The Databases tab in MongoDB Compass has a Create Database button. In MongoDB Compass, you create a database and add its first collection at the same time: Click Create Database to open the dialog Enter the name of the database and its first collection. Metabase backend on MongoDB. To install CPAN::Testers::Metabase::MongoDB, copy and paste the appropriate command in to your terminal. But with Metabase’s SQL interface, designers can write their own custom queries and share the results with their teams. And even beginners can create their own shareable dashboards. When you add the Panoply Metabase integration, the entire ETL process is managed securely and automatically. ETL is the flow that powers any data operations. Metabase maintains its own information about the various tables and fields in each database that is added to aid in querying. By default, Metabase performs this lightweight sync hourly to look for changes to the database such as new tables or fields. Metabase does not copy any data from your database.
Metabase::Archive::MongoDB - Metabase storage using MongoDB
version 1.000
This is an implementation of the Metabase::Archive role using MongoDB.
See Metabase::Backend::MongoDB for constructor attributes. See Metabase::Archive and Metabase::Librarian for details on usage.
David Golden <dagolden@cpan.org>
This software is Copyright (c) 2011 by David Golden.
This is free software, licensed under:
To install Metabase::Archive::MongoDB, copy and paste the appropriate command in to your terminal.
For more information on module installation, please visit the detailed CPAN module installation guide.
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Aggregation operations process data records and return computedresults. Aggregation operations group values from multiple documentstogether, and can perform a variety of operations on the grouped datato return a single result. MongoDB provides three ways to performaggregation: the aggregation pipeline, the map-reduce function, and single purpose aggregation methods.
Aggregation Pipeline¶
MongoDB's aggregation framework is modeled on the concept of dataprocessing pipelines. Documents enter a multi-stage pipeline thattransforms the documents into an aggregated result. For example:
First Stage: The $match stage filters the documents bythe status field and passes to the next stage those documents thathave status equal to 'A'.
Second Stage: The $group stage groups the documents bythe cust_id field to calculate the sum of the amount for eachunique cust_id.
The most basic pipeline stages provide filters that operate likequeries and document transformations that modify the formof the output document.
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Other pipeline operations provide tools for grouping and sortingdocuments by specific field or fields as well as tools for aggregatingthe contents of arrays, including arrays of documents. In addition,pipeline stages can use operators for tasks such as calculating theaverage or concatenating a string.
The pipeline provides efficient data aggregation using nativeoperations within MongoDB, and is the preferred method for dataaggregation in MongoDB.
The aggregation pipeline can operate on asharded collection.
The aggregation pipeline can use indexes to improve its performanceduring some of its stages. In addition, the aggregation pipeline has aninternal optimization phase. SeePipeline Operators and Indexes andAggregation Pipeline Optimization for details.
Single Purpose Aggregation Operations¶
MongoDB also provides db.collection.estimatedDocumentCount(),db.collection.count() and db.collection.distinct().
All of these operations aggregate documents from a single collection.While these operations provide simple access to common aggregationprocesses, they lack the flexibility and capabilities of an aggregationpipeline.
Map-Reduce¶
An aggregation pipeline providesbetter performance and usability than a map-reduce operation.
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Map-reduce operations can be rewritten using aggregation pipelineoperators, such as$group, $merge, and others.
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For map-reduce operations that require custom functionality, MongoDBprovides the $accumulator and $functionaggregation operators starting in version 4.4. Use these operators todefine custom aggregation expressions in JavaScript.
For examples of aggregation pipeline alternatives to map-reduceoperations, see Map-Reduce to Aggregation Pipeline andMap-Reduce Examples.
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Additional Features and Behaviors¶
For a feature comparison of the aggregation pipeline,map-reduce, and the special group functionality, seeAggregation Commands Comparison.