Implementing Aggregation Functions in MongoDB by Arun Viswanathan and Shruthi Kumar.
From the post:
With the amount of data that organizations generate exploding from gigabytes to terabytes to petabytes, traditional databases are unable to scale up to manage such big data sets. Using these solutions, the cost of storing and processing data will significantly increase as the data grows. This is resulting in organizations looking for other economical solutions such as NoSQL databases that provide the required data storage and processing capabilities, scalability and cost effectiveness. NoSQL databases do not use SQL as the query language. There are different types of these databases such as document stores, key-value stores, graph database, object database, etc.
Typical use cases for NoSQL database includes archiving old logs, event logging, ecommerce application log, gaming data, social data, etc. due to its fast read-write capability. The stored data would then require to be processed to gain useful insights on customers and their usage of the applications.
The NoSQL database we use in this article is MongoDB which is an open source document oriented NoSQL database system written in C++. It provides a high performance document oriented storage as well as support for writing MapReduce programs to process data stored in MongoDB documents. It is easily scalable and supports auto partitioning. Map Reduce can be used for aggregation of data through batch processing. MongoDB stores data in BSON (Binary JSON) format, supports a dynamic schema and allows for dynamic queries. The Mongo Query Language is expressed as JSON and is different from the SQL queries used in an RDBMS. MongoDB provides an Aggregation Framework that includes utility functions such as
count, distinctandgroup. However more advanced aggregation functions such assum, average, max, min, varianceandstandard deviationneed to be implemented using MapReduce.This article describes the method of implementing common aggregation functions like
sum, average, max, min, varianceandstandard deviationon a MongoDB document using its MapReduce functionality. Typical applications of aggregations include business reporting of sales data such as calculation of total sales by grouping data across geographical locations, financial reporting, etc.
Not terribly advanced but enough to get you started with creating aggregation functions.
Includes “testing” of the aggregation functions that are written in the article.
If Python is more your cup of tea, see: Aggregation in MongoDB (part1) and Aggregation in MongoDB (part 2).