The 'Subscription List' SQL Problem

Question

Phil Factor SQL Speed Phreak Competition: No 1

This competition is now over, but the winner, Peso, got an Amazon Voucher for $60, and the privilege of being able to display the 'Phil Factor SQL Speed Phreak' award on their own site

It was quite a struggle with some close competition from many of those who participated in this competition. However, Peso came up with a blindingly fast winner that produced an aggregation from a million rows in a third of a second. Now, we're all scratching our heads trying to come up with the fastest way of solving....

The ‘FIFO Stock Inventory’ SQL Problem

...on this site

(here is the original preamble.)

I really genuinely don't know the answer to this question: What is the fastest way in SQL Server (any version) to provide the following subscription report.

It is a reasonable request. We have a subscription list with 10,000 subscribers and we need to do a management report that gives the monthly breakdown of the Date, the number of current subscribers at the end of the month, the number of resignations in the month ('Unsubscribes'), and the number of new subscribers. The list should be in date order, and the date should be just the date of the first day of the month.

The table is in this form (simplified from the way we'd do it in a real system of course)

CREATE TABLE [dbo].[Registrations]
    (
     [Registration_ID] [int] IDENTITY(1, 1)
                             NOT NULL,
     [FirstName] [varchar](80) NOT NULL,
     [LastName] [varchar](80) NOT NULL,
     [DateJoined] [datetime] NOT NULL,
     [DateLeft] [datetime] NULL,
     CONSTRAINT [PK_Registrations] PRIMARY KEY CLUSTERED 
    	([DateJoined], [LastName], [FirstName])
    )
CREATE INDEX idxDateJoined 
    ON Registrations (DateJoined, DateLeft, Registration_ID)

You are welcome to change the two indexes to suit your solution. I'll give you a reasonable amount of data to try stuff out on. 10,000 faked data entries of subscribers is the most that I can reasonably ask you to download, (The list is here) but I shall be taking each solution, putting it in in a test harness consisting of a million subscribers. (I may even make it 1,169,187 to celebrate SQLServerCentral's subscription list) , and find the fastest way of doing it. I have ideas of my own of the way to do this but I suspect they're wrong.

Note that in the sample data the subscriptions that extend to sept 2010 are those people who’ve paid for a year’s subscription only rather than those which have ongoing renewals (e.g. direct Debit). ‘Now’ is the end of September 2009.

I will allow you to use a number table, but you can assume that every month has new subscribers in it. You can use views, or temporary table, but the time taken for their creation will be included in the timings. You can use a cursor if you don't mind a sharp intake of breath from Jeff. You can use any version of SQL Server that you like.

The winner will be amongst the tied fastest entrants (generally there is a group of these) and it will be the one with the highest number of votes. We'll announce the winner in a week's time on 19th October.

Who knows, if you are the only person who ever discovers this site and this competition, then the winner will be you!

OK End of day 1 and we've had some extremely good results (in milliseconds) from the original 10,000 data table

 Matt 16 Kev Riley 93 Graham 60 Peso 33 Gianluca 16 AndyM 170 Joe Harris 15533 William brewer 406

I've had to temporarily disqualify Kev, AndyM and Joe Harris as I couldn't get their results to match the correct values. All the other results agreed. The results for Matt, Peso and Gianluca are all very close indeed, too close to get a result from 10,000 rows, so I'll have to up the test table to a million rows. From experience, I know that a lot could change between now and next Monday. I feel sure that Graham's SQL can be tweaked. and I expect to see the slow ones to be back in the running with some fine-tuning. Anyone else?

OK End of Day 2, and Peso has streaked ahead with his third version. it is now too fast to measure on 10000 rows so I shall have to move to a larger database for the performance tests. Now I've put result-checking into the test harness Gianluca has had to be temporarily disqualified until he can cure his result, which is slightly out.

Matt                 16 ms          
Kev Riley            76 ms *         
Graham               60 ms         
Peso                 30 ms         
Gianluca             33 ms *         
AndyM                170 ms *       
William brewer       406 ms        
Peso 2               16 ms         
Peso 3               0 ms

Result of SQL is incorrect
Day 3. Due to a couple of glitches I haven't been able to complete all the timings on the million-row table. Here is what I've done so far!

graham 1        6986ms  
Peso 1           890ms  
Peso 2          1173ms  
Peso 3          1170ms  
Matt 1           596ms  
Matt 2           873ms  
Gianluca 1      4546ms  
Peso 4B          940ms  
Andriy Z        1200ms *
graham 2        1656ms

Day 5

Currently re-creating the test harness! timings on the 1 Million row tables are as follows. remember that these are preliminary timings and Peso and I are trying to scratch our heads to see why we are getting such different results. In the test harness, the results are inserted into a table variable and subsequently checked for validity.

Entry       Elapsed time in milliseconds
graham 1    6983 ms
Peso 1       890 ms
Peso 2      1186 ms
Peso 3      1173 ms
Matt 1       576 ms
Matt 2       860 ms
Gianluca 1  4550 ms
Peso 4B      936 ms
Peso 4d      830 ms
Peso 4e      313 ms
Andriy Z    1203 ms
Graham 2    1640 ms
Brewer 2     406 ms
Peso 1d     1076 ms
Gustavo 1    580 ms
Gianluca 4  2390 ms

..at the moment I write this, William Brewer's entry (incorporating ideas from Graham, Peso and Matt) seems to be in the lead! (Saturday, it is now Peso 4e) However, I'll be holding the competition open until Monday evening GMT to allow for people who didn't hear that the competition was on last Monday.

Just if you thought that some potential winners were emerging, look at the results with the original 10,000 row table. Yes, quite different. (We'd have to use a C# or VB test-harness to sort out the speed differences!) One would never be able to sort out the fastest from such close competition!

Entry       Elapsed time in milliseconds
graham 1    80 ms
Peso 1      13
Peso 2      16
Peso 3      16
Matt 1      13
Matt 2      33
Gianluca 1  30
Peso 4B     16
Andriy Z    13
Graham 2    16
Brewer 2    30
Peso 1d     33
Gustavo 1   16
Gianluca 4  13
Peso 4d     16

The complete million long test table is now on http://www.simple-talk.com/blogbits/philf/registrations.zip if you would like to fine-tune your winning entry

Monday. Here are the final rankings These are the simple 'elapsed time' measurements. I'll follow this up as soon as possible with the details. Matt has very kindly written a special C# test harness for accurate timings that I'm dusting out at the moment. I'm planning to do a full write-up on my SSC blog as there are some important lessons for anyone doing this sort of reporting task. After all, there is a world of difference between the time and CPU loading of the best entrants and the Ho-hums. Even the Ho-hums are a lot better than some of the production code I've seen.

These timings are very tight so in all fairness, I have to also award Gustavo, as runner-up, the right to proudly display the 'Phil Factor SQL Speed Phreak' award. After all, what is 30 milliseconds in processing a million rows. Peso has to get the title, not only for the best entry (Peso 4E), but for his energy (he was on paternity leave!) and for the way he helped and advised the other entrants. A true champion. Special Thanks to Matt for all the advice, and the test harness.

Peso4E was such a good entry that it even struck terror into Celko-Prizewinner Barry, and decided him not to enter the competition. However William and Matt deserve special commendation for their entries which remain brilliantly fast over a million rows.

Peso 4E       313
Gustavo 3     343
Gustavo 4     346
Brewer 2      423
Peso 1e       470
Peso 1        500
Gustavo 1     563
Matt 1        580
Matt 1B       593
Peso 1d       856
GianLuca Final 856
Peso 4d       856
Peso 1D       860
Matt 2        873
Matt3         923
Peso 4B       940
Graham 2      1106
Peso 3        1156
Peso 2        1170
Andriy Z      1233 *
Gustavo/peso  2800
Gianluca 1    4500
graham 1      4656

Remember that the winning routines were calculating aggregate reports on a million-row tables in between a third and half a second. These included calculations that you will sometimes read has to be done using a cursor!

(* in results means result needs minor tweaking to make it conform.

Answer 1

/*******************************************************************************
    Peso 4d - 20091015
*******************************************************************************/

-- Step 1 - Create an intermediate staging table
-- Table variable will not do (even with no logging) because table variables
-- cannot benefit from parallelism. I am using a temp table for the 103 months
-- or records between Mar 2001 and Sep 2009, with each record is 14 bytes
CREATE TABLE    #Stage
    	(
    		theMonth SMALLINT NOT NULL,
    		PeopleJoined INT NOT NULL,
    		PeopleLeft INT NOT NULL,
    		Subscribers INT NOT NULL
    	)

-- Step 2 - Populate the staging table
INSERT  	#Stage
    	(
    		theMonth,
    		PeopleJoined,
    		PeopleLeft,
    		Subscribers
    	)
SELECT  	u.theMonth,
        	-- Old school pivoting is slightly more efficient than PIVOT
        	-- It also gives us the ability to return 0 instead of NULL
    	SUM(CASE WHEN u.theCol = 'DateJoined' THEN u.Registrations ELSE 0 END) AS PeopleJoined,
    	SUM(CASE WHEN u.theCol = 'DateLeft' THEN u.Registrations ELSE 0 END) AS PeopleLeft,
    	0 AS Subscribers
FROM    	(
    		-- Do the full aggregation with final key before PIVOT
    		SELECT		DATEDIFF(MONTH, 0, DateJoined) AS DateJoined,
    				DATEDIFF(MONTH, 0, DateLeft) AS DateLeft,
    				SUM(Registrations) AS Registrations
    		FROM		(
    		    			-- Do some heavy-lifting pre-aggregation
    		    			-- It is better to UNPIVOT about 3,450 records (number of days since March 2001, plus 10% drop-offs)
        					-- than 1,169,187 records directly (average of 373 registrations per day)
    					SELECT		DateJoined,
    							DateLeft,
    							COUNT(*) AS Registrations
    					FROM		dbo.Registrations
    					GROUP BY	DateJoined,
    							DateLeft
    				) AS d
    		GROUP BY	DATEDIFF(MONTH, 0, DateJoined),
    				DATEDIFF(MONTH, 0, DateLeft)
    	) AS d
UNPIVOT 	(
    		theMonth
    		FOR theCol IN (d.DateJoined, d.DateLeft)
    	) AS u
GROUP BY    u.theMonth
    	-- Exclude those records for months not having any new subscribers
HAVING  	SUM(CASE WHEN u.theCol = 'DateJoined' THEN u.Registrations ELSE 0 END) > 0

-- Prepare running total
DECLARE @Subscribers INT = 0

-- Set up and prepare an ordered update CTE
;WITH Yak (theMonth, PeopleJoined, PeopleLeft, Subscribers)
AS (
    SELECT		TOP 2147483647
    		DATEADD(MONTH, theMonth, 0) AS theMonth,
    		PeopleJoined,
    		PeopleLeft,
    		Subscribers
    FROM		#Stage
    ORDER BY	theMonth
)

-- Step 3 - Do both the running total and the result output
UPDATE  Yak
SET @Subscribers = Subscribers = @Subscribers + PeopleJoined - PeopleLeft
OUTPUT  inserted.theMonth,
    inserted.PeopleJoined,
    inserted.PeopleLeft,
    inserted.Subscribers

/*******************************************************************************
    Peso 4e - 20091017
*******************************************************************************/
CREATE TABLE    #Stage
    	(
    		theMonth SMALLINT NOT NULL,
    		PeopleJoined INT NOT NULL,
    		PeopleLeft INT NOT NULL,
    		Subscribers INT NOT NULL
    	)

INSERT  	#Stage
    	(
    		theMonth,
    		PeopleJoined,
    		PeopleLeft,
    		Subscribers
    	)
SELECT  	u.theMonth,
    	SUM(CASE WHEN u.theCol = 'DateJoined' THEN u.Registrations ELSE 0 END) AS PeopleJoined,
    	SUM(CASE WHEN u.theCol = 'DateLeft' THEN u.Registrations ELSE 0 END) AS PeopleLeft,
    	0 AS Subscribers
FROM    	(
    		SELECT		DATEDIFF(MONTH, 0, DateJoined) AS DateJoined,
    				DATEDIFF(MONTH, 0, DateLeft) AS DateLeft,
    				COUNT(*) AS Registrations
    		FROM		dbo.Registrations2
    		GROUP BY	DATEDIFF(MONTH, 0, DateJoined),
    				DATEDIFF(MONTH, 0, DateLeft)
    	) AS d
UNPIVOT 	(
    		theMonth
    		FOR theCol IN (d.DateJoined, d.DateLeft)
    	) AS u
GROUP BY    u.theMonth
HAVING  	SUM(CASE WHEN u.theCol = 'DateJoined' THEN u.Registrations ELSE 0 END) > 0

DECLARE @Subscribers INT = 0

;WITH Yak (theMonth, PeopleJoined, PeopleLeft, Subscribers)
AS (
    SELECT		TOP 2147483647
    		DATEADD(MONTH, theMonth, 0) AS theMonth,
    		PeopleJoined,
    		PeopleLeft,
    		Subscribers
    FROM		#Stage
    ORDER BY	theMonth
)

UPDATE  Yak
SET @Subscribers = Subscribers = @Subscribers + PeopleJoined - PeopleLeft
OUTPUT  inserted.theMonth,
    inserted.PeopleJoined,
    inserted.PeopleLeft,
    inserted.Subscribers

Answer 2

Here's my answer....

WITH MonthlySummaries([Date], joined, [left])--a table collected from two aggregations, giving us
      AS (SELECT    joined.[date],--those who joined and those who left in each month
                    joined.joined,
                    COALESCE([left].[left], 0)
          FROM      (SELECT CAST('01 '+LEFT(DATENAME(month, DateJoined), 3)
                            +' '+DATENAME(year, DateJoined) AS DATETIME) AS [Date],
                            COUNT(*) AS [joined]
                     FROM   Registrations
                     GROUP BY CAST('01 '+LEFT(DATENAME(month, DateJoined),
                                              3)+' '+DATENAME(year, DateJoined) AS DATETIME)
                    ) joined
                    LEFT OUTER JOIN (SELECT COUNT(*) AS [left],
                                            [date] = CAST('01 '
                                            +LEFT(DATENAME(month, Dateleft),
                                                  3)+' '+DATENAME(year, Dateleft) AS DATETIME)
                                     FROM   Registrations
                                     GROUP BY CAST('01 '
                                            +LEFT(DATENAME(month, Dateleft),
                                                  3)+' '+DATENAME(year, Dateleft) AS DATETIME)
                                    ) [left] ON joined.date=[left].date
         )
SELECT  MonthlySummaries.date,
        [joined],
        [left],
        c.subscribers
FROM    MonthlySummaries
        INNER JOIN (SELECT  a.date,
                            SUM(b.joined)-SUM(b.[left]) AS subscribers
                    FROM    MonthlySummaries a
                            INNER JOIN Monthlysummaries b ON b.date<=a.date
                    GROUP BY a.date
                   ) c ON c.date=MonthlySummaries.date
ORDER BY monthlySummaries.date

Answer 3

Here is my second attempt without a "quirky" update

-- Peso 2
DECLARE @Stage TABLE
    (
    	theMonth DATE PRIMARY KEY CLUSTERED,
    	PeopleJoined INT NOT NULL,
    	PeopleLeft INT NOT NULL,
    	Subscribers INT NOT NULL
    )

INSERT  	@Stage
    	(
    		theMonth,
    		PeopleJoined,
    		PeopleLeft,
    		Subscribers
    	)
SELECT  	p.theMonth,
    	p.DateJoined AS PeopleJoined,
    	COALESCE(p.DateLeft, 0) AS PeopleLeft,
    	0 AS Subscribers
FROM    	(
    		SELECT		DATEADD(MONTH, DATEDIFF(MONTH, 0, u.theDate), 0) AS theMonth,
    				u.theCol,
    				COUNT(*) AS Items
    		FROM		dbo.Registrations AS r
    		UNPIVOT		(
    					theDate
    					FOR theCol IN (r.DateJoined, r.DateLeft)
    				) AS u
    		GROUP BY	DATEADD(MONTH, DATEDIFF(MONTH, 0, u.theDate), 0),
    				u.theCol
    	) AS d
PIVOT   	(
    		MAX(d.Items)
    		FOR theCol IN ([DateJoined], [DateLeft])
    	) AS p
WHERE   	p.DateJoined IS NOT NULL
ORDER BY    p.theMonth

UPDATE  x
SET x.Subscribers = (SELECT SUM(w.PeopleJoined) - SUM(w.PeopleLeft) FROM @Stage AS w WHERE w.theMonth <= x.theMonth)
FROM    @Stage AS x

SELECT  	theMonth,
    	PeopleJoined,
    	PeopleLeft,
    	Subscribers
FROM    	@Stage
ORDER BY    theMonth

Here is my third attempt, with a "quirky" update

-- Peso 3 "Quirky update"
DECLARE @Stage TABLE
    (
    	theMonth DATE PRIMARY KEY CLUSTERED,
    	PeopleJoined INT NOT NULL,
    	PeopleLeft INT NOT NULL,
    	Subscribers INT NOT NULL
    )

INSERT  	@Stage
    	(
    		theMonth,
    		PeopleJoined,
    		PeopleLeft,
    		Subscribers
    	)
SELECT  	p.theMonth,
    	p.DateJoined AS PeopleJoined,
    	COALESCE(p.DateLeft, 0) AS PeopleLeft,
    	0 AS Subscribers
FROM    	(
    		SELECT		DATEADD(MONTH, DATEDIFF(MONTH, 0, u.theDate), 0) AS theMonth,
    				u.theCol,
    				COUNT(*) AS Items
    		FROM		dbo.Registrations AS r
    		UNPIVOT		(
    					theDate
    					FOR theCol IN (r.DateJoined, r.DateLeft)
    				) AS u
    		GROUP BY	DATEADD(MONTH, DATEDIFF(MONTH, 0, u.theDate), 0),
    				u.theCol
    	) AS d
PIVOT   	(
    		MAX(d.Items)
    		FOR theCol IN ([DateJoined], [DateLeft])
    	) AS p
WHERE   	p.DateJoined IS NOT NULL
ORDER BY    p.theMonth

DECLARE @Subscribers INT = 0

UPDATE  @Stage
SET @Subscribers = Subscribers = @Subscribers + PeopleJoined - PeopleLeft

SELECT  	theMonth,
    	PeopleJoined,
    	PeopleLeft,
    	Subscribers
FROM    	@Stage
ORDER BY    theMonth

I don't think a quirky update is necessary here. Every month (every record) is 15 bytes (Date 3 bytes, Join 4 bytes, Leave 4 bytes and Subscribers 4 bytes; all non null).

Since a page is the minimum amount of data read by SQL Server anyway, you can have about 44 years (and 9 months) in a single page. The "triangular join" update will be sufficient for those records (537 records for 44.78 years of data).

SQLServerCentral started in March 2001, so there can be only 103 records (102 months since March 2001 to September 2009). A triangular join update for 103 records is really nothing.

Answer 4

Here is my attempt... Bearing in mind that I have only had 1 coffee so far today, this may yet get revised:

-- Matt 1
DECLARE @TempData TABLE (Joined [smallint], Unsubscribed [smallint], Num int)
INSERT INTO @TempData (Joined, Unsubscribed, Num)
SELECT Joined, Unsubscribed, COUNT(*) FROM
 (SELECT DATEDIFF(m, 0, [DateJoined]) AS Joined,
         DATEDIFF(m, 0, [DateLeft]) AS Unsubscribed
    FROM registrations) idat
GROUP BY [Joined], [Unsubscribed]

CREATE TABLE #TempMonths (MonthNumber [smallint], Joined [int], Unsubscribed [int], JoinedToDate [int], UnsubscribedToDate [int])
CREATE CLUSTERED INDEX [IX_TempMonths_1] ON #TempMonths (MonthNumber)
INSERT INTO #TempMonths (MonthNumber, Joined)
SELECT Joined, SUM(Num) FROM @TempData
GROUP BY Joined

UPDATE #TempMonths SET Unsubscribed = idat.UnsubscribedCount
FROM #TempMonths tm INNER JOIN
     (SELECT Unsubscribed AS UnsubscribedMonthNumber, SUM(Num) AS UnsubscribedCount FROM @TempData GROUP BY Unsubscribed) idat
  ON [idat].[UnsubscribedMonthNumber] = tm.MonthNumber

DECLARE @PrevJoined [int], @PrevUnsubscribed [int]
SET @PrevJoined = 0
SET @PrevUnsubscribed = 0

UPDATE #TempMonths
   SET @PrevJoined = JoinedToDate = @PrevJoined + Joined,
       @PrevUnsubscribed = UnsubscribedToDate = @PrevUnsubscribed + ISNULL(Unsubscribed, 0)
  FROM #TempMonths WITH(INDEX(0),TABLOCKX)
OPTION (MAXDOP 1)

SELECT DATEADD(m, MonthNumber, 0) AS MonthDate, 
       Joined AS JoinedThisMonth, 
       ISNULL(Unsubscribed, 0) AS UnsubscribedThisMonth, 
       JoinedToDate - UnsubscribedToDate AS EndOfMonthSubscribers 
  FROM #TempMonths 
 ORDER BY MonthDate

Here's version 2 (+2 cups of coffee)

-- Matt 2
CREATE TABLE #TempData (
  Joined       SMALLINT,
  Unsubscribed SMALLINT,
  Num          INT     
);

INSERT INTO #TempData (Joined, Unsubscribed, Num)
SELECT   Joined, Unsubscribed, SUM(Num)
FROM     (SELECT DATEDIFF(m, 0, [DateJoined]) AS Joined, DATEDIFF(m, 0, [DateLeft]) AS Unsubscribed, [Num]
          FROM   (SELECT   [DateJoined], [DateLeft], COUNT(*) AS Num
                  FROM     registrations WITH (TABLOCKX)
                  GROUP BY DateJoined, DateLeft) AS innerdat) AS idat
GROUP BY [Joined], [Unsubscribed];

CREATE TABLE #TempMonths (
  MonthNumber        SMALLINT,
  Joined             INT     ,
  Unsubscribed       INT     ,
  JoinedToDate       INT     ,
  UnsubscribedToDate INT     
);

CREATE CLUSTERED INDEX [IX_TempMonths_1]
  ON #TempMonths(MonthNumber);

INSERT INTO #TempMonths (MonthNumber, Joined, Unsubscribed)
SELECT td1.Joined, td1.Num, td2.Num
FROM   (SELECT   Joined, SUM(Num) AS Num
        FROM     #TempData
        GROUP BY Joined) AS td1
       LEFT OUTER JOIN
       (SELECT   Unsubscribed, SUM(Num) AS Num
        FROM     #TempData
        GROUP BY Unsubscribed) AS td2
       ON td2.Unsubscribed = td1.Joined;

DECLARE @PrevJoined AS INT, @PrevUnsubscribed AS INT;

SET @PrevJoined = 0;

SET @PrevUnsubscribed = 0;

UPDATE #TempMonths
SET    @PrevJoined       = JoinedToDate       = @PrevJoined + Joined,
       @PrevUnsubscribed = UnsubscribedToDate = @PrevUnsubscribed + ISNULL(Unsubscribed, 0)
FROM   #TempMonths WITH (INDEX (0), TABLOCKX)
OPTION (MAXDOP 1);

SELECT DATEADD(m, MonthNumber, 0) AS MonthDate, Joined AS JoinedThisMonth, ISNULL(Unsubscribed, 0) AS UnsubscribedThisMonth, JoinedToDate - UnsubscribedToDate AS EndOfMonthSubscribers
FROM   #TempMonths;

Here is version 3 - which is faster still on my machine, although only a slight tweak. Interesting to see the results Phil puts out given that 2 is slower on his rig than 1 is.

-- Matt 3
CREATE TABLE #TempData (
  Joined       SMALLINT,
  Unsubscribed SMALLINT,
  Num          INT     
);
CREATE CLUSTERED INDEX [IX_TempData_1]
  ON #TempData(Joined);
CREATE INDEX [IX_TempData_2]
  ON #TempData(Unsubscribed);

INSERT INTO #TempData (Joined, Unsubscribed, Num)
SELECT   Joined, Unsubscribed, SUM(Num)
FROM     (SELECT DATEDIFF(m, 0, [DateJoined]) AS Joined, DATEDIFF(m, 0, [DateLeft]) AS Unsubscribed, [Num]
          FROM   (SELECT   [DateJoined], [DateLeft], COUNT(*) AS Num
                  FROM     registrations WITH (TABLOCKX)
                  GROUP BY DateJoined, DateLeft) AS innerdat) AS idat
GROUP BY [Joined], [Unsubscribed];

CREATE TABLE #TempMonths (
  MonthNumber        SMALLINT,
  Joined             INT     ,
  Unsubscribed       INT     ,
  JoinedToDate       INT     ,
  UnsubscribedToDate INT     
);

CREATE CLUSTERED INDEX [IX_TempMonths_1]
  ON #TempMonths(MonthNumber);

INSERT INTO #TempMonths (MonthNumber, Joined, Unsubscribed)
SELECT td1.Joined, td1.Num, td2.Num
FROM   (SELECT   Joined, SUM(Num) AS Num
        FROM     #TempData
        GROUP BY Joined) AS td1
       LEFT OUTER JOIN
       (SELECT   Unsubscribed, SUM(Num) AS Num
        FROM     #TempData
        GROUP BY Unsubscribed) AS td2
       ON td2.Unsubscribed = td1.Joined;

DECLARE @PrevJoined AS INT, @PrevUnsubscribed AS INT;

SET @PrevJoined = 0;

SET @PrevUnsubscribed = 0;

UPDATE #TempMonths
SET    @PrevJoined       = JoinedToDate       = @PrevJoined + Joined,
       @PrevUnsubscribed = UnsubscribedToDate = @PrevUnsubscribed + ISNULL(Unsubscribed, 0)
FROM   #TempMonths WITH (INDEX (0), TABLOCKX)
OPTION (MAXDOP 1);

SELECT DATEADD(m, MonthNumber, 0) AS MonthDate, Joined AS JoinedThisMonth, ISNULL(Unsubscribed, 0) AS UnsubscribedThisMonth, JoinedToDate - UnsubscribedToDate AS EndOfMonthSubscribers
FROM   #TempMonths;

Ok this is my final one:

-- Matt 1b
DECLARE @TempData TABLE (Joined [smallint], Unsubscribed [smallint], Num int, UNIQUE (Joined, Unsubscribed))
INSERT INTO @TempData (Joined, Unsubscribed, Num)
SELECT Joined, Unsubscribed, COUNT(*) FROM
 (SELECT DATEDIFF(m, 0, [DateJoined]) AS Joined,
         DATEDIFF(m, 0, [DateLeft]) AS Unsubscribed
    FROM registrations WITH (TABLOCKX)) idat
GROUP BY [Joined], [Unsubscribed]

CREATE TABLE #TempMonths (MonthNumber [smallint], Joined [int], Unsubscribed [int], JoinedToDate [int], UnsubscribedToDate [int])
CREATE CLUSTERED INDEX [IX_TempMonths_1] ON #TempMonths (MonthNumber)

INSERT INTO #TempMonths (MonthNumber, Joined, Unsubscribed)
SELECT td1.Joined, td1.Num, td2.Num
FROM   (SELECT Joined, SUM(Num) AS Num FROM @TempData
        GROUP BY Joined) AS td1
       LEFT OUTER JOIN
       (SELECT Unsubscribed, SUM(Num) AS Num FROM @TempData
        GROUP BY Unsubscribed) AS td2
       ON td2.Unsubscribed = td1.Joined;

DECLARE @PrevJoined [int], @PrevUnsubscribed [int]
SET @PrevJoined = 0
SET @PrevUnsubscribed = 0

UPDATE #TempMonths
   SET @PrevJoined = JoinedToDate = @PrevJoined + Joined,
       @PrevUnsubscribed = UnsubscribedToDate = @PrevUnsubscribed + ISNULL(Unsubscribed, 0)
  FROM #TempMonths WITH(INDEX(0),TABLOCKX)
OPTION (MAXDOP 1)

SELECT DATEADD(m, MonthNumber, 0) AS MonthDate, 
       Joined AS JoinedThisMonth, 
       ISNULL(Unsubscribed, 0) AS UnsubscribedThisMonth, 
       JoinedToDate - UnsubscribedToDate AS EndOfMonthSubscribers 
  FROM #TempMonths

Answer 5

I'm using the "Quirky XML Select" for this:

DECLARE @monthly_joins int
DECLARE @monthly_leaves int
DECLARE @running_users int
DECLARE @current_month date
DECLARE @xchar nvarchar(max)
DECLARE @xml XML
DECLARE @now date

SELECT @xchar = '', @monthly_joins = 0, @monthly_leaves = 0, @current_month = NULL, @running_users = 0, @now = '20091001'

SELECT 
    @xchar = 
    	CASE 
    		WHEN eventDate = ISNULL(@current_month, eventDate) 
    			THEN @xchar 
    		ELSE 
    			CASE
    				WHEN @current_month < @now 
    					THEN @xchar + '<A MONTH="'+ CONVERT(char(8),ISNULL(@current_month,eventDate),112) +'" JOINS="'+ CAST(@monthly_joins AS varchar(10)) +'" LEAVES="'+ CAST(@monthly_leaves AS varchar(10)) +'" TOT="'+ CAST(@running_users AS varchar(10)) +'"/>' 
    					ELSE @xchar
    				END
    	END,
    @monthly_joins = 
    	CASE 
    		WHEN eventDate = ISNULL(@current_month,eventDate) 
    			THEN @monthly_joins + JOINS 
    		ELSE JOINS 
    	END,
    @monthly_leaves = 
    	CASE 
    		WHEN eventDate = ISNULL(@current_month,eventDate) 
    			THEN @monthly_leaves + LEAVES
    		ELSE LEAVES 
    	END,
    @running_users = @running_users + JOINS - LEAVES,
    @current_month = eventDate
FROM (
    	SELECT CAST(CONVERT(char(6), DateJoined, 112) + '01' AS DATE) AS eventDate, JOINS = 1, LEAVES = 0
    	FROM registrations
    	UNION ALL
    	SELECT CAST(CONVERT(char(6), DateLeft, 112) + '01' AS DATE) AS eventDate, JOINS = 0, LEAVES = CASE WHEN DateLeft IS NOT NULL THEN 1 ELSE 0 END
    	FROM registrations
) AS Data
ORDER BY eventDate

IF @current_month < @now
    SELECT @xchar = @xchar + '<A MONTH="'+ CONVERT(char(8), @current_month, 112) +'" JOINS="'+ CAST(@monthly_joins AS varchar(10)) +'" LEAVES="'+ CAST(@monthly_leaves AS varchar(10)) +'" TOT="'+ CAST(@running_users AS varchar(10)) +'"/>'
SELECT @xml = CAST('<R>' + @xchar + '</R>' AS XML)


SELECT DISTINCT CONVERT(date, T.results.value('@MONTH[1]','char(6)') + '01', 112) as [month],
    T.results.value('@JOINS[1]','int') as joins,
    T.results.value('@LEAVES[1]','int') as leaves,
    T.results.value('@TOT[1]','int') as tot
FROM @xml.nodes('/R/A') AS T(results)

There's still some index needed, but I'm going with this for the moment. I'll be back with indexes maybe.

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