We are now, simply, Redis
Unique items can be difficult to count. Usually this means storing every unique item then recalling this information somehow. With Redis, this can be accomplished by using a set and a single command, however both the storage and time complexity of this with very large sets is prohibitive. HyperLogLog provides a probabilistic alternative.
HyperLogLog is similar to a Bloom filter internally as it runs items through a non-cryptographic hash and sets bits in a form of a bitfield. Unlike a Bloom filter, HyperLogLog keeps a counter of items that is incremented when new items are added that have not been previously added. This provides a very low error rate when estimating the unique items (cardinality) of a set. HyperLogLog is built right into Redis.
There are three HyperLogLog commands in Redis: PFADD, PFCOUNT and PFMERGE. Let’s say you’re building a web crawler and you want to estimate the number of unique URLs the page has crawled during a given day. For every page you would run a command like this:
> PFADD crawled:20171124 "http://www.google.com/" (integer) 1 > PFADD crawled:20171124 "http://www.redis.com/" (integer) 1 > PFADD crawled:20171124 "http://www.redis.io/" (integer) 1 > PFADD crawled:20171125 "http://www.redisearch.io/" (integer) 1 > PFADD crawled:20171125 "http://www.redis.io/" (integer) 1
Each key above is indexed by day. To see how many pages were crawled on 2017-11-24, we would run the following command:
> PFCOUNT crawled:20171124 (integer) 3
To see how many pages were crawled on 2017-11-24 and 2017-11-25, we would use PFMERGE to produce a new key of the union of the two counts. Note that since http://redis.io/ was added to both keys it isn’t counted twice:
> PFMERGE crawled:20171124-25 crawled:20171124 crawled:20171125 OK > PFCOUNT crawled:20171124-25 (integer) 4
This is a multi-key operation, so take care in sharded environments as to make sure your keys end up on the same shard.