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Practical MongoDB Aggregations

Practical MongoDB Aggregations

By : Paul Done
5 (17)
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Practical MongoDB Aggregations

Practical MongoDB Aggregations

5 (17)
By: Paul Done

Overview of this book

Officially endorsed by MongoDB, Inc., Practical MongoDB Aggregations helps you unlock the full potential of the MongoDB aggregation framework, including the latest features of MongoDB 7.0. This book provides practical, easy-to-digest principles and approaches for increasing your effectiveness in developing aggregation pipelines, supported by examples for building pipelines to solve complex data manipulation and analytical tasks. This book is customized for developers, architects, data analysts, data engineers, and data scientists with some familiarity with the aggregation framework. It begins by explaining the framework's architecture and then shows you how to build pipelines optimized for productivity and scale. Given the critical role arrays play in MongoDB's document model, the book delves into best practices for optimally manipulating arrays. The latter part of the book equips you with examples to solve common data processing challenges so you can apply the lessons you've learned to practical situations. By the end of this MongoDB book, you’ll have learned how to utilize the MongoDB aggregation framework to streamline your data analysis and manipulation processes effectively.
Table of Contents (20 chapters)
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2
Part 1: Guiding Tips and Principles
7
Part 2: Aggregations by Example
16
Afterword

Performance tips for sharded aggregations

All the recommended aggregation optimization outlined in Chapter 3, Optimizing Pipelines for Performance, equally apply to a sharded cluster. In fact, in most cases, these same recommendations, repeated as follows, become even more important when executing aggregations on sharded clusters:

  • Sorting – use index sort: When the runtime has to split on a $sort stage, the shards part of the split pipeline running on each shard in parallel will avoid an expensive in-memory sort operation.
  • Sorting – use limit with sort: The runtime has to transfer fewer intermediate records over the network, from each shard performing the shards part of a split pipeline to the location that executes the pipeline's merger part.
  • Sorting – reduce records to sort: If you cannot adopt point 1 or 2, moving a $sort stage to as late as possible in a pipeline will typically benefit performance in a sharded cluster. Wherever the $sort...

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