Getting Started with Python and MongoDB

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You can get started with MongoDB and your favorite programming language by leveraging one of its drivers, many of which are maintained by MongoDB engineers, and others which are maintained by members of the community. MongoDB has a native Python driver, PyMongo, and a team of Driver engineers dedicated to making the driver fit to the Python community’s needs.

In this article, which is aimed at Python developers who are new to MongoDB, you will learn how to do the following:

• Create a free hosted MongoDB database using MongoDB Atlas
• Install PyMongo, the Python Driver
• Connect to MongoDB
• Explore MongoDB Collections and Documents
• Perform basic Create, Retrieve, Update and Delete (CRUD) operations using PyMongo

Let’s get started!

You can start working immediately with MongoDB by using a free MongoDB cluster via MongoDB Atlas. MongoDB Atlas is a hosted database service that allows you to choose your database size and get a connection string! If you are interested in using the free tier, follow the instructions in the Appendix section at the end of this article.

Install the Python Driver

For this article we will install the Python driver called “PyMongo”.

Although there are other drivers written by the community, PyMongo is the official Python driver for MongoDB. For detailed documentation of the driver check out the documentation here.

The easiest way to install the driver is through the pip package management system. Execute the following on a command line:

python -m pip install pymongo

Note: If you are using the Atlas M0 (Free Tier) cluster, you must use Python 2.7.9+ and use Python 3.4 or newer. You can check which version of Python and PyMongo you have installed by issuing python --version and pip list commands respectively.

For variations of driver installation check out the complete documentation:

Once PyMongo is installed we can write our first application that will return information about the MongoDB server. In your Python development environment or from a text editor enter the following code.

from pymongo import MongoClient
# pprint library is used to make the output look more pretty
from pprint import pprint
# connect to MongoDB, change the << MONGODB URL >> to reflect your own connection string
client = MongoClient(<<MONGODB URL>>)
db=client.admin
# Issue the serverStatus command and print the results
serverStatusResult=db.command("serverStatus")
pprint(serverStatusResult)

Replace <<MONGODB URL>> with your connection string to MongoDB. Save this file as mongodbtest.py and run it from the command line via python mongodbtest.py.

An example output appears as follows:

{u'asserts': {u'msg': 0,
              u'regular': 0,
              u'rollovers': 0,
              u'user': 0,
              u'warning': 0},
 u'connections': {u'available': 96, u'current': 4, u'totalCreated': 174L},
 u'extra_info': {u'note': u'fields vary by platform', u'page_faults': 0},
 u'host': u'cluster0-shard-00-00-6czvq.mongodb.net:27017',
 u'localTime': datetime.datetime(2017, 4, 4, 0, 18, 45, 616000),
.
.
.
}

Note that the u character comes from the Python output and it means that the strings are stored in Unicode. This example also uses the pprint library, which is not related to MongoDB but is used here only to make the output structured and visually appealing from a console.

In this example we are connecting to our MongoDB instance and issuing the db.serverStatus() command (reference). This command returns information about our MongoDB instance and is used in this example as a way to execute a command against MongoDB.

If your application runs successfully, you are ready to continue!

Exploring Collections and Documents

MongoDB stores data in documents. Documents are not like Microsoft Word or Adode PDF documents but rather JSON documents based on the JSON specification.

An example of a JSON document would be as follows:

Notice that documents are not just key/value pairs, but can include arrays and subdocuments. The data itself can be different data types like geospatial, decimal, and ISODate to name a few. Internally MongoDB stores a binary representation of JSON known as BSON. This allows MongoDB to provide data types like decimal that are not defined in the JSON specification. For more information on the BSON spec check out its site.

A collection in MongoDB is a container for documents. A database is the container for collections. This grouping is similar to relational databases and is pictured below:

There are many advantages to storing data in documents. While a deeper discussion is out of the scope of this article, some of the advantages like dynamic, flexible schema, and the ability to store arrays can be seen from our simple Python scripts. For more information on MongoDB document structure take a look at the online documentation.

Let’s take a look at how to perform basic CRUD operations on documents in MongoDB using PyMongo.

Performing Basic CRUD Operations Using PyMongo

To establish a connection to MongoDB with PyMongo you use the MongoClient class.

from pymongo import MongoClient
client = MongoClient('<<MongoDB URL>>’)

The <<MongoDB URL>> is a placeholder for the connection string to MongoDB. See the connection string documentation for detailed information on how to create your MongoDB connection string. If you are using Atlas for your MongoDB database, refer to the “testing your connection” section for more information on obtaining the connection string for MongoDB Atlas.

We can now create a database object referencing a new database, called business, as follows:

db = client.business

Once we create this object we can perform our CRUD operations. Since we want something useful to query let’s start by building a sample data generator application.

Generating Sample Data Code Example

Create a new file called createsamples.py using your development tool or command line text editor and copy the following code:

from pymongo import MongoClient
from random import randint
#Step 1: Connect to MongoDB - Note: Change connection string as needed
client = MongoClient(port=27017)
db=client.business
#Step 2: Create sample data
names = ['Kitchen','Animal','State', 'Tastey', 'Big','City','Fish', 'Pizza','Goat', 'Salty','Sandwich','Lazy', 'Fun']
company_type = ['LLC','Inc','Company','Corporation']
company_cuisine = ['Pizza', 'Bar Food', 'Fast Food', 'Italian', 'Mexican', 'American', 'Sushi Bar', 'Vegetarian']
for x in xrange(1, 501):
    business = {
        'name' : names[randint(0, (len(names)-1))] + ' ' + names[randint(0, (len(names)-1))]  + ' ' + company_type[randint(0, (len(company_type)-1))],
        'rating' : randint(1, 5),
        'cuisine' : company_cuisine[randint(0, (len(company_cuisine)-1))] 
    }
    #Step 3: Insert business object directly into MongoDB via isnert_one
    result=db.reviews.insert_one(business)
    #Step 4: Print to the console the ObjectID of the new document
    print('Created {0} of 100 as {1}'.format(x,result.inserted_id))
#Step 5: Tell us that you are done
print('finished creating 100 business reviews')

Be sure to change the MongoDB client connection URL to one that points to your MongoDB database instance. Once you run this application, 500 randomly named businesses with their corresponding ratings will be created in the MongoDB database called business. All of these businesses are created in a single collection called reviews. Notice that we do not have to explicitly create a database beforehand in order to use it. This is different from other databases that require statements like CREATE DATABASE to be performed first.

The command that inserts data into MongoDB in this example is the insert_one() function. A bit self-explanatory, insert_one will insert one document into MongoDB. The result set will return the single ObjectID that was created. This is one of a few methods that insert data. If you wanted to insert multiple documents in one call you can use the insert_many function.

In addition to an acknowledgement of the insertion, the result set for insert_many will include a list of the ObjectIDs that were created. For more information on insert_many see the documentation located here.

For details on the result set of insert_many check out this section of documentation as well.

We are now ready to explore querying and managing data in MongoDB using Python. To guide this exploration we will create another application that will manage our business reviews.

Exploring Business Review Data

Now that we have a good set of data in our database let’s query for some results using PyMongo.

In MongoDB the find_one command is used to query for a single document, much like select statements are used in relational databases. To use the find_one command in PyMongo we pass a Python dictionary that specifies the search criteria. For example, let’s find a single business with a review score of 5 by passing the dictionary, { ‘rating’ : 5 }.

fivestar = db.reviews.find_one({'rating': 5})
print(fivestar)

The result will contain data similar to the following:

{u'rating': 5,
 u'_id': ObjectId('58e65383ea0b650c867ef195'),
 u'name': u'Fish Salty Corporation', 
u'cuisine': u'Sushi Bar'}

Given that we created 500 sample pieces of data, there must be more than one business with a rating of 5. The find_one method is just one in a series of find statements that support querying MongoDB data. Another statement, called find, will return a cursor over all documents that match the search criteria. These cursors also support methods like count(), which returns the number of results in the query. To find the total count of businesses that are rated with a 5 we can use the count() method as follows:

fivestarcount = db.reviews.find({'rating': 5}).count()
print(fivestarcount)

Your results may vary since the data was randomly generated but in a test run the value of 103 was returned.

MongoDB can easily perform these straightforward queries. However, consider the scenario where you want to sum the occurrence of each rating across the entire data set. In MongoDB you could create 5 separate find queries, execute them and present the results, or you could simply issue a single query using the MongoDB aggregation pipeline as follows:

from pymongo import MongoClient
# Connect to the MongoDB, change the connection string per your MongoDB environment
client = MongoClient(port=27017)
# Set the db object to point to the business database
db=client.business
# Showcasing the count() method of find, count the total number of 5 ratings 
print('The number of 5 star reviews:')
fivestarcount = db.reviews.find({'rating': 5}).count()
print(fivestarcount)
# Not let's use the aggregation framework to sum the occurrence of each rating across the entire data set
print('\nThe sum of each rating occurance across all data grouped by rating ')
stargroup=db.reviews.aggregate(
# The Aggregation Pipeline is defined as an array of different operations
[
# The first stage in this pipe is to group data
{ '$group':
    { '_id': "$rating",
     "count" : 
                 { '$sum' :1 }
    }
},
# The second stage in this pipe is to sort the data
{"$sort":  { "_id":1}
}
# Close the array with the ] tag             
] )
# Print the result
for group in stargroup:
    print(group)

A deep dive into the aggregation framework is outside of the scope of this article, however, if you are interested in learning more about it check out this documentation.

Updating Data with PyMongo

Similar to insert_one and insert_many, there are functions to help you update your MongoDB data including update_one, update_many, and replace_one. The update_one method will update a single document based on a query that matches a document. For example, let’s assume that our business review application now has the ability for users to “like” a business. To illustrate updating a document with this new “likes” field, let’s first take a look at what an existing document looks like from our previous application’s insertion into MongoDB. Next, let’s update the document and requery the document and see the change.

from pymongo import MongoClient
#include pprint for readabillity of the 
from pprint import pprint

#change the MongoClient connection string to your MongoDB database instance
client = MongoClient(port=27020)
db=client.business

ASingleReview = db.reviews.find_one({})
print('A sample document:')
pprint(ASingleReview)

result = db.reviews.update_one({'_id' : ASingleReview.get('_id') }, {'$inc': {'likes': 1}})
print('Number of documents modified : ' + str(result.modified_count))

UpdatedDocument = db.reviews.find_one({'_id':ASingleReview.get('_id')})
print('The updated document:')
pprint(UpdatedDocument)

When running the sample code above you may see results similar to the following:

A sample document:
{'_id': ObjectId('58eba417ea0b6523b0fded4f'),
 'cuisine': 'Pizza',
 'name': 'Kitchen Goat Corporation',
 'rating': 1}

Number of documents modified : 1

The updated document:
{'_id': ObjectId('58eba417ea0b6523b0fded4f'),
 'cuisine': 'Pizza',
 'likes': 1,
 'name': 'Kitchen Goat Corporation',
 'rating': 1}

Notice that the original document did not have the “likes” field and an update allowed us to easily add the field to the document. This ability to dynamically add keys without the hassle of costly Alter_Table statements is the power of MongoDB’s flexible data model. It makes rapid application development a reality.

If you wanted to update all the fields of the document and keep the same ObjectID, you will want to use the replace_one function. For more details on replace_one check out the PyMongo documentation here.

The update functions also support an option called “upsert”. With upsert you can tell MongoDB to create a new document if the document you are trying to update does not exist.

Deleting Documents

Much like the other commands discussed so far, the delete_one and delete_many commands take a query that matches the document to delete as the first parameter. For example, if you wanted to delete all documents in the reviews collection where the category was “Bar Food”, you would issue the following:

result = db.restaurants.delete_many({“category”: “Bar Food“})

If you are deleting a large number of documents it may be more efficient to drop the collection instead of deleting all the documents.

Where to Go Next

There are lots of options when it comes to learning about MongoDB and Python. MongoDB University is a great place to start and learn about administration, development and other topics such as analytics with MongoDB. One course in particular is MongoDB for Developers (Python). This course covers the topics of this article in much more depth including a discussion on the MongoDB aggregation framework. Check it out here.

Appendix: Creating a free tier MongoDB Atlas database

MongoDB Atlas is a hosted database service that allows you to choose your database size and get a connection string! Follow the steps below to start using your free database.

Build your cluster for free

Follow the steps below to create a free MongoDB database:

1. Go to MongoDB Atlas.
2. Click the “Start Free” button.
3. Fill out the form to create an account. You will use this information to login to and manage your MongoDB.

Once you fill out the form, the website will create your account and you will be presented with the “Build Your New Cluster” pop up as shown:

To use the free tier, scroll down and select “M0”. When you do this the regions panel will be disabled. The free tier has some restrictions, with the ability to select a region being one of them. Your database size will be limited to 512MB of storage. Given that, when you are ready to use MongoDB for more than just some simple operations you can easily create another instance by choosing a size from the “Instance Size” list. Before you click “Confirm & Deploy” scroll down the page and notice the additional options shown here:

From the “Build Your New Cluster” pop up, you can see that there are other options available, including choosing a 3, 5 or 7 node replica set and up to a 12 shard cluster. Note that the free tier does not allow you to chose anything more than the 3 node cluster, but if you move into other sizes these options will become available.

At this point we are almost ready — the last thing to address is the admin username and password. You may also choose to have a random password generated for you by clicking the “Autogenerate Secure Password” button. Finally, click the “Confirm & Deploy” button to create your Atlas cluster.

Setting up your IP Whitelist

While Atlas is creating your database you will need to define which IPs are allowed access to your new database, since MongoDB Atlas does not allow access from the internet by default. This list of granted IP addresses is called the “IP Whitelist”. To add the IP of your machine to this list, click on the “Security” tab, then “IP Whitelist”, and then click the “+ ADD IP ADDRESS” button. This will pop up another dialog shown below.

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