Table of Contents for
Seven Databases in Seven Weeks, 2nd Edition

Version ebook / Retour

Cover image for bash Cookbook, 2nd Edition Seven Databases in Seven Weeks, 2nd Edition by Jim Wilson Published by Pragmatic Bookshelf, 2018
  1. Title Page
  2. Seven Databases in Seven Weeks, Second Edition
  3. Seven Databases in Seven Weeks, Second Edition
  4. Seven Databases in Seven Weeks, Second Edition
  5. Seven Databases in Seven Weeks, Second Edition
  6.  Acknowledgments
  7.  Preface
  8. Why a NoSQL Book
  9. Why Seven Databases
  10. What’s in This Book
  11. What This Book Is Not
  12. Code Examples and Conventions
  13. Credits
  14. Online Resources
  15. 1. Introduction
  16. It Starts with a Question
  17. The Genres
  18. Onward and Upward
  19. 2. PostgreSQL
  20. That’s Post-greS-Q-L
  21. Day 1: Relations, CRUD, and Joins
  22. Day 2: Advanced Queries, Code, and Rules
  23. Day 3: Full Text and Multidimensions
  24. Wrap-Up
  25. 3. HBase
  26. Introducing HBase
  27. Day 1: CRUD and Table Administration
  28. Day 2: Working with Big Data
  29. Day 3: Taking It to the Cloud
  30. Wrap-Up
  31. 4. MongoDB
  32. Hu(mongo)us
  33. Day 1: CRUD and Nesting
  34. Day 2: Indexing, Aggregating, Mapreduce
  35. Day 3: Replica Sets, Sharding, GeoSpatial, and GridFS
  36. Wrap-Up
  37. 5. CouchDB
  38. Relaxing on the Couch
  39. Day 1: CRUD, Fauxton, and cURL Redux
  40. Day 2: Creating and Querying Views
  41. Day 3: Advanced Views, Changes API, and Replicating Data
  42. Wrap-Up
  43. 6. Neo4J
  44. Neo4j Is Whiteboard Friendly
  45. Day 1: Graphs, Cypher, and CRUD
  46. Day 2: REST, Indexes, and Algorithms
  47. Day 3: Distributed High Availability
  48. Wrap-Up
  49. 7. DynamoDB
  50. DynamoDB: The “Big Easy” of NoSQL
  51. Day 1: Let’s Go Shopping!
  52. Day 2: Building a Streaming Data Pipeline
  53. Day 3: Building an “Internet of Things” System Around DynamoDB
  54. Wrap-Up
  55. 8. Redis
  56. Data Structure Server Store
  57. Day 1: CRUD and Datatypes
  58. Day 2: Advanced Usage, Distribution
  59. Day 3: Playing with Other Databases
  60. Wrap-Up
  61. 9. Wrapping Up
  62. Genres Redux
  63. Making a Choice
  64. Where Do We Go from Here?
  65. A1. Database Overview Tables
  66. A2. The CAP Theorem
  67. Eventual Consistency
  68. CAP in the Wild
  69. The Latency Trade-Off
  70.  Bibliography
  71. Seven Databases in Seven Weeks, Second Edition

Relaxing on the Couch

CouchDB lives up to its tag line: relax. Instead of focusing on working well only in massive clusters running on hundreds of nodes, CouchDB aims to support a variety of deployment scenarios from the datacenter down to the smartphone. You can run CouchDB on your Android phone, on your MacBook, and in your datacenter. Written in Erlang, CouchDB is built tough—the only way to shut it down is to kill the process! With its append-only storage model, your data is virtually incorruptible and easy to replicate, back up, and restore.

CouchDB is document oriented, using JSON as its storage and communication language. All calls to CouchDB happen through its REST interface. Replication can be one way (from one database to another) or bidirectional (back and forth between databases), and ad hoc (triggered at will) or continuous (triggered at periodic intervals). CouchDB gives you a lot of flexibility to decide how to structure, protect, and distribute your data.

Frontend-to-Backend Syncing with PouchDB

The past few years have seen a dramatic increase in interest in web apps that remain usable even when the user is offline. One of the core difficulties behind building such offline-friendly applications is the data synchronization problem. If a user uses an app offline for an hour and makes a wide variety of changes, what happens when their connection comes back and the discrepancy between client and server state needs to be resolved?

PouchDB is a very interesting tool that has emerged from the CouchDB ecosystem to address this problem. PouchDB is an open source database created in JavaScript that acts as a client-side, in-browser data store that automatically synchronizes data with CouchDB on the backend.

We won‘t cover PouchDB in this book, but we do find it to be a compelling solution to a problem that‘s widely encountered but has very few ready-made solutions. If you‘re looking to use CouchDB as a backend for web apps, you‘d be remiss not to take a look.

Comparing CouchDB and MongoDB

One of the big questions we wanted to address in this book is “What’s the difference between CouchDB and MongoDB?” On the surface, CouchDB and MongoDB—which we covered in Chapter 4, ​MongoDB​—can seem quite similar. They’re both document-oriented databases with an affinity for JavaScript that use JSON for data transport. There are many differences, though, ranging from project philosophy to implementation to scalability characteristics. We’ll cover many of these as we explore the beautiful simplicity of CouchDB.

During our three-day tour, we’ll explore many of CouchDB’s compelling features and design choices. We’ll start, as always, with individual CRUD commands and then move on to indexing through mapreduce views. As we’ve done with other databases, we’ll import some structured data and then use it to explore some advanced concepts. Finally, we’ll develop some simple event-driven client-side applications using Node.js and learn how CouchDB’s master-master replication strategy deals with conflicting updates. Let’s get to it!