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Jon of All Trades
Asked: 2011-05-25 10:50:05 +0800 CST

为什么这个查询在包装在 TVF 中时会变得非常慢？

  • 772

我有一个相当复杂的查询，它自己运行只需几秒钟，但是当包装在一个表值函数中时，它要慢得多；我实际上并没有让它完成，但它运行了长达十分钟而没有结束。唯一的变化是用日期参数替换两个日期变量（用日期文字初始化）：

七秒跑

DECLARE @StartDate DATE = '2011-05-21'
DECLARE @EndDate   DATE = '2011-05-23'

DECLARE @Data TABLE (...)
INSERT INTO @Data(...) SELECT...

SELECT * FROM @Data

运行至少十分钟

CREATE FUNCTION X (@StartDate DATE, @EndDate DATE)
  RETURNS TABLE AS RETURN
  SELECT ...

SELECT * FROM X ('2011-05-21', '2011-05-23')

我之前曾将该函数编写为带有 RETURNS @Data TABLE (...) 子句的多语句 TVF，但是将其替换为内联结构并没有产生明显的变化。TVF的长期运行时间为实际SELECT * FROM X时间；实际上创建 UDF 只需要几秒钟。

我可以发布有问题的查询，但它有点长（约 165 行），并且基于第一种方法的成功，我怀疑还有其他事情发生。浏览执行计划，它们似乎是相同的。

我尝试将查询分成更小的部分，而不做任何更改。单独执行时，没有一个部分需要超过几秒钟的时间，但 TVF 仍然挂起。

我看到一个非常相似的问题，https://stackoverflow.com/questions/4190506/sql-server-2005-table-valued-function-weird-performance，但我不确定该解决方案是否适用。也许有人已经看到了这个问题并且知道更通用的解决方案？谢谢！

这是经过几分钟处理后的 dm_exec_requests：

session_id              59
request_id              0
start_time              40688.46517
status                  running
command                 UPDATE
sql_handle              0x030015002D21AF39242A1101ED9E00000000000000000000
statement_start_offset  10962
statement_end_offset    16012
plan_handle             0x050015002D21AF3940C1E6B0040000000000000000000000
database_id                 21
user_id                 1
connection_id           314AE0E4-A1FB-4602-BF40-02D857BAD6CF
blocking_session_id         0
wait_type               NULL
wait_time                   0
last_wait_type          SOS_SCHEDULER_YIELD
wait_resource   
open_transaction_count  0
open_resultset_count    1
transaction_id              48030651
context_info            0x
percent_complete        0
estimated_completion_time   0
cpu_time                    344777
total_elapsed_time          348632
scheduler_id            7
task_address            0x000000045FC85048
reads                   1549
writes                  13
logical_reads           30331425
text_size               2147483647
language                us_english
date_format             mdy
date_first              7
quoted_identifier           1
arithabort              1
ansi_null_dflt_on       1
ansi_defaults           0
ansi_warnings           1
ansi_padding            1
ansi_nulls                  1
concat_null_yields_null 1
transaction_isolation_level 2
lock_timeout            -1
deadlock_priority           0
row_count                   105
prev_error              0
nest_level              1
granted_query_memory    170
executing_managed_code  0
group_id                2
query_hash              0xBE6A286546AF62FC
query_plan_hash         0xD07630B947043AF0

这是完整的查询：

CREATE FUNCTION Routine.MarketingDashboardECommerceBase (@StartDate DATE, @EndDate DATE)
RETURNS TABLE AS RETURN
    WITH RegionsByCode AS (SELECT CountryCode, MIN(Region) AS Region FROM Staging.Volusion.MarketingRegions GROUP BY CountryCode)
        SELECT
            D.Date, Div.Division, Region.Region, C.Category1, C.Category2, C.Category3,
            COALESCE(V.Visits,          0) AS Visits,
            COALESCE(Dem.Demos,         0) AS Demos,
            COALESCE(S.GrossStores,     0) AS GrossStores,
            COALESCE(S.PaidStores,      0) AS PaidStores,
            COALESCE(S.NetStores,       0) AS NetStores,
            COALESCE(S.StoresActiveNow, 0) AS StoresActiveNow
            -- This line causes the run time to climb from a few seconds to over an hour!
            --COALESCE(V.Visits,          0) * COALESCE(ACS.AvgClickCost, GAAC.AvgAdCost, 0.00) AS TotalAdCost
            -- This line alone does not inflate the run time
            --ACS.AvgClickCost
            -- This line is enough to increase the run time to at least a couple minutes
            --GAAC.AvgAdCost
        FROM
            --Dates AS D
            (SELECT SQLDate AS Date FROM Dates WHERE SQLDate BETWEEN @StartDate AND @EndDate) AS D
            CROSS JOIN (SELECT 'UK' AS Division UNION SELECT 'US' UNION SELECT 'IN' UNION SELECT 'Unknown') AS Div
            CROSS JOIN (SELECT Category1, Category2, Category3 FROM Routine.MarketingDashboardCampaignMap UNION SELECT 'Unknown', 'Unknown', 'Unknown') AS C
            CROSS JOIN (SELECT DISTINCT Region FROM Staging.Volusion.MarketingRegions) AS Region
            -- Visitors
            LEFT JOIN
                (
                SELECT
                    V.Date,
                    CASE    WHEN V.Country IN ('United Kingdom', 'Guernsey', 'Ireland', 'Jersey') THEN 'UK'
                        WHEN V.Country IN ('United States', 'Canada', 'Puerto Rico', 'U.S. Virgin Islands') THEN 'US'
                        ELSE 'IN' END AS Division,
                    COALESCE(MR.Region, 'Unknown') AS Region,
                    C.Category1, C.Category2, C.Category3,
                    SUM(V.Visits) AS Visits
                FROM
                             RawData.GoogleAnalytics.Visits        AS V
                    INNER JOIN Routine.MarketingDashboardCampaignMap AS C ON V.LandingPage = C.LandingPage AND V.Campaign = C.Campaign AND V.Medium = C.Medium AND V.Referrer = C.Referrer AND V.Source = C.Source
                    LEFT JOIN  Staging.Volusion.MarketingRegions     AS MR ON V.Country = MR.CountryName
                WHERE
                    V.Date BETWEEN @StartDate AND @EndDate
                GROUP BY
                    V.Date,
                    CASE    WHEN V.Country IN ('United Kingdom', 'Guernsey', 'Ireland', 'Jersey') THEN 'UK'
                        WHEN V.Country IN ('United States', 'Canada', 'Puerto Rico', 'U.S. Virgin Islands') THEN 'US'
                        ELSE 'IN' END,
                    COALESCE(MR.Region, 'Unknown'), C.Category1, C.Category2, C.Category3
                ) AS V ON D.Date = V.Date AND Div.Division = V.Division AND Region.Region = V.Region AND C.Category1 = V.Category1 AND C.Category2 = V.Category2 AND C.Category3 = V.Category3
            -- Demos
            LEFT JOIN
                (
                SELECT
                    OD.SQLDate,
                    G.Division,
                    COALESCE(MR.Region,   'Unknown') AS Region,
                    COALESCE(C.Category1, 'Unknown') AS Category1,
                    COALESCE(C.Category2, 'Unknown') AS Category2,
                    COALESCE(C.Category3, 'Unknown') AS Category3,
                    SUM(D.Demos) AS Demos
                FROM
                             Demos            AS D
                    INNER JOIN Orders           AS O  ON D."Order" = O."Order"
                    INNER JOIN Dates            AS OD ON O.OrderDate = OD.DateSerial
                    INNER JOIN MarketingSources AS MS ON D.Source = MS.Source
                    LEFT JOIN  RegionsByCode    AS MR ON MS.CountryCode = MR.CountryCode
                    LEFT JOIN
                        (
                        SELECT
                            G.TransactionID,
                            MIN (
                                CASE WHEN G.Country IN ('United Kingdom', 'Guernsey', 'Ireland', 'Jersey') THEN 'UK'
                                    WHEN G.Country IN ('United States', 'Canada', 'Puerto Rico', 'U.S. Virgin Islands') THEN 'US'
                                    ELSE 'IN' END
                                ) AS Division
                        FROM
                            RawData.GoogleAnalytics.Geography AS G
                        WHERE
                                TransactionDate BETWEEN @StartDate AND @EndDate
                            AND NOT EXISTS (SELECT * FROM RawData.GoogleAnalytics.Geography AS G2 WHERE G.TransactionID = G2.TransactionID AND G2.EffectiveDate > G.EffectiveDate)
                        GROUP BY
                            G.TransactionID
                        ) AS G  ON O.VolusionOrderID = G.TransactionID
                    LEFT JOIN  RawData.GoogleAnalytics.Referrers     AS R  ON O.VolusionOrderID = R.TransactionID AND NOT EXISTS (SELECT * FROM RawData.GoogleAnalytics.Referrers AS R2 WHERE R.TransactionID = R2.TransactionID AND R2.EffectiveDate > R.EffectiveDate)
                    LEFT JOIN  Routine.MarketingDashboardCampaignMap AS C  ON MS.LandingPage = C.LandingPage AND MS.Campaign = C.Campaign AND MS.Medium = C.Medium AND COALESCE(R.ReferralPath, '(not set)') = C.Referrer AND MS.SourceName = C.Source
                WHERE
                        O.IsDeleted = 'No'
                    AND OD.SQLDate BETWEEN @StartDate AND @EndDate
                GROUP BY
                    OD.SQLDate,
                    G.Division,
                    COALESCE(MR.Region,   'Unknown'),
                    COALESCE(C.Category1, 'Unknown'),
                    COALESCE(C.Category2, 'Unknown'),
                    COALESCE(C.Category3, 'Unknown')
                ) AS Dem ON D.Date = Dem.SQLDate AND Div.Division = Dem.Division AND Region.Region = Dem.Region AND C.Category1 = Dem.Category1 AND C.Category2 = Dem.Category2 AND C.Category3 = Dem.Category3
            -- Stores
            LEFT JOIN
                (
                SELECT
                    OD.SQLDate,
                    CASE WHEN O.VolusionCountryCode = 'GB' THEN 'UK'
                        WHEN A.CountryShortName IN ('U.S.', 'Canada', 'Puerto Rico', 'U.S. Virgin Islands') THEN 'US'
                        ELSE 'IN' END AS Division,
                    COALESCE(MR.Region,     'Unknown') AS Region,
                    COALESCE(CpM.Category1, 'Unknown') AS Category1,
                    COALESCE(CpM.Category2, 'Unknown') AS Category2,
                    COALESCE(CpM.Category3, 'Unknown') AS Category3,
                    SUM(S.Stores) AS GrossStores,
                    SUM(CASE WHEN O.DatePaid <> -1 THEN 1 ELSE 0 END) AS PaidStores,
                    SUM(CASE WHEN O.DatePaid <> -1 AND CD.WeekEnding <> OD.WeekEnding THEN 1 ELSE 0 END) AS NetStores,
                    SUM(CASE WHEN O.DatePaid <> -1 THEN SH.ActiveStores ELSE 0 END) AS StoresActiveNow
                FROM
                             Stores           AS S
                    INNER JOIN Orders           AS O   ON S."Order" = O."Order"
                    INNER JOIN Dates            AS OD  ON O.OrderDate = OD.DateSerial
                    INNER JOIN Dates            AS CD  ON O.CancellationDate = CD.DateSerial
                    INNER JOIN Customers        AS C   ON O.CustomerNow = C.Customer
                    INNER JOIN MarketingSources AS MS  ON C.Source = MS.Source
                    INNER JOIN StoreHistory     AS SH  ON S.MostRecentHistory = SH.History
                    INNER JOIN Addresses        AS A   ON C.Address = A.Address
                    LEFT JOIN  RegionsByCode    AS MR  ON MS.CountryCode = MR.CountryCode
                    LEFT JOIN  Routine.MarketingDashboardCampaignMap AS CpM ON CpM.LandingPage = 'N/A' AND MS.Campaign = CpM.Campaign AND MS.Medium = CpM.Medium AND CpM.Referrer = 'N/A' AND MS.SourceName = CpM.Source
                WHERE
                        O.IsDeleted = 'No'
                    AND OD.SQLDate BETWEEN @StartDate AND @EndDate
                GROUP BY
                    OD.SQLDate,
                    CASE WHEN O.VolusionCountryCode = 'GB' THEN 'UK'
                        WHEN A.CountryShortName IN ('U.S.', 'Canada', 'Puerto Rico', 'U.S. Virgin Islands') THEN 'US'
                        ELSE 'IN' END,
                    COALESCE(MR.Region,     'Unknown'),
                    COALESCE(CpM.Category1, 'Unknown'),
                    COALESCE(CpM.Category2, 'Unknown'),
                    COALESCE(CpM.Category3, 'Unknown')
                ) AS S ON D.Date = S.SQLDate AND Div.Division = S.Division AND Region.Region = S.Region AND C.Category1 = S.Category1 AND C.Category2 = S.Category2 AND C.Category3 = S.Category3
            -- Google Analytics spend
            LEFT JOIN
                (
                SELECT
                    AC.Date, C.Category1, C.Category2, C.Category3, SUM(AC.AdCost) / SUM(AC.Visits) AS AvgAdCost
                FROM
                    RawData.GoogleAnalytics.AdCosts AS AC
                    INNER JOIN
                        (
                        SELECT Campaign, Medium, Source, MIN(Category1) AS Category1, MIN(Category2) AS Category2, MIN(Category3) AS Category3
                        FROM Routine.MarketingDashboardCampaignMap
                        WHERE Category1 <> 'Affiliate'
                        GROUP BY Campaign, Medium, Source
                        ) AS C ON AC.Campaign = C.Campaign AND AC.Medium = C.Medium AND AC.Source = C.Source
                WHERE
                    AC.Date BETWEEN @StartDate AND @EndDate
                GROUP BY
                    AC.Date, C.Category1, C.Category2, C.Category3
                HAVING
                    SUM(AC.AdCost) > 0.00 AND SUM(AC.Visits) > 0
                ) AS GAAC ON D.Date = GAAC.Date AND C.Category1 = GAAC.Category1 AND C.Category2 = GAAC.Category2 AND C.Category3 = GAAC.Category3
            -- adCenter spend
            LEFT JOIN
                (
                SELECT Date, SUM(Spend) / SUM(Clicks) AS AvgClickCost
                FROM RawData.AdCenter.Spend
                WHERE Date BETWEEN @StartDate AND @EndDate
                GROUP BY Date
                HAVING SUM(Spend) > 0.00 AND SUM(Clicks) > 0
                ) AS ACS ON D.Date = ACS.Date AND C.Category1 = 'PPC' AND C.Category2 = 'adCenter' AND C.Category3 = 'N/A'
        WHERE
            V.Visits > 0 OR Dem.Demos > 0 OR S.GrossStores > 0
GO


SELECT * FROM Routine.MarketingDashboardECommerceBase('2011-05-21', '2011-05-23')
sql-server sql-server-2008

5 个回答

  1. Best Answer

    我将问题隔离到查询中的一行。请记住，查询有 160 行长，如果我从 SELECT 子句中禁用此行，我将包含相关表：

    COALESCE(V.Visits, 0) * COALESCE(ACS.AvgClickCost, GAAC.AvgAdCost, 0.00)

    ...运行时间从 63 分钟下降到5 秒（内联 CTE 使其比原来的 7 秒查询稍快）。包括ACS.AvgClickCost或GAAC.AvgAdCost导致运行时间爆炸。特别奇怪的是，这些字段来自两个子查询，它们分别有十行和三行！当它们独立运行时，它们每个都在零秒内运行，并且行数非常短，我希望即使使用嵌套循环，连接时间也很简单。

    关于为什么这个看似无害的计算会完全摆脱 TVF 的任何猜测，而它作为独立查询运行得非常快？

    • 4

  2. 我希望这与参数嗅探有关。

    这里有一些关于这些问题的讨论（您可以搜索 SO 以进行参数嗅探。）

    关联

    • 2

  3. 不幸的是，SQL 的查询优化引擎无法看到内部函数。

    所以我会使用快速执行计划来确定在 TF 中应用哪些提示。冲洗并重复，直到 TF 的执行计划接近更快的执行计划。

    http://sqlblog.com/blogs/tibor_karaszi/archive/2008/08/29/execution-plan-re-use-sp-executesql-and-tsql-variables.aspx

    • 1

  4. 请问这些值有什么区别？

    arithabort              1
ansi_null_dflt_on       1
ansi_defaults           0
ansi_warnings           1
ansi_padding            1
ansi_nulls              1

    这些（尤其是 arithabort）已被证明会以这种方式严重影响查询性能。

    • 0

  5. 对我来说，当我更改函数然后将其更改回来时，这个问题就解决了……所以，是的，看起来优化引擎把它搞砸了。我想它会再次开始缓慢运行......

    • 0

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