You can deduce some illuminating insights from this query plan: To view the explain plan for an aggregation pipeline, you can execute commands such as the following: db.coll.explain().aggregate([ It allows you to understand the database engine's applied optimisations and detect further potential optimisations you can manually implement in the pipeline. The availability of an explain plan for aggregations enables you to bridge this gap. ![]() It may not be able to make some types of judgement calls about what pipeline changes to apply to make it run faster. The database engine doesn't always have the extra context that your brain has, relating to the actual business problem at hand. A database engine should never optimise a pipeline in such a way as to risk changing the functional behaviour and outcome of the pipeline. Nevertheless, there could be some optimisations that only you can make. The MongoDB database engine will do its best to apply its own aggregation pipeline optimisations at runtime. There are far more opportunities for performance bottlenecks to occur, requiring optimisation. However, with aggregations, an explain plan tends to be even more critical because considerably more complex logic can be assembled and run in the database. The same applies to aggregation pipelines and the ability to view an explain plan for the executed pipeline. ![]() An explain plan allows you to fully understand the performance implications of the query you have created. ![]() When using the MongoDB Query Language (MQL) to develop queries, it is important to view the explain plan for a query to determine if you've used the appropriate index and if you need to optimise other aspects of the query or the data model. Summarising Arrays For First, Last, Min, Max & Average Advanced Use Of Expressions For Array Processing Embrace Composability For Increased Productivity
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