New Features in SQL Server 2022 for Developers

SQL Server 2022 brings a comprehensive suite of features designed to enhance the development experience, improve performance, and strengthen security. By leveraging these capabilities, developers can create more efficient, scalable, and secure applications, positioning their organisations to effectively handle modern data challenges and requirements. The focus on intelligent processing, machine learning, data integration, and robust security aligns with the evolving landscape of application development and data management.

Here’s a detailed overview of the new features in SQL Server 2022 tailored for developers.

1. Intelligent Query Processing Enhancements

Overview: SQL Server 2022 continues to build on the Intelligent Query Processing (IQP) capabilities introduced in previous versions. The aim is to enhance query performance automatically without requiring significant code changes. This means that developers can achieve better performance for their applications without extensive rewrites.

Key Feature:

• Parameter Sensitive Plan Optimisation: This feature allows SQL Server to create and maintain multiple execution plans for the same query based on different parameter values. It identifies the most efficient plan based on runtime parameter values rather than relying solely on the static plan generated during compilation.

Example Scenario: Consider a financial application that retrieves transaction records based on different filters, such as dates and account numbers. With parameter-sensitive plan optimization, SQL Server will adaptively select the best execution plan based on the most commonly accessed account numbers or date ranges, leading to reduced execution times and better resource utilisation.

2. Built-in Machine Learning Services

Overview: SQL Server 2022 provides built-in support for machine learning services directly within the database engine. This integration allows developers to run R and Python scripts without the need for external tools.

Key Features:

• Direct Data Access: R and Python scripts can access SQL Server data directly, allowing for real-time analysis without the need to export data to another platform.
• Model Deployment: Developers can train and deploy machine learning models directly within SQL Server, making it easier to use these models in production applications.
• Scalability: Since machine learning is performed on the SQL Server instance, it can take advantage of the existing hardware resources, which may lead to improved performance for large datasets.

Example Scenario: A marketing department might use machine learning to predict customer churn. By training a model directly within SQL Server, they can continuously feed it fresh data from their transactional systems, enabling real-time predictions that can drive immediate business actions, such as targeted retention strategies.

3. SQL Server Ledger

Overview: SQL Server Ledger introduces a new feature that provides blockchain-like functionality, enabling developers to build applications with strong data integrity and immutability. This is particularly useful for industries that require stringent audit trails and data verification.

Key Features:

• Immutable Ledger Tables: These tables track all transactions in a way that prevents any modifications after they are committed, creating an audit trail that can be independently verified.
• Cryptographic Verification: Each transaction is cryptographically hashed, and the hashes are stored in the ledger, ensuring the integrity and authenticity of the data over time.
• Integration with Existing Systems: Developers can integrate ledger functionality with existing applications seamlessly, making it easier to adopt this technology without major architectural changes.

Example Scenario: In a healthcare application, patient treatment records can be stored in a ledger table. Each update to a patient’s treatment plan is recorded in a way that cannot be altered, providing a verifiable history that meets regulatory compliance and can be audited as needed.

4. Enhancements to Query Store

Overview: The Query Store feature is designed to help developers manage query performance by providing historical data on query execution, plan choices, and runtime statistics. The enhancements in SQL Server 2022 provide deeper insights and improved capabilities for tracking query performance over time.

Key Features:

• Query Performance Insights: The enhancements include improved reporting capabilities, allowing developers to visualize query performance trends over time and quickly identify any regressions.
• Automatic Plan Correction: SQL Server can automatically identify when a query's performance has degraded and revert to a previously optimal execution plan, reducing the need for manual intervention.
• Improved Visibility into Query Execution: Developers can see detailed information about query execution times, resource consumption, and plan changes, making it easier to diagnose performance issues.

Example Scenario: A web application might experience slowdowns after a new feature is deployed. Developers can use the Query Store to identify which queries have started to perform poorly, understand the reason behind the changes, and either optimise the queries or revert to a better-performing plan.

5. Azure Synapse Link for SQL Server

Overview: Azure Synapse Link provides a seamless integration between on-premises SQL Server databases and Azure Synapse Analytics, enabling developers to perform analytics on operational data without the need to move data to the cloud.

Key Features:

• Near Real-Time Analytics: Developers can run analytics on data as it is ingested, allowing for timely insights that can influence business decisions.
• Simplified Data Pipelines: This integration allows for the creation of streamlined data pipelines, enabling developers to focus on analysis rather than data movement.
• Enhanced Reporting: Azure Synapse provides powerful analytical capabilities that can be directly applied to operational data, enhancing reporting and decision-making.

Example Scenario: An e-commerce company can use Azure Synapse Link to analyse customer behaviour in real-time. By connecting their on-premises SQL Server to Azure Synapse, they can quickly generate reports on customer purchasing patterns, allowing for immediate marketing adjustments based on current trends.

6. Enhanced Security Features

Overview: SQL Server 2022 introduces several new security enhancements designed to protect sensitive data and improve overall database security posture.

Key Features:

• Always Encrypted with Secure Enclaves: This enhancement allows certain operations to be performed on encrypted data without exposing it in plaintext, enhancing the security of sensitive information.
• Dynamic Data Masking Enhancements: These improvements offer developers more options for controlling how sensitive data is presented to users, allowing for greater flexibility in displaying data according to user roles.
• Security Monitoring Tools: New tools provide real-time alerts and recommendations for improving database security, helping developers maintain compliance with best practices.

Example Scenario: In a banking application, sensitive information such as customer account details can be protected using Always Encrypted. Developers can configure dynamic data masking to ensure that only authorised personnel can view sensitive data, thus enhancing customer trust and compliance with regulations.

7. SQL Server Management Studio (SSMS) Enhancements

Overview: The latest version of SQL Server Management Studio includes significant enhancements designed to improve the developer experience, making it easier to manage and develop SQL Server databases.

Key Features:

• Improved IntelliSense: Enhanced IntelliSense capabilities help developers write SQL queries more efficiently by providing better auto-completion and suggestions.
• Integrated Monitoring Tools: New monitoring features allow developers to track database performance metrics directly within SSMS, enabling faster troubleshooting and optimisation.
• User-Friendly Interface: A more intuitive user interface makes it easier for developers to navigate complex database structures and management tasks.

Example Scenario: A developer working on a complex SQL query can rely on the improved IntelliSense features to quickly find the right syntax and suggestions, reducing errors and speeding up the query development process.

8. Support for UTF-8 Encoding

Overview: With the introduction of UTF-8 encoding support, SQL Server 2022 allows developers to store and manipulate multilingual data more effectively, which is crucial for global applications.

Key Features:

• Flexible Data Storage: Developers can specify UTF-8 encoding when creating string columns, enabling efficient storage of various character sets.
• Reduced Storage Requirements: By using UTF-8 encoding, developers can save space compared to UTF-16 encoding, particularly for texts primarily in English or other Latin-based languages.

That's all for now.

Always On: Trying out Read-Only intent connections to secondary replicas

SQL Server Always On Availability Group enables us to connect Read-Only workloads to secondary replicas automatically by using differents ways. For instance, to connect .NET applications to secondary replicas, we must also include "ApplicationIntent=ReadOnly" besides the AG Listener Virtual Name in the connection string. More specifically, after configuring the Read-Only Routing list, this would be the connection string if we had an Availability Group with a Listener called "AGLISTENER" (in these examples it is used Windows Authentication).

Server=tcp:AGLISTENER,1433;IntegratedSecurity=SSPI;ApplicationIntent=ReadOnly;MultiSubnetFailover=True

But that is not all, it is also possible to establish connection to secondary replicas via SQLCMD tool by using the new parameter -K (and optionally -M for faster detection of and connection to the active server). If -K is not specified, the sqlcmd utility will not support connectivity to a secondary replica. So, this would be a simple example of how to use SQLCMD with -K and -M parameters. I couldn't have been simpler.

Sqlcmd -S AGLISTENER  -E  -K ReadOnly -M

And last but not least, we can also do it by using SSMS Connection Dialog window by using “ApplicationIntent=ReadOnly” in the "Additional Connection Parameter" tab. I also wrote another post about it with more details, take a look at it.

To sum up, these are the basic ways to try out Read-Only intent connections to secondary replicas, the first way is ideal for developers whereas the second and third if you are working as a DBA.
That is all for now. Let me know any remarks you may have. Stay tuned.

ObjectExplorer error: Dedicated administrator connections are not supported

Dedicated Administrator Connection (DAC) is a type of privileged connection that we can establish to SQL Server database engine when it does not respond because of too much workload. In this context, DAC is truly useful which allow to diagnosis and solve this sort of problems without restarting the database server any longer. Its is said that it is not possible to establish a DAC connection to the Object Explorer panel via SSMS so that we can get the following error while trying do it in that way:

   Cannot connect to admin:<SQLInstanceFullName>.
   Dedicated administrator connections are not supported. (ObjectExplorer)



It is true up to a point, but it does not mean that SSMS does not support DAC connections. Therefore, the question is:  can we ONLY establish a DAC connection to SQL Server by using SQLCMD? the answer is 'NO'. We also can do it via SSMS while creating a new SQL Query, which means that instead of clicking on 'Object Explorer' and 'Connect', we must click on 'New Query' and finally write 'admin:<Full Name of SQL Instance>' on 'Server Name' text box. That is all, it couldn't have been simpler.



Having done that, we will have a DAC connection ready via SSMS. Let me know any remarks you may have. Thanks for reading. Stay tuned.

New per-operator level performance stats included in SQL2016 SP1 and SQL2014 SP2

Traditionally, we were used to getting only basic runtime performance stats per operator regarding to CPU, I/O and Memory, which were not good enough to allow us to automate recollecting tasks of them. I strongly believe that DBAs are always overwhelmingly interested in diagnosing some issues proactively so as to prevent bad-written queries from using hardware resources inefficiently. Luckily, this convoluted situation has been changed radically in SQL Server 2016 and SQL Server 2014 SP2 since Microsoft has included very useful information per operator (and also per thread in the scope of each operator) which can be seen looking into the Showplan XML. For instance, we are going to see that information for the following query (in this case it has run under a serial plan, that is one thread).



This is its actual execution plan.


It is highlighted all what is new in SQL2016, SQL2016 SP1 and SQL2014 SP2. Therefore, we can appreciate CPU and I/O runtime performance stats for the Clustered Index Scan operator on 'PurchaseOrderDetail' table such as I/O reads, CPU time and Elapsed time. In a parallel plan you will be able to see information per thread in each operator.



And now we will see not only CPU and I/O performance stats for the Hash operator but also Memory performance stats.



All this information can also be seen in the Properties window in the context of each operator inside the Actual Execution Plan by using the latest version of SSMS. If you display the detail of each statistic, the detail per thread will be seen when working with parallel execution plan.



Having seen this, not only are we now able to look into useful performance stats by using Showplan XML and SSMS, but also we can create some scripts for administration and optimization purposes in order to pick up queries with poor performance and then optimise them. That is all for now. Let me know any remark you may have. Thanks for reading.

SQL Server Analysis Services Error: The following system error occurred: (Microsoft.AnalysisServices)

Clearly, while adding some users as Windows Administrators, not only do we do that, but also we add them as Administrator of Analysis Services (SSAS). So, if we delete them from Windows then they will become orphaned users inside SSAS. As a result of this action, not surprisingly, we are going to get the following error when we are trying to add other users to the server administrator role in SSAS via the GUI or by code.

“The following system error occurred: (Microsoft.AnalysisServices)”

This sort of error is raised at times as the orphaned users have not got a correct matching inside the Active Directory. So, only their invalid user IDs will still be registered.



What we should do to fix this issue and be able to add other users is, firstly, to remove these orphaned users and, finally, add the new ones. I hope you find this short post interesting. Thanks for reading!

Error: Index was outside the bounds of the array

It is said that we may work on SQL Server connecting to Object Explorer by using the SQL Management Studio tool (SSMS) of an older version. It is true up to a point, but there is a risk of getting some unexpected errors, for instance, we might get this error while working connected to SQL Server 2012 Object Explorer by using SQL Server 2005 SSMS.



More specifically, the error is raised because superior versions support more features, which means that each feature is likely to have one folder on the Object Explorer. Put differently, there is a bigger number of folders to display in SQL Server 2012 than SQL Server 2005 whose Object Explorer cannot display more because the max array index is outside the bounds that this supports. Ideally, it is highly advisable to always use the latest SSMS to connect to older versions (but not vice versa). As simple as water. That is all for now, let me know any remarks you may have. Thanks for reading.

How to connect to SQL Server 2005 much faster via SQL Management Studio

While connecting with SQL Server 2005, we have to deal with the 'splash' screen and then indicate, basically, the following information such as service type, server name, and authentication type. This process may sometimes slow us down, nevertheless, we can optimise it as long as we know where we want to establish a connection. Put differently, we can save some time by using 'sqlwb.exe' tool which allows to start SQL Management Studio rapidly. This tool has some parameters we can use them to speed up the logon process to SQL instances. Here I am coming up with an illustration how to use 'sqlwb.exe' with some basic parameters. For instance,  if we would like to connect to the 'TestDB' database on 'SVPRDB1' server and with Windows Authentication, it can be done in this way:

sqlwb.exe -E -S SVPRDB1 -d TestDB -nosplash

Only after successfully executing that will we connect directly to 'TestDB' database without specifying manually the parameters and also without seeing 'the splash' screen showing the presentation of SQL Management Studio version. Now we can also customise the shortcut as it is shown in the following picture:



As earlier I said, this is the fastest way to connect to SQL Server which will allow to save some seconds. It couldn't have been simpler. That is all for now. Thanks for reading. Stay tuned.