ClickHouse varSamp Function

The varSamp function in ClickHouse calculates the sample variance of a set of values. It's commonly used in statistical analysis to measure the variability or dispersion in a dataset, representing how far the values are spread out from their average.

Syntax

varSamp(x)

Official ClickHouse Documentation on varSamp

Example Usage

SELECT varSamp(value) AS sample_variance
FROM (
    SELECT 1 AS value
    UNION ALL SELECT 2
    UNION ALL SELECT 3
    UNION ALL SELECT 4
    UNION ALL SELECT 5
);

This query calculates the sample variance of the values 1, 2, 3, 4, and 5.

Common Issues

1. Returns nan (Not a Number) if the input contains less than two values.
2. May produce unexpected results if used with non-numeric data types.

Additional Information

• varSamp is an unbiased estimator of variance for a normal distribution.
• For population variance, use varPop instead.
• Consider using stddevSamp if you need the standard deviation instead of variance.

Frequently Asked Questions

Q: What's the difference between varSamp and varPop in ClickHouse?
A: varSamp calculates the sample variance and uses n-1 in the denominator, while varPop calculates the population variance and uses n. Use varSamp when working with a sample of a larger population.

Q: Can varSamp handle NULL values?
A: Yes, varSamp automatically ignores NULL values in its calculations.

Q: How does varSamp perform with large datasets?
A: varSamp is optimized for performance in ClickHouse and can handle large datasets efficiently. However, for extremely large datasets, consider using approximate aggregate functions if exact precision is not required.

Q: Is varSamp numerically stable?
A: Yes, ClickHouse implements varSamp using a numerically stable algorithm to minimize rounding errors, especially for large datasets or values with high variance.

Q: Can I use varSamp with window functions in ClickHouse?
A: Yes, varSamp can be used as a window function in ClickHouse, allowing you to calculate rolling or sliding window variances over ordered data.