What is cardinality algorithm?

What is cardinality algorithm?

Cardinality estimation algorithms make it possible to come up with good estimates of the number of distinct elements using memory sublinear in the number of distinct elements in the data stream. Existing cardinality estimation algorithms can be divided into two main cate- gories [3].

What is HyperLogLog used for?

A HyperLogLog is a probabilistic data structure used to count unique values — or as it’s referred to in mathematics: calculating the cardinality of a set. These values can be anything: for example, IP addresses for the visitors of a website, search terms, or email addresses.

How accurate is HLL?

As we discussed above, HLL is not 100% accurate. 99% of the time its margin of error is within 1%, with the remaining 1% of the time resulting in even larger margins of error. If the error does happen to be extremely large, it stands to reason that it would lead to extreme problems.

What is the disadvantage of Flajolet Martin algorithm?

Drawbacks of Flajolet–Martin algorithm One random occurrence of high frequency 0-prefix element from the hashing function will result in a bad estimate. A common solution to fix this is have multiple hashing functions , get the estimate from all of them and use average of them.

How accurate is HyperLogLog?

The HyperLogLog algorithm is able to estimate cardinalities of > 109 with a typical accuracy (standard error) of 2%, using 1.5 kB of memory. HyperLogLog is an extension of the earlier LogLog algorithm, itself deriving from the 1984 Flajolet–Martin algorithm.

What is HyperLogLog ++ HLL and why is it used in BigQuery?

HyperLogLog (HLL) is an algorithm that estimates how many unique elements the dataset contains. Google BigQuery has leveraged this algorithm to approximately count unique elements for very large dataset with 1 billion rows and above.

How accurate is Approx_distinct?

APPROX_DISTINCT provides a close estimate of the value returned by COUNT(DISTINCT) in far less computing time. For example, to count 100 million unique users, APPROX_DISTINCT(x) can estimate COUNT(DISTINCT x) within ±4% error rate at a quarter of the calculation time, according to BrainPad Inc.

What is FM algorithm in big data?

Flajolet Martin Algorithm, also known as FM algorithm, is used to approximate the number of unique elements in a data stream or database in one pass. The highlight of this algorithm is that it uses less memory space while executing.

What is HLL sketch?

HLL sketch is a construct that encapsulates the information about the distinct values in the data set. You can use HLL sketches to achieve significant performance benefits for queries that compute approximate cardinality over large data sets, with an average relative error between 0.01–0.6%.

What is counting distinct elements in a stream explain the Flajolet-Martin algorithm in detail?

Flajolet-Martin algorithm approximates the number of unique objects in a stream or a database in one pass. If the stream contains n elements with m of them unique, this algorithm runs in O(n) time and needs O(log(m)) memory.

Which of the following is true about bit vector length L used in Flajolet-Martin algorithm?

Wikipedia article on “United States Constitution” had 3978 unique words. When run ten times, Flajolet-Martin algorithm reported values of 4902, 4202, 4202, 4044, 4367, 3602, 4367, 4202, 4202 and 3891 for an average of 4198. As can be seen, the average is about right, but the deviation is between -400 to 1000.

What is Redis HyperLogLog?

Redis HyperLogLog is an algorithm that uses randomization in order to provide an approximation of the number of unique elements in a set using just a constant, and small amount of memory.