我正在尝试根据特定的日期和星期来计算数字列“消费”的总和。我已经成功地为当前周的一天以及从上周开始的单日成功生成了“消费”的数值。但是，我无法生成基于一周中特定天数总计“消费”的列。
我尝试按周分组，按天分组，并使用SUM（CASE WHEN THEN END）语句，但无法连续几天产生总消耗量
SELECT final.master_track_id,
final.artist,
final.track,
final.global_week,
final.dayofglobalweek,
final.transaction_date,
final.consumption,
final.consumption AS previous_consumption,
final.TW_Three_Day_Trend,
final.LW_Three_Day_Trend,
(final.TW_Three_Day_Trend - final.LW_Three_Day_Trend) / NULLIF(final.LW_Three_Day_Trend,0) AS Final_Delta
FROM (
#Select your columns here
SELECT DISTINCT
cur.master_track_id,
cur.artist,
cur.track,
cur.global_week,
cur.dayofglobalweek,
cur.transaction_date,
cur.consumption,
prev.consumption AS previous_consumption,
#The total change in consumption (TW - LW) / LW
((cur.consumption - prev.consumption) / prev.consumption) * 100.0 AS PER_DAY_DELTA,
#First case statement that creates category for the TW_Three_Day_Trend
SUM(CASE
WHEN cur.dayofglobalweek IN (6,7,1) THEN cur.consumption ELSE 0
END) AS TW_Three_Day_Trend,
#Second case statement that creates category for the LW_Three_Day_Trend
SUM(CASE
WHEN prev.dayofglobalweek IN (6,7,1) THEN prev.consumption ELSE 0
END) AS LW_Three_Day_Trend
#create the places from which you'll pull your various columns from. This will involve creating new tables
FROM (
SELECT
chae.master_track_id,
chae.artist,
chae.track,
t1.transaction_date,
t2.dayofglobalweek,
t2.global_week,
SUM(t1.consumption) AS consumption
#create the first set of transactions table
FROM (
SELECT
isrc,
transaction_date,
SUM(track_adjusted_units) AS consumption
FROM
`umg-marketing.consumption.transactions`
WHERE
transaction_date >= '2019-02-15'
AND transaction_date <= '2019-04-15'
AND sales_country_code = "US"
and sales_currency_code = "USD"
AND subject_area IN ("Streams Sales", "Digital Sales")
AND usage_type NOT IN ("Play / Stream (Webcasting)")
AND product_type IN ("Track")
AND transaction_type = "Sale"
GROUP BY
isrc,
transaction_date ) t1
#join first set of tables together
#create the first instance of the product table
LEFT JOIN (
SELECT
master_track_id,
master_artist,
master_track,
isrc
FROM
`umg-marketing.metadata.product` ) p
ON
p.isrc = t1.isrc
#join the day table
#create the first instance of the day table
LEFT JOIN (
SELECT
day,
global_week,
dayofglobalweek
FROM
`umg-marketing.metadata.day` ) t2
ON
t1.transaction_date = t2.day
#join the dchae table
#create the first instance of the chae table
LEFT JOIN (
SELECT
master_track_id,
artist,
track
FROM
`umg-user.chaed.test_FTL` ) chae
ON
chae.master_track_id = p.master_track_id
#Group by statement that brings the entire first instance together
GROUP BY
chae.master_track_id,
chae.artist,
chae.track,
t1.transaction_date,
t2.global_week,
t2.dayofglobalweek
#end of "cur" instance
) cur
#now you join the second instance of the tables labeled as "prev"
LEFT JOIN (
#pulling the second set of columns to pull from labeled as "prev"
SELECT
chae.master_track_id,
chae.artist,
chae.track,
t2.dayofglobalweek,
t2.global_week,
SUM(t1.consumption) AS consumption
FROM (
#create the second instance of transactions table
SELECT
isrc,
transaction_date,
SUM(track_adjusted_units) AS consumption
FROM
`umg-marketing.consumption.transactions`
WHERE
transaction_date >= '2019-02-15'
AND transaction_date <= '2019-04-15'
AND sales_country_code = "US"
and sales_currency_code = "USD"
AND subject_area IN ("Streams Sales", "Digital Sales")
AND usage_type NOT IN ("Play / Stream (Webcasting)")
AND product_type IN ("Track")
AND transaction_type = "Sale"
GROUP BY
isrc,
transaction_date ) t1
LEFT JOIN (
#create the second instance of product table
SELECT
master_track_id,
master_artist,
master_track,
isrc
FROM
`umg-marketing.metadata.product` ) p
ON
p.isrc = t1.isrc
LEFT JOIN (
#create the second instance of day table
SELECT
day,
dayofglobalweek,
global_week
FROM
`umg-marketing.metadata.day` ) t2
ON
t1.transaction_date = t2.day
LEFT JOIN (
#create the second instance of chae table
SELECT
master_track_id,
artist,
track
FROM
`umg-user.chaed.test_FTL` ) chae
ON
chae.master_track_id = p.master_track_id
#Group by statement for the prev
GROUP BY
chae.master_track_id,
chae.artist,
chae.track,
t2.dayofglobalweek,
t2.global_week
) prev
#most outermost join on these criteria
ON
cur.master_track_id = prev.master_track_id
AND cur.global_week - 1 = prev.global_week
AND cur.dayofglobalweek = prev.dayofglobalweek
#outermost Group By statement for the entire query
GROUP BY
cur.master_track_id,
cur.artist,
cur.track,
cur.global_week,
cur.dayofglobalweek,
cur.transaction_date,
cur.consumption,
prev.consumption,
prev.dayofglobalweek
) final
WHERE master_track_id IS NOT NULL
AND artist IS NOT NULL
AND track IS NOT NULL
ORDER BY global_week, dayofglobalweek, final.consumption
我希望每天的消费量是多天的总和（大约是三倍），但是TW / LW_Three_Day_Trend与单日的数字相同。output
