![]() Pipeline stages do not need to produce the same number of documents they receive. grouping: You can also process multiple documents together to form a summarized result. ![]() transforming: The ability to change the structure of documents means you can remove or rename certain fields, or perhaps rename or group fields within an embedded document for legibility.sorting: You can reorder documents based on a chosen field.filtering: This resembles queries, where the list of documents is narrowed down through a set of criteria.Stages can perform operations on data such as: Likewise, data entering an aggregation pipeline must go through a number of stages, each of which are responsible for a specific operation. In this analogy, vegetables go through a set of stations, each responsible for a single action: washing, peeling, chopping, cooking, and plating. It may help to think of this process like vegetables going through an assembly line in a restaurant kitchen. Documents from a chosen collection enter the pipeline and go through each stage, where the output coming from one stage forms the input for the next one and the final result comes at the end of the pipeline. Each stage inspects and transforms the documents as they pass through the pipeline, feeding the transformed results into the subsequent stages for further processing. These are built as a sequential series of declarative data processing operations known as stages. MongoDB enables you to perform aggregation operations through the mechanism called aggregation pipelines. In a document-oriented database like MongoDB, though, the database will pull data from multiple documents in the same collection. In a relational database, the database management system will typically pull data from multiple rows in the same table to execute an aggregate function. In order to analyze your data to find patterns or other information about the data - rather than the data itself - you’ll often need to perform another kind of operation known as an aggregation.Īggregations group data from multiple sources and then process that data in some way to return a single result. However, queries only return the data that already exists in the database. When working with a database management system, any time you want to retrieve data from the database you must execute an operation known as a query. It will generally work with any MongoDB installation regardless of the operating system as long as authentication has been enabled. This tutorial concentrates on MongoDB itself, not the underlying operating system. Note: The linked tutorials on how to configure your server, install, and then secure MongoDB installation refer to Ubuntu 20.04. To learn how to use MongoDB queries, follow the tutorial How To Create Queries in MongoDB. Familiarity with querying MongoDB collections and filtering results.To secure MongoDB like this, follow our tutorial on How To Secure MongoDB on Ubuntu 20.04. Your server’s MongoDB instance secured by enabling authentication and creating an administrative user.To set this up, follow our tutorial on How to Install MongoDB on Ubuntu 20.04. This tutorial was validated using a server running Ubuntu 20.04, and you can prepare your server by following this initial server setup tutorial for Ubuntu 20.04. A server with a regular, non-root user with sudo privileges and a firewall configured with UFW.You’ll filter, sort, group, and transform documents, and then use all these features together to form a multi-stage processing pipeline. In this tutorial, you’ll learn by example how to use the most common features of the aggregation pipelines. MongoDB provides aggregation operations through aggregation pipelines - a series of operations that process data documents sequentially. These allow you to process data records in a variety of ways, such as grouping data, sorting data into a specific order, or restructuring returned documents, as well as filtering data as one might with a query. This means your options for performing meaningful data analysis with MongoDB’s query mechanism alone are limited.Īs with many other database systems, MongoDB allows you to perform a variety of aggregation operations. You can run queries on collections to retrieve a subset of documents matching given conditions, but MongoDB’s query mechanism doesn’t allow you to group or transform the returned data. ![]() MongoDB is a database management system that allows you to store large amounts of data in documents that are held within larger structures known as collections. The author selected the Open Internet/Free Speech Fund to receive a donation as part of the Write for DOnations program.
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