Redis offers the HyperLogLog data structure for efficiently estimating the number of unique elements in large datasets. This probabilistic algorithm enables you to track unique counts using minimal memory, making it ideal for analytics and monitoring tasks where exact precision is less important than resource usage.

HyperLogLog is designed to estimate the cardinality—the count of distinct items—in a set without storing every item. Instead of tracking all elements, it uses clever hashing and mathematical techniques to provide an approximate count with a predictable, low error rate. This approach allows Redis to keep memory usage extremely low (just 12 KB per HyperLogLog object), even when counting millions of unique values. The tradeoff is that the count is an estimate, not a precise total, but in many real-world scenarios, this is an acceptable compromise for the huge savings in memory and speed.

Imagine you want to count the number of unique website visitors per day, but storing every visitor's ID would consume too much memory. With HyperLogLog, you can efficiently estimate the unique visitor count using a few simple Redis commands:

• Use PFADD to add elements to a HyperLogLog. For example, PFADD unique_visitors user123 adds a user ID to the HyperLogLog named unique_visitors.
• Use PFCOUNT to estimate the number of unique elements. After adding several users, run PFCOUNT unique_visitors to get an approximate count of unique visitors.
• Use PFMERGE to combine multiple HyperLogLogs. If you track visitors per region (us_visitors, eu_visitors), you can merge them with PFMERGE all_visitors us_visitors eu_visitors and then call PFCOUNT all_visitors to estimate the total unique visitors across all regions.

HyperLogLog is most useful when you need fast, memory-efficient estimates of unique items, such as counting unique users, IP addresses, or events in high-volume systems.