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.
Related
Hello I am trying to calculate the time difference of 2 consecutive rows for Date (either in hours or Days), as attached in the image
Highlighted in Yellow is the result I want which is basically the difference of the date in that row and 1 above.
How can we achieve it in the SQL? Attached is my complex code which has the rest of the fields in it
with cte
as
(
select m.voucher_no, CONVERT(VARCHAR(30),CONVERT(datetime, f.action_Date, 109),100) as action_date,f.col1_Value,f.col3_value,f.col4_value,f.comments,f.distr_user,f.wf_status,f.action_code,f.wf_user_id
from attdetailmap m
LEFT JOIN awftaskfin f ON f.oid = m.oid and f.client ='PC'
where f.action_Date !='' and action_date between '$?datef' and '$?datet'
),
.*select *, ROW_NUMBER() OVER(PARTITION BY action_Date,distr_user,wf_Status,wf_user_id order by action_Date,distr_user,wf_Status,wf_user_id ) as row_no_1 from cte
cte2 as
(
select *, ROW_NUMBER() OVER(PARTITION BY voucher_no,action_Date,distr_user,wf_Status,wf_user_id order by voucher_no ) as row_no_1 from cte
)
select distinct(v.dim_value) as resid,c.voucher_no,CONVERT(datetime, c.action_Date, 109) as action_Date,c.col4_value,c.comments,c.distr_user,v.description,c.wf_status,c.action_code, c.wf_user_id,v1.description as name,r.rel_value as pay_office,r1.rel_value as site
from cte2 c
LEFT OUTER JOIN aagviuserdetail v ON v.user_id = c.distr_user
LEFT OUTER JOIN aagviuserdetail v1 ON v1.user_id = c.wf_user_id
LEFT OUTER JOIN ahsrelvalue r ON r.resource_id = v.dim_Value and r.rel_Attr_id = 'P1' and r.period_to = '209912'
LEFT OUTER JOIN ahsrelvalue r1 ON r1.resource_id = v.dim_Value and r1.rel_Attr_id = 'Z1' and r1.period_to = '209912'
where c.row_no_1 = '1' and r.rel_value like '$?site1' and voucher_no like '$?trans'
order by voucher_no,action_Date
The key idea is lag(). However, date/time functions vary among databases. So, the idea is:
select t.*,
(date - lag(date) over (partition by transaction_no order by date)) as diff
from t;
I should note that this exact syntax might not work in your database -- because - may not even be defined on date/time values. However, lag() is a standard function and should be available.
For instance, in SQL Server, this would look like:
select t.*,
datediff(second, lag(date) over (partition by transaction_no order by date), date) / (24.0 * 60 * 60) as diff_days
from t;
I have a simple sql query with some aggregations, there is no problem with the query itself, I am looking into its execution plan and don't know where are those aggregations in the plan come from the query itself:
Table:
Query (this query contains string operation, group by, order by and join, purpose: to get the reporting period that total amount increased certain target over the years):
WITH cte
AS (SELECT Year(orderdate) AS yr,
Month(orderdate) AS mon,
Ltrim(Rtrim(Str(Year(orderdate)))) + '-'
+ Ltrim(Rtrim(Str(Month(orderdate)))) AS theMonth,
Sum(totalamount) AS theAmount
FROM [order]
GROUP BY Year(orderdate),
Month(orderdate),
Ltrim(Rtrim(Str(Year(orderdate)))) + '-'
+ Ltrim(Rtrim(Str(Month(orderdate)))))
SELECT TOP 3 cte.themonth,
cte_prev.themonth AS thePrevMonth,
cte.theamount,
cte_prev.theamount AS thePrevAmount,
( cte.theamount - cte_prev.theamount ) AS diff
FROM cte
JOIN cte cte_prev
ON cte.yr = cte_prev.yr + 1
AND cte.mon = cte_prev.mon
WHERE ( cte.theamount - cte_prev.theamount ) / cte_prev.theamount > 0.8
ORDER BY ( cte.theamount - cte_prev.theamount ) / cte_prev.theamount DESC
Execution plan:
I wonder how can I create a better/simpler query to calculate the difference between two reporting period? and the string trimming is really annoying here: why there is no simple and single trim but have to ltrim and rtrim?
I have a table that has data like following.
attr |time
----------------|--------------------------
abc |2018-08-06 10:17:25.282546
def |2018-08-06 10:17:25.325676
pqr |2018-08-05 10:17:25.366823
abc |2018-08-06 10:17:25.407941
def |2018-08-05 10:17:25.449249
I want to group them and count by attr column row wise and also create additional columns in to show their counts per day and percentages as shown below.
attr |day1_count| day1_%| day2_count| day2_%
----------------|----------|-------|-----------|-------
abc |2 |66.6% | 0 | 0.0%
def |1 |33.3% | 1 | 50.0%
pqr |0 |0.0% | 1 | 50.0%
I'm able to display one count by using group by but unable to find out how to even seperate them to multiple columns. I tried to generate day1 percentage with
SELECT attr, count(attr), count(attr) / sum(sub.day1_count) * 100 as percentage from (
SELECT attr, count(*) as day1_count FROM my_table WHERE DATEPART(week, time) = DATEPART(day, GETDate()) GROUP BY attr) as sub
GROUP BY attr;
But this also is not giving me correct answer, I'm getting all zeroes for percentage and count as 1. Any help is appreciated. I'm trying to do this in Redshift which follows postgresql syntax.
Let's nail the logic before presenting:
with CTE1 as
(
select attr, DATEPART(day, time) as theday, count(*) as thecount
from MyTable
)
, CTE2 as
(
select theday, sum(thecount) as daytotal
from CTE1
group by theday
)
select t1.attr, t1.theday, t1.thecount, t1.thecount/t2.daytotal as percentofday
from CTE1 t1
inner join CTE2 t2
on t1.theday = t2.theday
From here you can pivot to create a day by day if you feel the need
I am trying to enhance the query #johnHC btw if you needs for 7days then you have to those days in case when
with CTE1 as
(
select attr, time::date as theday, count(*) as thecount
from t group by attr,time::date
)
, CTE2 as
(
select theday, sum(thecount) as daytotal
from CTE1
group by theday
)
,
CTE3 as
(
select t1.attr, EXTRACT(DOW FROM t1.theday) as day_nmbr,t1.theday, t1.thecount, t1.thecount/t2.daytotal as percentofday
from CTE1 t1
inner join CTE2 t2
on t1.theday = t2.theday
)
select CTE3.attr,
max(case when day_nmbr=0 then CTE3.thecount end) as day1Cnt,
max(case when day_nmbr=0 then percentofday end) as day1,
max(case when day_nmbr=1 then CTE3.thecount end) as day2Cnt,
max( case when day_nmbr=1 then percentofday end) day2
from CTE3 group by CTE3.attr
http://sqlfiddle.com/#!17/54ace/20
In case that you have only 2 days:
http://sqlfiddle.com/#!17/3bdad/3 (days descending as in your example from left to right)
http://sqlfiddle.com/#!17/3bdad/5 (days ascending)
The main idea is already mentioned in the other answers. Instead of joining the CTEs for calculating the values I am using window functions which is a bit shorter and more readable I think. The pivot is done the same way.
SELECT
attr,
COALESCE(max(count) FILTER (WHERE day_number = 0), 0) as day1_count, -- D
COALESCE(max(percent) FILTER (WHERE day_number = 0), 0) as day1_percent,
COALESCE(max(count) FILTER (WHERE day_number = 1), 0) as day2_count,
COALESCE(max(percent) FILTER (WHERE day_number = 1), 0) as day2_percent
/*
Add more days here
*/
FROM(
SELECT *, (count::float/count_per_day)::decimal(5, 2) as percent -- C
FROM (
SELECT DISTINCT
attr,
MAX(time::date) OVER () - time::date as day_number, -- B
count(*) OVER (partition by time::date, attr) as count, -- A
count(*) OVER (partition by time::date) as count_per_day
FROM test_table
)s
)s
GROUP BY attr
ORDER BY attr
A counting the rows per day and counting the rows per day AND attr
B for more readability I convert the date into numbers. Here I take the difference between current date of the row and the maximum date available in the table. So I get a counter from 0 (first day) up to n - 1 (last day)
C calculating the percentage and rounding
D pivot by filter the day numbers. The COALESCE avoids the NULL values and switched them into 0. To add more days you can multiply these columns.
Edit: Made the day counter more flexible for more days; new SQL Fiddle
Basically, I see this as conditional aggregation. But you need to get an enumerator for the date for the pivoting. So:
SELECT attr,
COUNT(*) FILTER (WHERE day_number = 1) as day1_count,
COUNT(*) FILTER (WHERE day_number = 1) / cnt as day1_percent,
COUNT(*) FILTER (WHERE day_number = 2) as day2_count,
COUNT(*) FILTER (WHERE day_number = 2) / cnt as day2_percent
FROM (SELECT attr,
DENSE_RANK() OVER (ORDER BY time::date DESC) as day_number,
1.0 * COUNT(*) OVER (PARTITION BY attr) as cnt
FROM test_table
) s
GROUP BY attr, cnt
ORDER BY attr;
Here is a SQL Fiddle.
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.
My initial query looks like this:
select process_date, count(*) batchCount
from T1.log_comments
order by process_date asc;
I need to be able to do some quick analysis for weekends that are missing, but wanted to know if there was a quick way to fill in the missing dates not present in process_date.
I've seen the solution here but am curious if there's any magic hidden in db2 that could do this with only a minor modification to my original query.
Note: Not tested, framed it based on my exposure to SQL Server/Oracle. I guess this gives you the idea though:
*now amended and tested on DB2*
WITH MaxDateQry(MaxDate) AS
(
SELECT MAX(process_date) FROM T1.log_comments
),
MinDateQry(MinDate) AS
(
SELECT MIN(process_date) FROM T1.log_comments
),
DatesData(ProcessDate) AS
(
SELECT MinDate from MinDateQry
UNION ALL
SELECT (ProcessDate + 1 DAY) FROM DatesData WHERE ProcessDate < (SELECT MaxDate FROM MaxDateQry)
)
SELECT a.ProcessDate, b.batchCount
FROM DatesData a LEFT JOIN
(
SELECT process_date, COUNT(*) batchCount
FROM T1.log_comments
) b
ON a.ProcessDate = b.process_date
ORDER BY a.ProcessDate ASC;