What is the MongoDB aggregation language?
MongoDB's aggregation pipeline language is somewhat of a paradox. It can appear daunting, yet it is straightforward. It can seem verbose, yet it is lean and to the point. It is Turing complete and able to solve any business problem. Conversely, it is a strongly opinionated domain-specific language (DSL); if you attempt to veer away from its core purpose of mass data manipulation, it will try its best to resist you.
Invariably, for beginners, the aggregation framework seems difficult to understand and comes with an initially steep learning curve that you must overcome to become productive. In some programming languages, you only need to master a small set of the language's aspects to be largely effective. With MongoDB aggregations, the initial effort you must invest is slightly greater. However, once mastered, users find it provides an elegant, natural, and efficient solution to breaking down a complex set of data manipulations into a series of simple, easy-to-understand steps.
The MongoDB aggregation pipeline language is focused on data-oriented problem-solving rather than business process problem-solving. It can be regarded as a functional programming language rather than a procedural programming language. Since an aggregation pipeline is an ordered series of statements, called stages, the entire output of one stage forms the entire input of the next stage, with no side effects. This functional nature is why many users regard the aggregation framework as having a steeper learning curve than many languages—not because it is inherently more difficult to understand but because most developers come from a procedural programming background and not a functional one. Most developers also have to learn how to think like a functional programmer to learn the aggregation framework.
The functional characteristics of the aggregation framework ultimately make it especially powerful for processing massive datasets. Users focus more on defining the what in terms of the required outcome and less on the how of specifying the exact logic to apply to achieve each transformation. You provide one specific and clearly advertised purpose for each stage in the pipeline. At runtime, the database engine can then understand the exact intent of each stage. For example, the database engine can obtain clear answers to the questions it asks, such as, "Is this stage for performing a filter or is this stage for grouping on some fields?" With this knowledge, the database engine has the opportunity to optimize the pipeline at runtime. Figure 1.2 shows an example of the database performing a pipeline optimization. It may decide to reorder stages to optimally use an index while ensuring that the output hasn't changed. Alternatively, it may choose to execute some steps in parallel against subsets of the data in different shards, reducing the response time while again ensuring the output hasn't changed.
Figure 1.2: Database performing a pipeline optimization
Last and least in terms of importance is the syntax. So far, MongoDB aggregations have been described here as a programming language. However, what syntax do you use to construct a MongoDB aggregation pipeline? The answer is it depends, and the answer is mostly irrelevant.
This book will highlight pipeline examples using MongoDB Shell and the JavaScript interpreter it runs in. The book will express aggregation pipelines using a JSON-based syntax. However, if you are using one of the many programming language drivers that MongoDB offers, you will be using that language to construct an aggregation pipeline, not JSON. To learn more about MongoDB drivers, see https://docs.mongodb.com/drivers/. An aggregation is specified as an array of objects, regardless of how the programming language may facilitate it. This programmatic rather than textual format has a couple of advantages compared to querying with a string. It has a low vulnerability to injection attacks, and it is highly composable.
