Traditional databases are not flexible enough to fully support modern applications and satisfy all business requirements. Companies use various databases to store their data, but traditional relational databases impose predefined schemas that limit application flexibility. MongoDB, a widely used NoSQL database, offers a robust engine and a variety of features to meet the needs of modern applications.
Python is a popular programming language for developing scalable and flexible applications. Connecting Python with MongoDB can be achieved through the MongoClient from the PyMongo library. Python PyMongo MongoClient allows developers to establish a connection between their Python applications and MongoDB, making it easier to access the features of the NoSQL database. In this article, you will get a brief introduction to MongoDB, Python, and the PyMongo library, as well as learn the steps to connect Python PyMongo MongoClient and manipulate data.
Introduction to MongoDB
MongoDB is an open-source NoSQL Database program that is a cross-platform document-oriented Database. It uses JSON-type format documents with optional schemas. NoSQL is an alternative to traditional Relational Databases that allow users to easily manipulate, access, and manage large datasets of distributed data.
Relational Databases store data in tabular form with a predefined data model which is not much flexible as per modern applications. MongoDB offers NoSQL Database services to simplify data management and companies use it for ad-hoc queries, indexing, load balancing, aggregation, server-side JavaScript execution, and other features.
MongoDB comes in community and commercial versions, where the community version is an open-source and Enterprise server offers some additional security features, an in-memory storage engine, administration and authentication features, and monitoring capabilities through Ops Manager.
Key Features of MongoDB
Some of the main features of MongoDB are listed below.
- Scalability: MongoDB supports horizontal scalability using the Sharding technique. Its job is to distribute data on multiple servers where a large amount of data is divided into smaller data chunks using the shard key.
- Indexing: Every field in documents in MongoDB is indexed with primary and secondary indices making the data searching job lot easier and faster.
- High Performance: MongoDB delivers high performance in querying data as compared to other Databases because of its scalability, indexing, and replication features.
Integrate MongoDB to BigQuery
Integrate MongoDB to Redshift
Integrate MongoDB Atlas to PostgreSQL
Integrate MongoDB Atlas to Snowflake
Introduction to Python
Python is an open-source, high-level, general-purpose programming language with dynamic semantics. Guido Von Rossum developed it in 1991 to simplify the code readability using the indentation. It is dynamically typed and garbage-collected. Python supports modules and packages which encourage program modularity and code reusability.
Python lets you work quickly and integrate systems more efficiently. It has a huge robust global community with many tech giants like Google, Facebook, Netflix having dependencies on it. Also, it supports the integration of all the major Databases such as MongoDB using Python PyMongo MongoClient.
Hevo Data is a no-code data pipeline platform that can migrate data from various sources, such as MongoDb, to your desired destination with only three steps. It not only loads the data onto the desired destination but also enriches and transforms the data without coding. Hevo offers:
- Minimal Learning: Hevo’s simple and interactive UI makes it extremely simple for new customers to work on and perform operations.
- Live Support: The Hevo team is available 24/7 to extend exceptional support to its customers through chat, E-Mail, and support calls.
- Transformational Capabilities: It provides pre- and post-load transformational capabilities to ensure your data is always analysis ready.
- Fault-tolerant Architecture: Its secure, end-to-end encrypted architecture ensures your data is always safe and prevents data loss.
- Transparent Pricing: Hevo offers transparent pricing with no hidden fees, allowing you to budget effectively while scaling your data integration needs.
Try Hevo today to experience seamless data transformation and migration.
Get Started with Hevo for Free
Working of Python PyMongo MongoClient
The Python PyMongo MongoClient class allows Developers to make connections to MongoDB in development with the help of client instances. The use of the PyMongo driver with MongoClient class makes it easier to code and connect to MongoDB easily and quickly.
MongoClient is a part of the PyMongo library and it can be imported to the Python code using the “from pymongo import MongoClient“. With the help of Python Pymongo, MongoClient made API calls became easier and faster that can allow you to access your data from MongoDB Database.
Prerequisites
- Python installed on your local machine.
- MongoDB installed on your local machine.
- A brief knowledge of Python language.
Steps to Connect to Python PyMongo MongoClient
Now that you have understood about MongoDB and Python. In this section, you will learn about setting up a connection to Python PyMongo MongoClient and some other configurations. The Python language will be used as a wrapper for MongoDB using the PyMongo library that makes it easier to access MongoDB using Python code. The following steps for connecting to Python PyMongo MongoClient are listed below.
Step 1: Installing the Libraries
- Open up the code editor of your choice that supports Python on your local machine.
- From the command-line tool, install the PyMongo library by typing the command given below.
python -m pip install pymongo
- In case, if you are getting the error “ModuleNotFoundError: No module named ‘pymongo’” then uninstall the PyMongo and open the command prompt to install the pymongo library with the following command given below.
pip uninstall pymongo
- Similarly, install the library dnspython for the mongodb srv URI using the following command given below.
python -m pip install dnspython
Step 2: Making Connection with Python PyMongo MongoClient
- In the code editor first import the libraries from the code given below.
import pymongo
- Open up the terminal and start the MongoDB instance to the default host and port by the following command given below.
mongod
- Make sure the MongoDB instance is running properly. Now let’s create a MongoClient using the pymnogo library to the running instance. The code is given below.
myclient = pymongo.MongoClient("mongodb://localhost:27017/")
myclient = pymongo.MongoClient('localhost', 27017)
Step 3: Getting a Database
- Now that you have created a connection with MongoDB. A single instance of MongoDB can support multiple independent Databases. You can list the existing Databases using the code given below.
myclient.list_database_names()
- You can access a particular Database using the following code given below.
local_db = myclient.database_test
- If your Database name has “-” then you can use dictionary-style access using the following code given below.
local_db = myclient['database_test']
Step 4: Creating a Collection in Python
- A Collection is a group of documents stored in MongoDB and can be equivalent to a table in the Relational Database.
- To access the MongoDB Collection is given below.
local_collection = myclient.test_collection
Step 5: Inserting Document in Python
- You can insert many documents in one go by using the insert_many() method from the PyMongo library. The sample code to insert 2 documents is given below.
item_1 = {
"_id" : "U1IT00001",
"item_name" : "Blender",
"max_discount" : "10%",
"batch_number" : "RR450020FRG",
"price" : 340,
"category" : "kitchen appliance"
}
item_2 = {
"_id" : "U1IT00002",
"item_name" : "Egg",
"category" : "food",
"quantity" : 12,
"price" : 36,
"item_description" : "brown country eggs"
}
collection_name.insert_many([item_1,item_2])
- You can also insert one document using the insert_one() method. The sample code is given below.
item_3 = {
"item_name" : "Bread",
"quantity" : 2,
"ingredients" : "all-purpose flour",
"expiry_date" : expiry
}
collection_name.insert_one(item_3)
Seamlessly Migrate Data from MongoDb Using Hevo
No credit card required
Benefits of Python PyMongo MongoClient
A few benefits of using Python PyMongo MongoClient to access MongoDB are listed below:
- Python PyMongo MongoClient allows Developers to build scalable and flexible applications using MongoDB NoSQL Database to maintain flexible schema.
- Python PyMongo MongoClient is suited for JSON object data that is used in modern applications and supported by MongoDB. It enables developers to save the data as a single document while treating them as a whole.
- Python PyMongo MongoClient makes it easier for businesses to deploy and manage enterprise applications without creating any expensive environment.
Conclusion
In this article, you learnt about connecting to Python PyMongo MongoClient and its different operations to handle data in MongoDB using Python PyMongo MongoClient. You also read about the working of Python PyMongo MongoClient and its simplicity when building a Python MongoDB application. Python PyMongo MongoClient acts as a wrapper to establish a connection between Python MongoDB.
Nowadays, companies need to analyze data from multiple sources, like MongoDB. To get a holistic view, the data needs to be loaded to a single destination, like a data warehouse. Hevo Data is a no-code data pipeline solution that can help migrate data from various sources, like MongoDB, to the desired destination using pre-built connectors. It fully automates the process of transforming and migrating data to the destination. Sign up for Hevo’s 14-day free trial and experience seamless data migration.
FAQs
Which is better PyMongo or MongoEngine?
PyMongo is better for low-level MongoDB operations, offering more control and flexibility, while MongoEngine provides an Object-Document Mapper (ODM) for a more Pythonic, high-level approach to interacting with MongoDB.
What is the use of MongoClient?
MongoClient is used in PyMongo to connect to a MongoDB server or cluster, allowing applications to interact with MongoDB databases and collections.
How to fetch data from MongoDB using PyMongo?
Use find()
method with a MongoDB collection. Example: collection.find({})
fetches all documents from the specified collection.
Aditya Jadon is a data science enthusiast with a passion for decoding the complexities of data. He leverages his B. Tech degree, expertise in software architecture, and strong technical writing skills to craft informative and engaging content. Aditya has authored over 100 articles on data science, demonstrating his deep understanding of the field and his commitment to sharing knowledge with others.