Here are eight lesser-known specific syntax examples for rewriting SQL queries to make them run faster on SQL Server, along with explanations of how they improve execution speed and what indexes might need to be added to the fields:

1. Using CROSS APPLY instead of correlated subqueries:

   • Example:

     SELECT t1.*, t2.*
     FROM Table1 t1
     CROSS APPLY (SELECT TOP 1 * FROM Table2 t2 WHERE t2.ForeignKey = t1.PrimaryKey ORDER BY t2.SomeColumn) t2

     Copy

   • Explanation: CROSS APPLY executes the subquery for each row in the outer query, allowing for efficient filtering and joining of related data. It can be faster than using correlated subqueries, especially when the subquery returns a small number of rows for each outer row.
   • Index: Create an index on the ForeignKey column in Table2 to speed up the subquery execution.

2. Using OUTER APPLY for optional related data:

   • Example:

     SELECT t1.*, t2.*
     FROM Table1 t1
     OUTER APPLY (SELECT TOP 1 * FROM Table2 t2 WHERE t2.ForeignKey = t1.PrimaryKey ORDER BY t2.SomeColumn) t2

     Copy

   • Explanation: OUTER APPLY works similarly to CROSS APPLY but returns NULL values for the right table when no matching rows are found. This is useful when retrieving optional related data without using LEFT JOINs, which can be less efficient.
   • Index: Create an index on the ForeignKey column in Table2 to speed up the subquery execution.

3. Using filtered indexes:

   • Example:

     CREATE NONCLUSTERED INDEX IX_Table1_Column1_Column2 ON Table1 (Column1, Column2) WHERE Column3 = 'SomeValue'

     Copy

   • Explanation: Filtered indexes allow you to create indexes that only include rows satisfying a specific condition. This can significantly reduce the size of the index and improve query performance when the filter condition is selective.
   • Index: The filtered index itself is the optimization in this case.

4. Using computed columns with indexes:

   • Example:

     ALTER TABLE Table1 ADD ComputedColumn AS (ComplexExpression) PERSISTED
     CREATE NONCLUSTERED INDEX IX_Table1_ComputedColumn ON Table1 (ComputedColumn)

     Copy

   • Explanation: Computed columns allow you to store the result of a complex expression directly in the table. By creating an index on the computed column, you can avoid calculating the expression repeatedly during query execution, improving performance.
   • Index: Create an index on the computed column to speed up queries that use the complex expression.

5. Using the OPTIMIZE FOR query hint:

   • Example:

     SELECT * FROM Table1 WHERE Column1 = @Parameter OPTION (OPTIMIZE FOR (@Parameter = 'SomeValue'))

     Copy

   • Explanation: The OPTIMIZE FOR query hint instructs the query optimizer to use a specific value for a parameter when generating the execution plan. This can lead to a more optimal plan if the hinted value is representative of the typical parameter values used in the query.
   • Index: Create an index on the Column1 column to speed up the query execution.

6. Using the FORCESEEK query hint:

   • Example:

     SELECT * FROM Table1 WITH (FORCESEEK) WHERE Column1 = 'SomeValue'

     Copy

   • Explanation: The FORCESEEK query hint forces the query optimizer to use an index seek operation instead of an index scan or table scan. This can be beneficial when you know that an index seek is the most efficient way to retrieve the data.
   • Index: Ensure that an appropriate index exists on the Column1 column to enable the index seek operation.

7. Using the LOOP JOIN query hint:

   • Example:

     SELECT t1.*, t2.*
     FROM Table1 t1 WITH (LOOP JOIN)
     INNER JOIN Table2 t2 ON t1.PrimaryKey = t2.ForeignKey

     Copy

   • Explanation: The LOOP JOIN query hint forces the query optimizer to use a nested loop join instead of other join types like hash join or merge join. This can be faster when joining small tables or when one table has a highly selective filter.
   • Index: Create an index on the ForeignKey column in Table2 to speed up the join operation.

8. Using the HASH GROUP query hint:

   • Example:

     SELECT Column1, COUNT(*) AS Count
     FROM Table1 WITH (HASH GROUP)
     GROUP BY Column1

     Copy

   • Explanation: The HASH GROUP query hint forces the query optimizer to use a hash-based aggregation instead of a sort-based aggregation. This can be faster when dealing with a large number of groups and when the data is not already sorted.
   • Index: Create an index on the Column1 column to speed up the grouping operation.

Remember, while these techniques can be powerful, it's important to test and measure the performance impact in your specific environment. The effectiveness of these optimizations may vary depending on factors such as data distribution, hardware resources, and concurrent workload.