Example relevant table schema:
+---------------------------+-------------------+
| activity_date - TIMESTAMP | user_id - STRING |
+---------------------------+-------------------+
| 2017-02-22 17:36:08 UTC | fake_id_i24385787 |
+---------------------------+-------------------+
| 2017-02-22 04:27:08 UTC | fake_id_234885747 |
+---------------------------+-------------------+
| 2017-02-22 08:36:08 UTC | fake_id_i24385787 |
+---------------------------+-------------------+
I need to count active distinct users over a large data set over a rolling time period (90 days), and am running into issues due to the size of the dataset.
At first, I attempted to use a window function, similar to the answer here. https://stackoverflow.com/a/27574474
WITH
daily AS (
SELECT
DATE(activity_date) day,
user_id
FROM
`fake-table`)
SELECT
day,
SUM(APPROX_COUNT_DISTINCT(user_id)) OVER (ORDER BY day ROWS BETWEEN 89 PRECEDING AND CURRENT ROW) ninty_day_window_apprx
FROM
daily
GROUP BY
1
ORDER BY
1 DESC
However, this resulted in getting the distinct number of users per day, then summing these up - but distincts could be duplicated within the window, if they appeared multiple times. So this is not a true accurate measure of distinct users over 90 days.
The next thing I tried is to use the following solution https://stackoverflow.com/a/47659590 - concatenating all the distinct user_ids for each window to an array and then counting the distincts within this.
WITH daily AS (
SELECT date(activity_date) day, STRING_AGG(DISTINCT user_id) users
FROM `fake-table`
GROUP BY day
), temp2 AS (
SELECT
day,
STRING_AGG(users) OVER(ORDER BY UNIX_DATE(day) RANGE BETWEEN 89 PRECEDING AND CURRENT ROW) users
FROM daily
)
SELECT day,
(SELECT APPROX_COUNT_DISTINCT(id) FROM UNNEST(SPLIT(users)) AS id) Unique90Days
FROM temp2
order by 1 desc
However this quickly ran out of memory with anything large.
Next was to use a HLL sketch to represent the distinct IDs in a much smaller value, so memory would be less of an issue. I thought my problems were solved, but I'm getting an error when running the following: The error is simply "Function MERGE_PARTIAL is not supported." I tried with MERGE as well and got the same error. It only happens when using the window function. Creating the sketches for each day's value works fine.
I read through the BigQuery Standard SQL documentation and don't see anything about HLL_COUNT.MERGE_PARTIAL and HLL_COUNT.MERGE with window functions. Presumably this should take the 90 sketches and combine them into one HLL sketch, representing the distinct values between the 90 original sketches?
WITH
daily AS (
SELECT
DATE(activity_date) day,
HLL_COUNT.INIT(user_id) sketch
FROM
`fake-table`
GROUP BY
1
ORDER BY
1 DESC),
rolling AS (
SELECT
day,
HLL_COUNT.MERGE_PARTIAL(sketch) OVER (ORDER BY UNIX_DATE(day) RANGE BETWEEN 89 PRECEDING AND CURRENT ROW) rolling_sketch
FROM daily)
SELECT
day,
HLL_COUNT.EXTRACT(rolling_sketch)
FROM
rolling
ORDER BY
1
"Image of the error - Function MERGE_PARTIAL is not supported"
Any ideas why this error happens or how to adjust?
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