📊 Monggregate

📋 Overview

Monggregate is a library that aims at simplifying usage of MongoDB aggregation pipelines in Python. It's a lightweight QueryBuilder for MongoDB aggregation pipelines based on pydantic and compatible with all mongodb drivers and ODMs.

✨ Features

• 🔄 Provides an Object Oriented Programming (OOP) interface to the aggregation pipeline.
• 🎯 Allows you to focus on your requirements rather than MongoDB syntax.
• 📚 Integrates all the MongoDB documentation and allows you to quickly refer to it without having to navigate to the website.
• 🔍 Enables autocompletion on the various MongoDB features.
• 🔗 Offers a pandas-style way to chain operations on data.
• 💻 Mimics the syntax of your favorite tools like pandas

📥 Installation

💡 The package is available on PyPI:

pip install monggregate

🚀 Usage

📘 The below examples reference the MongoDB sample_mflix database

🔰 Basic Pipeline usage

import os

from dotenv import load_dotenv 
import pymongo
from monggregate import Pipeline, S

# Creating connexion string securely
# You need to create a .env file with your password
load_dotenv(verbose=True)
MONGODB_URI = os.environ["MONGODB_URI"] 


# Connect to your MongoDB cluster:
client = pymongo.MongoClient(MONGODB_URI)

# Get a reference to the "sample_mflix" database:
db = client["sample_mflix"]

# Creating the pipeline
pipeline = Pipeline()

# The below pipeline will return the most recent movie with the title "A Star is Born"
pipeline.match(
    title="A Star Is Born"
).sort(
    by="year"
).limit(
    value=1
)

# Executing the pipeline
curosr = db["movies"].aggregate(pipeline.export())

# Printing the results
results = list(curosr)
print(results)

🌟 Advanced Usage, with MongoDB Operators

import os

from dotenv import load_dotenv 
import pymongo
from monggregate import Pipeline, S


# Creating connexion string securely
load_dotenv(verbose=True)
MONGODB_URI = os.environ["MONGODB_URI"]

# Connect to your MongoDB cluster:
client = pymongo.MongoClient(MONGODB_URI)

# Get a reference to the "sample_mflix" database:
db = client["sample_mflix"]


# Creating the pipeline
pipeline = Pipeline()
pipeline.match(
    year=S.type_("number") # Filtering out documents where the year field is not a number
).group(
    by="year",
    query = {
        "movie_count":S.sum(1), # Aggregating the movies per year
        "movie_titles":S.push("$title")
    }
).sort(
    by="_id",
    descending=True
).limit(10)

# Executing the pipeline
cursor = db["movies"].aggregate(pipeline.export())

# Printing the results
results = list(cursor)
print(results)

🔥 Even More Advanced Usage with Expressions

import os

from dotenv import load_dotenv 
import pymongo
from monggregate import Pipeline, S

# Creating connexion string securely
load_dotenv(verbose=True)
MONGODB_URI = os.environ["MONGODB_URI"] 

# Connect to your MongoDB cluster:
client = pymongo.MongoClient(MONGODB_URI)

# Get a reference to the "sample_mflix" database:
db = client["sample_mflix"]

# Using expressions
comments_count = S.size(S.comments)

# Creating the pipeline
pipeline = Pipeline()
pipeline.lookup(
    right="comments",
    right_on="movie_id",
    left_on="_id",
    name="comments"
).add_fields(
    comments_count=comments_count
).match(
    expression=comments_count>2
).limit(1)

# Executing the pipeline
cursor = db["movies"].aggregate(pipeline.export())

# Printing the results
results = list(cursor)
print(results)

🔍 Going Further

• 📚 Check out the full documentation for more examples.
• 📝 Check out this medium article.