1.2.4 Hashes in Redis

  • 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

    1.2.4 Hashes in Redis

    Figure 1.4 An example of a HASH with two keys/values under the key hash-key

    Whereas LISTs and SETs in Redis hold sequences of items, Redis HASHes store a mapping of keys to values. The values that can be stored in HASHes are the same as what can be stored as normal STRINGs: strings themselves, or if a value can be interpreted as a number, that value can be incremented or decremented. Figure 1.4 shows a diagram of a hash with two values.

    In a lot of ways, we can think of HASHes in Redis as miniature versions of Redis itself. Some of the same commands that we can perform on STRINGs, we can perform on the values inside HASHes with slightly different commands. Try to follow listing 1.4 to see some commands that we can use to insert, fetch, and remove items from HASHes. Table 1.6 describes the commands.

    Table 1.6Commands used on HASH values

    Command

    What it does

    HSET

    Stores the value at the key in the hash

    HGET

    Fetches the value at the given hash key

    HGETALL

    Fetches the entire hash

    HDEL

    Removes a key from the hash, if it exists

    Listing 1.4
    The HSET, HGET, HGETALL, and HDEL commands in Redis
    redis 127.0.0.1:6379> hset hash-key sub-key1 value1
    (integer) 1
    redis 127.0.0.1:6379> hset hash-key sub-key2 value2
    (integer) 1
    redis 127.0.0.1:6379> hset hash-key sub-key1 value1
    (integer) 0
    

    When we add items to a hash, again we get a return value that tells whether the item is new in the hash.

    redis 127.0.0.1:6379> hgetall hash-key
    1) "sub-key1"
    2) "value1"
    3) "sub-key2"
    4) "value2"
    

    We can fetch all of the items in the HASH, which gets translated into a dictionary on the Python side of things.

    redis 127.0.0.1:6379> hdel hash-key sub-key2
    (integer) 1
    redis 127.0.0.1:6379> hdel hash-key sub-key2
    (integer) 0
    

    When we delete items from the hash, the command returns whether the item was there before we tried to remove it.

    redis 127.0.0.1:6379> hget hash-key sub-key1
    "value1"
    

    We can also fetch individual fields from hashes.

    redis 127.0.0.1:6379> hgetall hash-key
    1) "sub-key1"
    2) "value1"
    


    For those who are familiar with document stores or relational databases, we can consider a Redis HASH as being similar to a document in a document store, or a row in a relational database, in that we can access or change individual or multiple fields at a time. We’re now one structure from having seen all of the structures available in Redis. Keep reading to learn what ZSETs are and a few things that we can do with them.