A HyperLogLog is a probabilistic data structure used in order to count unique things (technically this is referred to estimating the cardinality of a set). Usually counting unique items requires using an amount of memory proportional to the number of items you want to count, because you need to remember the elements you have already seen in the past in order to avoid counting them multiple times. However there is a set of algorithms that trade memory for precision: you end with an estimated measure with a standard error, in the case of the Redis implementation, which is less than 1%. The magic of this algorithm is that you no longer need to use an amount of memory proportional to the number of items counted, and instead can use a constant amount of memory! 12k bytes in the worst case, or a lot less if your HyperLogLog (We'll just call them HLL from now) has seen very few elements.
HLLs in Redis, while technically a different data structure, is encoded as a Redis string, so you can call GET to serialize a HLL, and SET to deserialize it back to the server.
Conceptually the HLL API is like using Sets to do the same task. You would SADD every observed element into a set, and would use SCARD to check the number of elements inside the set, which are unique since SADD will not re-add an existing element.
While you don't really add items into an HLL, because the data structure only contains a state that does not include actual elements, the API is the same:
- Every time you see a new element, you add it to the count with PFADD.
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Every time you want to retrieve the current approximation of the unique elements added with PFADD so far, you use the PFCOUNT.
127.0.0.1:6379> PFADD hll a b c d (integer) 1 127.0.0.1:6379> PFCOUNT hll (integer) 4
An example of use case for this data structure is counting unique queries performed by users in a search form every day.