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

CAP in the Wild

The databases in this book largely occupy one corner or another of the CAP trade-off triangle. Redis, PostgreSQL, and Neo4J are consistent and available (CA); they don’t distribute data and so partitioning is not an issue (though arguably, CAP doesn’t make much sense in non-distributed systems). MongoDB and HBase are generally consistent and partition tolerant (CP). In the event of a network partition, they can become unable to respond to certain types of queries (for example, in a Mongo replica set you flag slaveok to false for reads). In practice, hardware failure is handled gracefully—other still-networked nodes can cover for the downed server—but strictly speaking, in the CAP theorem sense, they are unavailable. Finally, CouchDB is available and partition tolerant (AP). Even though two or more CouchDB servers can replicate data between them, CouchDB doesn’t guarantee consistency between any two servers.

It’s worth noting that most of these databases can be configured to change CAP type (Mongo can be CA, CouchDB can be CP), but here we’ve noted their default or common behaviors.