mjhjmtu

Columnstore is very heplful here compared to a nonclustered index which scans half the table. 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 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.

Paul White pointed out an answer to a similar question containing a link to an interesting article by Itzik Ben Gan. This describes the "Static Relational Interval Tree" model that allows this to be done efficiently.

In summary this approach involves storing a computed ("forknode") value based on the interval values in the row. When searching for ranges that intersect another range it is possible to precalculate the possible forknode values that matching rows must have and use this to find the results with a maximum of 31 seek operations (the below supports integers in the range 0 to the max signed 32 bit int)

Based on this I restructured the table as below.

CREATE TABLE dbo.MyTable3
(
 Id INT IDENTITY PRIMARY KEY,
 RangeFrom INT NOT NULL,
 RangeTo INT NOT NULL, 
 node AS RangeTo - RangeTo % POWER(2, FLOOR(LOG((RangeFrom - 1) ^ RangeTo, 2))) PERSISTED NOT NULL,
 CHECK (RangeTo > RangeFrom)
);

CREATE INDEX ix1 ON dbo.MyTable3 (node, RangeFrom) INCLUDE (RangeTo);
CREATE INDEX ix2 ON dbo.MyTable3 (node, RangeTo) INCLUDE (RangeFrom);

SET IDENTITY_INSERT MyTable3 ON

INSERT INTO MyTable3
 (Id,
 RangeFrom,
 RangeTo)
SELECT Id,
 RangeFrom,
 RangeTo
FROM MyTable

SET IDENTITY_INSERT MyTable3 OFF 

And then used the following query (the article is looking for intersecting intervals so finding an interval containing a point is a degenerate case of this)

DECLARE @value INT = 50000000;

;WITH N AS
(
SELECT 30 AS Level, 
 CASE WHEN @value > POWER(2,30) THEN POWER(2,30) END AS selected_left_node, 
 CASE WHEN @value < POWER(2,30) THEN POWER(2,30) END AS selected_right_node, 
 (SIGN(@value - POWER(2,30)) * POWER(2,29)) + POWER(2,30) AS node
UNION ALL
SELECT N.Level-1, 
 CASE WHEN @value > node THEN node END AS selected_left_node, 
 CASE WHEN @value < node THEN node END AS selected_right_node,
 (SIGN(@value - node) * POWER(2,N.Level-2)) + node AS node
FROM N 
WHERE N.Level > 0
)
SELECT I.id, I.RangeFrom, I.RangeTo
FROM dbo.MyTable3 AS I
 JOIN N AS L
 ON I.node = L.selected_left_node
 AND I.RangeTo >= @value
 AND L.selected_left_node IS NOT NULL
UNION ALL
SELECT I.id, I.RangeFrom, I.RangeTo
FROM dbo.MyTable3 AS I
 JOIN N AS R
 ON I.node = R.selected_right_node
 AND I.RangeFrom <= @value
 AND R.selected_right_node IS NOT NULL
UNION ALL
SELECT I.id, I.RangeFrom, I.RangeTo
FROM dbo.MyTable3 AS I
WHERE node = @value;

This typically executes in 1mson my machine when all pages are in cache - with IO stats.

Table 'MyTable3'. Scan count 24, logical reads 72, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.
Table 'Worktable'. Scan count 4, logical reads 374, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

and plan

NB: The source uses multistatement TVFs rather than a recursive CTE to get the nodes to join on but in the interests of making my answer self contained I have opted for the latter. For production use I'd probably use the TVFs.

I was able to find a row mode approach that is competitive with the N/CCI approach, but you need to know something about your data. Suppose that you had a column that contained the difference of RangeFrom and RangeTo and you indexed it along with RangeFrom:

ALTER TABLE dbo.MyTableWithDiff ADD DiffOfColumns AS RangeTo-RangeFrom;

CREATE INDEX IXDIFF ON dbo.MyTableWithDiff (DiffOfColumns,RangeFrom) INCLUDE (RangeTo);

If you knew all of the distinct values of DiffOfColumns then you could perform a seek for every value of DiffOfColumns with a range filter on RangeTo to get all of the relevant data. For example, if we know that DiffOfColumns = 2 then the only allowed values for RangeFrom are 49999998, 49999999 and 50000000. Recursion can be used to get all of the distinct values of DiffOfColumns and it works well for your data set because there are only 256 of them. The query below takes about 6 ms on my machine:

WITH RecursiveCTE
AS
(
 -- Anchor
 SELECT TOP (1)
 DiffOfColumns
 FROM dbo.MyTableWithDiff AS T
 ORDER BY
 T.DiffOfColumns

 UNION ALL

 -- Recursive
 SELECT R.DiffOfColumns
 FROM
 (
 -- Number the rows
 SELECT 
 T.DiffOfColumns,
 rn = ROW_NUMBER() OVER (
 ORDER BY T.DiffOfColumns)
 FROM dbo.MyTableWithDiff AS T
 JOIN RecursiveCTE AS R
 ON R.DiffOfColumns < T.DiffOfColumns
 ) AS R
 WHERE
 -- Only the row that sorts lowest
 R.rn = 1
)
SELECT ca.*
FROM RecursiveCTE rcte
CROSS APPLY (
 SELECT mt.Id, mt.RangeFrom, mt.RangeTo
 FROM dbo.MyTableWithDiff mt
 WHERE mt.DiffOfColumns = rcte.DiffOfColumns
 AND mt.RangeFrom >= 50000000 - rcte.DiffOfColumns AND mt.RangeFrom <= 50000000
) ca
OPTION (MAXRECURSION 0);

You can see the usual recursive part along with the index seek for every distinct value:

The flaw with this approach is that it starts to get slow when there are too many distinct values for DiffOfColumns. Let's do the same test, but use CRYPT_GEN_RANDOM(2)instead of CRYPT_GEN_RANDOM(1).

DROP TABLE IF EXISTS dbo.MyTableBigDiff;

CREATE TABLE dbo.MyTableBigDiff
(
Id INT IDENTITY PRIMARY KEY,
RangeFrom INT NOT NULL,
RangeTo INT NOT NULL,
CHECK (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 dbo.MyTableBigDiff
 (RangeFrom,
 RangeTo)
SELECT Num,
 Num + 1 + CRYPT_GEN_RANDOM(2) -- note the 2
FROM RandomNumbers;


ALTER TABLE dbo.MyTableBigDiff ADD DiffOfColumns AS RangeTo-RangeFrom;

CREATE INDEX IXDIFF ON dbo.MyTableBigDiff (DiffOfColumns,RangeFrom) INCLUDE (RangeTo);

The same query now finds 65536 rows from the recursive part and takes 823 ms of CPU on my machine. There are PAGELATCH_SH waits and other bad things going on. I can improve performance by bucketing the diff values to keep the number of unique values under control and adjusting for the bucketing in the CROSS APPLY. For this data set I'll try 256 buckets:

ALTER TABLE dbo.MyTableBigDiff ADD DiffOfColumns_bucket256 AS CAST(CEILING((RangeTo-RangeFrom) / 256.) AS INT);

CREATE INDEX [IXDIFF😎] ON dbo.MyTableBigDiff (DiffOfColumns_bucket256, RangeFrom) INCLUDE (RangeTo);

One way to avoid getting extra rows (now I'm comparing to a rounded value instead of the true value) is by filtering on RangeTo:

CROSS APPLY (
 SELECT mt.Id, mt.RangeFrom, mt.RangeTo
 FROM dbo.MyTableBigDiff mt
 WHERE mt.DiffOfColumns_bucket256 = rcte.DiffOfColumns_bucket256
 AND mt.RangeFrom >= 50000000 - (256 * rcte.DiffOfColumns_bucket256)
 AND mt.RangeFrom <= 50000000
 AND mt.RangeTo >= 50000000
) ca

The full query now takes 6 ms on my machine.

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

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.

As an homage to our new robot overlords, I decided to see if any of the new-ish R and Python functionality could help us out here. The answer is no, at least for the scripts that I could get working and returning correct results. If anyone with better knowledge comes along, well, feel free to spank me. My rates are reasonable.

To do this, I set up a VM with 4 cores and 16 GB RAM, thinking this would be enough to deal with a ~200MB data set.

Let's start with the language that doesn't exist in Boston!

R

EXEC sp_execute_external_script 
@language = N'R', 
@script = N'
tweener = 50000000
MO = data.frame(MartinIn)
MartinOut <- subset(MO, RangeFrom <= tweener & RangeTo >= tweener, select = c("Id","RangeFrom","RangeTo"))
', 
@input_data_1_name = N'MartinIn',
@input_data_1 = N'SELECT Id, RangeFrom, RangeTo FROM dbo.MyTable',
@output_data_1_name = N'MartinOut',
@parallel = 1
WITH RESULT SETS ((ID INT, RangeFrom INT, RangeTo INT));

This was a bad time.

Table 'MyTable'. Scan count 1, logical reads 22400

 SQL Server Execution Times:
 CPU time = 3219 ms, elapsed time = 5349 ms.

The execution plan is pretty uninteresting, though I don't know why the middle operator has to call us names.

Next up, coding with crayons!

Python

EXEC sp_execute_external_script 
@language = N'Python', 
@script = N'
import pandas as pd
MO = pd.DataFrame(MartinIn)
tweener = 50000000
MartinOut = MO[(MO.RangeFrom <= tweener) & (MO.RangeTo >= tweener)]
', 
@input_data_1_name = N'MartinIn',
@input_data_1 = N'SELECT Id, RangeFrom, RangeTo FROM dbo.MyTable',
@output_data_1_name = N'MartinOut',
@parallel = 1
WITH RESULT SETS ((ID INT, RangeFrom INT, RangeTo INT));

Just when you thought it couldn't get worse than R:

Table 'MyTable'. Scan count 1, logical reads 22400

 SQL Server Execution Times:
 CPU time = 3797 ms, elapsed time = 10146 ms.

Another foul-mouthed execution plan:

Hmm and Hmmer

So far, I'm not impressed. I can't wait to delete this VM.

I found a pretty good solution using a computed column, however it's only good for a single value. That being said, if you've got a magic value, maybe it's enough.

Starting with your given sample, then modifying the table:

ALTER TABLE dbo.MyTable
 ADD curtis_jackson 
 AS CONVERT(BIT, CASE 
 WHEN RangeTo >= 50000000
 AND RangeFrom < 50000000
 THEN 1 
 ELSE 0 
 END);

CREATE INDEX IX1_redo 
 ON dbo.MyTable (curtis_jackson) 
 INCLUDE (RangeFrom, RangeTo);

The query simply becomes:

SELECT *
FROM MyTable
WHERE curtis_jackson = 1;

Which returns the same results as your starting query. With execution plans turned off, here are the stats (truncated for brevity):

Table 'MyTable'. Scan count 1, logical reads 3...

SQL Server Execution Times:
 CPU time = 0 ms, elapsed time = 0 ms.

And here's the query plan:

My solution is based on the observation that the interval has a known maximum width W. For the sample data this is one byte or 256 integers. Hence for a given search parameter value P we know the smallest RangeFrom that can be in the result set is P - W. Adding this to the predicate gives

declare @P int = 50000000;
declare @W int = 256;

select
 *
from MyTable
where @P between RangeFrom and RangeTo
and RangeFrom >= (@P - @W);

Given the original set-up and query my machine (64 bit Windows 10, 4-core hyperthreaded i7, 2.8GHz, 16GB RAM) returns 13 rows. That query uses a parallel index seek of the (RangeFrom, RangeTo) index. The revised query also performs a parallel index seek on the same index.

The measurements for the original and revised queries are

 Original Revised
 -------- -------
Stats IO Scan count 9 6
Stats IO logical reads 11547 6

Estimated number of rows 1643170 1216080
Number of rows read 5109666 29
QueryTimeStats CPU 344 2
QueryTimeStats Elapsed 53 0

For the original query the number of rows read is equal to the number of rows that are less than or equal to @P. The query optimizer (QO) has no alternative but read them all since it cannot determine in advance which if these rows will satisfy the predicate. The multi-column index on (RangeFrom, RangeTo) is not useful in eliminating rows that do not match RangeTo as there is no correlation between the first index key and the second that can be applied. For example, the first row may have a small interval and be eliminated whereas the second row has a large interval and is returned, or vice versa.

In one failed attempt I tried to provide that certainty through a check constraint:

alter table MyTable with check
add constraint CK_MyTable_Interval
check
(
 RangeTo <= RangeFrom + 256
);

It made no difference.

By incorporating my external knowledge of the data distribution into the predicate I can cause the QO to skip low-valued RangeFrom rows, which can never be part of the resultset, and traverse the leading column of the index to the admissible rows. This shows in the different seek predicate for each query.

In a mirror argument the upper limit of RangeTo is P + W. This is not useful, however, because there is no correlation between RangeFrom and RangeTo that would allow the trailing column of a multi-column index to eliminate rows. Hence there is no benefit from adding this clause to the query.

This approach gains most of its benefit from the small interval size. As the possible interval size increases the number of low-valued rows skipped decreases, though some will still be skipped. In the limiting case, with the interval as large as the data range, this approach is no worse than the orginal query (which is cold comfort I admit).

I apologise for whatever off-by-one errors may exist in this answer.