Based on the bigquery query reference, currently Quantiles do not allow any kind of grouping by another column. I am mainly interested in getting medians grouped by a certain column. The only work around I see right now is to generate a quantile query per distinct group member where the group member is a condition in the where clause.
For example I use the below query for every distinct row in column-y if I want to get the desired result.
SELECT QUANTILE( <column-x>, 1001)
FROM <table>
WHERE
<column-y> == <each distinct row in column-y>
Thanks
To get percentiles, simply ask for 100 quantiles. select percentiles[offset(10)] as p10, percentiles[offset(25)] as p25, percentiles[offset(50)] as p50, percentiles[offset(75)] as p75, percentiles[offset(90)] as p90, from ( select approx_quantiles(char_length(text), 100) percentiles from `bigquery-public-data.
Percentiles are given as percent values, values such as 95%, 40%, or 27%. Quantiles are given as decimal values, values such as 0.95, 0.4, and 0.27. The 0.95 quantile point is exactly the same as the 95th percentile point.
APPROX_QUANTILES( [DISTINCT] expression, number [{IGNORE|RESPECT} NULLS] ) APPROX_QUANTILE function will return the approximate boundaries or the values for a group of expression values, in which number represents the number of quantiles you want to create.
With the recently announced percentile_cont() window function you can get medians.
Look at the example in the announcement blog post:
http://googlecloudplatform.blogspot.com/2013/06/google-bigquery-bigger-faster-smarter-analytics-functions.html
SELECT MAX(median) AS median, room FROM (
SELECT percentile_cont(0.5) OVER (PARTITION BY room ORDER BY data) AS median, room
FROM [io_sensor_data.moscone_io13]
WHERE sensortype='temperature'
)
GROUP BY room
While there are efficient algorithms to compute quantiles they are somewhat memory intensive - trying to do multiple quantile calculations in a single query gets expensive.
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