BigQuery SQL - 如果两列连续出现，则连接它们

Question

I am looking for a way to adjust this sql query running in BigQuery to return single count total for Sent EventTypes that happen two or even three days in a row.

SELECT date(EventDate) as EventDate, EventType, count(*) as count FROM `Database.Table`
    where date(EventDate) > DATE_SUB (CURRENT_DATE, INTERVAL 100 DAY)
    Group by 1,2 
    ORDER by 1,2

Response from above Query:

| Row    | EventDate | EventType | count |
| ------ | --------- |-----------|-------|
| 1      | 2019-02-06|  Sent     |    4  |
| 2      | 2019-02-07|  Sent     |    5  |
| 3      | 2019-02-12|  NotSent  |    7  |
| 4      | 2019-02-13|  Bounces  |    22 |
| 5      | 2019-02-14|  Bounces  |    22 |
| 6      | 2019-03-06|  Sent     |    2  |
| 7      | 2019-03-07|  Sent     |    4  |
| 8      | 2019-03-07|  NotSent  |    5  |
| 9      | 2019-03-12|  Bounces  |    7  |
| 10     | 2019-03-13|  Sent     |    22 |
| 11     | 2019-04-05|  Sent     |    2  |

Response I would like to get to:

| Row    | EventDate | EventType | count |
| ------ | --------- |-----------|-------|
| 1      | 2019-02-06|  Sent     |    9  |
| 2      | 2019-02-12|  NotSent  |    7  |
| 3      | 2019-02-13|  Bounces  |    22 |
| 4      | 2019-02-14|  Bounces  |    22 |
| 5      | 2019-03-06|  Sent     |    6  |
| 6      | 2019-03-07|  NotSent  |    5  |
| 7      | 2019-03-12|  Bounces  |    7  |
| 8      | 2019-03-13|  Sent     |    22 |
| 9      | 2019-04-05|  Sent     |    2  |

Something along those line, so I am able to concatenate two counts with the EventType of 'Sent' for consecutive days, and show other EventTypes without concatenating them, such as Bounces and NotSent.

Answer 1

I wrote a query that merges all consecutive 2 days in the table.
It gives the exact same output you want.

I think you meant '2019-03-06' in the 5th row, so I fixed it in my dummy data section.

WITH
data AS (
  SELECT CAST('2019-02-06' as date) as EventDate, 4 as count union all
  SELECT CAST('2019-02-07' as date) as EventDate, 5 as count union all
  SELECT CAST('2019-02-12' as date) as EventDate, 7 as count union all
  SELECT CAST('2019-02-13' as date) as EventDate, 22 as count union all
  SELECT CAST('2019-03-06' as date) as EventDate, 2 as count
),
data_with_steps AS (
  SELECT *, 
    IF(DATE_DIFF(EventDate, LAG(EventDate) OVER (ORDER BY EventDate), day) > 2, 1, 0) as new_step
  FROM data
),
data_grouped AS (
  SELECT *, 
    SUM(new_step) OVER (ORDER BY EventDate) as step_group
  FROM data_with_steps
)
SELECT MIN(EventDate) as EventDate, sum(count) as count
FROM data_grouped
GROUP BY step_group

So, how does it work?
First, I calculate the date difference to previous day. If it's more than 2 days, I set value 1, otherwise 0 for the new column new_step .
Then, I calculate the cumulative sum of new_step column and name it as step_group.
The output of the first two steps is:

At final step, I group table by step_group and get minimum date as event date, and sum counts to obtain group count.

Edit: To add other events without grouping by, I added a new version. I think the most intuitive and easiest way is to use Union All for that problem. So you can use that updated query to include other events without grouping.

WITH
data AS (
  SELECT CAST('2019-02-06' as date) as EventDate, 'Sent' as EventType, 4 as count union all
  SELECT CAST('2019-02-07' as date) as EventDate, 'Sent' as EventType, 5 as count union all
  SELECT CAST('2019-02-12' as date) as EventDate, 'Sent' as EventType, 7 as count union all
  SELECT CAST('2019-02-13' as date) as EventDate, 'Sent' as EventType, 22 as count union all
  SELECT CAST('2019-03-06' as date) as EventDate, 'Sent' as EventType, 2 as count union all
  SELECT CAST('2019-02-12' as date) as EventDate, 'NotSent' as EventType, 7 as count union all
  SELECT CAST('2019-03-07' as date) as EventDate, 'NotSent' as EventType, 5 as count union all
  SELECT CAST('2019-02-13' as date) as EventDate, 'Bounces' as EventType, 22 as count union all
  SELECT CAST('2019-02-14' as date) as EventDate, 'Bounces' as EventType, 22 as count union all
  SELECT CAST('2019-03-12' as date) as EventDate, 'Bounces' as EventType, 7 as count
),
data_with_steps AS (
  SELECT *, 
    IF(DATE_DIFF(EventDate, LAG(EventDate) OVER (ORDER BY EventDate), day) > 2, 1, 0) as new_step
  FROM data
  WHERE EventType = 'Sent'
),
data_grouped AS (
  SELECT *, 
    SUM(new_step) OVER (ORDER BY EventDate) as step_group
  FROM data_with_steps
)
SELECT EventType, MIN(EventDate) as EventDate, sum(count) as count
FROM data_grouped
GROUP BY EventType, step_group

UNION ALL

SELECT EventType, EventDate, count
FROM data
WHERE EventType != 'Sent'

Answer 2

This is a gaps-and-islands problem. The simplest method is to use row_number() and subtraction to identify the "islands". And then aggregate:

select min(row), eventType, min(eventDate), sum(count)
from (select t.*,
             row_number() over (partition by eventType order by eventDate) as seqnum
      from t
     ) t
group by eventType, dateadd(eventDate, interval -seqnum day)