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.
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.
When should I use HyperLogLog?
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.
What is a HyperLogLog sketch?
HyperLogLog is a novel algorithm that efficiently estimates the approximate number of distinct values in a data set. HLL sketch is a construct that encapsulates the information about the distinct values in the data set.
What is cardinality estimation in SQL Server?
Cardinality estimation (CE) in SQL Server is derived primarily from histograms that are created when indexes or statistics are created, either manually or automatically. Sometimes, SQL Server also uses constraint information and logical rewrites of queries to determine cardinality.
What is HyperLogLog ++ HLL and why is it used in Bigquery?
HyperLogLog++ functions. The HyperLogLog++ algorithm (HLL++) estimates cardinality from sketches. If you do not want to work with sketches and do not need customized precision, consider using approximate aggregate functions with system-defined precision. HLL++ functions are approximate aggregate functions.
What is HyperLogLog in Redis?
HyperLogLog is a data structure available in Redis that is used to count the number of unique elements in a set using a small, constant amount of memory. The element count is approximated with a standard error of 0.81%.
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.
How do you find cardinality estimation?
To make sure that the Cardinality Estimator of the Query Optimizer provides good estimates, you should first make sure that the AUTO_CREATE_STATISTICS and AUTO_UPDATE_STATISTICS database SET options are ON (the default setting), or that you have manually created statistics on all columns referenced in a query condition …
Why is cardinality estimation important?
If these initial steps produce inaccurate estimates all the next steps and the query execution may be very inefficient due to the inefficient plan. That is why cardinality estimation is so important mechanism in a query optimization process.
Is Redis university free?
Redis University is free to register and take classes. Even the certificate of completion is free. Our first course is an Introduction of Redis Data Structures. It will cover the practical uses of Keys, Strings, Hashes, Lists, Sets and Sorted Sets.
What is the accuracy of HyperLogLog?
The HyperLogLog algorithm is able to estimate cardinalities of > 10 9 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 the best cardinality estimation algorithm?
And here they decided to give this method a superior name: SuperLogLog. In 2007, our dear friend Flajolet finally found out his ultimate solution for the cardinality estimation problem. This solution is HyperLogLog, which he referred to as the “near-optimal cardinality estimation algorithm”. [3]
How does HyperLogLog work?
By using harmonic mean instead of geometric mean used in LogLog and only using 70 percent smallest values in SuperLogLog, HyperLogLog achieve an error rate of 1.04/√m, the lowest among all. Now we understand how HyperLogLog works. This algorithm can estimate the number of unique values within a very large dataset using little memory and time.
What is the standard error of loglog?
So this is LogLog, averaging the estimator to decrease the variance. The standard error of LogLog is 1.3/√m, given m is the number of buckets. After coming up with Flajolet-Martin Algorithm and LogLog, our friend Flajolet is unstoppable in terms of tackling the cardinality estimation problem.