Implementing Capped Collections in MongoDB with Pymongo

Implementing Capped Collections in MongoDB with Pymongo

Capped collections are a special type of collection within MongoDB that have a fixed size and support high throughput operations. They are designed to maintain insertion order, meaning documents are stored in the order in which they are inserted. Once the allocated space for a capped collection is filled, older documents are overwritten by new ones. This makes capped collections ideal for applications such as logging systems, where it’s necessary to keep only the most recent entries and the order of entries is significant.

One of the key features of capped collections is that they do not support the standard delete operation. Instead, documents automatically expire based on insertion order and space constraints. This behavior ensures that write operations remain constant and predictable, which is important for real-time data processing.

Another interesting characteristic of capped collections is that they can be tailed using a tailable cursor. This cursor type remains open after the client retrieves the last document and allows the client to wait and retrieve new documents as they are inserted. This feature is particularly useful for creating a stream of data that can be consumed by an application in real-time.

While capped collections come with limitations, such as the inability to remove specific documents or to update documents with larger sizes than their original, their performance benefits often outweigh these restrictions for use cases that fit their design.

Here is an example of how to create a capped collection in MongoDB using the mongo shell:

db.createCollection("log", { capped : true, size : 100000 })

This command will create a new capped collection named log with a maximum size of 100,000 bytes. Once the size limit is reached, MongoDB will start overwriting the oldest documents with new ones.

Setting Up MongoDB with Pymongo

Now that we have a basic understanding of what capped collections are and why they’re useful, let’s dive into how to set up MongoDB with Pymongo to start using capped collections in your Python applications. The first step is to ensure you have MongoDB installed and running on your system. You can download the latest version of MongoDB from their official website and follow the installation instructions for your operating system.

Once MongoDB is up and running, you will need to install Pymongo, which is the official Python driver for MongoDB. To install Pymongo, you can use pip, the Python package installer. Simply run the following command in your terminal:

pip install pymongo

With Pymongo installed, you can now connect to your MongoDB server using Python. Here’s how you can establish a connection:

from pymongo import MongoClient

# Replace 'localhost' with the IP address of your MongoDB server if needed
client = MongoClient('localhost', 27017)

# Access the database you wish to work with, or create it if it doesn't exist
db = client['mydatabase']

Once connected, you can begin working with collections within your database. To create a capped collection using Pymongo, you can use the create_collection method of the database object and specify the capped option as True, along with the desired size limit in bytes. Here’s an example:

# Create a capped collection named 'log' with a size limit of 100000 bytes
db.create_collection('log', capped=True, size=100000)

It is important to note that once a capped collection is created, its size cannot be altered. Therefore, it is important to plan and allocate the appropriate size for your use case. With your capped collection now set up, you can move on to implementing logic for inserting and managing data within it.

Implementing Capped Collections

To insert documents into your capped collection, you can use the insert_one or insert_many methods provided by Pymongo. Here’s an example of how to insert a single document:

# Insert a single document into the 'log' capped collection
log_entry = {"message": "User logged in", "timestamp": datetime.utcnow()}

If you have multiple documents to insert, you can use insert_many like this:

# Insert multiple documents into the 'log' capped collection
log_entries = [
    {"message": "User logged in", "timestamp": datetime.utcnow()},
    {"message": "User viewed page", "timestamp": datetime.utcnow()},
    {"message": "User logged out", "timestamp": datetime.utcnow()}

To retrieve documents from your capped collection, you can use the find method. If you want to take advantage of the tailable cursor feature, you can use the find method with the tailable and await_data options set to True. This will create a tailable cursor that waits for new documents to be inserted. Here’s how:

# Create a tailable cursor for the 'log' capped collection
cursor = db.log.find(cursor_type=pymongo.CursorType.TAILABLE_AWAIT)

# Loop through the cursor to retrieve new documents as they are inserted
while cursor.alive:
        doc =
    except StopIteration:
        time.sleep(1)  # wait for new documents to be inserted

This example shows a simple loop that prints out new log entries as they’re inserted into the capped collection. In a real-world application, you might process these entries in various ways, such as aggregating statistics or triggering alerts.

Keep in mind that while capped collections are powerful for certain use cases, they may not be appropriate for each scenarios. It is essential to consider the trade-offs and limitations before deciding to use capped collections in your application.

In the next section, we will explore how to manage capped collections, including how to view their properties and perform maintenance tasks such as compacting to reclaim wasted space.

Managing Capped Collections in MongoDB

Managing capped collections in MongoDB involves understanding how to view their properties and perform maintenance tasks. Since capped collections are fixed in size, it is important to monitor their usage and perform maintenance when necessary.

To view the properties of a capped collection, you can use the collstats command. This provides information about the collection’s size, the number of documents it contains, and more. Here’s an example of how to retrieve statistics for a capped collection using Pymongo:

collection_stats = db.command('collstats', 'log')

This command will print out a dictionary containing various statistics about the ‘log’ capped collection.

One maintenance task you might need to perform on a capped collection is compacting. Compacting is used to reclaim wasted space from deleted documents. While documents in a capped collection are automatically removed when space is needed, the space they occupied may not be efficiently reused. To compact a capped collection, you can use the compact command. However, it is important to note that compacting is an in-place operation and will block all other operations on the database while it runs. Here’s an example of how to compact a capped collection:

compact_result = db.command('compact', 'log')

This command will compact the ‘log’ capped collection and print out the result of the operation.

Another aspect of managing capped collections is handling document updates. As previously mentioned, documents in a capped collection cannot be updated if the update would cause the document to grow in size. However, updates that do not change the size of the document are allowed. Here’s an example of updating a document within a capped collection without changing its size:

# Assuming 'log_entry_id' is the ObjectId of the document we want to update
db.log.update_one({'_id': log_entry_id}, {'$set': {'message': 'User session updated'}})

In this example, we’re updating the ‘message’ field of a document with a new value that does not increase the size of the document.

It is also worth noting that capped collections do not support the drop operation. If you need to remove a capped collection, you must use the dropDatabase command to drop the entire database or rename the collection and then drop it.

Managing capped collections in MongoDB requires careful consideration of their fixed size and limitations. By monitoring collection statistics, performing maintenance tasks such as compacting, and understanding how to handle updates, you can effectively manage your capped collections and ensure they continue to provide high-performance data storage for your real-time applications.


No comments yet. Why don’t you start the discussion?

Leave a Reply

Your email address will not be published. Required fields are marked *