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HyperLogLog

HyperLogLog is a probabilistic data structure used for estimating the cardinality (unique count) of a set. It uses minimal memory (~12 KB) regardless of the number of elements, making it ideal for counting unique visitors, events, or any scenario requiring approximate distinct counts at scale.

API Reference

Method Description
HyperLogLogAddAsync<T>(key, value) Add a single element
HyperLogLogAddAsync<T>(key, values) Add multiple elements
HyperLogLogLengthAsync(key) Get approximate cardinality of one key
HyperLogLogLengthAsync(keys) Get approximate cardinality of the union of multiple keys
HyperLogLogMergeAsync(dest, sourceKeys) Merge multiple HyperLogLogs into a destination key

Usage

// Count unique visitors per page
await redis.HyperLogLogAddAsync("page:home:visitors", userId);

// Check how many unique visitors
var uniqueVisitors = await redis.HyperLogLogLengthAsync("page:home:visitors");

// Add multiple values at once
await redis.HyperLogLogAddAsync("page:about:visitors", new[] { "user1", "user2", "user3" });

// Count unique visitors across multiple pages
var totalUnique = await redis.HyperLogLogLengthAsync(new[] { "page:home:visitors", "page:about:visitors" });

// Merge daily counts into a monthly aggregate
await redis.HyperLogLogMergeAsync("visitors:2024:01", new[]
{
    "visitors:2024:01:01",
    "visitors:2024:01:02",
    "visitors:2024:01:03",
});

Notes

  • Values are serialized through the configured ISerializer before being added
  • HyperLogLog has a standard error rate of 0.81%
  • The Add methods return true if at least one internal register was altered (the cardinality estimate changed), false if the element was likely already counted
  • Memory usage is constant (~12 KB per key) regardless of the number of unique elements