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

Chapter 2
PostgreSQL

PostgreSQL is the hammer of the database world. It’s commonly understood, often readily available, and sturdy, and it solves a surprising number of problems if you swing hard enough. No one can hope to be an expert builder without understanding this most common of tools.

PostgreSQL (or just “Postgres”) is a relational database management system (or RDBMS for short). Relational databases are set-theory-based systems in which data is stored in two-dimensional tables consisting of data rows and strictly enforced column types. Despite the growing interest in newer database trends, the relational style remains the most popular and probably will for quite some time. Even in a book that focuses on non-relational “NoSQL” systems, a solid grounding in RDBMS remains essential. PostgreSQL is quite possibly the finest open source RDBMS available, and in this chapter you’ll see that it provides a great introduction to this paradigm.

While the prevalence of relational databases can be partially attributed to their vast toolkits (triggers, stored procedures, views, advanced indexes), their data safety (via ACID compliance), or their mind share (many programmers speak and think relationally), query pliancy plays a central role as well. Unlike some other databases, you don’t need to know in advance how you plan to use the data. If a relational schema is normalized, queries are flexible. You can start storing data and worrying about how exactly you’ll use it later on, even changing your entire querying model over time as your needs change.