Category: SQL Tips

The LAG() function in SQL Server is used to access data from a previous row within the same result set. It provides a way to look back at the previous row’s value and compare it with the current row’s value. Compare with Lead function, The LEAD function is used to retrieve the value from the next row in a query result set.

Syntax

LAG (scalar_expression [,offset] [,default_value]) 
OVER ([PARTITION BY partition_expression, ... ] 
ORDER BY order_expression [ASC | DESC], ... )

• scalar_expression: The expression to be returned from the previous row.
• offset: An optional argument that specifies the number of rows to move back from the current row. The default value is 1.
• default_value: An optional argument that specifies a default value to return if the offset is beyond the first row.

Example 1 

SELECT ProductName, Sales, 
  LAG(Sales) OVER (ORDER BY Sales) AS PrevSales
FROM SalesTable

In this example, the query returns the ProductName, Sales, and the previous row’s Sales value. The LAG() function is used to access the previous row’s value and compare it with the current row’s value. The result set is ordered by Sales.

Example 2

SELECT EmployeeName, Salary, 
  LAG(Salary, 2, 0) OVER (ORDER BY Salary) AS Prev2ndSalary
FROM EmployeeTable

In this example, the query returns the EmployeeName, Salary, and the second previous row’s Salary value. The LAG() function is used to access the second previous row’s value and compare it with the current row’s value. The result set is ordered by Salary, and the offset argument is set to 2, which specifies that the function should move two rows back from the current row. The default_value argument is set to 0, which specifies a default value to return if the offset is beyond the first row.

Lead function is available from SQL Server 2012. Lead function can accesses data from a subsequent row in the same result set without self-join. Use this function in a Select statement can compare values in the current row with values in the following row.

Syntax

LEAD ( scalar_expression [ ,offset ] , [ default ] )   
    OVER ( [ partition_by_clause ] order_by_clause )

scalar_expression the value to be compared and returned between the select result set

offset the number of rows forward from the current, the lead function will fetch the value based on this number.

default the default value returned from the lead function when reached last row of the select result set.

OVER ( [ partition_by_clause ] order_by_clause ) used to divide the result rows by partition_by_clause then order the result set by order_by_clause, the number of offset will be calculated based on the order_by result sets.

Demo data used in the following Example:

Example 1 Lead function with offset 1 and without a default value

In this example, we will calculate the price_to value for each records, the records will be grouped by PublicationSource and ordered by price_from. the first row’s price_to will be second rows price_from. before SQL Server 2012, we need to use self-join to do this logic. but since SQL Server 2012 , the new function LEAD can do this job as well.

SELECT ID,PublicationSource,PriceFrom
,LEAD(PriceFrom,1)OVER(PARTITION BY PublicationSource ORDER BY PriceFrom) price_to 
FROM #demo
ORDER BY PublicationSource,PriceFrom 

From the above output, we can see that the Lead function gets the value from the next row of the select result set and return NULL if no subsequent row is available be cause the offset is 1 and default value haven’t provide in the code.

Example 1 Lead function with offset 2 and without a default value

SELECT ID,PublicationSource,PriceFrom
,LEAD(PriceFrom,2)OVER(PARTITION BY PublicationSource ORDER BY PriceFrom) price_to
FROM #demo
ORDER BY PublicationSource,PriceFrom

From the above output, we can see that the Lead function gets the value from the second row of current data in the select result set and return NULL if no subsequent row is available

TRANSLATE and REPLACE are both SQL Server functions that can be used to replace characters in a string, but they have some key differences:

• REPLACE function replaces all occurrences of a specific string or character with another string or character. It takes three arguments: the source string, the string or character to be replaced, and the replacement string or character.
  REPLACE(string_expression, string_pattern, string_replacement)
• TRANSLATE function replaces multiple characters at once. It takes three arguments: the source string, the set of characters to be replaced, and the set of replacement characters. Each character in the first set is replaced by the corresponding character in the second set. If a character in the first set is not found in the second set, it is removed.
  TRANSLATE(string_expression, string_pattern, string_replacement)
SELECT TRANSLATE('2*[3+4]/{7-2}', '[]{}', '()()') –out put 2*(3+4)/(7-2)

The main difference between the two is that REPLACE can replace one or more specific characters or substrings in a string, while TRANSLATE can replace multiple characters at once. TRANSLATE can be useful when you need to replace multiple characters in a string at once, while REPLACE is useful when you need to replace a specific substring or character.

SQL provides 4 ranking functions:

RANK — Returns the rank of each row within the partition of a result set. The rank of a row is one plus the number of ranks that come before the row in question.

DENSE_RANK –This function returns the rank of each row within a result set partition, with no gaps in the ranking values. The rank of a specific row is one plus the number of distinct rank values that come before that specific row.

ROW_NUMBER — Numbers the output of a result set. More specifically, returns the sequential number of a row within a partition of a result set, starting at 1 for the first row in each partition.

NTILE — Distributes the rows in an ordered partition into a specified number of groups. The groups are numbered, starting at one. For each row, NTILE returns the number of the group to which the row belongs.

Examples

The following example shows the four ranking functions used in the same query. For function-specific examples, see each ranking function.

SELECT p.FirstName, p.LastName  
    ,ROW_NUMBER() OVER (ORDER BY a.PostalCode) AS "Row Number"  
    ,RANK() OVER (ORDER BY a.PostalCode) AS Rank  
    ,DENSE_RANK() OVER (ORDER BY a.PostalCode) AS "Dense Rank"  
    ,NTILE(4) OVER (ORDER BY a.PostalCode) AS Quartile  
    ,s.SalesYTD  
    ,a.PostalCode  
FROM Sales.SalesPerson AS s   
    INNER JOIN Person.Person AS p   
        ON s.BusinessEntityID = p.BusinessEntityID  
    INNER JOIN Person.Address AS a   
        ON a.AddressID = p.BusinessEntityID  
WHERE TerritoryID IS NOT NULL AND SalesYTD <> 0;  

FirstName	Row Number	Rank	Dense Rank	Quartile	SalesYTD	PostalCode
Michael	1	1	1	1	4557045.0459	98027
Linda	2	1	1	1	5200475.2313	98027
Jillian	3	1	1	1	3857163.6332	98027
Garrett	4	1	1	1	1764938.9859	98027
Tsvi	5	1	1	2	2811012.7151	98027
Shu	6	6	2	2	3018725.4858	98055
José	7	6	2	2	3189356.2465	98055
David	8	6	2	3	3587378.4257	98055
Tete	9	6	2	3	1931620.1835	98055
Lynn	10	6	2	3	1758385.926	98055
Rachel	11	6	2	4	2241204.0424	98055
Jae	12	6	2	4	5015682.3752	98055
Ranjit	13	6	2	4	3827950.238	98055

Overview

Once stored procedures deployed to production server, there is no easy way to safely debug or reproduce or tracking issues if we haven’t had a proper error and exception handling logic.

In this article, we’ll give one of example to catch the errors and save related information to table for issue debugging.

Handling errors using TRY…CATCH

Here’s how the syntax looks like. It’s pretty simple to get the hang of. We have two blocks of code:

BEGIN TRY  
     --function logic
END TRY  
BEGIN CATCH  
     --error handling logic
END CATCH

The code BEGIN TRY and END TRY is our function logic code that we want to monitor for an error. So, if an error would have happened inside this TRY statement, the CATCH statement will be triggered and then we need to handle those errors inside of CATCH statement . We can try to report the error, log the error, or event fix the error. But in this article, we are going to demo how to log the error, so we know can when it happened, who did it and other useful stuff. We even have access to some special data only available inside the CATCH statement:

• ERROR_NUMBER – Returns the internal number of the error
• ERROR_STATE – Returns the information about the source
• ERROR_SEVERITY – Returns the information about anything from informational errors to errors user of DBA can fix, etc.
• ERROR_LINE – Returns the line number at which an error happened on
• ERROR_PROCEDURE – Returns the name of the stored procedure or function
• ERROR_MESSAGE – Returns the most essential information and that is the message text of the error

The script below is as simple as it gets:

-- Basic example of TRY...CATCH
 
BEGIN TRY
-- Generate a divide-by-zero error  
  SELECT
    1 / 0 AS Error;
END TRY
BEGIN CATCH
  SELECT
    ERROR_NUMBER() AS ErrorNumber,
    ERROR_STATE() AS ErrorState,
    ERROR_SEVERITY() AS ErrorSeverity,
    ERROR_PROCEDURE() AS ErrorProcedure,
    ERROR_LINE() AS ErrorLine,
    ERROR_MESSAGE() AS ErrorMessage;
END CATCH;
GO

This is an example of how it looks and how it works. The only thing we’re doing in the BEGIN TRY is dividing 1 by 0, which will cause an error. If we execute the script from above, this is what we get:

From left to right, we got ErrorNumber, ErrorState, ErrorSeverity; there is no procedure in this case (NULL), ErrorLine, and ErrorMessage.

Now, let’s create a table called DB_Errors, which can be used to store tracking data:

 
CREATE TABLE DB_Errors
         (ErrorID        INT IDENTITY(1, 1),
          UserName       VARCHAR(100),
          ErrorNumber    INT,
          ErrorState     INT,
          ErrorSeverity  INT,
          ErrorLine      INT,
          ErrorProcedure VARCHAR(MAX),
          ErrorMessage   VARCHAR(MAX),
          ErrorDateTime  DATETIME)
GO

Now, let’s modify a custom stored procedure from the database and put an error handler in there:

ALTER PROCEDURE dbo.AddSale @employeeid INT,
@productid INT,
@quantity SMALLINT,
@saleid UNIQUEIDENTIFIER OUTPUT
AS
SET @saleid = NEWID()
BEGIN TRY
INSERT INTO Sales.Sales
SELECT @saleid,@productid,@employeeid,@quantity
END TRY
BEGIN CATCH
INSERT INTO dbo.DB_Errors
VALUES
(SUSER_SNAME(),
ERROR_NUMBER(),
ERROR_STATE(),
ERROR_SEVERITY(),
ERROR_LINE(),
ERROR_PROCEDURE(),
ERROR_MESSAGE(),
GETDATE());
END CATCH
GO

Altering this stored procedure simply wraps error handling in this case around the only statement inside the stored procedure. If we call this stored procedure and pass some valid data, here’s what happens:

A quick Select statement indicates that the record has been successfully inserted:

However, if we call the above-stored procedure one more time, passing the same parameters, the results grid will be populated differently:

This time, we got two indicators in the results grid:

0 rows affected – this line indicated that nothing actually went into the Sales table

1 row affected – this line indicates that something went into our newly created logging table

So, what we can do here is look at the errors table and see what happened. A simple Select statement will do the job:

Here we have all the information we set previously to be logged, only this time we also got the procedure field filled out and of course the SQL Server “friendly” technical message that we have a violation:

Violation of PRIMARY KEY constraint ‘PK_Sales_1′. Cannot insert duplicate key in object’ Sales.Sales’. The duplicate key value is (20).

So in this way, we have tracking what the error happened and then can fix issue based on the error information which make our issue tracking and fixing task more easily.

DECLARE @StartDate DATETIME
DECLARE @EndDate DATETIME
SET @StartDate = ‘2022/08/01’
SET @EndDate = ‘2022/08/25’

SELECT
(DATEDIFF(dd, @StartDate, @EndDate) + 1)
-(DATEDIFF(wk, @StartDate, @EndDate) * 2)
-(CASE WHEN DATENAME(dw, @StartDate) = ‘Sunday’ THEN 1 ELSE 0 END)
-(CASE WHEN DATENAME(dw, @EndDate) = ‘Saturday’ THEN 1 ELSE 0 END)

In the data world, duplicated data is a very common issue. It may duplicated for few fields or even worse you have more then two identical rows in the table and there is no way to distinguish those rows. So how to clean those duplicated data?

DELETE Duplicate Records Using TOP

Since SQL Server 2005 we can use the TOP command when we issue the delete, such as the following.

CREATE TABLE dbo.duplicateTest (
	[id] [INT] NULL,
	[name] [VARCHAR](100) 
)

INSERT INTO dbo.duplicateTest VALUES (1, 'a')
INSERT INTO dbo.duplicateTest VALUES (1, 'a')
INSERT INTO dbo.duplicateTest VALUES (1, 'a')
INSERT INTO dbo.duplicateTest VALUES (2, 'b')
INSERT INTO dbo.duplicateTest VALUES (3, 'c')

DECLARE @id int
WHILE EXISTS (SELECT COUNT(id) FROM dbo.duplicateTest  GROUP BY id HAVING COUNT(id) > 1 ) 
BEGIN
	SELECT TOP 1  @id = id
	FROM dbo.duplicateTest
	GROUP BY id 
	HAVING COUNT(id) > 1 

	DELETE TOP(1) FROM dbo.duplicateTest WHERE id =@id 
END

So as you can see using the top in while loop we can remove multiple duplicated records even you are not sure which id is duplicated.

DELETE Duplicate Records Using CTE

another way we can use to remove duplicated records is by using CTE.

TRUNCATE TABLE dbo.duplicateTest
INSERT INTO dbo.duplicateTest VALUES(1, 'Bob') 
INSERT INTO dbo.duplicateTest VALUES(2, 'Dave') 
INSERT INTO dbo.duplicateTest VALUES(3, 'Karen') 
INSERT INTO dbo.duplicateTest VALUES(1, 'Bob')
INSERT INTO dbo.duplicateTest VALUES(1, 'Bob')	


WITH cte1
AS (
   SELECT ROW_NUMBER() OVER ( PARTITION BY ID ORDER BY ID) rank1,*
   FROM dbo.duplicateTest 
)
DELETE FROM cte1 WHERE rank1 > 1

Index is used to speed-up query performance in SQL Server. It is similar to book or dictionary indexes. In the book, if you are looking for some particular chapter, you can looking the index and get the page number of the chapter then go directly to that page. Without index, it will take long time for you to find the desired chapter.

The similar logic for the database index. Well-designed indexes can reduce disk I/O operations and consume fewer system resources therefore improving query performance. In SQL Server table can have two types of indexes:

• Clustered Indexes
  Clustered index is as same as dictionary where the data is arranged by alphabetical order. The order clustered indexes stored the table or view data rows in the table is the order of key values order. Each table can have 1 clustered indexes only, because it can have one kind of order in the same. In SQL Server, the primary key constraint automatically creates a clustered index on that particular column.
  
  
  Example of clustered index

CREATE DATABASE schooldb
CREATE TABLE student
(
id INT PRIMARY KEY,
name VARCHAR(50) NOT NULL,
gender VARCHAR(50) NOT NULL,
DOB datetime NOT NULL,
total_score INT NOT NULL,
city VARCHAR(50) NOT NULL
)

The primary key of column ‘id’ will become clustered index automatically. You use system procedure “sp_helpindex” to see the indexes of the table.

USE schooldb
EXECUTE sp_helpindex student

Another way to view table indexes is by going to “Object Explorer-> Databases-> Database_Name-> Tables-> Table_Name -> Indexes”.

Non-clustered Indexes
Non-Clustered Index is similar to the index of textbook. The index of textbook listed the chapter name and related page number, you can directly go to the page by using the page number of the chapter if you are looking for some particular one.

The data and index are stored in different place. Therefore you can have multiple non-clustered index for each table, all the index will point to the same storage place.

To create a new clustered Index, execute the following script:

use schooldb
CREATE CLUSTERED INDEX IX_tblStudent_Gender_Score
ON student(gender ASC, total_score DESC)

Differences

differences between clustered and non-clustered indexes.

1. Can only have one clustered index per table. However, We can create multiple non-clustered indexes on the same table.
2. Clustered indexes only sort tables. Therefore, they do not consume extra storage. Non-clustered indexes are stored in a separate place from the actual table claiming more storage space.
3. Clustered indexes are faster than non-clustered indexes since they don’t involve any extra lookup step.
4. In clustered index, the clustered key defines order of data within table. In non-clustered index, the index key defines the order of data in the index only.

CHECKSUM function returns the checksum value computed over a table row, or over an expression list.

BINARY_CHECKSUM returns the binary checksum value computed over a row of a table or over a list of expressions.

HASHBYTES returns the MD2, MD4, MD5, SHA, SHA1, or SHA2 hash of its input in SQL Server.For SQL Server 2014 (12.x) and earlier, allowed input values are limited to 8000 bytes. Beginning with SQL Server 2016 (13.x), all algorithms other than SHA2_256, and SHA2_512 are deprecated.

BINARY_CHECKSUM and CHECKSUM return different values for the string data types, where locale can cause strings with different representation to compare as equal. 

However, CHECKSUM and BINARY_CHECKSUM  cannot is not guaranteed the values in the expression list is changed. It’s better to use HASHBYTES instead. 

Examples

If we have an incoming row of data (let’s say @column1, @column2, @column3, @column4) and I want to know if the incoming values match the ones already in the table and update the table only if they are different, what’s the best way to do that/

You might think that’s easy and you’d just add a WHERE clause to your UPDATE like:

Where Column1 = @column1
and Column2 = @column2
and Column3 = @column3
and Column4 = @column4

If you have large number of columns then you can imagine what this looks like. In addition, you may need to handle the the NULL value if column are nullable like this:

( ISNULL(Column1,'') = ISNULL(@column1,'')

The easier way to do this is by adding one more column that represents a checksum or hash value for all the columns then just compare that one field. The remain question is about which checksum or hashing function to use. Based on the information I mentioned in the above, the checksum and binary_checsum cannot guarantee the result is correct. Therefore hashbytes is the better options to do this job.

HASHBYTES('SHA2_256',
CONCAT(@column1, '|',
@column2, '|',
@column3, '|',
@column4,'|' ))

Tips: add a separator ‘|’ character between columns to allow for empty strings

SCOPE_IDENTITY, IDENT_CURRENT, and @@IDENTITY are similar functions because they return values that are inserted into identity columns. However, the scope and session on which last is defined in each of these functions differ:

• IDENT_CURRENT is not limited by scope and session; it is limited to a specified table. It returns the last identity value generated for a specific table in any session and any scope.
• @@IDENTITY returns the last identity value generated for any table in the current session, across all scopes.
• SCOPE_IDENTITY returns the last identity value generated for any table in the current session and the current scope.

Failed statements and transactions can change the current identity for a table and create gaps in the identity column values. The identity value is never rolled back even though the transaction that tried to insert the value into the table is not committed. For example, if an INSERT statement fails because of an IGNORE_DUP_KEY violation, the current identity value for the table is still incremented.

Examples

The following example shows the different identity values that are returned by IDENT_CURRENT, @@IDENTITY, and SCOPE_IDENTITY