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How to Find the Longest Consecutive Series of Events in SQL

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How to Find the Longest Consecutive Series of Events in SQL

Lukas Eder provides an interesting guide on how to present the longest consecutive series of events using SQL, which may come in more handy than one might expect.

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A very interesting problem that can be solved very easily with SQL is to find consecutive series of events in a time series. But what is a consecutive series of events in a time series?

Take Stack Overflow, for example. Stack Overflow has a cool reputation system that uses badges to reward certain behavior. As a social website, they encourage users to visit the platform every day. As such, two distinct badges are awarded:

stackoverflow-visits

Informally, it is obvious what this means. You’ll have to log in on day 1. Then again on day 2. Then again (perhaps several times, it doesn’t matter) on day 3. Forgot to log in on day 4? Ooops. We’ll start counting again.

How to do this in SQL?

On this blog, every problem will find its solution in SQL. So does this. And in order to solve this problem, we’re going to use the awesome Stack Exchange Data Explorer, which exposes a lot of Stack Exchange’s publicly available usage information.

Note that we won’t query the consecutive days of visits, as this information is not made available publicly. Instead, let’s query the consecutive days of posts a user has made.

The backing database is SQL Server, so we can run the following statement:


SELECT DISTINCT CAST(CreationDate AS DATE) AS date
FROM Posts
WHERE OwnerUserId = ##UserId##
ORDER BY 1

… which, for my own UserId generates something like:


date          
----------
2010-11-26
2010-11-27
2010-11-29
2010-11-30
2010-12-01
2010-12-02
2010-12-03
2010-12-05
2010-12-06
2010-12-07
2010-12-08
2010-12-09
2010-12-13
2010-12-14
...
(769 rows)

As we can see in the data, there have been gaps in the very early days:


date          
--------------------------------------
2010-11-26
2010-11-27 <---- Gap here after 2 days

2010-11-29
2010-11-30
2010-12-01
2010-12-02
2010-12-03 <---- Gap here after 5 days

2010-12-05
2010-12-06
2010-12-07
2010-12-08
2010-12-09 <---- Gap here after 5 days

2010-12-13
2010-12-14
...

Visually, it is very easy to see how many days in a row there were posts without any gaps. But how to do it with SQL?

To simplify the problem, let’s “store” individual queries in common table expressions. The above query, we’ll call dates:


WITH 

  -- This table contains all the distinct date 
  -- instances in the data set
  dates(date) AS (
    SELECT DISTINCT CAST(CreationDate AS DATE)
    FROM Posts
    WHERE OwnerUserId = ##UserId##
  )
...

Now, the goal of the resulting query is to put all consecutive dates in the same group, such that we can aggregate over this group. The following query is what we want to write:


SELECT 
  COUNT(*) AS consecutiveDates,
  MIN(week) AS minDate,
  MAX(week) AS maxDate
FROM groups
GROUP BY grp
ORDER BY 1 DESC, 2 DESC

We’d like to aggregate each group “grp” and count the number of dates in the group, as well as find the lowest and the highest date within each group.

Generating Groups for Consecutive Dates

Let’s look at the data again, and to illustrate the idea, we’ll add consecutive row numbers, regardless of the gaps in dates:


row number   date          
--------------------------------
1            2010-11-26
2            2010-11-27

3            2010-11-29 <-- gap before this row
4            2010-11-30
5            2010-12-01
6            2010-12-02
7            2010-12-03

8            2010-12-05 <-- gap before this row

As you can see, regardless whether there is a gap between dates (two dates are not consecutive), their row numbers will still be consecutive. We can do this with the ROW_NUMBER() window function, very easily:


SELECT
  ROW_NUMBER() OVER (ORDER BY date) AS [row number],
  date
FROM dates

Now, let’s check out the following, interesting query:


WITH 

  -- This table contains all the distinct date 
  -- instances in the data set
  dates(date) AS (
    SELECT DISTINCT CAST(CreationDate AS DATE)
    FROM Posts
    WHERE OwnerUserId = ##UserId##
  ),

  -- Generate "groups" of dates by subtracting the
  -- date's row number (no gaps) from the date itself
  -- (with potential gaps). Whenever there is a gap,
  -- there will be a new group
  groups AS (
    SELECT
      ROW_NUMBER() OVER (ORDER BY date) AS rn,
      dateadd(day, -ROW_NUMBER() OVER (ORDER BY date), date) AS grp,
      date
    FROM dates
  )
SELECT *
FROM groups
ORDER BY rn

The above query yields:


rn  grp          date          
--- ----------   ----------
1   2010-11-25   2010-11-26
2   2010-11-25   2010-11-27
3   2010-11-26   2010-11-29
4   2010-11-26   2010-11-30
5   2010-11-26   2010-12-01
6   2010-11-26   2010-12-02
7   2010-11-26   2010-12-03
8   2010-11-27   2010-12-05
9   2010-11-27   2010-12-06
10  2010-11-27   2010-12-07
11  2010-11-27   2010-12-08
12  2010-11-27   2010-12-09
13  2010-11-30   2010-12-13
14  2010-11-30   2010-12-14

All we did is subtract the row number from the date to get a new date “grp“. The actual date obtained this way is irrelevant. It’s just an auxiliary value.

What we can guarantee, though, is that for consecutive dates, the value of grp will be the same because for all consecutive dates, the following two equations yield true:


date2 - date1 = 1 // difference in days between dates
rn2   - rn1   = 1 // difference in row numbers

Yet, for non-consecutive dates, while the difference in row numbers is still 1, the difference in days is no longer 1. The groups can now be seen easily:


rn  grp          date          
--- ----------   ----------
1   2010-11-25   2010-11-26
2   2010-11-25   2010-11-27

3   2010-11-26   2010-11-29
4   2010-11-26   2010-11-30
5   2010-11-26   2010-12-01
6   2010-11-26   2010-12-02
7   2010-11-26   2010-12-03

8   2010-11-27   2010-12-05
9   2010-11-27   2010-12-06
10  2010-11-27   2010-12-07
11  2010-11-27   2010-12-08
12  2010-11-27   2010-12-09

13  2010-11-30   2010-12-13
14  2010-11-30   2010-12-14

Thus, the complete query can now be seen here:


WITH 

  -- This table contains all the distinct date 
  -- instances in the data set
  dates(date) AS (
    SELECT DISTINCT CAST(CreationDate AS DATE)
    FROM Posts
    WHERE OwnerUserId = ##UserId##
  ),

  -- Generate "groups" of dates by subtracting the
  -- date's row number (no gaps) from the date itself
  -- (with potential gaps). Whenever there is a gap,
  -- there will be a new group
  groups AS (
    SELECT
      ROW_NUMBER() OVER (ORDER BY date) AS rn,
      dateadd(day, -ROW_NUMBER() OVER (ORDER BY date), date) AS grp,
      date
    FROM dates
  )
SELECT 
  COUNT(*) AS consecutiveDates,
  MIN(week) AS minDate,
  MAX(week) AS maxDate
FROM groups
GROUP BY grp
ORDER BY 1 DESC, 2 DESC

And it yields:


consecutiveDates minDate       maxDate       
---------------- ------------- ------------- 
14               2012-08-13    2012-08-26
14               2012-02-03    2012-02-16
10               2013-10-24    2013-11-02
10               2011-05-11    2011-05-20
9                2011-06-30    2011-07-08
7                2012-01-17    2012-01-23
7                2011-06-14    2011-06-20
6                2012-04-10    2012-04-15
6                2012-04-02    2012-04-07
6                2012-03-26    2012-03-31
6                2011-10-27    2011-11-01
6                2011-07-17    2011-07-22
6                2011-05-23    2011-05-28
...

Bonus Query 1: Find Consecutive Weeks

The fact that we chose the granularity of days in the above query is a random choice. We simply took the timestamp from our time series and “collapsed” it to the desired granularity using a CAST function:


SELECT DISTINCT CAST(CreationDate AS DATE)

If we want to know the consecutive weeks, we’ll simply change that function to a different expression, e.g.


SELECT DISTINCT datepart(year, CreationDate) * 100 
              + datepart(week, CreationDate)

This new expression takes the year and the week and generates values like 201503 for week 03 in the year 2015. The rest of the statement remains exactly the same:


WITH 
  weeks(week) AS (
    SELECT DISTINCT datepart(year, CreationDate) * 100 
                  + datepart(week, CreationDate)
    FROM Posts
    WHERE OwnerUserId = ##UserId##
  ),
  groups AS (
    SELECT
      ROW_NUMBER() OVER (ORDER BY week) AS rn,
      dateadd(day, -ROW_NUMBER() OVER (ORDER BY week), week) AS grp,
      week
    FROM weeks
  )
SELECT 
  COUNT(*) AS consecutiveWeeks,
  MIN(week) AS minWeek,
  MAX(week) AS maxWeek
FROM groups
GROUP BY grp
ORDER BY 1 DESC, 2 DESC

And we’ll get the following result:


consecutiveWeeks minWeek maxWeek 
---------------- ------- ------- 
45               201401  201445  
29               201225  201253  
25               201114  201138  
23               201201  201223  
20               201333  201352  
16               201529  201544  
15               201305  201319  
12               201514  201525  
12               201142  201153  
9                201502  201510  
7                201447  201453  
7                201321  201327  
6                201048  201053  
4                201106  201109  
3                201329  201331  
3                201102  201104  
2                201301  201302  
2                201111  201112  
1                201512  201512  

Unsurprisingly, the consecutive weeks span much longer ranges, as I generally use Stack Overflow extensively.

Bonus Query 2: Simplify the Query Using DENSE_RANK()

In a previous article, we’ve shown that SQL Trick: ROW_NUMBER() is to SELECT what DENSE_RANK() is to SELECT DISTINCT.

If we go back to our consecutive days example, we can rewrite the query to find the distinct dates AND the groups in one go, using DENSE_RANK():


WITH 
  groups(date, grp) AS (
    SELECT DISTINCT 
      CAST(CreationDate AS DATE),
      dateadd(day, 
        -DENSE_RANK() OVER (ORDER BY CAST(CreationDate AS DATE)), 
        CAST(CreationDate AS DATE)) AS grp
    FROM Posts
    WHERE OwnerUserId = ##UserId##
  )
SELECT 
  COUNT(*) AS consecutiveDates,
  MIN(date) AS minDate,
  MAX(date) AS maxDate
FROM groups
GROUP BY grp
ORDER BY 1 DESC, 2 DESC

If the above doesn’t make sense, I recommend reading our previous article here, which explains it:

http://blog.jooq.org/2013/10/09/sql-trick-row_number-is-to-select-what-dense_rank-is-to-select-distinct/

Further Reading

The above has been one very useful example of using window functions (ROW_NUMBER()) in SQL. Learn more about window functions in any of the following articles:

Understand your options for deploying a database across multiple data centers - without the headache.

Topics:
sql query ,sql database

Published at DZone with permission of Lukas Eder, DZone MVB. See the original article here.

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