Database Patterns


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Database Patterns

About the Book

Node.js has been designed to do quick and efficient network I/O. It's event-driven streams make it ideal to be used as a kind of smart proxy, often working as the glue between back-end systems and clients. Node was originally designed with that intention in mind, but meanwhile it also has been successfully used to build traditional web applications: an HTTP server that serves HTML pages or replies JSON messages and uses a database to store the data. Even though web frameworks in other platforms and languages have preferred to stick with traditional open-source relational databases like MySQL or PostgreSQL, most of the existing Node web frameworks (like Express, Hapi and others) don't impose any database or even any type of database at all. This bring-your-own-database approach has been in part fed by the explosion in the variety of database servers now available, but also by the ease with which the Node module system and NPM allow you to install and use third-party libraries.

In this book we will analyze some of the existing solutions for interacting with some types of databases and what interesting uses can you give them. This first short book on databases starts with some of my favourites: LevelDB, Redis and CouchDB.

About the Author

Pedro Teixeira
Pedro Teixeira

Geek, programmer, freelancer, and entrepreneur. Author of some Node.js modules, the Node Tuts screencast show, and the Hands-on Node.js e-book and overall fervent proclaimer of the Node.js creed.

Wrote a book for Wiley on Node.js.

Partner of YLD.

Organizer of the Lisbon JavaScript Conference.

Table of Contents

  • 1. The source code
  • 2. Introduction
  • 3. An embedded database using LevelDB
    • 3.1 Installing LevelDB
    • 3.2 Using LevelDB
    • 3.3 Encodings
    • 3.4 Using JSON for encoding values
    • 3.5 Batch operations
    • 3.6 Using a readable stream
      • 3.6.1 Using ranges
      • 3.6.2 Limiting the number of results
      • 3.6.3 A consistent snapshot
      • 3.6.4 Using ranges to partition the data
    • 3.7 Using level-sublevel
      • 3.7.1 Batch in different sublevels
    • 3.8 Hooks
  • 4. Redis
    • 4.1 Redis primitives
      • 4.1.1 Strings
      • 4.1.2 Key expiration
      • 4.1.3 Transactions
      • 4.1.4 Command results in Multi
      • 4.1.5 Optimistic locking using WATCH
      • 4.1.6 Transactions using Lua scripts
        • Caching Lua scripts
        • Performance
      • 4.1.7 Integers
      • 4.1.8 Using counters
      • 4.1.9 Dictionaries
      • 4.1.10 Redis dictionary counters
      • 4.1.11 Lists
        • Avoid polling
        • Not losing work
      • 4.1.12 Sets
        • Intersecting sets
      • 4.1.13 Sorted Sets
      • 4.1.14 Pub-sub
      • 4.1.15 Distributed Emitter
        • Beware of race conditions
  • 5. CouchDB
    • 5.1 Starting off
    • 5.2 Ladies and Gentlemen, start your Nodes
    • 5.3 Overriding the HTTP agent socket pool
    • 5.4 The directory structure
    • 5.5 Creating documents with a specific ID
    • 5.6 Forcing a schema
    • 5.7 Unifying errors
      • 5.7.1 How to consume Boom errors
    • 5.8 Updating specific fields while handling conflicts
      • 5.8.1 Delegate conflicts entirely to the client.
      • 5.8.2 Diff doc with last write wins.
      • 5.8.3 Disallowing changes to specific fields
      • 5.9.1 Inverted indexes
        • Query
      • 5.9.2 Multi-value inverted indexes
      • 5.9.3 Paginating results
        • The wrong way of doing pagination
        • A better way of paginating
      • 5.9.4 Reducing
    • 5.10 Using the Changes Feed
      • 5.10.1 Minimising the chance of repeated jobs
      • 5.10.2 Recording the sequence
      • 5.10.3 Scaling: how to support more than one job in parallel
      • 5.10.4 Balancing work: how to use more than one worker process

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