How to store images in MongoDB (and why you should not)

Introduction

MongoDB, as a NoSQL database, offers flexibility in handling various data types. It is known for its scalability, ease of use, and dynamic schema design. One of the popular use cases in application development is storing images, but is it really the best option? This tutorial will walk you through the steps to store images in MongoDB while discussing the reasons why it might not be the most optimal solution.

To begin with, let’s address the ‘how’ before we delve into the ‘why not’.

Storing Images in MongoDB Database

Using GridFS

GridFS is a specification implemented in various MongoDB drivers that allows for storing and retrieving large files such as images. It works by breaking down a file into smaller chunks and storing each as a separate document, which bypasses MongoDB’s document size limit. Here’s a basic example of how to use GridFS in Python using the pymongo library:

from pymongo import MongoClient, gridfs
import os

def store_image(image_path):
    client = MongoClient('mongodb://localhost:27017/')
    db = client['mydatabase']
    fs = gridfs.GridFS(db)
    with open(image_path, 'rb') as image_file:
        fs.put(image_file, filename=os.path.basename(image_path))

store_image('/path/to/your/image.jpg')

This script connects to a MongoDB instance, selects a database, initializes GridFS, and uses the put method to store an image by reading it in binary mode. However, should you always store images directly in your database?

Using Binary Data (BSON)

You can also store images directly as binary data in a MongoDB document under the BSON type Binary. Here’s an example:

from pymongo import MongoClient
from bson.binary import Binary

def store_image_binary(image_path):
    client = MongoClient('mongodb://localhost:27017/')
    db = client['mydatabase']

    with open(image_path, 'rb') as image_file:
        image_data = Binary(image_file.read())

    db.images.insert_one({'filename': os.path.basename(image_path), 'image': image_data})

store_image_binary('/path/to/your/image.jpg')

This function reads an image as binary data and stores it in a document along with its filename. While this method is simple and straightforward, it’s not without its drawbacks, which we will explore later in this guide.

Serving Images Stored in MongoDB

Once you have stored your images in MongoDB, you might need to serve them through a web server. Below is an example using Flask, a micro web framework written in Python:

from flask import Flask, Response
from pymongo import MongoClient
import gridfs

app = Flask(__name__)
db = MongoClient('mongodb://localhost:27017/').mydatabase
fs = gridfs.GridFS(db)

@app.route('/image/')
def serve_image(filename):
    image = fs.find_one({'filename': filename})
    if not image:
        return 'Image not found', 404
    return Response(image.read(), mimetype='image/jpeg')

if __name__ == '__main__':
    app.run()

This example shows a Flask application that serves images from GridFS. When a GET request is made to the /image/ URL, the serve_image function looks up the image by filename and returns it, or a 404 if the image is not found. This illustrates how you could retrieve and display images when stored in MongoDB, though it’s not an exhaustive guide on how to serve media files effectively and securely.

Advanced Use Case: Storing and Processing Images

More advanced scenarios may involve not only storing images but also processing them, such as creating thumbnails or applying filters. Let’s see how this could work in a Python environment:

from PIL import Image
import io
from pymongo import MongoClient
from bson.binary import Binary

client = MongoClient('mongodb://localhost:27017/')
db = client['mydatabase']

def process_and_store_image(image_path):
    image = Image.open(image_path)
    thumbnail = image.copy()
    thumbnail.thumbnail((100, 100))
    thumb_io = io.BytesIO()
    thumbnail.save(thumb_io, format='JPEG')
    thumb_data = Binary(thumb_io.getvalue())
    db.images.insert_one({'filename': image_path, 'thumbnail': thumb_data})

process_and_store_image('/path/to/large/image.jpg')

This code uses the PIL library to create a thumbnail, which is then stored in MongoDB alongside the original image. The step involves in-memory bytes operations ensuring efficient handling of the manipulation before writing to the database.

Why NOT Store Images in MongoDB Database?

Storing images directly in MongoDB, or any database primarily designed for textual or structured data, is generally discouraged for several reasons:

1. Performance and Scalability: Large binary files like images can significantly increase the size of your database, which may lead to performance degradation over time. As the database grows, operations such as backup, recovery, and replication can become more cumbersome and time-consuming.
2. Storage Efficiency: Databases are optimized for transactional data access patterns, not for serving static content like images. Storing images in a database can lead to inefficient use of storage, as databases may not be as optimized for storing and retrieving large binary files compared to filesystems or dedicated object storage services.
3. Cost: Database storage typically costs more than file storage or object storage services. Storing large amounts of binary data like images can lead to increased costs, especially if the database is hosted in a cloud environment where storage costs can scale with usage.
4. Complexity: Serving images from a database can add unnecessary complexity to your application. It requires additional logic to retrieve and serve binary data, which might complicate your application’s architecture and increase the development and maintenance effort.
5. Bandwidth and Memory Usage: Retrieving images from a database can consume more bandwidth and memory than serving them from a dedicated static file server or content delivery network (CDN). This can impact the overall performance of your application, especially under high load.
6. Limited CDN and Caching Options: Serving images directly from a database limits your ability to leverage CDNs and caching strategies effectively. CDNs are optimized to serve static content from locations close to the user, reducing latency and improving load times. When images are stored in a database, you miss out on these optimizations.

Alternatives

• Object Storage Services: Services like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage are optimized for storing and serving large binary files. They provide high availability, durability, and scalability, along with features like CDN integration and fine-grained access controls.
• Filesystem: For smaller applications or where external services are not feasible, storing images on the filesystem and serving them through a web server might be a simpler and more efficient option.

When storing images, it’s common to store metadata about the image (e.g., filename, content type, size, and a reference URL or identifier for the object storage) in the database, while the actual binary data is stored in an object storage service or on the filesystem. This approach combines the strengths of both storage mechanisms, keeping the database responsive and scalable while efficiently managing large static files.

Conclusion

While storing images in MongoDB is feasible and there are tools designed for this purpose, such as GridFS, doing so comes with several disadvantages; bandwidth cost, potential performance issues with large data transactions, and difficulties in scaling. If your application requires extensive image data handling, consider alternative storage solutions such as file systems or object storage services that can efficiently handle media files.