The median function in ClickHouse calculates the median value of a numeric data set. It's commonly used in statistical analysis and data processing to find the middle value in a sorted list of numbers, providing a measure of central tendency that is less affected by outliers compared to the mean.
Syntax
median(x) or MEDIAN(x)
Example usage
SELECT median(salary) AS median_salary
FROM employees
WHERE department = 'Sales';
Common issues
- Performance can be slower compared to other aggregation functions for large datasets.
- Results may be approximate for very large datasets due to the use of reservoir sampling.
Best practices
- Consider using
medianExactfor smaller datasets where precision is critical. - For large datasets,
medianTDigestcan provide faster approximate results. - Use
medianin combination with other statistical functions likeavgandpercentilefor a comprehensive data analysis.
Frequently Asked Questions
Q: What's the difference between median and avg in ClickHouse?
A: median returns the middle value in a sorted list of numbers, while avg calculates the arithmetic mean. median is less affected by extreme outliers compared to avg.
Q: How accurate is the median function for large datasets?
A: For large datasets, ClickHouse uses an algorithm that may provide an approximate result. For exact results on large datasets, consider using medianExact, but be aware it may be slower.
Q: Can median be used with non-numeric data types?
A: No, median is designed for numeric data types. For other data types, you may need to use different aggregation functions or convert the data to a numeric representation first.
Q: How does median handle NULL values?
A: median ignores NULL values in its calculations. If you need to include NULL values in your analysis, you should handle them explicitly in your query.
Q: Is there a way to calculate weighted median in ClickHouse?
A: ClickHouse doesn't have a built-in weighted median function. For weighted median calculations, you might need to implement a custom solution using other available functions or consider using approximate functions like medianTDigest with weights.
