Counting Distinct Values With HyperLogLog

Counting distinct values is a trivial matter with small datasets, but it gets dramatically harder for streams with millions of distinct points. Absolute counting accuracy can be attained through a set, but then you have the undesirable tradeoff of linear memory growth.

What you really want when dealing with enormous datasets is something with predictable storage and performance characteristics. That is precisely what HyperLogLog (HLL) is — an algorithm optimized for counting the distinct values within a stream of data. Enormous streams of data. It is an algorithm that has become invaluable for real-time analytics. The canonical use case is tracking unique visitors to a website, but other use cases extend far beyond that.

Before HyperLogLog, there was the LogLog algorithm. Like HLL, it traded absolute accuracy for performance, speed, and a minor error rate. The author of LogLog, Philippe Flajolet, knew the error rate was too high and endeavored to create a successor that had fewer flaws. The result was HyperLogLog, which boasts a lower error rate (0.81%) and even better performance.

Fortunately, you don’t need to implement the data structure yourself in order to make use of the HLL goodness. Redis has a highly tuned implementation of HLL available as a native data structure.

Using HLL Within Redis

You can use the HyperLogLog data structure to count unique elements in a set using just a small constant amount of memory, specifically 12k bytes for every structure (plus a few bytes for the key itself).

Redis provides a couple of simple but powerful commands for working with HyperLogLog structures. The commands are all prefixed with PF, in honor of Philippe Flajolet, and are simple to experiment with. The Redis documentation is excellent and thoroughly describes the use of each command in more detail than we need.

For now, we’ll skim the basic usage of the PFADD, PFCOUNT, and PFMERGE commands. Start a new redis-cli session to experiment with and follow along:> PFADD words this is a sentence about stuff
(integer) 1> PFADD words this is another sentence about stuff
(integer) 1> PFCOUNT words
(integer) 7> PFADD alt-words stuff is a great word
(integer) 1> PFCOUNT words alt-words
(integer) 9

What we’ve done here is use PFADD to create two structures, one with the key words and another with the key alt-words. After creating the keys, we used PFCOUNT to check the cardinality at each key. After the creation of alt-words, we call PFCOUNT with multiple keys, which merges the structures in memory and gives us the combined cardinality.

It’s that simple. With such a tiny data set, we can easily see that the counts are correct. Of course, it gets far more interesting when estimating larger uncountable datasets.

Setting Up an Experiment

To make things interesting, let’s set up an experiment that leverages HLL outside the realm where it’s normally applied.

Cardinality as a measurement only has a single dimension. It becomes more informative when given a second dimension, such as change over time. We can use HLL to measure the changes in a stream of data, for example, the vocabulary of a writer over their career. This approach could be used on a Twitter stream, a blog feed, or a series of books.

Given that Redis uses linear counting for structures with less than 40,000 distinct values, we’ll want to use the most expansive sample set available. The complete works of Charles Dickens are freely available in plain text format. That should make for an expansive sample!

My hypothesis is that there will be a significant jump in vocabulary between each book — in addition to the small changes in subject and characters. Let’s see if Dickens was sporting a 40,000-word vocabulary…

Adding and Counting the Data

The interface for adding and counting values is extremely simple. Here we’ll define a simple Ruby script that will download a sample of books as raw text. Each book is then processed line by line, lightly normalized, and then pushed into Redis.

Unfortunately the URLs are not named in a very friendly way. Just know that this sample includes A Tale of Two Cities, Great Expectations, David Copperfield, and a few other popular classics.

require 'open-uri'
require 'redis'

URLS = %w[

REDIS = Redis.current

URLS.each do |url|
  text = open(url)
  name = File.basename(url)

  text.each_line do |line|
    REDIS.pfadd(name, line.split(/\s+/).map(&:downcase))

With all of the words stored, we can do some light exploration using PFCOUNT:

BOOKS.each do |name|
  puts "#{name}: #{REDIS.pfcount(name)}"

puts "All: #{REDIS.pfcount(*BOOKS)}"

That outputs the word count for each book, followed by the merged value for all of the works:

98.txt.utf-8: 18,583
1400.txt.utf-8:  21,384
730.txt.utf-8:   20,532
766.txt.utf-8:   31,601
19337.txt.utf-8: 7,405
700.txt.utf-8:   24,045
All:            65,454

Astoundingly, the cumulative distinct word count is over 65,000 words! Also astounding, but less apparent from the printed results, is that the values are reported instantaneously.

Powerful Analysis, Even Without Big Data

This is an intentionally simple example of how to use HLL for a trivial problem. However, once you grasp the fundamentals of the data structure, it opens up new avenues of analysis.

When given a problem dealing with unique values, most developers would instinctually reach for a set. Sets are perfectly suited to storing unique values, but that storage comes at a cost. In situations where the cardinality is important, such as unique visits, distinct search terms, or vocabulary, precise values are immaterial. Keep HyperLogLog in mind — it’s a data structure uniquely suited to the task of counting distinct values.

Reference: Counting Distinct Values With HyperLogLog from our WCG partner Florian Motlik at the Codeship Blog blog.

Parker Selbert

Parker is one half of Soren, a partnership that helps businesses make their production Rails apps better.
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