Optimizing numeric Range Search in SQL Server












4














This question is similar to Optimizing IP Range Search? but that one is restricted to SQL Server 2000.



Suppose I have 10 million ranges provisionally stored in a table structured and populated as below.



CREATE TABLE MyTable
(
Id INT IDENTITY PRIMARY KEY,
RangeFrom INT NOT NULL,
RangeTo INT NOT NULL,
CHECK (RangeTo > RangeFrom),
INDEX IX1 (RangeFrom,RangeTo),
INDEX IX2 (RangeTo,RangeFrom)
);

WITH RandomNumbers
AS (SELECT TOP 10000000 ABS(CRYPT_GEN_RANDOM(4)%100000000) AS Num
FROM sys.all_objects o1,
sys.all_objects o2,
sys.all_objects o3,
sys.all_objects o4)
INSERT INTO MyTable
(RangeFrom,
RangeTo)
SELECT Num,
Num + 1 + CRYPT_GEN_RANDOM(1)
FROM RandomNumbers


I need to know all ranges containing the value 50,000,000. I try the following query



SELECT *
FROM MyTable
WHERE 50000000 BETWEEN RangeFrom AND RangeTo


SQL Server shows that there were 10,951 logical reads and nearly 5 million rows were read to return the 12 matching ones.



enter image description here



Can I improve on this performance? Any restructuring of the table or additional indexes is fine.










share|improve this question






















  • If I'm understanding the set up of the table correctly, you're picking random numbers uniformly to form your ranges, with no constraints on the "size" of each range. And your probe is for the middle of the overall range 1..100M. In that case - no apparent clustering due to uniform randomness - I don't know why an index on either lower bound or upper bound would be helpful. Can you explain that?
    – davidbak
    30 mins ago












  • @davidbak the conventional indexes on this table are indeed not very helpful in the worst case as it has to scan half the range hence asking for potential improvements on it. There is a nice improvement in the linked question for SQL Server 2000 with the introduction of the "granule" I was hoping spatial indexes could help here as they support contains queries and whilst they work well at reducing the amount of data read they seem to add other overhead that counteracts this.
    – Martin Smith
    21 mins ago












  • I don't have the facility to try it - but I wonder if two indexes - one on the lower bound, one on the upper - and then an inner join - would let the query optimizer work something out.
    – davidbak
    2 mins ago


















4














This question is similar to Optimizing IP Range Search? but that one is restricted to SQL Server 2000.



Suppose I have 10 million ranges provisionally stored in a table structured and populated as below.



CREATE TABLE MyTable
(
Id INT IDENTITY PRIMARY KEY,
RangeFrom INT NOT NULL,
RangeTo INT NOT NULL,
CHECK (RangeTo > RangeFrom),
INDEX IX1 (RangeFrom,RangeTo),
INDEX IX2 (RangeTo,RangeFrom)
);

WITH RandomNumbers
AS (SELECT TOP 10000000 ABS(CRYPT_GEN_RANDOM(4)%100000000) AS Num
FROM sys.all_objects o1,
sys.all_objects o2,
sys.all_objects o3,
sys.all_objects o4)
INSERT INTO MyTable
(RangeFrom,
RangeTo)
SELECT Num,
Num + 1 + CRYPT_GEN_RANDOM(1)
FROM RandomNumbers


I need to know all ranges containing the value 50,000,000. I try the following query



SELECT *
FROM MyTable
WHERE 50000000 BETWEEN RangeFrom AND RangeTo


SQL Server shows that there were 10,951 logical reads and nearly 5 million rows were read to return the 12 matching ones.



enter image description here



Can I improve on this performance? Any restructuring of the table or additional indexes is fine.










share|improve this question






















  • If I'm understanding the set up of the table correctly, you're picking random numbers uniformly to form your ranges, with no constraints on the "size" of each range. And your probe is for the middle of the overall range 1..100M. In that case - no apparent clustering due to uniform randomness - I don't know why an index on either lower bound or upper bound would be helpful. Can you explain that?
    – davidbak
    30 mins ago












  • @davidbak the conventional indexes on this table are indeed not very helpful in the worst case as it has to scan half the range hence asking for potential improvements on it. There is a nice improvement in the linked question for SQL Server 2000 with the introduction of the "granule" I was hoping spatial indexes could help here as they support contains queries and whilst they work well at reducing the amount of data read they seem to add other overhead that counteracts this.
    – Martin Smith
    21 mins ago












  • I don't have the facility to try it - but I wonder if two indexes - one on the lower bound, one on the upper - and then an inner join - would let the query optimizer work something out.
    – davidbak
    2 mins ago
















4












4








4







This question is similar to Optimizing IP Range Search? but that one is restricted to SQL Server 2000.



Suppose I have 10 million ranges provisionally stored in a table structured and populated as below.



CREATE TABLE MyTable
(
Id INT IDENTITY PRIMARY KEY,
RangeFrom INT NOT NULL,
RangeTo INT NOT NULL,
CHECK (RangeTo > RangeFrom),
INDEX IX1 (RangeFrom,RangeTo),
INDEX IX2 (RangeTo,RangeFrom)
);

WITH RandomNumbers
AS (SELECT TOP 10000000 ABS(CRYPT_GEN_RANDOM(4)%100000000) AS Num
FROM sys.all_objects o1,
sys.all_objects o2,
sys.all_objects o3,
sys.all_objects o4)
INSERT INTO MyTable
(RangeFrom,
RangeTo)
SELECT Num,
Num + 1 + CRYPT_GEN_RANDOM(1)
FROM RandomNumbers


I need to know all ranges containing the value 50,000,000. I try the following query



SELECT *
FROM MyTable
WHERE 50000000 BETWEEN RangeFrom AND RangeTo


SQL Server shows that there were 10,951 logical reads and nearly 5 million rows were read to return the 12 matching ones.



enter image description here



Can I improve on this performance? Any restructuring of the table or additional indexes is fine.










share|improve this question













This question is similar to Optimizing IP Range Search? but that one is restricted to SQL Server 2000.



Suppose I have 10 million ranges provisionally stored in a table structured and populated as below.



CREATE TABLE MyTable
(
Id INT IDENTITY PRIMARY KEY,
RangeFrom INT NOT NULL,
RangeTo INT NOT NULL,
CHECK (RangeTo > RangeFrom),
INDEX IX1 (RangeFrom,RangeTo),
INDEX IX2 (RangeTo,RangeFrom)
);

WITH RandomNumbers
AS (SELECT TOP 10000000 ABS(CRYPT_GEN_RANDOM(4)%100000000) AS Num
FROM sys.all_objects o1,
sys.all_objects o2,
sys.all_objects o3,
sys.all_objects o4)
INSERT INTO MyTable
(RangeFrom,
RangeTo)
SELECT Num,
Num + 1 + CRYPT_GEN_RANDOM(1)
FROM RandomNumbers


I need to know all ranges containing the value 50,000,000. I try the following query



SELECT *
FROM MyTable
WHERE 50000000 BETWEEN RangeFrom AND RangeTo


SQL Server shows that there were 10,951 logical reads and nearly 5 million rows were read to return the 12 matching ones.



enter image description here



Can I improve on this performance? Any restructuring of the table or additional indexes is fine.







sql-server optimization






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked 2 hours ago









Martin Smith

61.5k10166245




61.5k10166245












  • If I'm understanding the set up of the table correctly, you're picking random numbers uniformly to form your ranges, with no constraints on the "size" of each range. And your probe is for the middle of the overall range 1..100M. In that case - no apparent clustering due to uniform randomness - I don't know why an index on either lower bound or upper bound would be helpful. Can you explain that?
    – davidbak
    30 mins ago












  • @davidbak the conventional indexes on this table are indeed not very helpful in the worst case as it has to scan half the range hence asking for potential improvements on it. There is a nice improvement in the linked question for SQL Server 2000 with the introduction of the "granule" I was hoping spatial indexes could help here as they support contains queries and whilst they work well at reducing the amount of data read they seem to add other overhead that counteracts this.
    – Martin Smith
    21 mins ago












  • I don't have the facility to try it - but I wonder if two indexes - one on the lower bound, one on the upper - and then an inner join - would let the query optimizer work something out.
    – davidbak
    2 mins ago




















  • If I'm understanding the set up of the table correctly, you're picking random numbers uniformly to form your ranges, with no constraints on the "size" of each range. And your probe is for the middle of the overall range 1..100M. In that case - no apparent clustering due to uniform randomness - I don't know why an index on either lower bound or upper bound would be helpful. Can you explain that?
    – davidbak
    30 mins ago












  • @davidbak the conventional indexes on this table are indeed not very helpful in the worst case as it has to scan half the range hence asking for potential improvements on it. There is a nice improvement in the linked question for SQL Server 2000 with the introduction of the "granule" I was hoping spatial indexes could help here as they support contains queries and whilst they work well at reducing the amount of data read they seem to add other overhead that counteracts this.
    – Martin Smith
    21 mins ago












  • I don't have the facility to try it - but I wonder if two indexes - one on the lower bound, one on the upper - and then an inner join - would let the query optimizer work something out.
    – davidbak
    2 mins ago


















If I'm understanding the set up of the table correctly, you're picking random numbers uniformly to form your ranges, with no constraints on the "size" of each range. And your probe is for the middle of the overall range 1..100M. In that case - no apparent clustering due to uniform randomness - I don't know why an index on either lower bound or upper bound would be helpful. Can you explain that?
– davidbak
30 mins ago






If I'm understanding the set up of the table correctly, you're picking random numbers uniformly to form your ranges, with no constraints on the "size" of each range. And your probe is for the middle of the overall range 1..100M. In that case - no apparent clustering due to uniform randomness - I don't know why an index on either lower bound or upper bound would be helpful. Can you explain that?
– davidbak
30 mins ago














@davidbak the conventional indexes on this table are indeed not very helpful in the worst case as it has to scan half the range hence asking for potential improvements on it. There is a nice improvement in the linked question for SQL Server 2000 with the introduction of the "granule" I was hoping spatial indexes could help here as they support contains queries and whilst they work well at reducing the amount of data read they seem to add other overhead that counteracts this.
– Martin Smith
21 mins ago






@davidbak the conventional indexes on this table are indeed not very helpful in the worst case as it has to scan half the range hence asking for potential improvements on it. There is a nice improvement in the linked question for SQL Server 2000 with the introduction of the "granule" I was hoping spatial indexes could help here as they support contains queries and whilst they work well at reducing the amount of data read they seem to add other overhead that counteracts this.
– Martin Smith
21 mins ago














I don't have the facility to try it - but I wonder if two indexes - one on the lower bound, one on the upper - and then an inner join - would let the query optimizer work something out.
– davidbak
2 mins ago






I don't have the facility to try it - but I wonder if two indexes - one on the lower bound, one on the upper - and then an inner join - would let the query optimizer work something out.
– davidbak
2 mins ago












2 Answers
2






active

oldest

votes


















3














One alternative way of representing a range would be as points on a line.



The below migrates all the data into a new table with the range represented as a geometry datatype.



CREATE TABLE MyTable2
(
Id INT IDENTITY PRIMARY KEY,
Range GEOMETRY NOT NULL,
RangeFrom AS Range.STPointN(1).STX,
RangeTo AS Range.STPointN(2).STX,
CHECK (Range.STNumPoints() = 2 AND Range.STPointN(1).STY = 0 AND Range.STPointN(2).STY = 0)
);

SET IDENTITY_INSERT MyTable2 ON

INSERT INTO MyTable2
(Id,
Range)
SELECT ID,
geometry::STLineFromText(CONCAT('LINESTRING(', RangeFrom, ' 0, ', RangeTo, ' 0)'), 0)
FROM MyTable

SET IDENTITY_INSERT MyTable2 OFF


CREATE SPATIAL INDEX index_name
ON MyTable2 ( Range )
USING GEOMETRY_GRID
WITH (
BOUNDING_BOX = ( xmin=0, ymin=0, xmax=110000000, ymax=1 ),
GRIDS = (HIGH, HIGH, HIGH, HIGH),
CELLS_PER_OBJECT = 16);


The equivalent query to find ranges containing the value 50,000,000 is below.



SELECT Id,
RangeFrom,
RangeTo
FROM MyTable2
WHERE Range.STContains(geometry::STPointFromText ('POINT (50000000 0)', 0)) = 1


The reads for this show an improvement on the 10,951 from the original query.



Table 'MyTable2'. Scan count 0, logical reads 505, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table 'Workfile'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table 'extended_index_1797581442_384000'. Scan count 4, logical reads 17, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.


However there is no significant improvement over the original in terms of time elapsed. Typical execution results are 250 ms vs 252 ms.



The execution plan is more complex as below



enter image description here



The only case where the rewrite reliably performs better for me is with a cold cache.



So disappointing in this case and difficult to recommend this rewrite but publication of negative results can also be useful.






share|improve this answer





























    2














    Not sure if you want other answers, but you can get significantly better results with a columnstore index. A nonclustered columnstore index provides most of the benefit but inserting ordered data into a clustered columnstore index is even better.



    DROP TABLE IF EXISTS dbo.MyTableCCI;

    CREATE TABLE dbo.MyTableCCI
    (
    Id INT PRIMARY KEY,
    RangeFrom INT NOT NULL,
    RangeTo INT NOT NULL,
    CHECK (RangeTo > RangeFrom),
    INDEX CCI CLUSTERED COLUMNSTORE
    );

    INSERT INTO dbo.MyTableCCI
    SELECT TOP (987654321) *
    FROM dbo.MyTable
    ORDER BY RangeFrom ASC
    OPTION (MAXDOP 1);


    By design I can get rowgroup elimination on the RangeFrom column which will eliminate half of my rowgroups. But due to the nature of the data I also get rowgroup elimination on the RangeTo column as well:



    Table 'MyTableCCI'. Segment reads 1, segment skipped 9.


    For what it's worth, for larger tables with more variable data there are different ways to load the data to guarantee the best possible rowgroup elimination on both columns. For your data in particular, the query takes 1 ms.



    I'll look for other algorithms but I don't think I'll be able to beat that.






    share|improve this answer





















    • yep definitely looking for other approaches to consider without the 2000 restriction. Doesnt sound like that will be beaten.
      – Martin Smith
      26 mins ago











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    2 Answers
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    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    3














    One alternative way of representing a range would be as points on a line.



    The below migrates all the data into a new table with the range represented as a geometry datatype.



    CREATE TABLE MyTable2
    (
    Id INT IDENTITY PRIMARY KEY,
    Range GEOMETRY NOT NULL,
    RangeFrom AS Range.STPointN(1).STX,
    RangeTo AS Range.STPointN(2).STX,
    CHECK (Range.STNumPoints() = 2 AND Range.STPointN(1).STY = 0 AND Range.STPointN(2).STY = 0)
    );

    SET IDENTITY_INSERT MyTable2 ON

    INSERT INTO MyTable2
    (Id,
    Range)
    SELECT ID,
    geometry::STLineFromText(CONCAT('LINESTRING(', RangeFrom, ' 0, ', RangeTo, ' 0)'), 0)
    FROM MyTable

    SET IDENTITY_INSERT MyTable2 OFF


    CREATE SPATIAL INDEX index_name
    ON MyTable2 ( Range )
    USING GEOMETRY_GRID
    WITH (
    BOUNDING_BOX = ( xmin=0, ymin=0, xmax=110000000, ymax=1 ),
    GRIDS = (HIGH, HIGH, HIGH, HIGH),
    CELLS_PER_OBJECT = 16);


    The equivalent query to find ranges containing the value 50,000,000 is below.



    SELECT Id,
    RangeFrom,
    RangeTo
    FROM MyTable2
    WHERE Range.STContains(geometry::STPointFromText ('POINT (50000000 0)', 0)) = 1


    The reads for this show an improvement on the 10,951 from the original query.



    Table 'MyTable2'. Scan count 0, logical reads 505, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
    Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
    Table 'Workfile'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
    Table 'extended_index_1797581442_384000'. Scan count 4, logical reads 17, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.


    However there is no significant improvement over the original in terms of time elapsed. Typical execution results are 250 ms vs 252 ms.



    The execution plan is more complex as below



    enter image description here



    The only case where the rewrite reliably performs better for me is with a cold cache.



    So disappointing in this case and difficult to recommend this rewrite but publication of negative results can also be useful.






    share|improve this answer


























      3














      One alternative way of representing a range would be as points on a line.



      The below migrates all the data into a new table with the range represented as a geometry datatype.



      CREATE TABLE MyTable2
      (
      Id INT IDENTITY PRIMARY KEY,
      Range GEOMETRY NOT NULL,
      RangeFrom AS Range.STPointN(1).STX,
      RangeTo AS Range.STPointN(2).STX,
      CHECK (Range.STNumPoints() = 2 AND Range.STPointN(1).STY = 0 AND Range.STPointN(2).STY = 0)
      );

      SET IDENTITY_INSERT MyTable2 ON

      INSERT INTO MyTable2
      (Id,
      Range)
      SELECT ID,
      geometry::STLineFromText(CONCAT('LINESTRING(', RangeFrom, ' 0, ', RangeTo, ' 0)'), 0)
      FROM MyTable

      SET IDENTITY_INSERT MyTable2 OFF


      CREATE SPATIAL INDEX index_name
      ON MyTable2 ( Range )
      USING GEOMETRY_GRID
      WITH (
      BOUNDING_BOX = ( xmin=0, ymin=0, xmax=110000000, ymax=1 ),
      GRIDS = (HIGH, HIGH, HIGH, HIGH),
      CELLS_PER_OBJECT = 16);


      The equivalent query to find ranges containing the value 50,000,000 is below.



      SELECT Id,
      RangeFrom,
      RangeTo
      FROM MyTable2
      WHERE Range.STContains(geometry::STPointFromText ('POINT (50000000 0)', 0)) = 1


      The reads for this show an improvement on the 10,951 from the original query.



      Table 'MyTable2'. Scan count 0, logical reads 505, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
      Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
      Table 'Workfile'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
      Table 'extended_index_1797581442_384000'. Scan count 4, logical reads 17, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.


      However there is no significant improvement over the original in terms of time elapsed. Typical execution results are 250 ms vs 252 ms.



      The execution plan is more complex as below



      enter image description here



      The only case where the rewrite reliably performs better for me is with a cold cache.



      So disappointing in this case and difficult to recommend this rewrite but publication of negative results can also be useful.






      share|improve this answer
























        3












        3








        3






        One alternative way of representing a range would be as points on a line.



        The below migrates all the data into a new table with the range represented as a geometry datatype.



        CREATE TABLE MyTable2
        (
        Id INT IDENTITY PRIMARY KEY,
        Range GEOMETRY NOT NULL,
        RangeFrom AS Range.STPointN(1).STX,
        RangeTo AS Range.STPointN(2).STX,
        CHECK (Range.STNumPoints() = 2 AND Range.STPointN(1).STY = 0 AND Range.STPointN(2).STY = 0)
        );

        SET IDENTITY_INSERT MyTable2 ON

        INSERT INTO MyTable2
        (Id,
        Range)
        SELECT ID,
        geometry::STLineFromText(CONCAT('LINESTRING(', RangeFrom, ' 0, ', RangeTo, ' 0)'), 0)
        FROM MyTable

        SET IDENTITY_INSERT MyTable2 OFF


        CREATE SPATIAL INDEX index_name
        ON MyTable2 ( Range )
        USING GEOMETRY_GRID
        WITH (
        BOUNDING_BOX = ( xmin=0, ymin=0, xmax=110000000, ymax=1 ),
        GRIDS = (HIGH, HIGH, HIGH, HIGH),
        CELLS_PER_OBJECT = 16);


        The equivalent query to find ranges containing the value 50,000,000 is below.



        SELECT Id,
        RangeFrom,
        RangeTo
        FROM MyTable2
        WHERE Range.STContains(geometry::STPointFromText ('POINT (50000000 0)', 0)) = 1


        The reads for this show an improvement on the 10,951 from the original query.



        Table 'MyTable2'. Scan count 0, logical reads 505, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
        Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
        Table 'Workfile'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
        Table 'extended_index_1797581442_384000'. Scan count 4, logical reads 17, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.


        However there is no significant improvement over the original in terms of time elapsed. Typical execution results are 250 ms vs 252 ms.



        The execution plan is more complex as below



        enter image description here



        The only case where the rewrite reliably performs better for me is with a cold cache.



        So disappointing in this case and difficult to recommend this rewrite but publication of negative results can also be useful.






        share|improve this answer












        One alternative way of representing a range would be as points on a line.



        The below migrates all the data into a new table with the range represented as a geometry datatype.



        CREATE TABLE MyTable2
        (
        Id INT IDENTITY PRIMARY KEY,
        Range GEOMETRY NOT NULL,
        RangeFrom AS Range.STPointN(1).STX,
        RangeTo AS Range.STPointN(2).STX,
        CHECK (Range.STNumPoints() = 2 AND Range.STPointN(1).STY = 0 AND Range.STPointN(2).STY = 0)
        );

        SET IDENTITY_INSERT MyTable2 ON

        INSERT INTO MyTable2
        (Id,
        Range)
        SELECT ID,
        geometry::STLineFromText(CONCAT('LINESTRING(', RangeFrom, ' 0, ', RangeTo, ' 0)'), 0)
        FROM MyTable

        SET IDENTITY_INSERT MyTable2 OFF


        CREATE SPATIAL INDEX index_name
        ON MyTable2 ( Range )
        USING GEOMETRY_GRID
        WITH (
        BOUNDING_BOX = ( xmin=0, ymin=0, xmax=110000000, ymax=1 ),
        GRIDS = (HIGH, HIGH, HIGH, HIGH),
        CELLS_PER_OBJECT = 16);


        The equivalent query to find ranges containing the value 50,000,000 is below.



        SELECT Id,
        RangeFrom,
        RangeTo
        FROM MyTable2
        WHERE Range.STContains(geometry::STPointFromText ('POINT (50000000 0)', 0)) = 1


        The reads for this show an improvement on the 10,951 from the original query.



        Table 'MyTable2'. Scan count 0, logical reads 505, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
        Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
        Table 'Workfile'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
        Table 'extended_index_1797581442_384000'. Scan count 4, logical reads 17, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.


        However there is no significant improvement over the original in terms of time elapsed. Typical execution results are 250 ms vs 252 ms.



        The execution plan is more complex as below



        enter image description here



        The only case where the rewrite reliably performs better for me is with a cold cache.



        So disappointing in this case and difficult to recommend this rewrite but publication of negative results can also be useful.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered 2 hours ago









        Martin Smith

        61.5k10166245




        61.5k10166245

























            2














            Not sure if you want other answers, but you can get significantly better results with a columnstore index. A nonclustered columnstore index provides most of the benefit but inserting ordered data into a clustered columnstore index is even better.



            DROP TABLE IF EXISTS dbo.MyTableCCI;

            CREATE TABLE dbo.MyTableCCI
            (
            Id INT PRIMARY KEY,
            RangeFrom INT NOT NULL,
            RangeTo INT NOT NULL,
            CHECK (RangeTo > RangeFrom),
            INDEX CCI CLUSTERED COLUMNSTORE
            );

            INSERT INTO dbo.MyTableCCI
            SELECT TOP (987654321) *
            FROM dbo.MyTable
            ORDER BY RangeFrom ASC
            OPTION (MAXDOP 1);


            By design I can get rowgroup elimination on the RangeFrom column which will eliminate half of my rowgroups. But due to the nature of the data I also get rowgroup elimination on the RangeTo column as well:



            Table 'MyTableCCI'. Segment reads 1, segment skipped 9.


            For what it's worth, for larger tables with more variable data there are different ways to load the data to guarantee the best possible rowgroup elimination on both columns. For your data in particular, the query takes 1 ms.



            I'll look for other algorithms but I don't think I'll be able to beat that.






            share|improve this answer





















            • yep definitely looking for other approaches to consider without the 2000 restriction. Doesnt sound like that will be beaten.
              – Martin Smith
              26 mins ago
















            2














            Not sure if you want other answers, but you can get significantly better results with a columnstore index. A nonclustered columnstore index provides most of the benefit but inserting ordered data into a clustered columnstore index is even better.



            DROP TABLE IF EXISTS dbo.MyTableCCI;

            CREATE TABLE dbo.MyTableCCI
            (
            Id INT PRIMARY KEY,
            RangeFrom INT NOT NULL,
            RangeTo INT NOT NULL,
            CHECK (RangeTo > RangeFrom),
            INDEX CCI CLUSTERED COLUMNSTORE
            );

            INSERT INTO dbo.MyTableCCI
            SELECT TOP (987654321) *
            FROM dbo.MyTable
            ORDER BY RangeFrom ASC
            OPTION (MAXDOP 1);


            By design I can get rowgroup elimination on the RangeFrom column which will eliminate half of my rowgroups. But due to the nature of the data I also get rowgroup elimination on the RangeTo column as well:



            Table 'MyTableCCI'. Segment reads 1, segment skipped 9.


            For what it's worth, for larger tables with more variable data there are different ways to load the data to guarantee the best possible rowgroup elimination on both columns. For your data in particular, the query takes 1 ms.



            I'll look for other algorithms but I don't think I'll be able to beat that.






            share|improve this answer





















            • yep definitely looking for other approaches to consider without the 2000 restriction. Doesnt sound like that will be beaten.
              – Martin Smith
              26 mins ago














            2












            2








            2






            Not sure if you want other answers, but you can get significantly better results with a columnstore index. A nonclustered columnstore index provides most of the benefit but inserting ordered data into a clustered columnstore index is even better.



            DROP TABLE IF EXISTS dbo.MyTableCCI;

            CREATE TABLE dbo.MyTableCCI
            (
            Id INT PRIMARY KEY,
            RangeFrom INT NOT NULL,
            RangeTo INT NOT NULL,
            CHECK (RangeTo > RangeFrom),
            INDEX CCI CLUSTERED COLUMNSTORE
            );

            INSERT INTO dbo.MyTableCCI
            SELECT TOP (987654321) *
            FROM dbo.MyTable
            ORDER BY RangeFrom ASC
            OPTION (MAXDOP 1);


            By design I can get rowgroup elimination on the RangeFrom column which will eliminate half of my rowgroups. But due to the nature of the data I also get rowgroup elimination on the RangeTo column as well:



            Table 'MyTableCCI'. Segment reads 1, segment skipped 9.


            For what it's worth, for larger tables with more variable data there are different ways to load the data to guarantee the best possible rowgroup elimination on both columns. For your data in particular, the query takes 1 ms.



            I'll look for other algorithms but I don't think I'll be able to beat that.






            share|improve this answer












            Not sure if you want other answers, but you can get significantly better results with a columnstore index. A nonclustered columnstore index provides most of the benefit but inserting ordered data into a clustered columnstore index is even better.



            DROP TABLE IF EXISTS dbo.MyTableCCI;

            CREATE TABLE dbo.MyTableCCI
            (
            Id INT PRIMARY KEY,
            RangeFrom INT NOT NULL,
            RangeTo INT NOT NULL,
            CHECK (RangeTo > RangeFrom),
            INDEX CCI CLUSTERED COLUMNSTORE
            );

            INSERT INTO dbo.MyTableCCI
            SELECT TOP (987654321) *
            FROM dbo.MyTable
            ORDER BY RangeFrom ASC
            OPTION (MAXDOP 1);


            By design I can get rowgroup elimination on the RangeFrom column which will eliminate half of my rowgroups. But due to the nature of the data I also get rowgroup elimination on the RangeTo column as well:



            Table 'MyTableCCI'. Segment reads 1, segment skipped 9.


            For what it's worth, for larger tables with more variable data there are different ways to load the data to guarantee the best possible rowgroup elimination on both columns. For your data in particular, the query takes 1 ms.



            I'll look for other algorithms but I don't think I'll be able to beat that.







            share|improve this answer












            share|improve this answer



            share|improve this answer










            answered 29 mins ago









            Joe Obbish

            20.4k32880




            20.4k32880












            • yep definitely looking for other approaches to consider without the 2000 restriction. Doesnt sound like that will be beaten.
              – Martin Smith
              26 mins ago


















            • yep definitely looking for other approaches to consider without the 2000 restriction. Doesnt sound like that will be beaten.
              – Martin Smith
              26 mins ago
















            yep definitely looking for other approaches to consider without the 2000 restriction. Doesnt sound like that will be beaten.
            – Martin Smith
            26 mins ago




            yep definitely looking for other approaches to consider without the 2000 restriction. Doesnt sound like that will be beaten.
            – Martin Smith
            26 mins ago


















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