Questo potrebbe non essere completamente in argomento, tuttavia è un giorno lento qui.

Esiste un metodo più efficiente per ottenere un elenco di numeri da 1 a 49 con una colonna contenente le parole FIZZquando il numero può essere diviso uniformemente per 3, BUZZquando il numero può essere diviso uniformemente per 5 e FIZZBUZZquando il numero può essere diviso uniformemente da entrambi 3 e 5?

I miei tentativi sono (ATTENZIONE, questo svuoterà la cache delle procedure, quindi NON ESEGUIRE UNA SCATOLA DI PRODUZIONE):

DBCC FREEPROCCACHE
GO
/*VARIANT1*/
;WITH t AS (
    SELECT RowNum = ROW_NUMBER() OVER (ORDER BY o.object_id)
    FROM sys.objects o
)
SELECT t.RowNum
    , CASE WHEN ((t.RowNum % 3) + (t.RowNum % 5)) = 0  THEN 'FIZZBUZZ' 
    ELSE 
        CASE WHEN t.RowNum % 3 = 0 THEN 'FIZZ' 
        ELSE 
            CASE WHEN t.RowNum % 5 = 0 THEN 'BUZZ' 
            ELSE '' 
            END 
        END 
    END
FROM t
WHERE t.RowNum < 50;
GO 100

/*VARIANT2*/
DECLARE @t TABLE
(
    Num INT NOT NULL PRIMARY KEY CLUSTERED
);
INSERT INTO @t (Num)
SELECT ROW_NUMBER() OVER (ORDER BY o.object_id)
FROM sys.objects o;

SELECT t.Num
    , CASE WHEN ((t.Num % 3) + (t.Num % 5)) = 0  THEN 'FIZZBUZZ' 
    ELSE 
        CASE WHEN t.Num % 3 = 0 THEN 'FIZZ' 
        ELSE 
            CASE WHEN t.Num % 5 = 0 THEN 'BUZZ' 
            ELSE '' 
            END 
        END 
    END
FROM @t t
WHERE t.Num < 50;
GO 100

SELECT CASE WHEN dest.text LIKE '%/*VARIANT1*/%' THEN 'VARIANT1' ELSE 'VARIANT2' END
    , MAX(deqs.execution_count)
    , SUM(deqs.total_worker_time)
    , AvgWorkerTime = SUM(deqs.total_worker_time) / MAX(deqs.execution_count)
FROM sys.dm_exec_query_stats deqs
CROSS APPLY sys.dm_exec_sql_text(deqs.sql_handle) dest
WHERE (dest.text LIKE '%/*VARIANT1*/%'
    OR dest.text LIKE '%/*VARIANT2*/%')
    AND dest.text NOT LIKE '%/*NOT_ME!*/%'
GROUP BY CASE WHEN dest.text LIKE '%/*VARIANT1*/%' THEN 'VARIANT1' ELSE 'VARIANT2' END
ORDER BY CASE WHEN dest.text LIKE '%/*VARIANT1*/%' THEN 'VARIANT1' ELSE 'VARIANT2' END
/*NOT_ME!*/;

Come consigliato da @AaronBertrand, ho modificato i miei tentativi di eseguire ogni serie di istruzioni 100 volte ciascuna, quindi mostrare i tempi registrati da SQL Server tramite sys.dm_exec_query_stats .

I risultati:

            Runs    total_time      average time
VARIANT1    100     42533           425
VARIANT2    100     138677          1386

Di seguito è una soluzione T-SQL che scrive i primi milioni di numeri in una tabella temporanea. Ci vogliono circa 84 ms sul mio computer. I principali colli di bottiglia sono in attesa del latch NESTING_TRANSACTION_FULL e CXPACKET, entrambi i quali non so come affrontare se non cambiare MAXDOP. Volevo un piano di query che potesse sfruttare i cicli nidificati paralleli e il parallelismo basato sulla domanda, che è quello che sono riuscito a ottenere:

Il codice è un po 'lungo. In breve, unisco due tabelle derivate di 246 righe e 271 righe per un totale di 66666 righe. Tali righe sono unite a una tabella derivata di 15 righe che sfrutta il fatto che il modello FIZZBUZZ viene ripetuto per ogni 15 righe. Le ultime dieci righe vengono aggiunte con a UNION ALL.

DROP TABLE IF EXISTS #t;

SELECT res.fizzbuzz INTO #t
FROM
(
VALUES
(0),
(15),
(30),
(45),
(60),
(75),
(90),
(105),
(120),
(135),
(150),
(165),
(180),
(195),
(210),
(225),
(240),
(255),
(270),
(285),
(300),
(315),
(330),
(345),
(360),
(375),
(390),
(405),
(420),
(435),
(450),
(465),
(480),
(495),
(510),
(525),
(540),
(555),
(570),
(585),
(600),
(615),
(630),
(645),
(660),
(675),
(690),
(705),
(720),
(735),
(750),
(765),
(780),
(795),
(810),
(825),
(840),
(855),
(870),
(885),
(900),
(915),
(930),
(945),
(960),
(975),
(990),
(1005),
(1020),
(1035),
(1050),
(1065),
(1080),
(1095),
(1110),
(1125),
(1140),
(1155),
(1170),
(1185),
(1200),
(1215),
(1230),
(1245),
(1260),
(1275),
(1290),
(1305),
(1320),
(1335),
(1350),
(1365),
(1380),
(1395),
(1410),
(1425),
(1440),
(1455),
(1470),
(1485),
(1500),
(1515),
(1530),
(1545),
(1560),
(1575),
(1590),
(1605),
(1620),
(1635),
(1650),
(1665),
(1680),
(1695),
(1710),
(1725),
(1740),
(1755),
(1770),
(1785),
(1800),
(1815),
(1830),
(1845),
(1860),
(1875),
(1890),
(1905),
(1920),
(1935),
(1950),
(1965),
(1980),
(1995),
(2010),
(2025),
(2040),
(2055),
(2070),
(2085),
(2100),
(2115),
(2130),
(2145),
(2160),
(2175),
(2190),
(2205),
(2220),
(2235),
(2250),
(2265),
(2280),
(2295),
(2310),
(2325),
(2340),
(2355),
(2370),
(2385),
(2400),
(2415),
(2430),
(2445),
(2460),
(2475),
(2490),
(2505),
(2520),
(2535),
(2550),
(2565),
(2580),
(2595),
(2610),
(2625),
(2640),
(2655),
(2670),
(2685),
(2700),
(2715),
(2730),
(2745),
(2760),
(2775),
(2790),
(2805),
(2820),
(2835),
(2850),
(2865),
(2880),
(2895),
(2910),
(2925),
(2940),
(2955),
(2970),
(2985),
(3000),
(3015),
(3030),
(3045),
(3060),
(3075),
(3090),
(3105),
(3120),
(3135),
(3150),
(3165),
(3180),
(3195),
(3210),
(3225),
(3240),
(3255),
(3270),
(3285),
(3300),
(3315),
(3330),
(3345),
(3360),
(3375),
(3390),
(3405),
(3420),
(3435),
(3450),
(3465),
(3480),
(3495),
(3510),
(3525),
(3540),
(3555),
(3570),
(3585),
(3600),
(3615),
(3630),
(3645),
(3660),
(3675)
) v246 (n)
CROSS JOIN 
(
VALUES
(0),
(15),
(30),
(45),
(60),
(75),
(90),
(105),
(120),
(135),
(150),
(165),
(180),
(195),
(210),
(225),
(240),
(255),
(270),
(285),
(300),
(315),
(330),
(345),
(360),
(375),
(390),
(405),
(420),
(435),
(450),
(465),
(480),
(495),
(510),
(525),
(540),
(555),
(570),
(585),
(600),
(615),
(630),
(645),
(660),
(675),
(690),
(705),
(720),
(735),
(750),
(765),
(780),
(795),
(810),
(825),
(840),
(855),
(870),
(885),
(900),
(915),
(930),
(945),
(960),
(975),
(990),
(1005),
(1020),
(1035),
(1050),
(1065),
(1080),
(1095),
(1110),
(1125),
(1140),
(1155),
(1170),
(1185),
(1200),
(1215),
(1230),
(1245),
(1260),
(1275),
(1290),
(1305),
(1320),
(1335),
(1350),
(1365),
(1380),
(1395),
(1410),
(1425),
(1440),
(1455),
(1470),
(1485),
(1500),
(1515),
(1530),
(1545),
(1560),
(1575),
(1590),
(1605),
(1620),
(1635),
(1650),
(1665),
(1680),
(1695),
(1710),
(1725),
(1740),
(1755),
(1770),
(1785),
(1800),
(1815),
(1830),
(1845),
(1860),
(1875),
(1890),
(1905),
(1920),
(1935),
(1950),
(1965),
(1980),
(1995),
(2010),
(2025),
(2040),
(2055),
(2070),
(2085),
(2100),
(2115),
(2130),
(2145),
(2160),
(2175),
(2190),
(2205),
(2220),
(2235),
(2250),
(2265),
(2280),
(2295),
(2310),
(2325),
(2340),
(2355),
(2370),
(2385),
(2400),
(2415),
(2430),
(2445),
(2460),
(2475),
(2490),
(2505),
(2520),
(2535),
(2550),
(2565),
(2580),
(2595),
(2610),
(2625),
(2640),
(2655),
(2670),
(2685),
(2700),
(2715),
(2730),
(2745),
(2760),
(2775),
(2790),
(2805),
(2820),
(2835),
(2850),
(2865),
(2880),
(2895),
(2910),
(2925),
(2940),
(2955),
(2970),
(2985),
(3000),
(3015),
(3030),
(3045),
(3060),
(3075),
(3090),
(3105),
(3120),
(3135),
(3150),
(3165),
(3180),
(3195),
(3210),
(3225),
(3240),
(3255),
(3270),
(3285),
(3300),
(3315),
(3330),
(3345),
(3360),
(3375),
(3390),
(3405),
(3420),
(3435),
(3450),
(3465),
(3480),
(3495),
(3510),
(3525),
(3540),
(3555),
(3570),
(3585),
(3600),
(3615),
(3630),
(3645),
(3660),
(3675),
(3690),
(3705),
(3720),
(3735),
(3750),
(3765),
(3780),
(3795),
(3810),
(3825),
(3840),
(3855),
(3870),
(3885),
(3900),
(3915),
(3930),
(3945),
(3960),
(3975),
(3990),
(4005),
(4020),
(4035),
(4050)
) v271 (n)
CROSS APPLY
(
VALUES
(CAST(v246.n * 271 + v271.n + 1 AS CHAR(8))),
(CAST(v246.n * 271 + v271.n + 2 AS CHAR(8))),
(CAST('FIZZ' AS CHAR(8))),
(CAST(v246.n * 271 + v271.n + 4 AS CHAR(8))),
(CAST('BUZZ' AS CHAR(8))),
(CAST('FIZZ' AS CHAR(8))),
(CAST(v246.n * 271 + v271.n + 7 AS CHAR(8))),
(CAST(v246.n * 271 + v271.n + 8 AS CHAR(8))),
(CAST('FIZZ' AS CHAR(8))),
(CAST('BUZZ' AS CHAR(8))),
(CAST(v246.n * 271 + v271.n + 11 AS CHAR(8))),
(CAST('FIZZ' AS CHAR(8))),
(CAST(v246.n * 271 + v271.n + 13 AS CHAR(8))),
(CAST(v246.n * 271 + v271.n + 14 AS CHAR(8))),
(CAST('FIZZBUZZ' AS CHAR(8)))
) res (fizzbuzz)

UNION ALL

SELECT v.fizzbuzz
FROM (
VALUES 
('999991'),
('999992'),
('FIZZ'),
('999994'),
('BUZZ'),
('FIZZ'),
('999997'),
('999998'),
('FIZZ'),
('BUZZ')
) v (fizzbuzz)

OPTION (MAXDOP 6, NO_PERFORMANCE_SPOOL);

Utilizzo di una tabella ottimizzata per la memoria di SQL Server 2014 e una procedura compilata in modo nativo:

-- Setup
CREATE DATABASE InMem;
GO
ALTER DATABASE InMem
ADD FILEGROUP FG1
CONTAINS MEMORY_OPTIMIZED_DATA;
GO
ALTER DATABASE InMem
ADD FILE 
(
    NAME = 'FN1', 
    -- Change to suit your system
    FILENAME = 'C:\Program Files\Microsoft SQL Server\MSSQL12.SQL2014\MSSQL\DATA\FN1.mod'
)
TO FILEGROUP FG1;
GO
USE InMem;
GO
CREATE TYPE dbo.FizzBuzzTableType AS TABLE 
(
    n integer NOT NULL INDEX i,
    FizzBuzz varchar(8) NOT NULL
) WITH (MEMORY_OPTIMIZED = ON);
GO

Procedura nativa:

CREATE PROCEDURE dbo.FizzBuzz
WITH 
    NATIVE_COMPILATION, 
    SCHEMABINDING, 
    EXECUTE AS OWNER
AS
BEGIN ATOMIC 
WITH 
(
    TRANSACTION ISOLATION LEVEL = SNAPSHOT, 
    LANGUAGE = N'english'
)   
    DECLARE @n AS dbo.FizzBuzzTableType;

    DECLARE @i integer = 1;
    WHILE @i < 50
    BEGIN
        IF @i % 15 = 0
        BEGIN
            INSERT @n (n, FizzBuzz) 
            VALUES (@i, 'FizzBuzz')
        END
        ELSE 
        BEGIN
            IF @i % 3 = 0
            BEGIN
                INSERT @n (n, FizzBuzz)
                VALUES (@i, 'Fizz')
            END
            ELSE 
            BEGIN
                IF @i % 5 = 0
                BEGIN
                    INSERT @n (n, FizzBuzz) 
                    VALUES (@i, 'Buzz')
                END
                ELSE
                BEGIN
                    INSERT @n (n, FizzBuzz) 
                    VALUES (@i, CONVERT(varchar(8), @i));
                END;
            END;
        END;

        SET @i += 1;
    END;

    SELECT
        N.n, 
        N.FizzBuzz
    FROM @n AS N
    ORDER BY
        N.n;
END;

Test:

SET NOCOUNT ON;
PRINT SYSUTCDATETIME();
GO
DECLARE @T AS dbo.FizzBuzzTableType;

INSERT @T (n, FizzBuzz)
EXECUTE dbo.FizzBuzz;
GO 100

PRINT SYSUTCDATETIME();

Risultati tipici:

-- 95ms for 100 iterations, < 1ms each
2014-12-31 10:07:13.7993355
Beginning execution loop
Batch execution completed 100 times.
2014-12-31 10:07:13.8943409

Questo scrive l'output della procedura in una variabile di tabella in memoria, perché altrimenti stiamo solo testando la velocità di visualizzazione dei risultati in SSMS.

Un milione di file

La procedura nativa sopra descritta richiede circa 12 secondi per essere eseguita su 1.000.000 di numeri. Ci sono molti modi più veloci per fare la stessa cosa in T-SQL. Quello che ho scritto prima segue. Funziona in circa 500 ms sul mio laptop su un milione di righe quando viene raggiunto il piano parallelo previsto:

IF  OBJECT_ID(N'tempdb..#Result', N'U') IS NOT NULL
    DROP TABLE #Result;

IF  OBJECT_ID(N'tempdb..#Thousand', N'U') IS NOT NULL
    DROP TABLE #Thousand;

SET NOCOUNT ON;
DECLARE @start datetime2(7) = SYSUTCDATETIME();

CREATE TABLE #Thousand 
(
    n integer NOT NULL,

    CONSTRAINT PK_#Thousand
    PRIMARY KEY CLUSTERED (n)
);

-- Add 1,000 rows numbered 0-999 to #Thousand
WITH 
    L1 (n) AS
(
    SELECT  V.n
    FROM    
    (
        VALUES  (0), (1), (2), (3), (4),
                (5), (6), (7), (8), (9)
    ) AS V (n)
),
    Thousand AS
(
    SELECT  n = 
        CONVERT
        (
            integer,
            ROW_NUMBER() OVER (
            ORDER BY (SELECT NULL))
            - 1
        )
    FROM L1
    CROSS JOIN L1 AS L2
    CROSS JOIN L1 AS L3
)
INSERT #Thousand (n)
SELECT n
FROM Thousand;

-- To hold the Fizz Buzz output
CREATE TABLE #Result 
(
    n integer NOT NULL, 
    result varchar(8) NOT NULL
);

INSERT #Result
SELECT 
    Million.n, 
    Million.result
FROM
(
    -- Modulo operation to encourage few outer rows parallelism
    SELECT  n
    FROM    #Thousand
    WHERE   n % 1 = 0
) AS T1
-- Outer Apply to keep the Compute Scalar parallel
OUTER APPLY
(
    SELECT
        F2.n, 
        F2.result
    FROM #Thousand AS T2
    CROSS APPLY
    (
        -- Row numbers 1 to 1,000,000
        SELECT  (T1.n * 1000) + T2.n + 1
    ) AS F1 (n)
    CROSS APPLY
    (
        -- The Fizz Buzz bit
        SELECT
            F1.n,
            result =
                CASE 
                    WHEN F1.n % 15 = 0 THEN 'FizzBuzz'
                    WHEN F1.n % 3 = 0 THEN 'Buzz'
                    WHEN F1.n % 5 = 0 THEN 'Fizz'
                    ELSE CONVERT(varchar(8), F1.n)
                END
    ) AS F2
) AS Million
OPTION  (MAXDOP 4, QUERYTRACEON 9481);

PRINT DATEDIFF(MILLISECOND, @start, SYSUTCDATETIME());

Questo funziona allo stesso modo sul mio computer come il tuo primo (0ms). Non sono sicuro che si ridimensionerebbe più velocemente o meno.

;WITH t AS (
    SELECT RowNum = ROW_NUMBER() OVER (ORDER BY o.object_id)
    FROM sys.objects o
)
SELECT t.RowNum
    , Cxa.Fizz + CxB.Buzz
FROM t
CROSS APPLY (SELECT CASE WHEN t.RowNum % 3 = 0 THEN 'FIZZ' ELSE '' END) CxA(Fizz)
CROSS APPLY (SELECT CASE WHEN t.RowNum % 5 = 0 THEN 'BUZZ' ELSE '' END) CxB(Buzz)
WHERE t.RowNum < 50;

La migliore versione che mi è venuta in mente funziona a 30ms sul mio computer:

WITH t AS (
    SELECT 1 As RowNum
    Union ALL
    Select RowNum + 1
    From t
    Where RowNum < 49
)
SELECT t.RowNum
, SubString('FIZZ', (t.RowNum % 3)*10, 5) + SubString('BUZZ', (t.RowNum % 5)*10, 5)
FROM t;

Secondo sqlfiddle.com ci vogliono 7 ms:

select coalesce(fizz + buzz, fizz, buzz, cast(n as varchar)) as FizzBuzz
  from (
    select n0 + 3 * n3 + 9 * n9 + 27 * n27 + 81 * n81 as n
        from
            (select 0 as n0  union all select 1 union all select 2 as n0)  as n0,
            (select 0 as n3  union all select 1 union all select 2 as n3)  as n3,
            (select 0 as n9  union all select 1 union all select 2 as n9)  as n9,
            (select 0 as n27 union all select 1 union all select 2 as n27) as n27,
            (select 0 as n81 union all select 1                    as n81) as n81
  ) as stupidalias1
  left outer join
    (select 3 as fizzstep, 'Fizz' as fizz) as stupidalias2 on n % fizzstep = 0
  left outer join
    (select 5 as buzzstep, 'Buzz' as buzz) as stupidalias3 on n % buzzstep = 0
  where n between 1 and 100
  order by n;

Non utilizza tabelle, processi memorizzati o CTE.

Ho ottenuto una versione ragionevole del proc memorizzato compilato in modo nativo che funziona per 1 milione di righe in ~ 500-800ms. Questa è una conversione T-SQL che ho fatto dell'algoritmo bit per bit da qui con un piccolo aiuto dal blog di Adam Machanic sulle operazioni bit per bit qui .

Sto (si spera) seguendo le stesse regole del proc di 500ms / 1 milione di righe di @ PaulWhite, ovvero generare i risultati ma non visualizzarli / non farli passare come parte dei tempi. Devono essere gli indici di hash sulle tabelle in memoria per velocità e dimensioni della benna di 4.194.304 o 8.388.608 sembra essere il punto debole per me, anche se ovviamente ciò dà un numero elevato di bucket vuote.

USE hekatondb
GO

--NB: SQLCMD script, Enable via: Query menu > SQLCMD Mode
:setvar bucketCount 4194304
--:setvar bucketCount 8388608

IF OBJECT_ID('dbo.usp_hekatonFizzBuzz') IS NOT NULL
DROP PROC dbo.usp_hekatonFizzBuzz 
GO
IF OBJECT_ID('dbo.FizzBuzz') IS NOT NULL
DROP TABLE dbo.FizzBuzz
GO


IF OBJECT_ID('dbo.FizzBuzz') IS NULL
CREATE TABLE dbo.FizzBuzz (
    Number      INT NOT NULL,
    Result      VARCHAR(8) NULL,

    CONSTRAINT PK_FizzBuzz PRIMARY KEY NONCLUSTERED HASH ( Number ) WITH ( BUCKET_COUNT = $(bucketCount) )

) WITH ( MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_ONLY )
GO


CREATE PROC dbo.usp_hekatonFizzBuzz 

    @limit  INT

WITH
    NATIVE_COMPILATION, 
    SCHEMABINDING, 
    EXECUTE AS OWNER
AS
BEGIN ATOMIC
WITH
(
    TRANSACTION ISOLATION LEVEL = SNAPSHOT, 
    LANGUAGE = N'english'
)   

    DECLARE @acc INT = 810092048    -- 11 00 00 01 00 10 01 00 00 01 10 00 01 00 00
    DECLARE @i INT = 1
    DECLARE @c INT

    WHILE @i <= @limit
    BEGIN 

        SELECT
            @c = @acc & 3,
            @acc = ( @acc / 4 ) | ( @c * 268435456 )

        INSERT dbo.FizzBuzz ( Number, Result )
        SELECT @i, SUBSTRING( '       Fizz    Buzz    FizzBuzz', @c * 8, @c * 4 )

        SET @i += 1

    END

END
GO

DELETE dbo.FizzBuzz
DECLARE @startDate DATETIME2 = SYSUTCDATETIME();

EXEC dbo.usp_hekatonFizzBuzz 1000000

SELECT DATEDIFF( millisecond, @startDate, SYSUTCDATETIME() ) diff
GO 10

Ho trovato e giocato con questa singola selezione secondaria senza CTE. max_elapsed_time nelle statistiche della query mostra 1036

 SELECT num,
        CASE    WHEN mod3 + mod5 = 0 THEN 'FizzBuzz'
                WHEN mod5 = 0 THEN 'Buzz'
                WHEN mod3 = 0 THEN 'Fizz'
                ELSE CONVERT(VARCHAR(8), num)
        END
 FROM 
 (
    SELECT  number as num,
            number % 3 AS mod3,
            number % 5 AS mod5
    FROM    master.dbo.spt_values
    WHERE   name IS NULL
            AND number BETWEEN 1 AND 101
 ) AS numbers;

Non mi merito per il codice scritto, volevo solo vedere quanto tempo ci sarebbe voluto

UN MILIONE DI FILE!

;WITH T(N) AS (SELECT N FROM (VALUES (NULL),(NULL),(NULL),(NULL),(NULL),(NULL),(NULL),(NULL),(NULL),(NULL)) AS X(N))
    ,NUMS(N) AS (SELECT TOP(1000000000) ROW_NUMBER() OVER (ORDER BY (SELECT NULL))  AS N FROM T T1,T T2,T T3,T T4,T T5,T T6,T T7,T T8,T T9, T T10)
    SELECT  n, ca.fb
    INTO #fizzywizzy
    FROM    NUMS n
            CROSS APPLY ( SELECT    CASE WHEN n.N % 15 = 0 THEN 'FizzBuzz'
                                         WHEN n.N % 3 = 0 THEN 'Fizz'
                                         WHEN n.N % 5 = 0 THEN 'Buzz'
                                         ELSE CAST(n AS VARCHAR)
                                    END AS [fb]
                        ) ca

La risposta è: circa 10 minuti.

SQL Server parse and compile time: 
   CPU time = 13 ms, elapsed time = 13 ms.

 SQL Server Execution Times:
   CPU time = 648625 ms,  elapsed time = 618025 ms.

PostgreSQL

PostgreSQL fornisce generate_seriesuna funzione di valore di tabella (funzione Set-Returning) che la rende sostanzialmente più semplice. Suppongo che non desideri ottenere alcun risultato quando il numero non contiene né 3 né 5.

SELECT x, str
FROM generate_series(1,49) AS gs(x)
CROSS JOIN LATERAL (VALUES (CASE
  WHEN x % 15 =0 THEN 'Fizzbuzz'
  WHEN x % 3  =0 THEN 'Fizz'
  WHEN x % 5  =0 THEN 'Buzz'
END)) AS c(str)
WHERE str IS NOT NULL;