I have a SQL query in BigQuery:
SELECT
creator.country,
(SUM(length) / 60) AS total_minutes,
COUNT(DISTINCT creator.id) AS total_users,
(SUM(length) / 60 / COUNT(DISTINCT creator.id)) AS minutes_per_user
FROM
...
You may have noticed that the last column is equivalent to total_minutes / total_users.
I tried this, but it doesn't work:
SELECT
creator.country,
(SUM(length) / 60) AS total_minutes,
COUNT(DISTINCT creator.id) AS total_users,
(total_minutes / total_users) AS minutes_per_user
FROM
...
Is there any way to make this simpler?
Not really. That is, you cannot re-use column aliases in expressions in the same SELECT. If you really want, you can use a subquery or CTE:
SELECT c.*,
total_minutes / total_users
FROM (SELECT creator.country,
(SUM(length) / 60) AS total_minutes,
COUNT(DISTINCT creator.id) AS total_users
FROM
) c;
Another option is to move all business logic of metrics calculation into UDF (temp or permanent depends on usage needs) ...
create temp function custom_stats(arr any type) as ((
select as struct
sum(length) / 60 as total_minutes,
count(distinct id) as total_users,
sum(length) / 60 / count(distinct id) as minutes_per_user
from unnest(arr)
));
... and thus keep query itself simple and least verbose - as in below example
select creator.country,
custom_stats(array_agg(struct(length, creator.id))).*
from `project.dataset.table`
group by country
Related
I'm having trouble with the SELECT portion of this query. I can calculate the absolute change just fine, but when I want to also find out the percent change I get lost in all the subqueries. Using BigQuery. Thank you!
SELECT
station_name,
ridership_2013,
ridership_2014,
absolute_change_2014 / ridership_2013 * 100 AS percent_change,
(ridership_2014 - ridership_2013) AS absolute_change_2014,
It will probably be beneficial to organize your query with CTEs and descriptive aliases to make things a bit easier. For example...
with
data as (select * from project.dataset.table),
ridership_by_year as (
select
extract(year from ride_date) as yr,
count(*) as rides
from data
group by 1
),
ridership_by_year_and_station as (
select
extract(year from ride_date) as yr,
station_name,
count(*) as rides
from data
group by 1,2
),
yearly_changes as (
select
this_year.yr,
this_year.rides,
prev_year.rides as prev_year_rides,
this_year.rides - coalesce(prev_year.rides,0) as absolute_change_in_rides,
safe_divide( this_year.rides - coalesce(prev_year.rides), prev_year.rides) as relative_change_in_rides
from ridership_by_year this_year
left join ridership_by_year prev_year on this_year.yr = prev_year.yr + 1
),
yearly_station_changes as (
select
this_year.yr,
this_year.station_name,
this_year.rides,
prev_year.rides as prev_year_rides,
this_year.rides - coalesce(prev_year.rides,0) as absolute_change_in_rides,
safe_divide( this_year.rides - coalesce(prev_year.rides), prev_year.rides) as relative_change_in_rides
from ridership_by_year this_year
left join ridership_by_year prev_year on this_year.yr = prev_year.yr + 1
)
select * from yearly_changes
--select * from yearly_station_changes
Yes this is a bit longer, but IMO it is much easier to understand.
Running into some issues figuring out how to add in an extra column that will give me the percentage of the total of the aggregate of the count function. The query I have looks like this:
Select
count(*) AS num_rides,
member_casual
FROM `2020_bikeshare_data`
GROUP BY member_casual
ORDER BY num_rides DESC
And returns me this result:
num_rides
member_casual
2134988
member
1341217
casual
And what I'd like to do is add a 3rd column that lists the percent of the total each membership makes up
num_rides
member_casual
perc_tot
2134988
member
61.4%
1341217
casual
38.6
thoughts?
You window functions:
SELECT member_casual,
COUNT(*) AS num_rides,
COUNT(*) * 1.0 / SUM(COUNT(*)) OVER ()
FROM `2020_bikeshare_data`
GROUP BY member_casual
ORDER BY num_rides DESC;
No subquery is needed.
Consider below approach
select distinct member_casual,
count(num_rides) over type as num_rides,
round(count(num_rides) over type * 100.0 / count(num_rides) over(), 2) as perc_tot
from `2020_bikeshare_data`
window type as (partition by member_casual)
# order by num_rides desc
if applied to sample data in your question - output is
The simplest way is use a subquery as part of the column expression to calculate your percentage:
select
count(1) as num_rides,
member_casual,
sum(100) / (select sum(1.0) from `2020_bikeshare_data`) as perc_tot -- return as percentage
from
`2020_bikeshare_data`
group by
member_casual
Using the subquery, get the total number of rows and calculate the percentage accordingly.
Select
count(*) AS num_rides,
member_casual,
Concat(count(*) * 100 / totalRecord,' %') as perc_tot
FROM (SELECT *,COUNT(*) as totalRecord FROM `2020_bikeshare_data`)
GROUP BY member_casual
or
Select
count(*) AS num_rides,
member_casual,
Concat(count(*) * 100 / (SELECT COUNT(*) FROM `2020_bikeshare_data`) ,' %') as perc_tot
FROM `2020_bikeshare_data`
GROUP BY member_casual
In addition to the other answers, you can also break this down into simple SQL (without window functions) by organizing with CTEs.
with
data as (select * from `2020_bikeshare_data`),
total as (select count(*) as ride_count from data),
by_type as (select member_casual, count(*) as ride_count from data group by 1)
select
member_casual,
by_type.ride_count as num_rides,
by_type.ride_count / total.ride_count as perc_tot
from by_type
cross join total
In my opinion, this is much easier to see the perc_tot calculation.
This query would be too heavy, needs to be refactored. How can I do that?
Please help
SELECT
contract_type, SUM(fte), ROUND(SUM(fte * 100 / t.s ), 0) AS "% of total"
FROM
design_studio_testing.empfinal_tableau
CROSS JOIN
(SELECT SUM(fte) AS s
FROM design_studio_testing.empfinal_tableau) t
GROUP BY
contract_type;
Output should be like this:
Use window functions:
SELECT contract_type,
SUM(fte),
ROUND(SUM(fte) * 100.0 / SUM(SUM(fte)) OVER (), 0) AS "% of total"
FROM design_studio_testing.empfinal_tableau
GROUP BY contract_type;
That said, your original version should not be that much slower than this, unless perhaps empfinal_tableau is a view.
If it is a table, you could further speed this with an index on empfinal_tableau(contract_type, fte).
There is no need to sum over the expression:
fte * 100 / t.s
which may slow the process.
Get SUM(fte) and then multiply and divide:
SELECT g.contract_type, g.sum_fte,
ROUND(100.0 * g.sum_fte / t.s, 0) AS [% of total]
FROM (
SELECT
contract_type,
SUM(fte) AS sum_fte
FROM design_studio_testing.empfinal_tableau
GROUP BY contract_type
) AS g CROSS JOIN (SELECT SUM(fte) AS s FROM design_studio_testing.empfinal_tableau) t
Edit for Oracle:
SELECT g.contract_type, g.sum_fte,
ROUND(100.0 * g.sum_fte / t.s, 0) AS "% of total"
FROM (
SELECT
contract_type,
SUM(fte) AS sum_fte
FROM empfinal_tableau
GROUP BY contract_type
) g CROSS JOIN (SELECT SUM(fte) AS s FROM empfinal_tableau) t
I am trying to get a claim denied percentage count (total_count / denied_count * 100) for providers with under 100 claims. I am able to get the total count and denied count with separate queries, but I am having trouble pulling everything together.
SELECT
PROVID,
COUNT(CLAIMID) AS TOTAL_COUNT,
COUNT(CLAIMID) / (SELECT COUNT(CLAIMID) * 100
FROM #TEMPSTAGE
WHERE STATUS = 'DENY') AS DENIED_PERCENTAGE
FROM
#TEMPSTAGE
WHERE
PROVID IN (SELECT DISTINCT PROVID
FROM #TEMPSTAGE
GROUP BY PROVID
HAVING COUNT(CLAIMID) <= 100)
GROUP BY
PROVID
Results example:
ProvID / Total_Count / Denied Percentage
-----------------------------------------
X12345 / 77 / 0
I am getting zero denied percentage for everything as my subquery in the select statement isn't allowing me to group by provid.
Error
Only one expression can be specified in the select list when the subquery is not introduced with EXISTS.
What's the best way to go about this??
As per most languages, if you do 1 / 2 with integers, the result is 0, because there is no integer for 0.5. To get a decimal (fixed point of floating point) you need to convert the datatypes.
How depends on your dialect of SQL (MySQL, SQL Server, Oracle, PostgreSQL, etc).
CAST(COUNT(CLAIMID) AS FLOAT)
CAST(COUNT(CLAIMID) AS DECIMAL(10, 4))
COUNT(CLAIMID) * 1.0
etc, etc
Next, to use IN the list needs to be in braces IN (1, 2, 3), but to use a sub query the query needs to be in braces (SELECT x FROM y).
That means to use both you need two pairs of braces IN ((SELECT x FROM y))
So, the smallest changes to your query are...
SELECT
PROVID,
COUNT(CLAIMID) AS TOTAL_COUNT,
COUNT(CLAIMID) / (SELECT COUNT(CLAIMID) * 100.0
FROM #TEMPSTAGE
WHERESTATUS = 'DENY') AS DENIED_PERCENTAGE
FROM
#TEMPSTAGE
WHERE
PROVID IN ((SELECT PROVID
FROM #TEMPSTAGE
GROUP BY PROVID
HAVING COUNT(CLAIMID) <= 100))
GROUP BY
PROVID
That said, the subquery in where clause can just be moved to the main query...
SELECT
PROVID,
COUNT(CLAIMID) AS TOTAL_COUNT,
COUNT(CLAIMID) / (SELECT COUNT(CLAIMID) * 100.0
FROM #TEMPSTAGE
WHERE STATUS = 'DENY') AS DENIED_PERCENTAGE
FROM
#TEMPSTAGE
GROUP BY
PROVID
HAVING
COUNT(CLAIMID) <= 100
Also, I've removed the DISTINCT keywords. If you're using GROUP BY the way you are you don't need it.
EDITTED : Following comment
You can skip the sub-query and just sum the number of rows in the group where the status is 'DENY'.
Also, a percentage is (x * 100) / y not x / (y * 100), so I reversed the calculation.
SELECT
PROVID,
COUNT(CLAIMID) AS TOTAL_COUNT,
SUM(CASE WHEN STATUS = 'DENY' THEN 1 ELSE 0 END) * 100.0 / COUNT(CLAIMID) AS DENIED_PERCENTAGE
FROM
#TEMPSTAGE
GROUP BY
PROVID
HAVING
COUNT(CLAIMID) <= 100
I need help formulating a cohort/retention query
I am trying to build a query to look at visitors who performed ActionX on their first visit (in the time frame) and then how many days later they returned to perform Action X again
The output I (eventually) need looks like this...
The table I am dealing with is an export of Google Analytics to BigQuery
If anyone could help me with this or anyone who has written a query similar that I can manipulate?
Thanks
Just to give you simple idea / direction
Below is for BigQuery Standard SQL
#standardSQL
SELECT
Date_of_action_first_taken,
ROUND(100 * later_1_day / Visits) AS later_1_day,
ROUND(100 * later_2_days / Visits) AS later_2_days,
ROUND(100 * later_3_days / Visits) AS later_3_days
FROM `OutputFromQuery`
You can test it with below dummy data from your question
#standardSQL
WITH `OutputFromQuery` AS (
SELECT '01.07.17' AS Date_of_action_first_taken, 1000 AS Visits, 800 AS later_1_day, 400 AS later_2_days, 300 AS later_3_days UNION ALL
SELECT '02.07.17', 1000, 860, 780, 860 UNION ALL
SELECT '29.07.17', 1000, 780, 120, 0 UNION ALL
SELECT '30.07.17', 1000, 710, 0, 0
)
SELECT
Date_of_action_first_taken,
ROUND(100 * later_1_day / Visits) AS later_1_day,
ROUND(100 * later_2_days / Visits) AS later_2_days,
ROUND(100 * later_3_days / Visits) AS later_3_days
FROM `OutputFromQuery`
The OutputFromQuery data is as below:
Date_of_action_first_taken Visits later_1_day later_2_days later_3_days
01.07.17 1000 800 400 300
02.07.17 1000 860 780 860
29.07.17 1000 780 120 0
30.07.17 1000 710 0 0
and the final output is:
Date_of_action_first_taken later_1_day later_2_days later_3_days
01.07.17 80.0 40.0 30.0
02.07.17 90.0 78.0 86.0
29.07.17 80.0 12.0 0.0
30.07.17 70.0 0.0 0.0
I found this query on Turn Your App Data into Answers with Firebase and BigQuery (Google I/O'19)
It should work :)
#standardSQL
###################################################
# Part 1: Cohort of New Users Starting on DEC 24
###################################################
WITH
new_user_cohort AS (
SELECT DISTINCT
user_pseudo_id as new_user_id
FROM
`[your_project].[your_firebase_table].events_*`
WHERE
event_name = `[chosen_event] ` AND
#set the date from when starting cohort analysis
FORMAT_TIMESTAMP("%Y%m%d", TIMESTAMP_TRUNC(TIMESTAMP_MICROS(event_timestamp), DAY, "Etc/GMT+1")) = '20191224' AND
_TABLE_SUFFIX BETWEEN '20191224' AND '20191230'
),
num_new_users AS (
SELECT count(*) as num_users_in_cohort FROM new_user_cohort
),
#############################################
# Part 2: Engaged users from Dec 24 cohort
#############################################
engaged_users_by_day AS (
SELECT
FORMAT_TIMESTAMP("%Y%m%d", TIMESTAMP_TRUNC(TIMESTAMP_MICROS(event_timestamp), DAY, "Etc/GMT+1")) as event_day,
COUNT(DISTINCT user_pseudo_id) as num_engaged_users
FROM
`[your_project].[your_firebase_table].events_*`
INNER JOIN
new_user_cohort ON new_user_id = user_pseudo_id
WHERE
event_name = 'user_engagement' AND
_TABLE_SUFFIX BETWEEN '20191224' AND '20191230'
GROUP BY
event_day
)
####################################################################
# Part 3: Daily Retention = [Engaged Users / Total Users]
####################################################################
SELECT
event_day,
num_engaged_users,
num_users_in_cohort,
ROUND((num_engaged_users / num_users_in_cohort), 3) as retention_rate
FROM
engaged_users_by_day
CROSS JOIN
num_new_users
ORDER BY
event_day
So I think I may have cracked it... from this output I then would need to manipulate it (pivot table it) to make it look like the desired output.
Can anyone review this for me and let me know what you think?
`WITH
cohort_items AS (
SELECT
MIN( TIMESTAMP_TRUNC(TIMESTAMP_MICROS((visitStartTime*1000000 +
h.time*1000)), DAY) ) AS cohort_day, fullVisitorID
FROM
TABLE123 AS U,
UNNEST(hits) AS h
WHERE _TABLE_SUFFIX BETWEEN "20170701" AND "20170731"
AND 'ACTION TAKEN'
GROUP BY 2
),
user_activites AS (
SELECT
A.fullVisitorID,
DATE_DIFF(DATE(TIMESTAMP_TRUNC(TIMESTAMP_MICROS((visitStartTime*1000000 + h.time*1000)), DAY)), DATE(C.cohort_day), DAY) AS day_number
FROM `Table123` A
LEFT JOIN cohort_items C ON A.fullVisitorID = C.fullVisitorID,
UNNEST(hits) AS h
WHERE
A._TABLE_SUFFIX BETWEEN "20170701 AND "20170731"
AND 'ACTION TAKEN'
GROUP BY 1,2),
cohort_size AS (
SELECT
cohort_day,
count(1) as number_of_users
FROM
cohort_items
GROUP BY 1
ORDER BY 1
),
retention_table AS (
SELECT
C.cohort_day,
A.day_number,
COUNT(1) AS number_of_users
FROM
user_activites A
LEFT JOIN cohort_items C ON A.fullVisitorID = C.fullVisitorID
GROUP BY 1,2
)
SELECT
B.cohort_day,
S.number_of_users as total_users,
B.day_number,
B.number_of_users / S.number_of_users as percentage
FROM retention_table B
LEFT JOIN cohort_size S ON B.cohort_day = S.cohort_day
WHERE B.cohort_day IS NOT NULL
ORDER BY 1, 3
`
Thank you in advance!
If you use some techniques available in BigQuery, you can potentially solve this type of problem with very cost and performance effective solutions. As an example:
SELECT
init_date,
ARRAY((SELECT AS STRUCT days, freq, ROUND(freq * 100 / MAX(freq) OVER(), 2) FROM UNNEST(data) ORDER BY days)) data
FROM(
SELECT
init_date,
ARRAY_AGG(STRUCT(days, freq)) data
FROM(
SELECT
init_date,
data AS days,
COUNT(data) freq
FROM(
SELECT
init_date,
ARRAY(SELECT DATE_DIFF(PARSE_DATE("%Y%m%d", dts), PARSE_DATE("%Y%m%d", init_date), DAY) AS dt FROM UNNEST(dts) dts) data
FROM(
SELECT
MIN(date) init_date,
ARRAY_AGG(DISTINCT date) dts
FROM `Table123`
WHERE TRUE
AND EXISTS(SELECT 1 FROM UNNEST(hits) where eventinfo.eventCategory = 'recommendation') -- This is your 'ACTION TAKEN' filter
AND _TABLE_SUFFIX BETWEEN "20170724" AND "20170731"
GROUP BY fullvisitorid
)
),
UNNEST(data) data
GROUP BY init_date, days
)
GROUP BY init_date
)
I tested this query against our G.A data and selected customers who interacted with our recommendation system (as you can see in the filter selection WHERE EXISTS...). Example of result (omitted absolute values of freq for privacy reasons):
As you can see, at day 28th for instance, 8% of customers came back 1 day later and interacted with the system again.
I recommend you to play around with this query and see if it works well for you. It's simpler, cheaper, faster and hopefully easier to maintain.