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Querying Data Using Map-reduce in MongoDB
MongoDB’s Map-reduce stands strong as the number one choice for big data analytics. Learn about condensing a large volume of document data into a small set of aggregated results by creating versatile map and reduce functions in JavaScript.
What you'll learn
Working with large volumes of document data for analytics requires the power and flexibility of MongoDB’s Map-reduce feature. In this course, Querying Data Using Map-reduce in MongoDB, you’ll gain the ability to get yourself fully equipped to confidently apply the Map-reduce pattern to any data set no matter how large it could be. First, you’ll discover the requirement for Map-reduce among other aggregation capabilities of MongoDB. Next, you’ll explore how to create a custom JavaScript map function and a reduce function that are needed to perform map and reduce operations. Finally, you’ll learn how to use these custom functions to perform a Map-reduce operation on a MongoDB data set to aggregate results across documents. When you’re finished with this course, you’ll have the skills and knowledge of working with the Map-reduce function in MongoDB that will help you leverage the power of MongoDB’s aggregation feature for data crunching requirements in your next project.
Table of contents
- Version Check | 15s
- Introduction | 3m 56s
- Aggregation Mechanisms in MongoDB | 4m 7s
- Demo: Creating the Database and Loading the Data | 4m 58s
- Demo: Using Single Purpose Aggregation Operations | 2m 4s
- Demo: Using the Aggregation Pipeline | 3m 53s
- Lack of Flexibility and Power for Complex Operations | 3m 5s
- The map() and reduce() Methods in JavaScript Array | 2m 25s
- Linking JS Array Map and Reduce to Document Aggregation | 1m 40s
- Summary | 1m 14s