When i m running this query giving result in two different rows of same date one contains zero other contains events count????
How to solve this, any help will be really appreciated!
(Select
distinct(case
when event_text = 'poll_vote' THEN device_id Else 0 END) as
pollvote,event_date from
(Select event_date,event_text,count(distinct users) as device_id from
(SELECT event.name as event_text, ( user.value.value.string_value)
AS users,
CAST(TIMESTAMP_ADD(TIMESTAMP_MICROS(event.timestamp_micros),
INTERVAL 330 MINUTE) AS date) AS event_date
FROM
`dataset.tablename`,
UNNEST(event_dim) AS event,
UNNEST(user_dim.user_properties) AS user
where
user.key="context_device_id"
GROUP BY
event_date,event_text,users)
GROUP BY
event_text,event_date))
Using ‘GROUP BY’ for event_date only should give you only one column as you wanted. Here are some of the GROUP BY examples.
Related
I am writing a large query with many JOINs (shortened it in example here) and I am trying to reference values form other sub-queries but can't figure out how.
This is my example query:
DROP TABLE IF EXISTS breakdown;
CREATE TEMP TABLE breakdown AS
SELECT * FROM
(
SELECT COUNT(DISTINCT s_id) AS before, date_trunc('day', time) AS day FROM table_a
WHERE date_trunc('sec',earliest) < date_trunc('sec',time) GROUP BY day
)
JOIN
(
SELECT ROUND(before * 100.0 / total, 1) AS Percent_1, day
FROM breakdown
GROUP BY day
) USING (day)
JOIN
(
SELECT COUNT(DISTINCT s_id) AS equal, date_trunc('day', time) AS day FROM table_a
WHERE date_trunc('sec',earliest) = date_trunc('sec',time) GROUP BY day
) USING (day)
JOIN
(
SELECT COUNT(DISTINCT s_id) AS after, date_trunc('day', time) AS day FROM table_a
WHERE date_trunc('sec',earliest) > date_trunc('sec',time) GROUP BY day
) USING (day)
JOIN
(
SELECT COUNT(DISTINCT s_id) AS total, date_trunc('day', earliest) AS day
FROM first
GROUP BY 2
) USING (day)
ORDER BY day;
SELECT * FROM breakdown ORDER BY day;
The last query gives me the total and for each of the previous subqueries I want to get the percentages as well.
I found the code for getting the percentage (second JOIN) but I don't know how to reference the values from the other tables.
E.g. for getting the percentage from the first query I want to use the COUNT of the first query which I renamed before and then divide that by the COUNT of the last query which I renamed total (If there is an easier solution to do this i.e. get the percentage for each of the sub-queries please let me know), But I cant seem to find how to reference them. I tried adding AS x to the end of each subquery and calling by that (x.total) as well as trying to reference via the parent table (breakdown.total) but neither worked.
How can I do this without changing my table too much as it is a long table with a lot of sub-queries.
This is what my table looks like I would like to add percentage for each column
Using redshift BTW.
Thanks
I'm a little confused by all that is going on as you drop table breakdown and then in the second subquery of the create table you reference breakdown. I suspect that there are some issues in the provided sample of SQL. Please update if there are issues.
For a number of these subqueries it looks like you are using a subquery where a case statement will do. In Redshift you don't want to scan the same table over and over if you can prevent it. For example if we look at the the 3rd and 4th subqueries you can replace these with one query. Also in these cases I like to use the DECODE() statement rather than CASE since it is more readable in these simple cases.
(
SELECT COUNT(DISTINCT s_id) AS equal, date_trunc('day', time) AS day
FROM table_a
WHERE date_trunc('sec',earliest) = date_trunc('sec',time)
GROUP BY day
) USING (day)
JOIN
(
SELECT COUNT(DISTINCT s_id) AS after, date_trunc('day', time) AS day
FROM table_a
WHERE date_trunc('sec',earliest) > date_trunc('sec',time)
GROUP BY day
)
Becomes:
(
SELECT COUNT(DISTINCT DECODE(date_trunc('sec',earliest) = date_trunc('sec',time), true, s_id, NULL)) AS equal,
COUNT(DISTINCT DECODE(date_trunc('sec',earliest) > date_trunc('sec',time), true, s_id, NULL)) AS after,
date_trunc('day', time) AS day
FROM table_a
GROUP BY day
)
Read each table once (if at all possible) and calculate the desired results. then you will have all your values in one layer of query and can reference these new values. This will be faster (especially on Redshift).
=============================
Expanding based on comment made by poster.
It appears that using DECODE() and referencing derived columns in a single query can produce what you want. I don't have your data so I cannot test this but here is what I'd want to move to:
SELECT
COUNT(DISTINCT DECODE(date_trunc('sec',earliest) < date_trunc('sec',time), true, s_id)) AS before,
ROUND(before * 100.0 / total, 1) AS Percent_1,
COUNT(DISTINCT DECODE(date_trunc('sec',earliest) = date_trunc('sec',time), true, s_id)) AS equal,
COUNT(DISTINCT DECODE(date_trunc('sec',earliest) > date_trunc('sec',time), true, s_id)) AS after,
COUNT(DISTINCT s_id) AS total
FROM table_a
GROUP BY date_trunc('day', time);
This should be a complete replacement for the SELECT currently inside your CREATE TEMP TABLE. However, I don't have sample data so this is untested.
I have written the sql query:
SELECT id
date_diff("day", create_date, date) as day
action_type
FROM "my_database"
It brings this:
id day action_type
1 0 upload
1 0 upload
1 0 upload
1 1 upload
1 1 upload
2 0 upload
2 0 upload
2 1 upload
How to change my query to get table with unique days in column day and average number "upload" action_type among all id's. So desired result must look like this:
day avg_num_action
0 2.5
1 1.5
It is 2.5, because (3+2)/2 (3 uploads of id:1 and 2 uploads for id:2). same for 1.5
Please try this. Consider your given query as a table. If any WHERE condition needed then please enable this other wise disable where clause.
SELECT t.day
, COUNT(*) / COUNT(DISTINCT t.id) avg_num_action
FROM (SELECT id,
date_diff("day", create_date, date) as day,
action_type
FROM "my_database") t
WHERE t.action_type = 'upload'
GROUP BY t.day
Create a table from your given result set and write query based on that.
SELECT t.tday
, COUNT(*) / COUNT(DISTINCT t.id) avg_num_action
FROM my_database t
GROUP BY t.tday
Please check from url https://dbfiddle.uk/?rdbms=mysql_8.0&fiddle=871935ea2b919c4e24eb83fcbce78973
Update: I think my two-steps approach is more complicated than needed. Rahul Biswas shows how this can be done in one step. I suggest you use and accept his answer.
Original answer:
Two steps:
Count entries per ID and day
Take the average count per day
The query:
with rows as (select id, date_diff('day', create_date, date) as day from mytable)
, per_id_and_day as (select id, day, count(*) as cnt from rows group by id, day)
select day, avg(cnt)
from per_id_and_day
group by day
order by day;
You don't need a subquery for this logic:
SELECT date_diff("day", create_date, date) as day,
COUNT(*) * 1.0 / COUNT(DISTINCT id)
FROM "my_database"
GROUP BY date_diff("day", create_date, date)
Got something that I cant get my head around
raw data shows every 15 min intervals and I would like to group them based on if they are consecutive 15 min intervals (see screenshot below) I will like to do this multiple times for each user and for alot of users... Any ideas on how to do this using sql only that can scale to 1000's users?
Any help would be appreicated
Thanks
This is a type of gaps-and-islands problem. Use lag() to get the difference, then a cumulative sum to identify the group:
select user_id, min(start_time), max(end_time)
from (select t.*,
sum( case when prev_end_time <> start_time then 0 else 1 end) over (partition by user_id order by start_time) as grp
from (select t.*,
lag(end_time) over (partition by user_id order by start_time) as prev_end_time
from t
) t
) t
group by user_id, grp;
I have two queries:
select COUNT(DISTINCT (CASE WHEN EVENT_NAME = 'event' THEN UPPER(user END)) AS SIGNUP_COUNT,
from table
WHERE date BETWEEN '2020-07-01' AND '2020-09-01'
and
with EVENTS_FILTERED_with_count as (
select *
, COUNT(DISTINCT (CASE WHEN EVENT_NAME = 'event' THEN UPPER(user END)) AS SIGNUP_COUNT
from table
group by 1)
SELECT sum(SIGNUP_COUNT) FROM EVENTS_FILTERED_with_count
WHERE date BETWEEN '2020-07-01' AND '2020-09-01'
The first query returns 2.5K rows as result, and the second one returns 3K rows.
Why would adding the group by make the result larger? I'm wondering if it has to do with NULL values.
Because the same user has multiple events, so the event is counted multiple times when counted at the user level.
It is hard to be more descriptive without sample data.
Based on table below in Presto I need a column for all new 'rid'. What I managed to do is the same what I can achieve with partition by but it's not exactly what I'm looking for (db<>fiddle demo).
Goal is to have many groupings counts but I think this should describe problem sufficiently.
I need data truncated by days and column for new users every day as shown at example below. In simple words - if value repeats don't count it. I've tried to find correlation between this and relational division problem but I just stuck.
You could use row_number() to rank the records of each rid by time; then you can aggregate and count in only the top record per group.
select
date_trunc(day, t.time) dy,
count(*) rid_count,
sum(case when t.rn = 1 then 1 else 0 end) new_rid_count
from (
select
t.*
row_number() over(partition by t.rid order by t.time) rn
from mytable t
) t
group by date_trunc(day, t.time)
I think of this as two levels of aggregation. The inner one to get the earliest date. The outer to aggregate:
select first_day, count(*)
from (select rid, date_trunc('day', min(time))::date as first_day
from orders o
group by rid
) r
group by 1