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Counting events before a specific event


Counting events before a specific event

By : Ross Kan
Date : September 20 2020, 10:00 PM
wish helps you Let's say, I have a table with the following columns: , I went to SQL Fiddle and ran the test via MS SQL engine.
code :
CREATE TABLE add4ad (date date, event varchar(10), user_id int,
                   unit_id int, cost float, ad_id float, spend float);
INSERT INTO add4ad (date, Event, user_id,unit_id,cost,ad_id,spend)
VALUES
    ('2018-03-15','impression','2353','3436','0.15',NULL,NULL),
    ('2018-03-15','impression','2353','3436','0.12',NULL,NULL),
    ('2018-03-15','impression','2353','3436','0.10',NULL,NULL),
    ('2018-03-15','click','1234','5678', NULL, NULL,NULL),
    ('2018-03-15','create_ad','2353','5678', NULL, 6789,10);
with e10 as (select  user_id, event, date, rowid=row_number() over (Partition by user_id order by date)
from add4ad
where event='create_ad'
),
e20 as ( -- get the first create_ad event
select user_id, date
  from e10
  where rowid=1
  )
  select a.user_id, count(1) as N
  from e20 inner join add4ad a
  on e20.user_id=a.user_id
  and a.date<=e20.date
  and a.event='impression'
  group by a.user_id


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Detach event listeners for domain events? or how to stop executing otherwise required post events on specific use-cases

Detach event listeners for domain events? or how to stop executing otherwise required post events on specific use-cases


By : deepak
Date : March 29 2020, 07:55 AM
help you fix your problem You have a few options.
First of all, make the use-case explicit. You can, for example, add a bool to the UserRegistered event indicating that the user was registered as part of creation of an order. This would allow the email handlers to send appropriate emails.
Counting occurrences of specific events in CUDA kernels

Counting occurrences of specific events in CUDA kernels


By : J.Rcr
Date : March 29 2020, 07:55 AM
I wish this helpful for you The atomicAdd is is one possibility and i would probably go that route. If you do not use the result of the atomicAdd function call the compiler will emit a reduction operation such as RED.E.ADD. Reduction is very fast as long as there are not many conflicts happening (i actually use it sometimes even if i do not need the operation to be atomic because it can be quicker than loading value from global memory, doing an arithmetic operation and saving back to global memory).
The second option you have is to use a profiler counter and use the profiler to analyze the result. Please see Profiler Counter Function for more details.
Resampling Event DataFrame to 10 mints interval and counting events

Resampling Event DataFrame to 10 mints interval and counting events


By : Robert A.
Date : March 29 2020, 07:55 AM
I wish this helpful for you I have a pandas dataframe which shows some information about some events taking place. It is basicall like this , Set the timestamp as the dataframe index:
code :
event_df.index = pd.to_datetime(event_df.Timestamp)
count_138 = (event_df['Event Code']==138).astype(int)\
                                         .resample('10 min').sum()
count_0 = (event_df['Event Code']==0).astype(int)\
                                     .resample('10 min').sum()
pd.DataFrame({'count_0': count_0, 'count_138': count_138})
counting events by event history with R

counting events by event history with R


By : madan
Date : March 29 2020, 07:55 AM
will be helpful for those in need You could combine this idiom with a non-equi join:
code :
library(data.table)
library(lubridate)

df <- read.table(header=T, text="
process_id    date         event
00001       00/01/20     1
00002       00/01/20     1
00003       00/01/20     0
00001       01/01/19     1
00002       01/01/19     0
00003       01/01/19     1")

dt <- as.data.table(df)

dt[, date := as.POSIXct(date, format = "%y/%m/%d")]
dt[, prev_year := date - lubridate::dyears(1L)]

positives <- dt[.(1), .(process_id, date, event), on = "event"]

dt[, prev_event := positives[.SD,
                             .(x.event),
                             on = .(process_id, date < date, date >= prev_year),
                             mult = "last"]]

print(dt)
   process_id       date event  prev_year prev_event
1:          1 2000-01-20     1 1999-01-20         NA
2:          2 2000-01-20     1 1999-01-20         NA
3:          3 2000-01-20     0 1999-01-20         NA
4:          1 2001-01-19     1 2000-01-20          1
5:          2 2001-01-19     0 2000-01-20          1
6:          3 2001-01-19     1 2000-01-20         NA
dt[, `:=`(
  c("prev_event", "prev_date"),
  positives[.SD, .(x.event, x.date), on = .(process_id, date < date, date >= prev_year), mult = "last"]
)]
library(table.express)
library(data.table)
library(lubridate)

dt <- as.data.table(df) %>%
  start_expr %>%
  mutate(date = as.POSIXct(date, format = "%y/%m/%d")) %>%
  mutate(prev_year = date - lubridate::dyears(1L)) %>%
  end_expr

positives <- dt %>%
  start_expr %>%
  filter_on(event = 1) %>%
  select(process_id, date, event) %>%
  end_expr

dt %>%
  start_expr %>%
  mutate_join(positives,
              process_id, date > date, prev_year <= date,
              mult = "last",
              .SDcols = c(prev_event = "event", prev_date = "date")) %>%
  end_expr

print(dt)
   process_id       date event  prev_year prev_event  prev_date
1:          1 2000-01-20     1 1999-01-20         NA       <NA>
2:          2 2000-01-20     1 1999-01-20         NA       <NA>
3:          3 2000-01-20     0 1999-01-20         NA       <NA>
4:          1 2001-01-19     1 2000-01-20          1 2000-01-20
5:          2 2001-01-19     0 2000-01-20          1 2000-01-20
6:          3 2001-01-19     1 2000-01-20         NA       <NA>
Counting events only once if an event happens more than once every X minutes

Counting events only once if an event happens more than once every X minutes


By : user3094840
Date : March 29 2020, 07:55 AM
Any of those help Use lag() to determine when the previous event was created for the user. Then some date filtering and aggregation:
code :
select userid, count(*)
from (select t.*,
             lag(created_at) over (partition by userid order by created_at) as prev_created_at
      from t
     ) t
where prev_created_at is null or prev_created_at < created_at - interval '10 minute'
group by userid
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