or just backing things up, the MongoDB export to JSON is a common task. But how do you approach it? This guide dives into 3 easy methods, each tailored to your technical skills and preference.

We’ll walk you through using the built-in mongoexport tool, leveraging the Python pymongo driver, and even exploring user-friendly GUI options like Studio 3T and Robo 3T.

Follow our easy step-by-step guide to help you master the skill to efficiently learn the MongoDB export to JSON format using Mongo’s rich environment, various other tools & programming language drivers.

3 Easy Methods: MongoDB Export To JSON

The MongoDB export to JSON can be done in three easy ways –

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MongoDB Export to JSON Method 1: Using mongoexport

Mongoexport is a command-line utility that is used for MongoDB export to JSON. mongoexport runs directly on the MongoDB command line and not on the Mongo Shell & it can be used in the following way:

mongoexport --collection=<coll> [options]

mongoexport is part of MongoDB’s tools package, which is available through the MongoDB Download Center. The tools package must be installed well in advance to access the command line.

The user must specify the collection they want to export and an output file name. 

Mongoexport helps in MongoDB export to JSON format in two simple steps:

Step 1: Establishing A Connection To A MongoDB Instance

A MongoDB instance running on port “27017”, doesn’t necessarily require a host or port to be specified.

Example query: Here director represents the database from which movies collection is being exported in JSON format.

mongoexport --collection=movies --db=director --out=movies.json

Options that are specified as a part of the query used to export MongoDB data in JSON format:

–host: It is an optional parameter that helps mention the remote server MongoDB instances.

–username & –passwords: These are the optional parameter used for authentication.

–db: Gives information about the database.

–collection: Gives the collection name.

a) Specifying the collection that you want to export, can be done in two ways:

--collection=<collection> or -c=<collection>

b) Specifying the output file that you want to export to, can similarly be done in two ways:

--out=<output_name> or -o=<output_name>

c) Specifying the database you will be exporting from is represented as: 

--db=<database_name> or -d=<database_name>

Step 2: Specifying Host And/Or Port Of The MongoDB Instance

The port and/or hostname can be specified in multiple ways:

  1. Using the –uri connection string

This method requires specifying the database as a part of the string.

mongoexport --uri="mongodb://mongodb0.example.com:27017/conduct"  --collection=webinars  --out=webinars.json [additional options]

The --db command line option can’t be used directly with the --uri connection string.

2. Using –host option

The –host option allows using either of the following ways to specify the value:

<--host=<hostname><:port> or -h=<hostname><:port>

Example query:

mongoexport --host="mongodb0.example.com:27017" --collection=movies --db=director --out=movies.json [additional options]

3. Using –host & –port options

The --host & --port options allow using either of the following ways to specify the values:

<--host=<hostname><:port>, -h=<hostname><:port> 
--port=<port>

Example query:

mongoexport --host="mongodb0.example.com" --port=27017 --collection=movies--db=director--out=movies.json [additional options]

The output format of an exported document can vary with the version of JSON being used, therefore simply exporting MongoDB data isn’t enough, it’s important to understand how the output varies:

Output in extended JSON v2.0 (Relaxed Mode)

{"_id":1.0,"volume":2980000,"date":{"$date":"2019-08-05T16:18:29.559Z"}}

Output in extended JSON v2.0 (Canonical Mode)

To export MongoDB data in canonical mode, you must use the parameter –jsonFormat=canonical in your query.

{"_id":{"$numberDouble":"1.0"},"volume":{"$numberLong":"2980000"},"date":{"$date":{"$numberLong":"1565363188675"}}}

Limitations of Using Mongoexport to Export MongoDB to JSON

  • Limited Representation of Complex Structure: Using mongoexport can lead to loss of data relationships as nested documents and arrays of MongoDB may not be supported in JSON. 
  • Data Conversion Issues: mongoexport various data types, but JSON has fewer data types compared to it. So, using mongoexport may result in data loss or inaccuracy.

MongoDB Export to JSON Method 2: Using The Pymongo Driver

MongoDB Export to JSON: Pymongo Driver

The Pymongo driver is another way to export MongoDB data in the desired JSON format. The Pymongo driver helps interact with MongoDB using Python.

It is available starting from MongoDB v2.6 and supports all versions up to v4.2.

This method involves the following steps:

Step 1: Installing Pymongo

The recommended way to install a Pymongo driver is through pip, which usually gets installed along with the Python environment. You can use the following code:

C:Users/YourName/AppData/LocalPrograms/Python/Python36-32Scripts>python -m pip install pymongo

Once the installation is done, import the driver:

import pymongo

Once Pymongo is installed & imported, install the pandas library:

pip3 install pandas
import pandas

Step 2: Connecting With MongoClient

To export MongoDB data, it’s essential to connect & create a MongoClient on the running MongoDB instance:

from pymongo import MongoClient
Client_Mongo_new=MongoClient()

This code will connect to the default host and port, to give a specific host & port use the following code:

Client_Mongo_new=MongoClient(‘localhost’,27017)

Connect with the desired database & collection to export MongoDB data as follows:

db=Client_Mongo_new.database_name
c=db.collection_name // This is used to check whether the collection is there or not.

Step 3: Document Retrieval Using API Calls

To retrieve all documents, use the find() method and make sure not to pass any arguments for now. The output thus obtained will be in the form of pymongo.cursor.cursor object.

Once the API call is made, use the list() method to receive all documents. 

cursor=c.find()
docs=list(cursor)// retrieves exported MongoDB data from database.

It’s always a good practice to restrict the amount of MongoDB data being exported to make the process more efficient:

docs=docs[:25]// Retrieves only 25 documents initially.

Step 4: Using pandas.series() method

Export MongoDB data in a restricted amount as mentioned above and convert it to pandas.series.Series format as follows:

Series_obj=pandas.Series({“one”:”index”})
Series_obj.index=[“one”]
Print(“index”:series_obj.index) 

Step 5: Creating A Pandas Dataframe Object

Begin by creating an empty object as follows:

docus=pandas.Dataframe(colums=[])

Step 6: Enumeration & Appending The Series Object

To iterate through the documents efficiently, make use of the enumerate function in a python for-loop as follows:

for n,doc in enumerate(docs):
    doc[“_id”]=str(doc[“_id”])
    doc_id=doc[“_id”]
series_obj=pandas.Series(doc,name=doc_id)// Series Object Creation.
docs=docs.append(series_obj)

Step 7: Using .to_json() Function

The inherent methods of Pandas Series and DataFrame objects allow streamlined exporting of different file formats including to_html(), to_json(), and to_csv().

json_export = docs.to_json() # return JSON data
print ("nJSON data:", json_export)

These seven steps can be used for successful MongoDB export to JSON file format with the help of Pymongo driver.

Limitations of Using Pymongo to Export MongoDB to JSON

  • Scalability: Using Pymongo to export JSON can be slow, time-consuming, and resource-intensive for large datasets.
  • Basic Data Manipulation: You may have to use additional libraries or do scripting for complex data transformation and specific data formatting in JSON while using Pymongo. 

MongoDB Export to JSON Method 3: Using Tools Such as Studio 3T & Robo 3T

MongoDB Export to JSON: Studio 3T Logo.
Image Source: studio3T.com

Using Studio 3T

Studio 3T provides the Export Wizard facility, which helps in MongoDB export to JSON, query results, and even collections in various forms like JSON. The process begins by connecting with the desired MongoDB instance and then working on the collection and its documents using the Studio 3T wizard. 

Using Studio 3T, you can export MongoDB data in 2 steps:

Step 1: Using The Wizard Toolbar

Wizard Toolbar.

Click on the Export option and then use a right-click to select any database followed by a collection that you want to export.

MongoDB Export to JSON: Exporting Collections.

With Studio 3T, you can export data & simultaneously query in applications like Visual Query Builder, IntelliShell, SQL, etc.

The Export configuration allows to dynamically change the source of export i.e database, collection, etc.

MongoDB Export to JSON: Export Source

The progress of an export process can be tracked using the operations window, usually found in the bottom left corner of the wizard.

MongoDB Export to JSON: Reveal in Folder Option.

Step 2: Exporting Documents In JSON

Begin by launching the wizard, and then select the source from where you want to export the document.

MongoDB Export to JSON: Selecting Current Query.

Select the final export format as JSON.

MongoDB Export to JSON: JSON Export Format.

This will open a new tab called Export Overview along with a default tab by the name of Export Unit #1-JSON.

MongoDB Export to JSON: Query Overview.

There are five parts of the Export unit#1-JSON tab:

  • Source: Make sure the source is entered correctly.
  • Format Option: Choose JSON-mongo shell/ JSON-mongoexport.
  • Target: Choose between clipboard/file & make sure the file path is defined.
  • Others: Choose whether to export with commas between documents or export as a document array.
  • Output Preview: Displays the final JSON document.

Click on execute for a successful MongoDB export to JSON format using Studio3T.

Using Robo 3T

MongoDB Export to JSON: Robo 3T Logo.

Robo 3T (formerly Robomongo) doesn’t directly provide a feature to export MongoDB data in JSON format however, it can be implemented by tweaking the export query to provide data in a format similar to JSON.

This can be achieved in two ways:

1. Using tojson Function

var cursor = db.getCollection('foo').find({}, {});
while(cursor.hasNext()) {
print(tojson(cursor.next())) }

This code produces a JSON like array of documents.

2. Write A Query

db.getCollection('collection').find({}).toArray()

Once the query has executed, use a right-click, and select copy JSON option to export MongoDB data.

MongoDB Export to JSON: Copying JSON.

Let’s compare Studio 3T and Robo 3T for data presentation and modification.

FeatureRobo 3TStudio 3T
Data Display Table, Tree, and JSON ViewsYesYes
Search in JSON ViewYesYes
Search in Tree and Table ViewsYes
Batch updates of fieldsYes
Edit fields in placeYes
Source: Studio3t

Limitations of Using Studio 3T and Robo 3T to Export MongoDB to JSON

  • Unsupportive to Complex Structure: Due to limitations of format, nested documents and arrays of MongoDB cannot be represented properly in JSON. This may lead to loss of information or may require manual intervention. 
  • Scalability:  Using Studio 3T to export massive collections directly to JSON can be slow and resource-intensive, especially for complex data structures. 
  • Robo 3T only supports MongoDB 4.2 and not its latest versions. 

Use Cases for Mongo Export to JSON

  • Data Exchange and Sharing: Through MongoDB export JSON, you can do API integrations and configure specific files if required. You can also back up your data temporarily. 
  • Data Analysis: you can do small-scale data exploration, model training, and log analysis when you export the MongoDB database to JSON.

Conclusion

This article offers a step-by-step solution for MongoDB export to JSON format, considering multiple tools and techniques like Studio 3T. It provides a brief introduction of these methods and their operations to help the users understand them better and use them to export MongoDB data in a JSON file format in the most efficient way possible.

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We would love to hear from you about your experience with these methods! Reach out to us in the comments section below.

Divij Chawla
Former Marketing Operations and Analytics Manager, Hevo Data

Divij Chawla worked with Hevo in driving the Marketing Operations and Analytics team. He has a keen interest in data analysis, data, software architecture, and the creation of technical content related to the realm.

mm
Customer Experience Engineer, Hevo

Sarthak brings two years of expertise in JDBC, MongoDB, REST API, and AWS, playing a pivotal role in Hevo's triumph through adept problem-solving and superior issue management.

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