Practical MongoDB Aggregations : The Official Guide to Developing Optimal Aggregation Pipelines with MongoDB 7. 0 / Paul Done and Asya Kamsky.

Format:

Book

Author/Creator:

Done, Paul, author.

Kamsky, Asya, author.

Language:

English

Subjects (All):

MongoDB.

Database management--Software.

Database management.

Open source software.

Object-oriented databases.

Non-relational databases.

Physical Description:

1 online resource (313 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2023]

Biography/History:

Done Paul: Paul Done is a Field CTO at MongoDB Inc. , having been a Solutions Architect for the past decade at MongoDB. He has previously held roles in various software disciplines, including engineering, consulting, and pre-sales, at companies like Oracle, Novell, and BEA Systems. Paul specializes in databases and middleware, focusing on resiliency, scalability, transactions, event processing, and applying evolvable data model approaches. He spent most of the early 2000s building Java EE (J2EE) transactional systems on WebLogic, integrated with relational databases like Oracle RAC and messaging systems like MQ Series.

Summary:

Begin your journey toward efficient data manipulation with this robust technical guide and enhance your aggregation skills while building efficient pipelines for a variety of tasks Key Features Build effective aggregation pipelines for increased productivity and performance Solve common data manipulation and analysis problems with the help of practical examples Learn essential strategies to aggregate time series data in financial datasets and IoT Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. What you will learn Develop dynamic aggregation pipelines tailored to changing business requirements Master essential techniques to optimize aggregation pipelines for rapid data processing Achieve optimal efficiency for applying aggregations to vast datasets with effective sharding strategies Eliminate the performance penalties of processing data externally by filtering, grouping, and calculating aggregated values directly within the database Use pipelines to help you secure your data access and distribution Who this book is for This book is for intermediate-level developers, architects, analysts, engineers, and data scientists who are interested in learning about aggregation capabilities in MongoDB. Working knowledge of MongoDB is needed to get the most out of this book.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Acknowledgements

Foreword

Table of Contents

Preface

Chapter 1: MongoDB Aggregations Explained

What is the MongoDB aggregation framework?

What is the MongoDB aggregation language?

What do developers use the aggregation framework for?

A short history of MongoDB aggregations

Aggregation capabilities in MongoDB server releases

Getting going

Setting up your environment

Database

Client tool

Getting further help

Summary

Part 1: Guiding Tips and Principles

Chapter 2: Optimizing Pipelines for Productivity

Embrace composability for increased productivity

Guiding principles to promote composability

Using macro functions

So, what's the best way of factoring out code?

Better alternatives for a projection stage

When to use set and unset

When to use project

The hidden danger of project

Key projection takeaways

Chapter 3: Optimizing Pipelines for Performance

Using explain plans to identify performance bottlenecks

Viewing an explain plan

Understanding the explain plan

Guidance for optimizing pipeline performance

Be cognizant of streaming vs blocking stages ordering

Avoid unwinding and regrouping documents just to process each array's elements

Encourage match filters to appear early in the pipeline

Chapter 4: Harnessing the Power of Expressions

Aggregation expressions explained

What do expressions produce?

Chaining operator expressions together

Can all stages use expressions?

What is using expr inside match all about?

Restrictions when using expressions within match

Advanced use of expressions for array processing

if-else conditional comparison

The power array operators

for-each looping to transform an array.

for-each looping to compute a summary value from an array

for-each looping to locate an array element

Reproducing map behavior using reduce

Adding new fields to existing objects in an array

Rudimentary schema reflection using arrays

Chapter 5: Optimizing Pipelines for Sharded Clusters

A brief summary of MongoDB sharded clusters

Sharding implications for pipelines

Sharded aggregation constraints

Where does a sharded aggregation run?

Pipeline splitting at runtime

Execution of the split pipeline shards

Execution of the merger part of the split pipeline

Difference in merging behavior for grouping versus sorting

Performance tips for sharded aggregations

Part 2: Aggregations by Example

Chapter 6: Foundational Examples: Filtering, Grouping, and Unwinding

Filtered top subset

Scenario

Populating the sample data

Defining the aggregation pipeline

Executing the aggregation pipeline

Expected pipeline results

Pipeline observations

Group and total

Expected pipeline result

Unpack arrays and group differently

Distinct list of values

Chapter 7: Joining Data Examples

One-to-one join

Pipeline observations.

Multi-field join and one-to-many

Chapter 8: Fixing and Generating Data Examples

Strongly typed conversion

Converting incomplete date strings

Generating mock test data

Chapter 9: Trend Analysis Examples

Faceted classification

Largest graph network

Incremental analytics

Chapter 10: Securing Data Examples

Redacted view

Mask sensitive fields

Executing the aggregation pipeline.

Expected pipeline result

Role programmatic restricted view

Chapter 11: Time-Series Examples

IoT power consumption

State change boundaries

Chapter 12: Array Manipulation Examples

Summarizing arrays for first, last, minimum, maximum, and average values

Pivoting array items by a key

Array sorting and percentiles

Array element grouping

Array fields joining

Comparison of two arrays

Populating the sample data.

Defining the aggregation pipeline

Jagged array condensing

Chapter 13: Full-Text Search Examples

What is Atlas Search?

Compound text search criteria

Facets and counts text search

Appendix

Afterword

Index

Other books you may enjoy.

Notes:

Description based on print version record.

ISBN:

9781835086841

1835086845

OCLC:

1396974184