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.
Tip Complete Aggregation TutorialsYou can find tutorials that provide detailed explanations of common aggregation tasks in the Complete Aggregation Pipeline Tutorials section of the Server manual. Select a tutorial, and then pick Python from the Select your language drop-down menu in the upper-right corner of the page.
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
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 the aggregate() method.
The $graphLookup stage has a strict memory limit of 100 megabytes and ignores the allowDiskUse parameter.
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 $group stage to group the matching documents by the borough field, accumulating a count of documents for each distinct value.
Select the Synchronous or Asynchronous tab to see the corresponding code:
pipeline = [ { "$match": { "cuisine": "Bakery" } }, { "$group": { "_id": "$borough", "count": { "$sum": 1 } } }]aggCursor = collection.aggregate(pipeline)for document in aggCursor: print(document)pipeline = [ { "$match": { "cuisine": "Bakery" } }, { "$group": { "_id": "$borough", "count": { "$sum": 1 } } }]aggCursor = await collection.aggregate(pipeline)async for document in aggCursor: print(document)The preceding code example produces output similar to the following:
{'_id': 'Bronx', 'count': 71}{'_id': 'Brooklyn', 'count': 173}{'_id': 'Missing', 'count': 2}{'_id': 'Manhattan', 'count': 221}{'_id': 'Queens', 'count': 204}{'_id': 'Staten Island', 'count': 20}To view information about how MongoDB executes your operation, you can instruct MongoDB to explain it. When MongoDB explains an operation, it returns execution plans and performance statistics. An execution plan is a potential way MongoDB can complete an operation. When you instruct MongoDB to explain an operation, it returns both the plan MongoDB executed and any rejected execution plans.
To explain an aggregation operation, you can use either the PyMongoExplain library or a database command. Select the corresponding tab below to see an example of each method.
Use pip to install the pymongoexplain library, as shown in the following example:
python3 -m pip install pymongoexplainThe following code example runs the preceding aggregation example and prints the explanation returned by MongoDB:
pipeline = [ { "$match": { "cuisine": "Bakery" } }, { "$group": { "_id": "$borough", "count": { "$sum": 1 } } }]result = ExplainableCollection(collection).aggregate(pipeline)print(result)...'winningPlan': {'queryPlan': {'stage': 'GROUP', 'planNodeId': 3, 'inputStage': {'stage': 'COLLSCAN', 'planNodeId': 1, 'filter': {'cuisine': {'$eq': 'Bakery'}}, 'direction': 'forward'}}, ...The following code example runs the preceding aggregation example and prints the explanation returned by MongoDB:
pipeline = [ { $match: { cuisine: "Bakery" } }, { $group: { _id: "$borough", count: { $sum: 1 } } }]result = database.command("aggregate", "collection", pipeline=pipeline, explain=True)print(result)...'command': {'aggregate': 'collection', 'pipeline': [{'$match': {'cuisine': 'Bakery'}}, {'$group': {'_id': '$borough', 'count': {'$sum': 1}}}], 'explain': True,...You can use Python's pprint module to make explanation results easier to read:
import pprint...pprint.pp(result)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.
For more information about executing aggregation operations with PyMongo, see the following API documentation:
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