I have a table defined like so:
CREATE TABLE Items (
Barcode CHAR(50) PRIMARY KEY NOT NULL
Location CHAR(15) ,
ManufacturedAt TIMESTAMP WITH TIMEZONE,
ShippedOutAt TIMESTAMP WITH TIMEZONE,
ReceivedAt TIMESTAMP WITH TIMEZONE,
SoldAt TIMESTAMP WITH TIMEZONE,
DiscardedAt TIMESTAMP WITH TIMEZONE,
);
I am trying to get a sum of each date field for a location over the last twelve months.
So example results I am trying to get:
Date NumManu NumShip NumRece NumSold NumDisc
DEC 5 3 3 2 1
NOV 3 5 5 3 2
I am no sql expert by any means, but I am unsure of how to do this without doing 12 different sql queries (one for each month), or is that the only way? Thanks in advance!
It can be done with a single query with either sub-selects (and with set-returning functions):
SELECT lo, hi, to_char(lo, 'MON') Date,
(SELECT count(*) FROM Items WHERE ManufacturedAt BETWEEN lo AND hi) NumManu,
(SELECT count(*) FROM Items WHERE ShippedOutAt BETWEEN lo AND hi) NumShip,
(SELECT count(*) FROM Items WHERE ReceivedAt BETWEEN lo AND hi) NumRece,
(SELECT count(*) FROM Items WHERE SoldAt BETWEEN lo AND hi) NumSold,
(SELECT count(*) FROM Items WHERE DiscardedAt BETWEEN lo AND hi) NumDisc
FROM generate_series(current_timestamp, current_timestamp - interval '11 mon', interval '-1 mon') ts,
LATERAL (select date_trunc('month', ts)) lo(lo),
LATERAL (select lo + interval '1 mon') hi(hi)
... or with multiple joins of the same table:
SELECT lo, hi, to_char(lo, 'MON') Date,
count(DISTINCT JManu) NumManu,
count(DISTINCT JShip) NumShip,
count(DISTINCT JRece) NumRece,
count(DISTINCT JSold) NumSold,
count(DISTINCT JDisc) NumDisc
FROM generate_series(current_timestamp, current_timestamp - interval '11 mon', interval '-1 mon') ts,
LATERAL (select date_trunc('month', ts)) lo(lo),
LATERAL (select lo + interval '1 mon') hi(hi)
LEFT JOIN Items JManu ON JManu.ManufacturedAt BETWEEN lo AND hi
LEFT JOIN Items JShip ON JShip.ShippedOutAt BETWEEN lo AND hi
LEFT JOIN Items JRece ON JRece.ReceivedAt BETWEEN lo AND hi
LEFT JOIN Items JSold ON JSold.SoldAt BETWEEN lo AND hi
LEFT JOIN Items JDisc ON JDisc.DiscardedAt BETWEEN lo AND hi
GROUP BY lo, hi
I may be missing something but it seems as though you could just use COUNT and then either ORDER BY or GROUP BY month.
Although I see now that you are only keeping track of the month as actual dates, so you would have to perform some sort of logic check to get dates that are BETWEEN the start and end of each month.
I believe you would have to manually account for each month in the where clause if this is the case.
Related
In PostgreSQL, I have a table that logs activity for all users, with an account ID and a timestamp field:
SELECT account_id, created FROM activity_log;
A single account_id can appear many times in a day, or not at all.
I would like a chart showing the number of "active users" each day, where "active users"
means "users who have done any activity within the previous X days".
If X is 1, then we can just truncate timestamp to 'day' and aggregate:
SELECT date_trunc('day', created) AS date, count(DISTINCT account_id)
FROM activity_log
GROUP BY date_trunc('day', created) ORDER BY date;
If X is exactly 7, then we could truncate to 'week' and aggregate - although this gives
me only one data point for a week, when I actually want one data point per day.
But I need to solve for the general case of different X, and give a distinct data point for each day.
One method is to generate the dates and then count using left join and group by or similar logic. The following uses a lateral join:
select gs.dte, al.num_accounts
from generate_series('2021-01-01'::date, '2021-01-31'::date, interval '1 day'
) gs(dte) left join lateral
(select count(distinct al.account_id) as num_accounts
from activity_log al
where al.created >= gs.dte - (<n - 1>) * interval '1 day' and
al.created < gs.dte + interval '1 day'
) al
on 1=1
order by gs.dte;
<n - 1> is one less than the number of days. So for one week, it would be 6.
If your goal is to get day wise distinct account_id for last X days you can use below query. Instead of 7 you can use any number as you wise:
SELECT date_trunc('day', created) AS date, count(DISTINCT account_id)
FROM activity_log
where date_trunc('day', created)>=date_trunc('day',CURRENT_DATE) +interval '-7' day
GROUP BY date_trunc('day', created)
ORDER BY date
(If there is no activity in any given date then the date will not be in the output.)
I am trying to get data to populate a multi-line graph. The table jobs has the columns id, created_at, and partner_id. I would like to display the sum of jobs for each partner_id each day. My current query has 2 problems. 1) It is missing a lot of jobs. 2) It only contains an entry for a given day if there was a row on that day. My current query is where start is an integer denoting how many days back we are looking for data:
SELECT d.date, count(j.id), j.partner_id FROM (
select to_char(date_trunc('day', (current_date - offs)), 'YYYY-MM-DD')
AS date
FROM generate_series(0, #{start}, 1)
AS offs
) d
JOIN (
SELECT jobs.id, jobs.created_at, jobs.partner_id FROM jobs
WHERE jobs.created_at > now() - INTERVAL '#{start} days'
) j
ON (d.date=to_char(date_trunc('day', j.created_at), 'YYYY-MM-DD'))
GROUP BY d.date, j.partner_id
ORDER BY j.partner_id, d.date;
This returns records like the following:
[{"date"=>"2019-06-21", "count"=>3, "partner_id"=>"099"},
{"date"=>"2019-06-22", "count"=>1, "partner_id"=>"099"},
{"date"=>"2019-06-21", "count"=>3, "partner_id"=>"075"},
{"date"=>"2019-06-23", "count"=>1, "partner_id"=>"099"}]
what I want is something like this:
[{"date"=>"2019-06-21", "count"=>3, "partner_id"=>"099"},
{"date"=>"2019-06-22", "count"=>1, "partner_id"=>"099"},
{"date"=>"2019-06-21", "count"=>3, "partner_id"=>"075"},
{"date"=>"2019-06-22", "count"=>0, "partner_id"=>"075"},
{"date"=>"2019-06-23", "count"=>0, "partner_id"=>"075"},
{"date"=>"2019-06-23", "count"=>1, "partner_id"=>"099"}]
So that for every day in the query I have an entry for every partner even if that count is 0. How can I adjust the query to populate data even when the count is 0?
Use a LEFT JOIN. You also don't need so many subqueries and there is no need to translate to a date to a string and then back to a date:
SELECT d.date, count(j.id), j.partner_id
FROM (SELECT to_char(dte, 'YYYY-MM-DD') AS date , dte
FROM generate_series(current_date - {start} * interval '1 day', current_date, interval '1 day') gs(dte)
) d LEFT JOIN
jobs j
ON DATE_TRUNC('day', j.created_at) = d.dte
GROUP BY d.date, j.partner_id
ORDER BY j.partner_id, d.date;
New here. I am trying to get the Daily and Weekly active users over time. they have 30 days before they are considered inactive. My goal is to create graph's that can be split by user_id to show cohorts, regions, categories, etc.
I have created a date table to get every day for the time period and I have the simplified orders table with the base info that I need to calculate this.
I am trying to do a Left Join to get the status by date using the following SQL Query:
WITH daily_use AS (
SELECT
__key__.id AS user_id
, DATE_TRUNC(date(placeOrderDate), day) AS activity_date
FROM `analysis.Order`
where isBuyingGroupOrder = TRUE
AND testOrder = FALSE
GROUP BY 1, 2
),
dates AS (
SELECT DATE_ADD(DATE "2016-01-01", INTERVAL d.d DAY) AS date
FROM
(
SELECT ROW_NUMBER() OVER(ORDER BY __key__.id) -1 AS d
FROM `analysis.Order`
ORDER BY __key__.id
LIMIT 1096
) AS d
ORDER BY 1 DESC
)
SELECT
daily_use.user_id
, wd.date AS date
, MIN(DATE_DIFF(wd.date, daily_use.activity_date, DAY)) AS days_since_last_action
FROM dates AS wd
LEFT JOIN daily_use
ON wd.date >= daily_use.activity_date
AND wd.date < DATE_ADD(daily_use.activity_date, INTERVAL 30 DAY)
GROUP BY 1,2
I am getting this Error: LEFT OUTER JOIN cannot be used without a condition that is an equality of fields from both sides of the join. In BigQuery and was wondering how can I go around this. I am using Standard SQL within BigQuery.
Thank you
Below is for BigQuery Standard SQL and mostly reproduce logic in your query with exception of not including days where no activity at all is found
#standardSQL
SELECT
daily_use.user_id
, wd.date AS DATE
, MIN(DATE_DIFF(wd.date, daily_use.activity_date, DAY)) AS days_since_last_action
FROM dates AS wd
CROSS JOIN daily_use
WHERE wd.date BETWEEN
daily_use.activity_date AND DATE_ADD(daily_use.activity_date, INTERVAL 30 DAY)
GROUP BY 1,2
-- ORDER BY 1,2
if for whatever reason you still need to exactly reproduce your logic - you can embrace above with final left join as below:
#standardSQL
SELECT *
FROM dates AS wd
LEFT JOIN (
SELECT
daily_use.user_id
, wd.date AS date
, MIN(DATE_DIFF(wd.date, daily_use.activity_date, DAY)) AS days_since_last_action
FROM dates AS wd
CROSS JOIN daily_use
WHERE wd.date BETWEEN
daily_use.activity_date AND DATE_ADD(daily_use.activity_date, INTERVAL 30 DAY)
GROUP BY 1,2
) AS daily_use
USING (date)
-- ORDER BY 1,2
I am trying to calculate the median response time for conversations on each date for the last X days.
I use the following query below, but for some reason, it will generate multiple rows with the same date.
with grouping as (
SELECT a.id, d.date, extract(epoch from (first_response_at - started_at)) as response_time
FROM (
select to_char(date_trunc('day', (current_date - offs)), 'YYYY-MM-DD') AS date
FROM generate_series(0, 2) AS offs
) d
LEFT OUTER JOIN apps a on true
LEFT OUTER JOIN conversations c ON (d.date=to_char(date_trunc('day'::varchar, c.started_at), 'YYYY-MM-DD')) and a.id = c.app_id
and c.app_id = a.id and c.first_response_at > (current_date - (2 || ' days')::interval)::date
)
select
*
from grouping
where grouping.id = 'ASnYW1-RgCl0I'
Any ideas?
First a number of issues with your query, assuming there aren't any parts you haven't shown us:
You don't need a CTE for this query.
From table apps you only use column id whose value is the same as c.app_id. You can remove the table apps and select c.app_id for the same result.
When you use to_char() you do not first have to date_trunc() to a date, the to_char() function handles that.
generate_series() also works with timestamps. Just enter day values with an interval and cast the end result to date before using it.
So, removing all the flotsam we end up with this which does exactly the same as the query in your question but now we can at least see what is going on.
SELECT c.app_id, to_date(d.date, 'YYYY-MM-DD') AS date,
extract(epoch from (first_response_at - started_at)) AS response_time
FROM generate_series(CURRENT_DATE - 2, CURRENT_DATE, interval '1 day') d(date)
LEFT JOIN conversations c ON d.date::date = c.started_at::date
AND c.app_id = 'ASnYW1-RgCl0I'
AND c.first_response_at > CURRENT_DATE - 2;
You don't calculate the median response time anywhere, so that is a big problem you need to solve. This only requires data from table conversations and would look somewhat like this to calculate the median response time for the past 2 days:
SELECT app_id, started_at::date AS start_date,
percentile_disc(0.5) WITHIN GROUP (ORDER BY first_response_at - started_at) AS median_response
FROM conversations
WHERE app_id = 'ASnYW1-RgCl0I'
AND first_response_at > CURRENT_DATE - 2
GROUP BY 2;
When we fold the two queries, and put the parameters handily in a single place, this is the final result:
SELECT p.id, to_date(d.date, 'YYYY-MM-DD') AS date,
extract(epoch from (c.median_response)) AS response_time
FROM (VALUES ('ASnYW1-RgCl0I', 2)) p(id, days)
JOIN generate_series(CURRENT_DATE - p.days, CURRENT_DATE, interval '1 day') d(date) ON true
LEFT JOIN LATERAL (
SELECT started_at::date AS start_date,
percentile_disc(0.5) WITHIN GROUP (ORDER BY first_response_at - started_at) AS median_response
FROM conversations
WHERE app_id = p.id
AND first_response_at > CURRENT_DATE - p.days
GROUP BY 2) c ON d.date::date = c.start_date;
If you want to change the id of the app or the number of days to look back, you only have to change the VALUES clause accordingly. You can also wrap the whole thing in a SQL function and convert the VALUES clause into two parameters.
Here are the two cases:
Members Lost: Get the distinct count of user ids from 365 days ago who haven't had any activity since then
Members Added: Get the distinct count of user ids from today who don't exist in the previous 365 days.
Here are the SQL statements I've been writing. Logically I feel like this should work (and it does for sample data), but the dataset is 5Million+ rows and takes forever! Is there any way to do this more efficiently? (base_date is a calendar that I'm joining on to build out a 2 year trend. I figured this was faster than joining the 5million table on itself...)
-- Members Lost
SELECT
effective_date,
COUNT(DISTINCT dwuserid) as members_lost
FROM base_date
LEFT JOIN site_visit
-- Get Login Activity for 365th day
ON DATEDIFF(day, srclogindate, effective_date) = 365
WHERE dwuserid NOT IN (
-- Get Distinct Login activity for Current Day (PY) + 1 to Current Day (CY) (i.e. 2013-01-02 to 2014-01-01)
SELECT DISTINCT dwuserid
FROM site_visit b
WHERE DATEDIFF(day, b.srclogindate, effective_date) BETWEEN 0 AND 364
)
GROUP BY effective_date
ORDER BY effective_date;
-- Members Added
SELECT
effective_date,
COUNT(DISTINCT dwuserid) as members_added
FROM base_date
LEFT JOIN site_visit ON srclogindate = effective_date
WHERE dwuserid NOT IN (
SELECT DISTINCT dwuserid
FROM site_visit b
WHERE DATEDIFF(day, b.srclogindate, effective_date) BETWEEN 1 AND 365
)
GROUP BY effective_date
ORDER BY effective_date;
Thanks in advance for any help.
UPDATE
Thanks to #JohnR for pointing me in the right direction. I had to tweak your response a bit because I need to know on any login day how many were "Member Added" or "Member Lost" so it had to be a 365 rolling window looking back or looking forward. Finding the IDs that didn't have a match in the LEFT JOIN was much faster.
-- Trim data down to one user login per day
CREATE TABLE base_login AS
SELECT DISTINCT "dwuserid", "srclogindate"
FROM site_visit
-- Members Lost
SELECT
current."srclogindate",
COUNT(DISTINCT current."dwuserid") as "members_lost"
FROM base_login current
LEFT JOIN base_login future
ON current."dwuserid" = future."dwuserid"
AND current."srclogindate" < future."srclogindate"
AND DATEADD(day, 365, current."srclogindate") >= future."srclogindate"
WHERE future."dwuserid" IS NULL
GROUP BY current."srclogindate"
-- Members Added
SELECT
current."srclogindate",
COUNT(DISTINCT current."dwuserid") as "members_added"
FROM base_login current
LEFT JOIN base_login past
ON current."dwuserid" = past."dwuserid"
AND current."srclogindate" > past."srclogindate"
AND DATEADD(day, 365, past."srclogindate") >= current."srclogindate"
WHERE past."dwuserid" IS NULL
GROUP BY current."srclogindate"
NOT IN should generally be avoided because it has to scan all data.
Instead of joining to the site_visit table (which is presumably huge), try joining to a sub-query that selects UserID and the most recent login date -- that way, there is only one row per user instead of one row per visit.
For example:
SELECT dwuserid, min (srclogindate) as first_login, max(srclogindate) as last_login
FROM site_visit
GROUP BY dwuserid
You could then simplify the queries to something like:
-- Members Lost: Last login was between 12 and 13 months ago
SELECT
COUNT(*)
FROM
(
SELECT dwuserid, min(srclogindate) as first_login, max(srclogindate) as last_login
FROM site_visit
GROUP BY dwuserid
)
WHERE
last_login BETWEEN current_date - interval '13 months' and current_date - interval '12 months'
-- Members Added: First visit in last 12 months
SELECT
COUNT(*)
FROM
(
SELECT dwuserid, min(srclogindate) as first_login, max(srclogindate) as last_login
FROM site_visit
GROUP BY dwuserid
)
WHERE
first_login > current_date - interval '12 months'