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.

Where is HyperLogLog used?

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 HyperLogLog ++ HLL?

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 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 HyperLogLog in Redis?

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.

What is cardinality estimation in SQL Server?

Cardinality Estimation (CE) is how the Query Optimizer can estimate the total number of rows processed at each level of a query plan. Cardinality estimation in SQL Server is derived primarily from histograms created when indexes or statistics are created, either manually or automatically.

What is Datasketch?

datasketch gives you probabilistic data structures that can process and search very large amount of data super fast, with little loss of accuracy. This package contains the following data sketches: Data Sketch.

What are Redis streams?

Redis Streams is a data structure that, among other functions, can effectively manage data consumption, persist data when consumers are offline with a data fail-safe, and create a data channel between many producers and consumers.

What is probabilistic data structure?

Probabilistic data structures are a group of data structures that are extremely useful for big data and streaming applications. Generally speaking, these data structures use hash functions to randomize and compactly represent a set of items.

What are the different types of cardinality?

When dealing with columnar value sets, there are three types of cardinality: high-cardinality, normal-cardinality, and low-cardinality. High-cardinality refers to columns with values that are very uncommon or unique. High-cardinality column values are typically identification numbers, email addresses, or user names.

Why is cardinality estimation important?

Cardinality estimation (CardEst) plays a significant role in generating high-quality query plans for a query optimizer in DBMS. In the last decade, an increasing number of advanced CardEst methods (especially ML-based) have been proposed with outstanding estimation accuracy and inference latency.

How is LSH implemented in Python?

Implementing LSH in Python

  1. Step 1: Load Python Packages. import numpy as np.
  2. Step 2: Exploring Your Data.
  3. Step 3: Preprocess your data.
  4. Step 4: Choose your parameters.
  5. Step 5: Create Minhash Forest for Queries.
  6. Step 6: Evaluate Queries.