SQL Server 不会优化两个等效分区表上的并行合并连接

提前为非常详细的问题道歉。我已经包含查询以生成完整的数据集以重现问题，并且我在 32 核机器上运行 SQL Server 2012。但是，我不认为这特定于 SQL Server 2012，并且我已为此特定示例强制 MAXDOP 为 10。

我有两个使用相同分区方案分区的表。当在用于分区的列上将它们连接在一起时，我注意到 SQL Server 无法像预期的那样优化并行合并连接，因此选择使用 HASH JOIN。在这种特殊情况下，我可以通过根据分区函数将查询拆分为 10 个不相交的范围并在 SSMS 中同时运行每个查询来手动模拟更优化的并行 MERGE JOIN。使用 WAITFOR 在完全相同的时间运行它们，结果是所有查询在原始并行 HASH JOIN 使用的总时间的约 40% 内完成。

在等效分区表的情况下，有什么方法可以让 SQL Server 自行进行这种优化？我知道 SQL Server 通常可能会产生大量开销以使 MERGE JOIN 并行，但在这种情况下似乎有一种非常自然的分片方法，开销最小。也许这只是优化器还不够聪明识别的特殊情况？

这是设置简化数据集以重现此问题的 SQL：

/* Create the first test data table */
CREATE TABLE test_transaction_properties 
    ( transactionID INT NOT NULL IDENTITY(1,1)
    , prop1 INT NULL
    , prop2 FLOAT NULL
    )

/* Populate table with pseudo-random data (the specific data doesn't matter too much for this example) */
;WITH E1(N) AS (
    SELECT 1 UNION ALL SELECT 1 UNION ALL SELECT 1 UNION ALL SELECT 1 UNION ALL SELECT 1 
    UNION ALL SELECT 1 UNION ALL SELECT 1 UNION ALL SELECT 1 UNION ALL SELECT 1 UNION ALL SELECT 1
)
, E2(N) AS (SELECT 1 FROM E1 a CROSS JOIN E1 b)
, E4(N) AS (SELECT 1 FROM E2 a CROSS JOIN E2 b)
, E8(N) AS (SELECT 1 FROM E4 a CROSS JOIN E4 b)
INSERT INTO test_transaction_properties WITH (TABLOCK) (prop1, prop2)
SELECT TOP 10000000 (ABS(CAST(CAST(NEWID() AS VARBINARY) AS INT)) % 5) + 1 AS prop1
                , ABS(CAST(CAST(NEWID() AS VARBINARY) AS INT)) * rand() AS prop2
FROM E8

/* Create the second test data table */
CREATE TABLE test_transaction_item_detail
    ( transactionID INT NOT NULL
    , productID INT NOT NULL
    , sales FLOAT NULL
    , units INT NULL
    )

 /* Populate the second table such that each transaction has one or more items
     (again, the specific data doesn't matter too much for this example) */
INSERT INTO test_transaction_item_detail WITH (TABLOCK) (transactionID, productID, sales, units)
SELECT t.transactionID, p.productID, 100 AS sales, 1 AS units
FROM test_transaction_properties t
JOIN (
    SELECT 1 as productRank, 1 as productId
    UNION ALL SELECT 2 as productRank, 12 as productId
    UNION ALL SELECT 3 as productRank, 123 as productId
    UNION ALL SELECT 4 as productRank, 1234 as productId
    UNION ALL SELECT 5 as productRank, 12345 as productId
) p
    ON p.productRank <= t.prop1

/* Divides the transactions evenly into 10 partitions */
CREATE PARTITION FUNCTION [pf_test_transactionId] (INT)
AS RANGE RIGHT
FOR VALUES
(1,1000001,2000001,3000001,4000001,5000001,6000001,7000001,8000001,9000001)

CREATE PARTITION SCHEME [ps_test_transactionId]
AS PARTITION [pf_test_transactionId]
ALL TO ( [PRIMARY] )

/* Apply the same partition scheme to both test data tables */
ALTER TABLE test_transaction_properties
ADD CONSTRAINT PK_test_transaction_properties
PRIMARY KEY (transactionID)
ON ps_test_transactionId (transactionID)

ALTER TABLE test_transaction_item_detail
ADD CONSTRAINT PK_test_transaction_item_detail
PRIMARY KEY (transactionID, productID)
ON ps_test_transactionId (transactionID)

现在我们终于准备好重现次优查询了！

/* This query produces a HASH JOIN using 20 threads without the MAXDOP hint,
    and the same behavior holds in that case.
    For simplicity here, I have limited it to 10 threads. */
SELECT COUNT(*)
FROM test_transaction_item_detail i
JOIN test_transaction_properties t
    ON t.transactionID = i.transactionID
OPTION (MAXDOP 10)

但是，使用单个线程来处理每个分区（下面的第一个分区示例）将导致更有效的计划。我通过在完全相同的时刻对 10 个分区中的每一个运行如下查询来测试这一点，所有 10 个分区都在 1 秒多的时间内完成：

SELECT COUNT(*)
FROM test_transaction_item_detail i
INNER MERGE JOIN test_transaction_properties t
    ON t.transactionID = i.transactionID
WHERE t.transactionID BETWEEN 1 AND 1000000
OPTION (MAXDOP 1)