6.1 Autocomplete

  • Redis in Action – Home
  • Foreword
  • Preface
  • Part 1: Getting Started
  • Part 2: Core concepts
  • 1.3.1 Voting on articles
  • 1.3.2 Posting and fetching articles
  • 1.3.3 Grouping articles
  • 4.2.1 Configuring Redis for replication
  • 4.2.2 Redis replication startup process
  • 4.2.3 Master/slave chains
  • 4.2.4 Verifying disk writes
  • 5.1 Logging to Redis
  • 5.2 Counters and statistics
  • 5.3 IP-to-city and -country lookup
  • 5.4 Service discovery and configuration
  • 5.1.1 Recent logs
  • 5.1.2 Common logs
  • 5.2.2 Storing statistics in Redis
  • 5.3.1 Loading the location tables
  • 5.3.2 Looking up cities
  • 5.4.1 Using Redis to store configuration information
  • 5.4.2 One Redis server per application component
  • 5.4.3 Automatic Redis connection management
  • 8.1.1 User information
  • 8.1.2 Status messages
  • 9.1.1 The ziplist representation
  • 9.1.2 The intset encoding for SETs
  • Chapter 10: Scaling Redis
  • Chapter 11: Scripting Redis with Lua
  • 10.1 Scaling reads
  • 10.2 Scaling writes and memory capacity
  • 10.3 Scaling complex queries
  • 10.2.2 Creating a server-sharded connection decorator
  • 10.3.1 Scaling search query volume
  • 10.3.2 Scaling search index size
  • 10.3.3 Scaling a social network
  • 11.1.1 Loading Lua scripts into Redis
  • 11.1.2 Creating a new status message
  • 11.2 Rewriting locks and semaphores with Lua
  • 11.3 Doing away with WATCH/MULTI/EXEC
  • 11.4 Sharding LISTs with Lua
  • 11.5 Summary
  • 11.2.1 Why locks in Lua?
  • 11.2.2 Rewriting our lock
  • 11.2.3 Counting semaphores in Lua
  • 11.4.1 Structuring a sharded LIST
  • 11.4.2 Pushing items onto the sharded LIST
  • 11.4.4 Performing blocking pops from the sharded LIST
  • A.1 Installation on Debian or Ubuntu Linux
  • A.2 Installing on OS X
  • B.1 Forums for help
  • B.4 Data visualization and recording
  • Buy the paperback
  • Redis in Action – Home
  • Foreword
  • Preface
  • Part 1: Getting Started
  • Part 2: Core concepts
  • 1.3.1 Voting on articles
  • 1.3.2 Posting and fetching articles
  • 1.3.3 Grouping articles
  • 4.2.1 Configuring Redis for replication
  • 4.2.2 Redis replication startup process
  • 4.2.3 Master/slave chains
  • 4.2.4 Verifying disk writes
  • 5.1 Logging to Redis
  • 5.2 Counters and statistics
  • 5.3 IP-to-city and -country lookup
  • 5.4 Service discovery and configuration
  • 5.1.1 Recent logs
  • 5.1.2 Common logs
  • 5.2.2 Storing statistics in Redis
  • 5.3.1 Loading the location tables
  • 5.3.2 Looking up cities
  • 5.4.1 Using Redis to store configuration information
  • 5.4.2 One Redis server per application component
  • 5.4.3 Automatic Redis connection management
  • 8.1.1 User information
  • 8.1.2 Status messages
  • 9.1.1 The ziplist representation
  • 9.1.2 The intset encoding for SETs
  • Chapter 10: Scaling Redis
  • Chapter 11: Scripting Redis with Lua
  • 10.1 Scaling reads
  • 10.2 Scaling writes and memory capacity
  • 10.3 Scaling complex queries
  • 10.2.2 Creating a server-sharded connection decorator
  • 10.3.1 Scaling search query volume
  • 10.3.2 Scaling search index size
  • 10.3.3 Scaling a social network
  • 11.1.1 Loading Lua scripts into Redis
  • 11.1.2 Creating a new status message
  • 11.2 Rewriting locks and semaphores with Lua
  • 11.3 Doing away with WATCH/MULTI/EXEC
  • 11.4 Sharding LISTs with Lua
  • 11.5 Summary
  • 11.2.1 Why locks in Lua?
  • 11.2.2 Rewriting our lock
  • 11.2.3 Counting semaphores in Lua
  • 11.4.1 Structuring a sharded LIST
  • 11.4.2 Pushing items onto the sharded LIST
  • 11.4.4 Performing blocking pops from the sharded LIST
  • A.1 Installation on Debian or Ubuntu Linux
  • A.2 Installing on OS X
  • B.1 Forums for help
  • B.4 Data visualization and recording
  • Buy the paperback

    6.1 Autocomplete

    In the web world, autocomplete is a method that allows us to quickly look up things that we want to find without searching. Generally, it works by taking the letters that we’re typing and finding all words that start with those letters. Some autocomplete tools will even let us type the beginning of a phrase and finish the phrase for us. As an example, autocomplete in Google’s search shows us that Betty White’s SNL appearance is still popular, even years later (which is no surprise—she’s a firecracker). It shows us the URLs we’ve recently visited and want to revisit when we type in the address bar, and it helps us remember login names. All of these functions and more are built to help us access information faster. Some of them, like Google’s search box, are backed by many terabytes of remote information. Others, like our browser history and login boxes, are backed by much smaller local databases. But they all get us what we want with less work.

    We’ll build two different types of autocomplete in this section. The first uses lists to remember the most recent 100 contacts that a user has communicated with, trying to minimize memory use. Our second autocomplete offers better performance and scalability for larger lists, but uses more memory per list. They differ in their structure, the methods used, and the time it takes for the operations to complete. Let’s first start with an autocomplete for recent contacts.