The sessions from Microsoft Ignite 2018

SQL Server 2017 has brought to market a new modern data platform including support for Linux, Docker Containers and rich features in intelligent performance, HADR, machine learning, and graph database. Come learn about the roadmap and new functionality planned for SQL Server including intelligent query processing, data virtualization, new features for mission critical security and HADR, and new scenarios for Linux and Docker Containers.

Release announcement for SQL Server 2019 preview
A detailed introduction to SQL Server big data clusters
General Availability of Azure Data Studio
SQL Server 2019 preview and Containers
Detailed blog post on database engine enhancements for SQL Server 2019 preview

Below are the videos from the SQL Server 2019 public preview sessions at Ignite.

It’s a deep dive across the investments and features released in SQL Server 2019. There is more than 10 hours of content. Enjoy!




Scalar Functions vs Table-Valued Functions

Today I’m going to give you another tip, but now focusing on Scalar Functions vs Table-Valued Functions.

Many developers love to use Scalar Functions inside of SQL Server because it lets them easily reuse code, which is a common goal in object-oriented programming.
Why would you ever write the same piece of logic more than once after all?

But the problem with Scalar Functions is that the query optimizer performs row-by-row processing instead of set-based logic.
Scalar Functions are known as “silent performance killer“, because even if we use some of the techniques in our benchmark toolkit, like looking at SET Statistics IO, and execution plans, we wouldn’t be able to see the row-by-row processing.

Let’s take a look at a sample query using Scalar Function:

CREATE FUNCTION GetmaxProductQty_Scalar (@ProductId INT)
returns INT
      DECLARE @maxQty INT 

      SELECT @maxQty = Max(sod.orderqty)
      FROM   Sales.SalesOrderDetail sod
      WHERE  sod.productid = @ProductId 

      RETURN ( @maxQty )

Here’s a query that uses the new function. It does nothing more than getting the max quantity sold for each product in the Production.Product table.

SELECT productid,
       dbo.GetMaxProductqty_scalar(productid) m
FROM   Production.Product
ORDER  BY productid

If we look at the SET Statistics IO, you might think this is great query just because we only have 15 logical reads. But does that mean that we don’t have anything else to improve here?


Keep in mind that this is just the Product’s table, and our function is referencing SalesOrderDetail. That is the reason why this is a “silent performance killer”.
If we check the execution plan, there is only an Index Scan from Product’s table.


However, using SQL Server Profiler, we are able to see the row-by-row processing:


Now, let’s create the Table-Valued Function, doing exactly the same thing on SalesOrderDetail.
You will notice that there is a slight difference compared to the previous function GetMaxProductQty_Scalar.

CREATE FUNCTION GetMaxProductQty_inline (@ProductId INT)
returns TABLE
      (SELECT Max(sod.orderqty) AS maxqty
       FROM   sales.SalesOrderdetail sod
       WHERE  sod.productid = @ProductId)

You can check the following examples using the new function:

SELECT      productid,
FROM        production.product
CROSS apply dbo.GetMaxProductqty_Inline(productid) MaxQTy
ORDER BY    productid

SELECT   productid,
         ( SELECT maxqty
FROM dbo.GetMaxProductqty_Inline(productid)) MaxQty
FROM     production.product
ORDER BY productid

If we now look at the SET Statistics IO, we can see the information from SalesOrderDetail:


The other thing to notice is that our execution plan has changed, and now includes everything. We can see the index scan on SalesOrderDetail, so we still may have some opportunities for improvement.
The main point here is that our silent performance killer is gone, and we don’t see the row-by-row processing anymore. So, if you have the opportunity to push that over to a Table Function, it will allow you to use set-based logic, which gives you much faster execution times.

Specify ORDER BY if you need ordered data

Some of you guys may not know that, so I’d like to share it.

If you need ordered data and your query does not have an ORDER BY clause, the data might return in any order. Yeah. It’s not guaranteed that it will return based in the order of the “primary key”.
So, you can’t  assume that SQL Server has some “default” order. You might believe there is, because usually when you SELECT a specific table it will use the cluster index(PK) with identity IDs.

But, it’s not always the case. Check the following example:

The following query will run just fine, and “as expected”.

Now, let’s create the following index.

CREATE INDEX Test_RandomInt ON dbo.Test(RandomInt);

The same query executed before is now broken, just because SQL Server has decided to change the index in the execution plan .

I’m not saying that you should start sorting every single query (it’s very expensive performance wise). But, if you really need it, you must provide the ORDER BY clause.

It’s preferable to sort on the app side from a resource perspective, because generally it’s easier to add more servers or add more CPU and Memory to the application layer.  It’s much more difficult, and expensive, to do so for the SQL servers.

Recommend MaxDop settings for SQL Server

The Microsoft SQL Server max degree of parallelism (MAXDOP) configuration option controls the number of processors that are used for the execution of a query in a parallel plan. This option determines the computing and thread resources that are used for the query plan operators that perform the work in parallel. Depending on whether SQL Server is set up on a symmetric multiprocessing (SMP) computer, a non-uniform memory access (NUMA) computer, or hyperthreading-enabled processors, you have to configure the max degree of parallelism option appropriately.

When setting up a new SQL Server, I usually use the following code to determine a good starting point for the MAXDOP setting:

I recommend setting MAXDOP to a setting that makes sense for your server load. Only you can determine the best setting for your environment. This script is just a good way to start.

Error updating temporal tables


At this point, you have probably heard about the feature called System Versioned Temporal Tables released on SQL Server 2016. So, let’s imagine that your team has decided to  implement that on a table called “Orders”.

The application is using different access patterns to modify the data in this table. There are direct statements coming from the application or the application is running long batches in explicit transactions where it makes several changes to multiple tables. The update on the “Orders” table is not the first statement in those long batches! So, suddenly you start seeing the following error message:

Data modification failed on system-versioned table “Orders” because transaction time was earlier than period start time for affected records.

You can simulate the error message running the following script:

, [OrderValue] DECIMAL(19,4)
INSERT dbo.Orders ([OrderId], [OrderValue])
VALUES (1, 9.99), (2, 9.99);
SELECT * FROM dbo.Orders;

--Run first query
WAITFOR DELAY '00:00:15';
UPDATE dbo.Orders
SET [OrderValue] = [OrderValue] + 1;

--Run Query 2 in another session
UPDATE dbo.Orders
SET [OrderValue] = [OrderValue] + 1;


But, what’s the problem here?

The system generated start and end times are keyed to the server’s system time at the moment the BEGIN TRANSACTION is executed.

The times recorded in the system datetime2 columns are based on the begin time of the transaction itself. For example, all rows inserted within a single transaction will have the same UTC time recorded in the column corresponding to the start of the SYSTEM_TIME period.

Is that a bug?

No, this is a standard behavior of System Versioned Temporal Tables.
More info:


  • Build in exception handling that can retry the transaction.
  • Use triggers instead of Temporal Tables
  • Wait for Microsoft to build/release the Application-time feature (that is part of the Standard). More info:

SQL Server Concepts and Best Practices [Part 1]

Hi Folks,

It’s been a while that I don’t post anything here, so it’s time to start a series of SQL Server concepts and Best Practices. Most of them, I’ve already shared in my repository on GitHub.
So, take a peek when you get a chance.

MAX vs Top 1 – which one is better?

When there is a clustered index on the table and the column to be queried, both the MAX() operator and the query SELECT TOP 1 will have almost identical performance.

When there is no clustered index (Heap) on the table and the column to be queried, the MAX() operator offers the better performance. So, consider using MAX rather than SELECT TOP 1.

CTE vs #temp table


* Are unindexable (but can use existing indexes on referenced objects)
* Cannot have constraints
* Are essentially disposable VIEWs
* Persist only until the next query is run
* Can be recursive
* Do not have dedicated stats (rely on stats on the underlying objects)

Temp Tables…

* Are real materialized tables that exist in tempdb
* Can be indexed
* Can have constraints
* Persist for the life of the current CONNECTION
* Can be referenced by other queries or subprocedures
* Have dedicated stats generated by the engine

As far as when to use each, they have very different use cases. If you will have a very large result set, or need to refer to it more than once, put it in a #temp table. If it needs to be recursive, is disposable, or is just to simplify something logically, a CTE is preferred. Also, a CTE should never be used for performance. You will almost never speed things up by using a CTE, because, again, it’s just a disposable view. You can do some neat things with them but speeding up a query isn’t really one of them.

When to use char/varchar/nchar/nvarchar?

  • nchar and nvarchar can store Unicode characters.
  • char and varchar cannot store Unicode characters.
  • char and nchar are fixed-length which will reserve storage space for number of characters you specify even if you don’t use up all that space.
  • varchar and nvarchar are variable-length which will only use up spaces for the characters you store. It will not reserve storage like char or nchar. nchar and nvarchar will take up twice as much storage space, so it may be wise to use them only if you need Unicode support.

It is worth highlighting the importance to keep your code (variables) using the same data type definition as the column definition. Mismatch data types will probably result in Non-Sargable queries.

Using the same column name in different tables but with different data types

Probably anybody will assume that columns with the same name in different tables have the same data type. As a result, they won’t verify data types. Different types is an accident waiting to happen.

Check that before creating a new column with the same name:

SELECT'.' AS ObjectName, as ColumnName,
CASE WHEN IN ('char','varchar') THEN'('+CASE WHEN s.max_length<0 then 'MAX' ELSE CONVERT(varchar(10),s.max_length) END+')'
WHEN IN ('nvarchar','nchar') THEN'('+CASE WHEN s.max_length<0 then 'MAX' ELSE CONVERT(varchar(10),s.max_length/2) END+')'
WHEN IN ('numeric') THEN'('+CONVERT(varchar(10),s.precision)+','+CONVERT(varchar(10),s.scale)+')'
,CASE WHEN s.is_nullable=1 THEN 'NULL' ELSE 'NOT NULL' END AS Nullable     
FROM sys.columns s
INNER JOIN sys.types t ON s.system_type_id=t.user_type_id and t.is_user_defined=0
INNER JOIN sys.objects o ON s.object_id=o.object_id
INNER JOIN sys.schemas sh on o.schema_id=sh.schema_id
WHERE IN (select table_name from information_schema.tables)
--AND = 'NewColumn'
ORDER BY'.',s.column_id

Float and real datatypes

Why we should not use float and real datatypes to store money in SQL Server? The main problem with them is that they are approximate numerics, so they don’t store exact values.

Take a look at this:

image2017-6-29 10-35-6

The float and real data types are known as approximate data types. The behavior of float and real follows the IEEE 754 specification on approximate numeric data types. Approximate numeric data types do not store the exact values specified for many numbers; they store an extremely close approximation of the value. For many applications, the tiny difference between the specified value and the stored approximation is not noticeable. At times, though, the difference becomes noticeable. Because of the approximate nature of the float and real data types, do not use these data types when exact numeric behavior is required, such as in financial applications, in operations involving rounding, or in equality checks. Instead, use the integer, decimal, money, or smallmoney data types.

Decimal is always a better choice even compared to money datatype, where we might have issues in cases of division.

image2017-6-29 10-35-47

I hope I could help you somehow.
See you soon!


Determining a setting for Cost threshold for parallelism

The cost threshold for parallelism option specifies the threshold at which SQL Server creates and runs parallel plans for queries. SQL Server creates and runs a parallel plan for a query only when the estimated cost to run a serial plan for the same query is higher than the value set in cost threshold for parallelism.


The default is set to 5, which is a really low setting. Although there is no right and wrong number, I’d recommend starting with this setting at 50 or so and tuning up or down as appropriate.

The following script returns a list of parallel query plans and their subtree costs which will help you decide an appropriate number.

SQL Server ignores the cost threshold for parallelism value under the following conditions:

  • Your computer has only one processor.
  • Only a single CPU is available to SQL Server because of the affinity mask configuration option.
  • The max degree of parallelism option is set to 1.

core AS (
eqp.query_plan AS [QueryPlan],
ecp.plan_handle [PlanHandle],
q.[Text] AS [Statement],
n.value('(@StatementOptmLevel)[1]', 'VARCHAR(25)') AS OptimizationLevel ,
ISNULL(CAST(n.value('(@StatementSubTreeCost)[1]', 'VARCHAR(128)') as float),0) AS SubTreeCost ,
ecp.usecounts [UseCounts],
ecp.size_in_bytes [SizeInBytes]
sys.dm_exec_cached_plans AS ecp
CROSS APPLY sys.dm_exec_query_plan(ecp.plan_handle) AS eqp
CROSS APPLY sys.dm_exec_sql_text(ecp.plan_handle) AS q
CROSS APPLY query_plan.nodes ('/ShowPlanXML/BatchSequence/Batch/Statements/StmtSimple') AS qn ( n )

SubTreeCost * UseCounts [GrossCost],
--GrossCost DESC
SubTreeCost DESC

Fore more details: Cost Threshold for Parallelism Option