MongoDB Recipes

With Data Modeling and Query Building Strategies
1. Auflage 2019
ISBN: 978-1-4842-4890-4
Verlag: Apress

Buch, Englisch, 247 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 411 g

Get the most out of MongoDB using a problem-solution approach. This book starts with recipes on the MongoDB query language, including how to query various data structures stored within documents. These self-contained code examples allow you to solve your MongoDB problems without fuss.
MongoDB Recipes describes how to use advanced querying in MongoDB, such as indexing and the aggregation framework. It demonstrates how to use the Compass function, a GUI client interacting with MongoDB, and how to apply data modeling to your MongoDB application. You’ll see recipes on the latest features of MongoDB 4 allowing you to manage data in an efficient manner using MongoDB.
What You Will Learn 

• Work with the MongoDB document model 
  
• Design MongoDB schemas 
  
• Use the MongoDB query language
  
• Harness the aggregation framework
  
• Create replica sets and sharding in MongoDB
  

Who This Book Is ForDevelopers and professionals who work with MongoDB. 
        
     

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Ganesan, Dharanitharan

Chellappan, Subhashini

Fachgebiete

• Mathematik | Informatik EDV | Informatik Betriebssysteme Linux Betriebssysteme, Open Source Betriebssysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data

Inhaltsverzeichnis

Chapter 1: MongoDB Features and Installation

Chapter Goal:  In this chapter, you will learn NoSQL databases, CAP theorem, MongoDB Features and MongoDB tools. This chapter will also cover installation of MongoDB and its associated tools.

Sub Topics:

NoSQL Databases and Categories

CAP Theorem

MongoDB Features

MongoDB Tools

Describe JSON and BSON

Installing MongoDB on Windows, Linux

MongoDB Terms

MongoDB Data Types

Chapter 2: CRUD Operations

Chapter Goal:  In this chapter, you will learn how to perform CRUD operations with MongoDB. This chapter also help you to understand how to query embedded documents and arrays.

Sub Topics:

Basic CRUD operations

Query Embedded Documents

Query Arrays

Bulk Write Operations

Chapter 3: Data Modelling

Chapter Goal:  In this chapter, you will learn schema design and various data modelling patterns in MongoDB.

Sub Topics:

Data Modelling Concepts

Data Model Patterns

Model Relationship between documents

Model Tree Structures

Chapter 4: Indexing and Aggregation Framework

Chapter Goal:  In this chapter, you will learn indexes types and Aggregation Framework in MongoDB.

Sub Topics:

Introduction to indexes

Index Types

Creating Indexes

Introduction to Aggregation Framework

Aggregation Framework Types

Chapter 5: MongoDB Replication and Sharding

Chapter Goal:  In this chapter, you will learn the replication set up and sharding set up.

Sub Topics:

Replication Concepts

Master Slave Replication

Replication Setup

Introduction to Sharding and concepts

Shard Setup

Types of Sharding

Chapter 6: MongoDB Transaction

Chapter Goal:  In this chapter, you will learn transactions in MongoDB.

Sub Topics:

Atomicity

Multi-Document Transaction

Concurrency Control

Chapter 7: MongoDB Administration

Chapter Goal:  In this chapter, you will learn Database Profiler, MongoDB Backup Methods and Monitoring MongoDB.

Sub Topics:

Database Profiler

MongoDB Backup Methods

Monitoring MongoDB

Chapter 8: MongoDB Security

Chapter Goal:  In this chapter, you will learn security aspects of MongoDB.

Sub Topics:

Creating Users

Creating and Assigning custom roles

Authenticating Server

Über Autor(innen)

Subhashini Chellappan is a technology enthusiast with expertise in the big data and cloud space. She has rich experience in both academia and the software industry. Her areas of interest and expertise are centered on business intelligence, big data analytics and cloud computing.
Dharanitharan Ganesan is an MBA in Technology management with high level of exposure and experience in big data – Apache Hadoop, Apache Spark and various Hadoop ecosystem components. He has a proven track record of improving efficiency and productivity through the automation of various routine and administrative functions in business intelligence and big data technologies. His areas of interest and expertise are centered on machine learning algorithms, Blockchain in Bigdata, statistical modelling and predictive analytics.