HyperLogLog in Presto: A significantly faster way to handle cardinality estimation

Mehrdad Honarkhah

Computing the count of distinct elements in massive data sets is often necessary but computationally intensive. Say you need to determine the number of distinct people visiting Facebook in the past…

Overview

The article discusses the implementation of the HyperLogLog (HLL) algorithm in Presto, a distributed SQL query engine, to improve the efficiency of cardinality estimation in large data sets. It highlights how HLL can significantly reduce computation time and memory usage, allowing for approximate distinct counts in massive datasets.

What You'll Learn

1

How to implement the HyperLogLog algorithm in Presto for efficient cardinality estimation

2

Why using HyperLogLog can reduce memory usage to less than 1 MB for large datasets

3

How to leverage Presto functions like APPROX_DISTINCT and MERGE for distinct count calculations

Key Questions Answered

How does HyperLogLog improve cardinality estimation in Presto?

HyperLogLog improves cardinality estimation by providing an approximate count of distinct elements with significantly reduced memory usage and computation time. Instead of requiring terabytes of memory and days of processing, HLL allows for the same calculations to be performed in about 12 hours with less than 1 MB of memory.

What are the advantages of using the APPROX_DISTINCT function in Presto?

The APPROX_DISTINCT function in Presto allows users to estimate the number of distinct values in a dataset efficiently. It leverages the HyperLogLog algorithm, reducing the standard error to about 2.3 percent for cardinalities above 256, thus providing quick and memory-efficient distinct counts.

What is the difference between sparse and dense layouts in Presto's HLL implementation?

In Presto's HLL implementation, a sparse layout is used initially to save memory for low-cardinality datasets, while a dense layout is automatically adopted for high-cardinality datasets. The dense layout allocates a fixed number of buckets from the start, optimizing performance for larger datasets.

Key Statistics & Figures

Memory usage for cardinality estimation

less than 1 MB

This is achieved using the HyperLogLog algorithm in Presto, compared to traditional methods requiring terabytes of memory.

Time to perform distinct count calculations

12 hours

This is the time taken to compute distinct counts using HyperLogLog, significantly faster than traditional methods.

Standard error for APPROX_DISTINCT function

2.3 percent

This error rate is observed for cardinalities above 256, indicating the accuracy of the HyperLogLog implementation.

Technologies & Tools

Some links below are affiliate links. We may earn a commission if you make a purchase.

Backend

Presto

Used as a distributed SQL query engine to implement the HyperLogLog algorithm for cardinality estimation.

Key Actionable Insights

1
Utilizing HyperLogLog in Presto can drastically reduce the time and resources needed for cardinality estimation.
This is particularly beneficial for large datasets where traditional methods are computationally expensive, allowing for quicker insights and analysis.

2
Implementing the APPROX_DISTINCT function can enhance performance in queries involving distinct counts.
By integrating this function, users can avoid the overhead of full data scans and leverage approximate counts for faster query responses.

3
Switching between sparse and dense layouts in Presto's HLL can optimize memory usage based on dataset characteristics.
Understanding when to use each layout allows for better resource management and performance tuning in data-intensive applications.

Common Pitfalls

1

Relying solely on traditional methods for distinct counts can lead to inefficient resource usage.

This often results in excessive memory consumption and longer processing times, especially with large datasets. Adopting HyperLogLog can mitigate these issues.

Related Concepts

Cardinality Estimation

Approximate Algorithms

Data Structures For Large Datasets