Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Practical MongoDB Aggregations

You're reading from   Practical MongoDB Aggregations The official guide to developing optimal aggregation pipelines with MongoDB 7.0

Arrow left icon
Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781835080641
Length 312 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Paul Done Paul Done
Author Profile Icon Paul Done
Paul Done
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

Preface 1. Chapter 1: MongoDB Aggregations Explained FREE CHAPTER 2. Part 1: Guiding Tips and Principles
3. Chapter 2: Optimizing Pipelines for Productivity 4. Chapter 3: Optimizing Pipelines for Performance 5. Chapter 4: Harnessing the Power of Expressions 6. Chapter 5: Optimizing Pipelines for Sharded Clusters 7. Part 2: Aggregations by Example
8. Chapter 6: Foundational Examples: Filtering, Grouping, and Unwinding 9. Chapter 7: Joining Data Examples 10. Chapter 8: Fixing and Generating Data Examples 11. Chapter 9: Trend Analysis Examples 12. Chapter 10: Securing Data Examples 13. Chapter 11: Time-Series Examples 14. Chapter 12: Array Manipulation Examples 15. Chapter 13: Full-Text Search Examples 16. Afterword
17. Index 18. Other books you may enjoy Appendix

Array sorting and percentiles

The need to sort arrays and calculate summary data, such as the 99th percentile, is common. Recent versions of the MongoDB aggregation framework offer enhanced capabilities in this area. This example will guide you through implementing this using both an earlier and a more recent version of MongoDB.

Scenario

You've conducted performance testing of an application with the results of each test run captured in a database. Each record contains a set of response times for the test run. You want to analyze the data from multiple runs to identify the slowest ones. You calculate the median (50th percentile) and 90th percentile response times for each test run and only keep results where the 90th percentile response time is greater than 100 milliseconds.

Note

For MongoDB version 5.0 and earlier, the example will use a macro function for inline sorting of arrays. Adopting this approach avoids the need for you to use the combination of the $unwind...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image