Transform Your Data with Aggregation
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Overview
In this guide, you can learn how to use PyMongo to perform aggregation operations.
Aggregation operations process data in your MongoDB collections and return computed results. The MongoDB Aggregation framework, which is part of the Query API, is modeled on the concept of data processing pipelines. Documents enter a pipeline that contains one or more stages, and this pipeline transforms the documents into an aggregated result.
An aggregation operation is similar to a car factory. A car factory has an assembly line, which contains assembly stations with specialized tools to do specific jobs, like drills and welders. Raw parts enter the factory, and then the assembly line transforms and assembles them into a finished product.
The aggregation pipeline is the assembly line, aggregation stages are the assembly stations, and operator expressions are the specialized tools.
Aggregation Versus Find Operations
You can use find operations to perform the following actions:
Select which documents to return
Select which fields to return
Sort the results
You can use aggregation operations to perform the following actions:
Perform find operations
Rename fields
Calculate fields
Summarize data
Group values
Limitations
Keep the following limitations in mind when using aggregation operations:
Returned documents must not violate the BSON document size limit of 16 megabytes.
Pipeline stages have a memory limit of 100 megabytes by default. You can exceed this limit by using the
allowDiskUse
keyword argument of theaggregate()
method.
Important
$graphLookup exception
The $graphLookup stage has a strict
memory limit of 100 megabytes and ignores the allowDiskUse
parameter.
Aggregation Example
Note
This example uses the sample_restaurants.restaurants
collection
from the Atlas sample datasets. To learn how to create a
free MongoDB Atlas cluster and load the sample datasets, see Get Started with PyMongo.
To perform an aggregation, pass a list of aggregation stages to the
collection.aggregate()
method.
The following code example produces a count of the number of bakeries in each borough of New York. To do so, it uses an aggregation pipeline with the following stages:
A $match stage to filter for documents whose
cuisine
field contains the value"Bakery"
.A $group stage to group the matching documents by the
borough
field, accumulating a count of documents for each distinct value.
# Define an aggregation pipeline with a match stage and a group stage pipeline = [ { "$match": { "cuisine": "Bakery" } }, { "$group": { "_id": "$borough", "count": { "$sum": 1 } } } ] # Execute the aggregation aggCursor = collection.aggregate(pipeline) # Print the aggregated results for document in aggCursor: print(document)
The preceding code example produces output similar to the following:
{'_id': 'Bronx', 'count': 71} {'_id': 'Brooklyn', 'count': 173} {'_id': 'Missing', 'count': 2} {'_id': 'Manhattan', 'count': 221} {'_id': 'Queens', 'count': 204} {'_id': 'Staten Island', 'count': 20}
Explain an Aggregation
To view information about how MongoDB executes your operation, you can instruct MongoDB to explain it. When MongoDB explains an operation, it returns execution plans and performance statistics. An execution plan is a potential way MongoDB can complete an operation. When you instruct MongoDB to explain an operation, it returns both the plan MongoDB executed and any rejected execution plans.
To explain an aggregation operation, you can use either the PyMongoExplain library or a database command. Select the corresponding tab below to see an example of each method.
Use pip to install the pymongoexplain
library, as shown in the
following example:
python3 -m pip install pymongoexplain
The following code example runs the preceding aggregation example and prints the explanation returned by MongoDB:
# Define an aggregation pipeline with a match stage and a group stage pipeline = [ { "$match": { "cuisine": "Bakery" } }, { "$group": { "_id": "$borough", "count": { "$sum": 1 } } } ] # Execute the operation and print the explanation result = ExplainableCollection(collection).aggregate(pipeline) print(result)
The following code example runs the preceding aggregation example and prints the explanation returned by MongoDB:
# Define an aggregation pipeline with a match stage and a group stage pipeline = [ { $match: { cuisine: "Bakery" } }, { $group: { _id: "$borough", count: { $sum: 1 } } } ] # Execute the operation and print the explanation result = database.command("aggregate", "collection", pipeline=pipeline, explain=True) print(result)
... 'command': {'aggregate': 'collection', 'pipeline': [{'$match': {'cuisine': 'Bakery'}}, {'$group': {'_id': '$borough', 'count': {'$sum': 1}}}], 'explain': True, ...
Tip
You can use Python's pprint
module to make explanation results easier to read:
import pprint ... pprint.pp(result)
Additional Information
MongoDB Server Manual
To view a full list of expression operators, see Aggregation Operators.
To learn about assembling an aggregation pipeline and view examples, see Aggregation Pipeline.
To learn more about creating pipeline stages, see Aggregation Stages.
To learn more about explaining MongoDB operations, see Explain Output and Query Plans.
Aggregation Tutorials
To view step-by-step explanations of common aggregation tasks, see Aggregation Tutorials.
API Documentation
For more information about executing aggregation operations with PyMongo, see the following API documentation: