Book Image

Learning Couchbase

By : Henry Potsangbam
Book Image

Learning Couchbase

By: Henry Potsangbam

Overview of this book

This book achieves its goal by taking up an end-to-end development structure, right from understanding NOSQL document design to implementing full fledged eCommerce application design using Couchbase as a backend. Starting with the architecture of Couchbase to get you up and running, this book quickly takes you through designing a NoSQL document and implementing highly scalable applications using Java API. You will then be introduced to document design and get to know the various ways to administer Couchbase. Followed by this, learn to store documents using bucket. Moving on, you will then learn to store, retrieve and delete documents using smart client base on Java API. You will then retrieve documents using SQL like syntax call N1QL. Next, you will learn how to write map reduce base views. Finally, you will configure XDCR for disaster recovery and implement an eCommerce application using Couchbase.

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Table of Contents (12 chapters)
Free Chapter
1
Introduction to Couchbase
Introduction to Couchbase
What is NoSQL and why do we need it?
The architecture of Couchbase
Concepts of Couchbase
Summary
2
The Couchbase Administration Interface
The Couchbase Administration Interface
The need for the Couchbase administrative interface
The web admin UI
Buckets and servers
Views
XDCR
Log
Settings
Couchbase administrative REST API
The command line interface
Summary
3
Storing Documents in Couchbase Using Buckets
Storing Documents in Couchbase Using Buckets
Buckets
Understanding documents
vBuckets
Understanding some internals of Couchbase
Summary
4
Designing a Document for Couchbase
Designing a Document for Couchbase
Understanding JSON and non JSON data
Document versus RDBMS
Document modeling
Document relationships
Using the document editor
Summary
5
Introducing Client SDK
Introducing Client SDK
A Couchbase SDK overview
Understanding write operation in the Couchbase cluster
Understanding the Couchbase API
Understanding Java SDK
Understanding locking
Understanding counters
async operations
Connection management
Summary
6
Retrieving Documents without Keys Using Views
Retrieving Documents without Keys Using Views
An overview of MapReduce
Views
Indexes
Understanding stale parameters
Built-in reduce functions
Custom reduce functions
Filtering and transforming data
Mapping with SQL to MapReduce
Understanding geospatial views
View writing guidance
Summary
7
Understanding SQL-Like Queries – N1QL
Understanding SQL-Like Queries – N1QL
The N1QL overview
Operation types
Understanding N1QL syntax
Indexing properties
Using the N1QL API
Summary
8
Full Text Search Using ElasticSearch
Full Text Search Using ElasticSearch
Understanding content-driven applications
Full text search overview
Configuration and query
Using the ES query API
An API to connect to ES
Summary
9
Data Replication and Compaction
Data Replication and Compaction
Understanding the XDCR architecture
Configuration and monitoring
Monitoring ongoing replications
The detailed replication progress
XDCR use cases
XDCR topologies
XDCR impact
Compaction
Summary
10
Administration, Tuning, and Monitoring
Administration, Tuning, and Monitoring
Overview
Backup and restoration
cbbackup
Rebalancing
Adding and removing a node from the cluster
Performing a bulk set
Monitoring
Best practices
Summary
11
Case Study – An E-Commerce Application
Case Study – An E-Commerce Application
Overview
The conceptual model
Summary
Index
Index

Configuration and query

In this section, we will configure ElasticSearch and integrate it with Couchbase.

The Couchbase and ES integration architecture (Courtesy - Couchbase Documentation)

Let me briefly explain the architecture and usage pattern of ElasticSearch with the Couchbase cluster. Couchbase can replicate documents to ElasticSearch using XDCR, which will be discussed in detail in the next chapter. ElasticSearch will perform indexing on documents replicated from the Couchbase cluster by each attribute, and the developer will execute the search query in ES and use the resultset returned by ES to get the actual document from the Couchbase cluster, as shown in the preceding diagram. Why are we not storing the actual document in ES and fetching it from ES itself? In order to answer this query, you need to remember that ES is good for searching text and Couchbase is fast at data retrieval, since all documents are, by default, stored in the memory. Here, we are trying to use the best features...

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