5.2 Counters and statistics

  • 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

    5.2 Counters and statistics

    As you saw way back in chapter 2 when I introduced the concept of counting individual page visits, having basic hit count information can (for example) change the way we choose our caches. But our example from chapter 2 was very simple, and reality is rarely that simple, especially when it involves a real website.

    The fact that our site received 10,000 hits in the last 5 minutes, or that the database handled 200 writes and 600 reads in the last 5 seconds, is useful information to know. If we add the ability to see that information over time, we can notice sudden or gradual increases in traffic, predict when server upgrades are necessary, and ultimately save ourselves from downtime due to an overloaded system.

    This section will work through two different methods for recording both counters and statistics in Redis, and will finish by discussing how to simplify the collection of our example statistics. Both of these examples are driven by real use cases and requirements. Our next stop on the road of application introspection is collecting time series counters in Redis.