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Today, I will talk to you about what MongoDB aggregation refers to. Many people may not know much about it. In order to make you understand better, the editor has summarized the following for you. I hope you can get something according to this article.

Introduction to MongoDB aggregation

Aggregation is the operation of processing data records and returning calculation results. MongoDB provides a rich set of aggregation operations to detect and perform calculations on datasets. Running the data set on the mongod instance simplifies the application code and limits the use of resources.

Like queries, aggregation operations in MongoDB use a collection of documents as input and return results in the form of one or more documents.

Polymerization mode

Polymerization pipeline

MongoDB 2.2 introduces a new aggregation framework modeled around data processing pipelines. The document goes into a multi-level pipeline and is converted into an aggregate result.

The most basic pipeline level provides filtering like query operations, and document conversion to modify the form of the output document.

Other pipe operations provide tools for grouping and sorting documents by specifying one or more fields, and for aggregating the contents of an array of documents. In addition, the pipe level can use operators such as averages or connection strings for tasks.

In MongoDB, pipes use local operations to provide efficient data aggregation, which is a better way of data aggregation.

Map-Reduce

MongoDB also provides map-reduce operations to implement aggregation. Typically, map-reduce operations have two phases: the map phase processes each document, outputting one or more objects for each input document, and the reduce phase combines the output of the map operation. Optionally, map-reduce can have a finalize level to make final changes to the results. Like other aggregation operations, map-reduce can specify a query condition to query the input document and to sort and restrict the results.

Map-reduce uses client-side JavaScript functions to implement map and reduce operations, as well as optional finalize operations. On the other hand, the client-side JavaScript provides more scalability than the aggregation pipeline, and generally, map-reduce is less efficient and more complex than the aggregation pipeline.

Note:

Starting with MongoDB 2.4, some mongo shell features and attributes are not accessible in map-reduce operations. MongoDB 2.4 also provides support for running multiple JavaScript operations at the same time. Before MongoDB 2.4, JavaScript code was executed in a single thread, which led to the parallelism problem of map-reduce.

Single-purpose aggregation operation

For a large number of ordinary single-purpose aggregation operations, MongoDB provides purpose-specific database commands. These common aggregation operations are to return the number of records that match the document, return the uncommon value of a field, and group data based on the value of a field. All of these operations aggregate documents from a single collection. These operations, which provide a single access to normal aggregation processing, lack the scalability and extensibility of the aggregation pipeline and map-reduce.

After reading the above, do you have any further understanding of what MongoDB aggregation refers to? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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