Question: How does the `$facet` stage impact performance in MongoDB aggregation pipelines?


MongoDB's aggregation framework provides a powerful way to transform and analyze data directly within the database. The $facet stage, introduced in MongoDB version 3.4, allows for performing multiple aggregation operations in a single stage. This can be particularly useful for building complex queries that require multiple views of the same data, such as generating summaries, counts, and categorical breakdowns simultaneously. However, understanding how $facet impacts performance is crucial for optimizing your MongoDB queries.

Performance Considerations

The $facet stage allows you to execute several sub-pipelines on the same input documents concurrently. While this feature is powerful, it has several performance implications:

  1. Memory Usage: Each sub-pipeline in a $facet stage operates on the same set of input documents. This means that the memory used by the $facet stage can increase significantly with the number of sub-pipelines and the size of the input documents. MongoDB limits the amount of RAM for each aggregation pipeline stage to 100 MB by default. If a stage exceeds this limit, MongoDB will attempt to write data to temporary files on disk, which can severely degrade performance.

  2. CPU Utilization: Since $facet enables executing multiple pipelines in parallel, it can lead to increased CPU utilization. This is generally beneficial when the server has ample CPU resources. However, in resource-constrained environments, running complex facets could potentially lead to CPU bottlenecks, affecting overall server performance.

  3. Optimization Opportunities: MongoDB's query optimizer can optimize individual stages of an aggregation pipeline but optimizing across multiple sub-pipelines in a $facet stage is more challenging. This can sometimes result in less efficient execution plans compared to running each facet's sub-pipeline as a separate query.

Best Practices

To mitigate potential performance issues with $facet, consider the following best practices:

  • Limit the Number of Sub-pipelines: Only include necessary sub-pipelines within a $facet stage to minimize resource consumption.

  • Filter Early: Apply any filtering stages ($match) early in the pipeline before the $facet stage to reduce the volume of documents processed by each sub-pipeline.

  • Use Indexes Effectively: Ensure your queries leverage indexes effectively, especially in the stages preceding the $facet. This can significantly reduce the amount of data that needs to be processed.

  • Monitor Performance: Use MongoDB's monitoring tools to track the performance of your aggregation queries. Pay special attention to queries that use $facet to identify potential bottlenecks.


db.collection.aggregate([ { $match: { status: 'A' } }, // Pre-filter documents { $facet: { "categories": [{ $group: { _id: "$category", count: { $sum: 1 } } }], "averagePrice": [{ $group: { _id: null, avgPrice: { $avg: "$price" } } }], "topSellers": [{ $sort: { quantity: -1 } }, { $limit: 5 }] }} ]);

In this example, documents are first filtered by status, reducing the workload for the subsequent $facet stage. The $facet stage then concurrently processes three sub-pipelines to compute categories, average price, and top sellers.


While the $facet stage offers a flexible way to perform multiple aggregations simultaneously, it is important to be mindful of its potential impact on performance. By following best practices and carefully designing your aggregation pipelines, you can leverage the power of $facet without significantly degrading query performance.

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