Introducing Microsoft SQL Server 2019

By Kellyn Gorman , Allan Hirt , Dave Noderer and 5 more
    What do you get with a Packt Subscription?

  • Instant access to this title and 7,500+ eBooks & Videos
  • Constantly updated with 100+ new titles each month
  • Breadth and depth in over 1,000+ technologies
  1. Free Chapter
    1. Optimizing for performance, scalability and real‑time insights
About this book

Microsoft SQL Server comes equipped with industry-leading features and the best online transaction processing capabilities. If you are looking to work with data processing and management, getting up to speed with Microsoft Server 2019 is key.

Introducing SQL Server 2019 takes you through the latest features in SQL Server 2019 and their importance. You will learn to unlock faster querying speeds and understand how to leverage the new and improved security features to build robust data management solutions. Further chapters will assist you with integrating, managing, and analyzing all data, including relational, NoSQL, and unstructured big data using SQL Server 2019. Dedicated sections in the book will also demonstrate how you can use SQL Server 2019 to leverage data processing platforms, such as Apache Hadoop and Spark, and containerization technologies like Docker and Kubernetes to control your data and efficiently monitor it.

By the end of this book, you'll be well versed with all the features of Microsoft SQL Server 2019 and understand how to use them confidently to build robust data management solutions.

Publication date:
April 2020


1. Optimizing for performance, scalability and real-time insights

Companies are optimizing their computing resources to get more transactional performance out of the same hardware resources. At the same time, the demand and pace of business and customer focus is increasing; they need real-time insights on the transactional data.

In recent years, many companies have turned to No-SQL solutions that allow very high write performance of transactions while allowing eventual consistency, but that later require data mining and analysis.

Microsoft SQL Server has taken on this challenge and, with every release, continues to expand the workloads in many dimensions. This chapter will discuss many of the features that allow both high-performance transaction processing while simultaneously allowing real-time analytics on transactional data without the need for a separate set of ETL processes, a separate data warehouse, and the time to do that processing.

Microsoft SQL Server 2019 is built on a database engine that is number one for TPC-E (On-Line Transaction Processing Benchmark) and TCP-H (Decision Support Benchmark). See for more information.

Changes in hardware architecture allow dramatic speed increases with Hybrid Buffer Pool, which utilizes persistent memory (PMEM), also known as Storage Class Memory (SCM).

Microsoft SQL Server 2019 can be used in the most demanding computing environments required today. Using a variety of features and techniques, including in-memory database operations, can make dramatic increases in your transaction processing rate while still allowing near-real-time analysis without having to move your transaction data to another "data warehouse" for reporting and analysis.

Microsoft SQL Server 2019 has also expanded the number of opportunities to tune database operations automatically, along with tools and reports to allow monitoring and optimization of queries and workloads. Comprehensive diagnostic features including Query Store allow SQL Server 2019 to identify performance issues quickly.

By upgrading to SQL Server 2019, the customer will be able to boost query performance without manual tuning or management. Intelligent Query Processing (IQP) helps many workloads to run faster without making any changes to the application.


Hybrid transactional and analytical processing (HTAP)

Hybrid transactional and analytical processing (HTAP), is the application of tools and features to be able to analyze live data without affecting transactional operations.

In the past, data warehouses were used to support the reporting and analysis of transactional data. A data warehouse leads to many inefficiencies. First, the data has to be exported from the transactional database and imported into a data warehouse using ETL or custom tools and processes. Making a copy of data takes more space, takes time, may require specialized ETL tools, and requires additional processes to be designed, tested, and maintained. Second, access to analysis is delayed. Instead of immediate access, business decisions are made, meaning the analysis may be delayed by hours or even days. Enterprises can make business decisions faster when they can get real-time operational insights. In some cases, it may be possible to affect customer behavior as it is happening.

Microsoft SQL Server 2019 provides several features to enable HTAP, including memory-optimized tables, natively compiled stored procedures, and Clustered Columnstore Indexes.

This chapter covers many of these features and will give you an understanding of the technology and features available.

A more general discussion of HTAP is available here:


Clustered Columnstore Indexes

Clustered Columnstore indexes can make a dramatic difference and are the technology used to optimize real-time analytics. They can achieve an order of magnitude performance gain over a normal row table, a dramatic compression of the data, and minimize interference with real-time transaction processing.

A columnstore has rows and columns, but the data is stored in a column format.

A rowgroup is a set of rows that are compressed into a columnstore format — a maximum of a million rows (1,048,576).

There are an optimum number of rows in a rowgroup that are stored column-wise, and this represents a trade-off between large overhead, if there are too few rows, and an inability to perform in-memory operations if the rows are too big.

Each row consists of column segments, each of which represents a column from the compressed row.

Columnstore is illustrated in Figure 1.1, showing how to load data into a non-clustered columnstore index:

Figure 1.1: Loading data into a non-clustered columnstore index
Figure 1.1: Loading data into a non-clustered columnstore index

A clustered columnstore index is how the columnstore table segments are stored in physical media. For performance reasons, and to avoid fragmenting the data, the columnstore index may store some data in a deltastore and a list of the IDs of deleted rows. All deltastore operations are handled by the system and not visible directly to the user. Deltastore and columnstore data is combined when queried.

A delta rowgroup is used to store columnstore indexes until there are enough to store in the columnstore. Once the maximum number of rows is reached, the delta rowgroup is closed, and a background process detects, compresses, and writes the delta rowgroup into the columnstore.

There may be more than one delta rowgroup. All delta rowgroups are described as the deltastore. While loading data, anything less than 102,400 rows will be kept in the deltastore until they group to the maximum size and are written to the columnstore.

Batch mode execution is used during a query to process multiple rows at once.

Loading a clustered columnstore index and the deltastore are shown in Figure 1.2.

Figure 1.2: Loading a clustered columnstore index
Figure 1.2: Loading a clustered columnstore index

Further information can be found here:

Adding Clustered Columnstore Indexes to memory-optimized tables

When using a memory-optimized table, add a non-clustered columnstore index. A clustered columnstore index is especially useful for running analytics on a transactional table.

A clustered columnstore index can be added to an existing memory-optimized table, as shown in the following code snippet:

-- Add a clustered columnstore index to a memory-optimized table
ALTER TABLE MyMemOpttable 
ADD INDEX MyMemOpt_ColIndex clustered columnstore

Disk-based tables versus memory-optimized tables

There are several differences between memory-optimized and disk-based tables.

One difference is the fact that, in a disk-based table, rows are stored in 8k pages and a page only stores rows from a single table. With memory-optimized tables, rows are stored individually, such that one data file can contain rows from multiple memory-optimized tables.

Indexes in a disk-based table are stored in pages just like data rows. Index changes are logged, as are data row changes. A memory-optimized table persists the definition of the index but is regenerated each time the memory-optimized table is loaded, such as restarting the database. No logging of index "pages" is required.

Data operations are much different. With a memory-optimized table, all operations are done in memory. Log records are created when an in-memory update is performed. Any log records created in-memory are persisted to disk through a separate thread. Disk-based table operations may perform in-place updates on non-key-columns, but key-columns require a delete and insert. Once the operation is complete, changes are flushed to disk.

With disk-based tables, pages may become fragmented. As changes are made, there may be partially filled pages and pages that are not consecutive. With memory-optimized tables, storing as rows removes fragmentation, but inserts, deletes, and updates will leave rows that can be compacted. Compaction of the rows is executed by means of a merge thread in the background.

Additional information can be found at this Microsoft docs link:


In-memory OLTP

In-memory on-line transaction processing (OLTP) is available in Microsoft SQL Server for optimizing the performance of transaction processing. In-memory OLTP is also available for all premium Azure SQL databases. While dependent on your application, performance gains of 2-30x have been observed.

Most of the performance comes from removing lock and latch contention between concurrently executing transactions and is optimized for in-memory data. Although performed in-memory, changes are logged to disk so that once committed, the transaction is not lost even if the machine should fail.

To fully utilize in-memory OLTP, the following features are available:

  • Memory-optimized tables are declared when you create the table.
  • Non-durable tables, basically in-memory temporary tables for intermediate results, are not persisted so that they do not use any disk I/O. A non-durable table is declared with DURABILITY=SCHEMA_ONLY.
  • Table values and table-valued parameters can be declared as in-memory types as well.
  • Natively compiled stored procedures, triggers, and scalar user-defined functions are compiled when created and avoid having to compile them at execution time, thereby speeding up operations.

Additional information can be found at the following links:


Planning data migration to memory-optimized tables

Microsoft SQL Server Management Studio (SSMS) contains tools to help analyze and migrate tables to memory-optimized storage.

When you right-click on a database in SSMS and click on Reports | Standard Reports | Transaction Performance Analysis Overview, a four-quadrant report of all tables in the database will be made:

Figure 1.3: Choosing Transaction Performance Analysis
Figure 1.3: Choosing Transaction Performance Analysis

The report will look at each table and place it on the chart to show the ease of migration versus the expected gain by migrating the table to be memory-optimized:

Figure 1.4: Recommended Tables Based on Usage
Figure 1.4: Recommended Tables Based on Usage

Once you have identified tables that might benefit, you can right-click on individual tables and run the Memory Optimization Advisor:

Figure 1.5: Selecting the Memory Optimization Advisor
Figure 1.5: Selecting the Memory Optimization Advisor

The Table Memory Optimization Advisor is a "wizard" style of user interface that will step you through the configurations:

Figure 1.6: The Table Memory Optimization Advisor dialogue
Figure 1.6: The Table Memory Optimization Advisor dialogue

The wizard will take you through a checklist with any failed issues:

Figure 1.7: Memory Optimization Checklist
Figure 1.7: Memory Optimization Checklist

The warnings dialogue will flag up other important issues.

Figure 1.8: Memory Optimization Warnings
Figure 1.8: Memory Optimization Warnings

Next enter file names and check paths in the migration option dialogue.

Figure 1.9: Review Optimization options
Figure 1.9: Review Optimization options

The wizard will detect the primary keys and populates the list of columns based on the primary key metadata. To migrate to a durable memory-optimized table, a primary key needs to be created. If there is no primary key and the table is being migrated to a non-durable table, the wizard will not show this screen.

Figure 1.10: Review Primary Key Conversion
Figure 1.10: Review Primary Key Conversion

By clicking Script you can generate a Transact-SQL script in the summary screen.

Figure 1.11: Verify Migration Actions Summary Screen
Figure 1.11: Verify Migration Actions Summary Screen

The wizard will the display a report as the table migrates.

Figure 1.12: Migration progress report
Figure 1.12: Migration progress report

Memory-optimized tables are a great feature, but you will need to plan carefully to make sure you get the performance and transactional reliability you require.

You can create a new database specifying memory-optimized, or alter an existing database to handle memory-optimized data. In either case, a filegroup for containing the memory-optimized data must be created.

In the following sample, we will create a memory-optimized database using SQL script:

-- Create Memory-Optimized Database
  ON (Name = MemOptDB_Data, FileName = 'c:\sqldata\memoptdb_data.mdf', size = 10 mb, maxsize = 20 mb, filegrowth = 5 mb)
  LOG ON (Name = MemOptDB_Log, FileName = 'c:\sqldata\memoptdb_log.ldf', size = 2 mb, maxsize = 10 mb, filegrowth = 1 mb);
-- Must declare a memory-optimized filegroup
  ADD FILE (Name = 'MemOptDB_MOFG', FileName = 'c:\sqldata\memoptdb_mofg')

You can also make a memory-optimized database by using SQL Server Management Studio and adding a memory-optimized filegroup:

Figure 1.13: The new database dialogue window
Figure 1.13: The new database dialogue window

Natively compiled stored procedures

Natively compiled stored procedures are compiled when created and bypass the query execution engine. The procedure is compiled when created, and also manually or when the database or server are restarted.

A few additional concepts are introduced here, including SCHEMABINDING and BEGIN ATOMIC, both of which are required for natively compiled stored procedures.

SCHEMABINDING locks the table definition to prevent alteration after the stored procedure is created. SCHEMABINDING allows the compiled stored procedure to be certain of the data types involved. The tables involved in the natively compiled stored procedure cannot be altered without dropping the SCHEMABINDING, making changes and then reapplying the SCHEMABINDING. SHEMABINDING also requires that explicit field names are used in the query; "select *…" will not work.

BEGIN ATOMIC is required in a natively compiled stored procedure and is only available for a natively compiled stored procedure. In interactive (non-natively compiled) procedures, you would use a BEGIN TRAN statement block. Using the ATOMIC block and transaction settings will be independent of the current connection/settings as the stored procedure may be used in different execution sessions.

If there is an existing active transaction, BEGIN ATOMIC will set a save point and roll back to that if it fails. Otherwise, a new transaction is created and completed or rolled back.

You indicated a natively compiled stored procedure in the create declaration of the stored procedure using the "NATIVE_COMPILATION" directive.

In the following sample, we will create a memory-optimized table and a natively stored procedure. Note that memory-optimized tables cannot have clustered indexes. Memory-optimized tables are stored as rows, not in pages, as with a disk-based table:

-- Create Memory-Optimized Table
CREATE TABLE dbo.MyMemOptTable
  id int not null,
  dtCreated datetime not null,
  orderID nvarchar(10) not null
-- Create Natively Stored Procedure
CREATE PROCEDURE dbo.myNativeProcedure (@id int)
  SELECT id, dtCreated, orderID
  FROM dbo.MyMemOptTable
  WHERE id = @id

The table schema is locked due to the reference to a natively compiled stored procedure. If you try to alter the table, an exception will be thrown, as shown here:

-- Try to alter the schema!
ALTER TABLE [dbo].[MyMemOpttable]
  ALTER COLUMN orderId nvarchar(20)
Msg 5074, Level 16, State 1, Line 55
The object 'myNativeProcedure' is dependent on column 'orderId'.
Msg 4922, Level 16, State 9, Line 55
ALTER TABLE ALTER COLUMN orderId failed because one or more objects access this column.

More information on natively compiled procedures can be found here:


TempDB enhancements

We have introduced another scalability enhancement with memory-optimized TempDB metadata. Historically, TempDB metadata contention has been a bottleneck to scalability for workloads running on SQL Server.

The system tables used for managing temp table metadata can be moved into latch-free non-durable memory-optimized tables.

Enabling memory-optimized TempDB metadata

Enabling this feature in SQL Server is a two-step process:

  • First, alter the server configuration with T-SQL
  • Restart the service

The following T-SQL command can be used to verify whether tempdb is memory-optimized:

SELECT SERVERPROPERTY('IsTempdbMetadataMemoryOptimized')

Limitations of memory-optimized TempDB metadata

There are a few limitations associated with using this new feature.

  • Toggling the feature on and off requires a service restart.
  • A single transaction may not access memory-optimized tables in more than one database. This means that any transactions that involve a memory-optimized table in a user database will not be able to access TempDB System views in the same transaction. If you attempt to access TempDB system views in the same transaction as a memory-optimized table in a user database, you will receive the following error:
    A user transaction that accesses memory-optimized tables or natively compiled modules cannot access more than one user database or databases model and msdb, and it cannot write to master.
  • Queries against memory-optimized tables do not support locking and isolation hints, so queries against memory-optimized TempDB catalog views will not honor locking and isolation hints. As with other system catalog views in SQL Server, all transactions against system views will be in READ COMMITTED (or, in this case, READ COMMITTED SNAPSHOT) isolation.
  • There may be some issues with columnstore indexes on temporary tables when memory-optimized TempDB metadata is enabled. It is best to avoid columnstore indexes on temporary tables when using memory-optimized TempDB metadata.

Intelligent Query Processing

Intelligent Query Processing (IQP) is a family of features that were introduced in Microsoft SQL Server 2017 as adaptive query processing and has been expanded with new features in Microsoft SQL Server 2019. By upgrading to SQL Server 2019 and with compatibility level 150, most workloads will see performance improvements due to added intelligence in the query optimizer.

Intelligent Query Processing features are automatically enabled based on the "COMPATIBLITY_LEVEL" of the database. To take advantage of the latest IQP features, set the database compatibility to 150.

Most of these are also available in Azure SQL, but it is best to check current documentation on exactly what is available there as this changes.

The following table summarizes some of the IQP features.

Table 1.14: Table summarizing IQP features
Table 1.14: Table summarizing IQP features

Hybrid Buffer Pool

Microsoft SQL Server 2019 introduces Hybrid Buffer Pool. This feature allows access to Persistent MEMory (PMEM) devices. These persistent memory devices add a new layer to server memory hierarchy and filling the gap between high performance / high cost of DRAM (Dynamic Random Access Memory) and the lower cost lower performance of file storage drives using SSD.

This memory architecture has been implemented by Intel as Intel® Optane™ Technology; refer to for more information:

Figure 1.15: Intel memory architecture
Figure 1.15: Intel memory architecture

Persistent memory is integrated at the memory controller level of the CPU chip and will retain data even when the server is powered off.

While many aspects of persistent memory devices can be realized without any software changes, features such as Hybrid Buffer Pool can take advantage of the new storage hierarchy and provide direct memory access to files.

For clean database pages, those that have not been modified, SQL server can directly access them as memory. When an update is made, and then marked as dirty, the page is copied to DRAM, changes persisted, and the page is then written back into the persistent memory area.

To enable Hybrid Buffer Pool, the feature must be enabled at the instance level of SQL Server. It is off by default. After enabling, the instance must be restarted:


Furthermore, the Hybrid Buffer Pool will only operate on memory-optimized databases:


Or, in order to disable, execute the following command:


To see the Hybrid Buffer Pool configurations and memory-optimized databases on an instance, you can run the following queries:

SELECT * FROM sys.configurations WHERE name = 'hybrid_buffer_pool';
SELECT name, is_memory_optimized_enabled FROM sys.databases;

There are many considerations when configuring a server with persistent memory, including the ratio of DRAM to PMEM. You can read more here:


Query Store

The Query Store in SQL Server, first introduced in SQL Server 2016, streamlines the process of troubleshooting query execution plans. The Query Store, once enabled, automatically captures query execution plans and runtime statistics for your analysis. You can then use the sys.dm_db_tuning_recommendations view to discover where query execution plan regression has occurred and use the stored procedure, sp_query_store_force_plan, to force a specific plan that performs better.

In SQL Server 2019, we now have made some additional enhancements to the default Query Store features. In this section, we will discuss the following topics:

  • Changes to default parameter values when enabling Query Store
  • A new QUERY_CAPTURE_MODE custom
  • Support for fast forward and static cursors

You can configure Query Store with SQL Server Management Studio (SSMS) or with T-SQL statements. SSMS configuration includes turning it on and off by setting the operation mode (off, read-only, or read/write), the Query Store size, and other settings. You can find Query Store parameters in the properties of a database by right-clicking on the database and selecting Query Store:

Figure 1.16: Database properties dialogue window
Figure 1.16: Database properties dialogue window

Changes to default parameter values

Two of the existing parameters have new default values compared to SQL Server 2017. These parameters are MAX_STORAGE_SIZE_MB and QUERY_CAPTURE_MODE. The new default values as of SQL Server 2019 are listed here:

  • MAX_STORAGE_SIZE_MB has a default value of 1000 (MB)
  • The QUERY_CAPTURE_MODE has a default value of AUTdO


In previous versions of SQL Server, the default value for the QUERY_CAPTURE_MODE was set to ALL, and therefore all query plans were captured and stored. As mentioned in the previous section, the default value has now been changed to AUTO.

Setting the QUERY_CAPTURE_MODE to AUTO means that no query plans or associated runtime statistics will be captured for the first 29 executions in a single day. Query plans and runtime statistics are not captured until the 30th execution of a plan. This default setting can be changed by using the new custom mode.


Before 2019, there were three available values for the query_capture_mode; those values were NONE, ALL, and AUTO. We have now added a fourth option, which is CUSTOM.

The CUSTOM mode provides you with a mechanism for changing the default settings of the Query Store. For example, the following settings can be modified when working in CUSTOM mode:


First, you can verify and validate the current Query Store settings by using the sys.database_query_store_options view:

SELECT actual_state_desc, stale_query_threshold_days, query_capture_mode_desc, 
  capture_policy_execution_count, capture_policy_total_compile_cpu_time_ms, 
FROM sys.database_query_store_options

The output is as follows:

Figure 1.17: Verifying and validating the Query Store settings
Figure 1.17: Verifying and validating the Query Store settings

To modify the default settings, you will first change the query capture mode to custom and then apply changes to the default values. Look at the following code by way of an example:

ALTER DATABASE AdventureWorks2017 

The output is as follows:

Figure 1.18: Modifying the default settings
Figure 1.18: Modifying the default settings

Support for FAST_FORWARD and STATIC Cursors

We have added another exciting update to the Query Store. You can now force query execution plans for fast forward and static cursors. This functionality supports T-SQL and API cursors. Forcing execution plans for fast forward and static cursors is supported through SSMS or T-SQL using sp_query_store_force_plan.


Automatic tuning

Automatic tuning identifies potential query performance problems, recommends solutions, and automatically fixes problems identified.

By default, automatic tuning is disabled and must be enabled. There are two automatic tuning features available:

  • Automatic plan correction
  • Automatic index management

Automatic plan correction

To take advantage of automatic plan correction, the Query Store must be enabled on your database. Automatic plan correction is made possible by constantly monitoring data that is stored by the Query Store.

Automatic plan correction is the process of identifying regression in your query execution plans. Plan regression occurs when the SQL Server Query Optimizer uses a new execution plan that performs worse than the previous plan. To identify plan regression, the Query Store captures compile time and runtime statistics of statements being executed.

The database engine uses the data captured by the Query Store to identify when plan regression occurs. More specifically, to identify plan regression and take necessary action, the database engine uses the sys.dm_db_tuning_recommendations view. This is the same view you use when manually determining which plans have experienced regressions and which plans to force.

When plan regression is noticed, the database engine will force the last known good plan.

The great news is that the database engine doesn't stop there; the database engine will monitor the performance of the forced plan and verify that the performance is better than the regressed plan. If the performance is not better, then the database engine will unforce the plan and compile a new query execution plan.

Enabling automatic plan correction

Automatic plan correction is disabled by default. The following code can be used to verify the status of automatic plan correction on your database:

SELECT name, desired_state_desc, actual_state_desc
FROM sys.database_automatic_tuning_options

The output is as follows:

Figure 1.19: Automatic plan correction is turned off
Figure 1.19: Automatic plan correction is turned off

You enable automatic plan correction by using the following code:


If you have not turned the Query Store on, then you will receive the following error:

Figure: 1.20: Error report if the Query Store is off
Figure: 1.20: Error report if the Query Store is off

Automatically forced plans

The database engine uses two criteria to force query execution plans:

  • Where the estimated CPU gain is higher than 10 seconds
  • The number of errors in the recommended plan is lower than the number of errors in the new plan

Forcing execution plans improves performance where query execution plan regression has occurred, but this is a temporary solution, and these forced plans should not remain indefinitely. Therefore, automatically forced plans are removed under the following two conditions.

  • Plans that are automatically forced by the database engine are not persisted between SQL Server restarts.
  • Forced plans are retained until a recompile occurs, for example, a statistics update or schema change.

The following code can be used to verify the status of automatic tuning on the database:

SELECT name, desired_state_desc, actual_state_desc
FROM sys.database_automatic_tuning_options;
Figure 1.21: Verifying the status of automatic tuning on the database

Figure 1.21: Verifying the status of automatic tuning on the database


Lightweight query profiling

Lightweight query profiling (LWP) provides DBAs with the capability to monitor queries in real time at a significantly reduced cost of the standard query profiling method. The expected overhead of LWP is at 2% CPU, as compared to an overhead of 75% CPU for the standard query profiling mechanism.

For a more detailed explanation on the query profiling infrastructure, refer to

New functionality in 2019

In SQL Server 2019, we have now improved LWP with new features and enhancements to the existing capabilities.

  • In SQL Server 2016 and 2017, lightweight query profiling was deactivated by default and you could enable LWP at the instance level by using trace flag 7412. In 2019, we have now turned this feature ON by default.
  • You can also now manage this at the database level through Database Scoped Configurations. In 2019, you have a new database scoped configuration, lightweight_query_profiling, to enable or disable the lightweight_query_profiling infrastructure at the database level.
  • We have also introduced a new extended event. The new query_post_execution_plan_profile extended event collects the equivalent of an actual execution plan based on lightweight profiling,unlike query_post_execution_showplan, which uses standard profiling.
  • We also have a new DMF sys.dm_exec_query_plan_stats; this DMF returns the equivalent of the last known actual execution plan for most queries, based on lightweight profiling.

The syntax for sys.dm_exec_query_plan_stats is as follows:


For a more detailed analysis, refer to this online documentation:


If you are not certain of the current status of LWP, you can use the following code to check the status of your database scoped configurations. The value column is 1; therefore, using the sys.database_scoped_configurations view, you see that Query Plan Stats is currently enabled:

SELECT * FROM sys.database_scoped_configurations 

The output is as follows:

Figure 1.23: Check the status of the database scoped configurations
Figure 1.22: Check the status of the database scoped configurations

To enable or disable LWP, you will use the database scoped configuration lightweight_query_profiling. Refer to the following example:


Activity monitor

With LWP enabled, you can now look at active expensive queries in the activity monitor. To launch the activity monitor, right-click on the instance name from SSMS and select Activity Monitor. Below Active Expensive Queries, you will see currently running queries, and if you right-click on an active query, you can now examine the Live Execution Plan!

Figure 1.24: The activity monitor
Figure 1.23: The activity monitor

Columnstore stats in DBCC CLONEDATABASE

DBCC CLONEDATABASE creates a clone of the database that contains a copy of the schema and statistics for troubleshooting and diagnostic purposes. More specifically, with DBCC CLONEDATABASE, you have a lightweight, minimally invasive way to investigate performance issues related to the query optimizer. In SQL Server 2019, we now extend the capabilities of DBCC CLONEDATABASE by adding support for columnstore statistics.

Columnstore statistics support

In SQL Server 2019, support has been added for columnstore statistics. Before SQL Server 2019, manual steps were required to capture these statistics (refer to the following link). We now automatically capture stats blobs, and therefore, these manual steps are no longer required:


DBCC CLONEDATABASE performs the following validation checks. If any of these checks fail, the operation will fail, and a copy of the database will not be provided.

  • The source database must be a user database.
  • The source database must be online or readable.
  • The clone database name must not already exist.
  • The command must not be part of a user transaction.

Understanding DBCC CLONEDATABASE syntax

DBCC CLONEDATABASE syntax with optional parameters:

    source_database_name, target_database_name

The following T-SQL script will create a clone of the existing database. The statistics and Query Store data are included automatically.

DBCC CLONEDATABASE ('Source', 'Destination');

The following messages are provided upon completion:

Figure 1.25: Cloned database output
Figure 1.24: Cloned database output

To exclude statistics, you rewrite the code to include WITH NO_STATISTICS:

DBCC CLONEDATABASE ('Source', 'Destination_NoStats') 

To exclude statistics and Query Store data, execute the following code:

DBCC CLONEDATABASE ('Source', 'Destination_NoStats_NoQueryStore') 

Making the clone database production-ready

Thus far, the database clones provisioned are purely for diagnostic purposes. The option VERIFY_CLONEDB is required if you want to use the cloned database for production use. VERIFY_CLONEDB will verify the consistency of the new database.

For example:

DBCC CLONEDATABASE ('Source', 'Destination_ProdReady') 

The output is as follows:

Figure 1.26: Verifying the cloned database
Figure 1.25: Verifying the cloned database

Estimate compression for Columnstore Indexes

The stored procedure sp_estimate_data_compression_savings estimates the object size for the requested compression state. Furthermore, you can evaluate potential compression savings for whole tables or parts of tables; we will discuss the available options shortly. Prior to SQL Server 2019, you were unable to use sp_estimate_data_compression_savings for columnstore indexes and, thus, we were unable to estimate compression for columnstore or columnstore_archive.

We have extended the capability for sp_estimate_data_compression_savings to include support for COLUMNSTORE and COLUMNSTORE_ARCHIVE.

sp_estimate_data_compression_savings Syntax

Look at the following T-SQL syntax:

     [ @schema_name = ] 'schema_name'    
   , [ @object_name = ] 'object_name'   
   , [@index_id = ] index_id   
   , [@partition_number = ] partition_number   
   , [@data_compression = ] 'data_compression'   

The following argument descriptions are provided by

Table 1.27: Description of the arguments
Table 1.26: Description of the arguments

There are currently eight available outputs; you will primarily focus on the four outputs related to size.


size_with_current_compression_setting (KB)
size_with_requested_compression_setting (KB)
sample_size_with_current_compression_setting (KB)
sample_size_with_current_requested_setting (KB)

The following is an example of the procedure in action, followed by a comparison of the space savings for page and columnstore compression:

EXEC sp_estimate_data_compression_savings 
    @schema_name = 'dbo', 
    @object_name = 'MySourceTable', 
    @index_id = NULL, 
    @partition_number = NULL, 
    @data_compression = 'PAGE'

Example with PAGE Compression:

Figure 1.28: PAGE Compression
Figure 1.27: PAGE Compression
EXEC sp_estimate_data_compression_savings 
    @schema_name = 'dbo', 
    @object_name = 'MySourceTable', 
    @index_id = NULL, 
    @partition_number = NULL, 
    @data_compression = 'COLUMNSTORE'

Example with COLUMNSTORE compression:

Figure 1.29: COLUMNSTORE compression
Figure 1.28: COLUMNSTORE compression

In this example, page compression has estimated space savings of roughly 45%, and columnstore compression has estimated space savings of 68%.


Troubleshooting page resource waits

A new and exciting feature in SQL Server 2019 is sys.dm_db_page_info. This new dynamic management function (DMF) retrieves useful page information, such as page_id, file_id, index_id, object_id, and page_type, that can be used for troubleshooting and debugging performance issues in SQL Server. Historically, troubleshooting has involved the use of DBCC Page and the undocumented DMF sys.dm_db_page_allocations.

Unlike DBCC Page, which provides the entire contents of a page, sys.dm_db_page_info only returns header information about pages. Fortunately, this will be sufficient for most troubleshooting and performance tuning scenarios.

This section will discuss the following topics:

  • Database State permissions
  • sys.dm_db_page_info parameters
  • New column page_resource in (sys.dm_exec_requests, sys.processes)
  • sys.fn_PageResCracker


First, to leverage this new DMF, we require the VIEW DATABASE STATE permission. The following code can be used to provide access:


There are four required parameters:

sys.dm_db_page_info ( DatabaseId, FileId, PageId, Mode )

The following argument descriptions are provided by

Table 1.30: The description of the arguments
Table 1.29: The description of the arguments

You can execute the function by itself if you have all the requisite parameters. The mode is set to Limited in this example, and this will return NULL values for all description columns:

SELECT OBJECT_NAME(object_id) as TableName,* 
FROM SYS.dm_db_page_info(6, 1, 1368, 'Limited')

The output is as follows:

Figure 1.31: Output with LIMITED mode
Figure 1.30: Output with LIMITED mode

Using the Detailed mode, you will get much more descriptive information than provided in the previous example. In this example, you can see that the NULL values have been replaced with descriptive information.

SELECT OBJECT_NAME(object_id) as TableName,* 
FROM SYS.dm_db_page_info(6, 1, 1368, 'Detailed')

The output is as follows:

Figure 1.32: Output with Detailed mode
Figure 1.31: Output with Detailed mode

To see a full list of all the columns returned, go to


In the previous example, you saw how to pass parameters to this new function manually. Fortunately, the parameters can be directly retrieved from sys.dm_exec_requests or sys.processes. To make this work, we added a new column called page_resource. The page_resource column returns the page ID, the file ID, and the database ID. It is also important to highlight that the new page_resource column in sys.dm_exec_request will be NULL when WAIT_RESOURCE does not have a valid value.

However, the page_resource column stores the data as an 8-byte hexadecimal value that needs to be converted. Therefore, we have added a new function called sys.fn_pagerescracker. This function returns the page ID, the file ID, and the database ID for the given page_resource value.

It is important to note that we require the user to have VIEW SERVER STATE permission on the server to run sys.fn_PageResCracker.

In this example, the page_resource column is being passed into the sys.fn_PageResCracker function, and then the database ID, file ID, and Page ID are passed to sys.dm_db_page_info:

SELECT OBJECT_NAME(page_info.object_id) AS TableName,page_info.* 
FROM sys.dm_exec_requests AS d 
CROSS APPLY sys.fn_PageResCracker (d.page_resource) AS r 
CROSS APPLY sys.dm_db_page_info(r.db_id, r.file_id, r.page_id, 
'Detailed') AS page_info

The output is as follows:

Figure 1.33: Page resource column is being passed into a function
Figure 1.32: Page resource column is being passed into a function

You can read more here:

About the Authors
  • Kellyn Gorman

    Kellyn Gorman is a Customer Success Engineer at Microsoft specializing on Oracle and data platforms on Azure. An alumnus of both Microsoft's Idera ACE and Oracle ACE Director programs, a Friend of Redgate, she has been recognized with numerous awards over the years for her technical contributions and community volunteerism. She is one of only six women part of the Oak Table, a network for the Oracle scientist. She has extensive experience in environment migrations, optimization, automation and architecture. Kellyn is well known for her technical content and thought leadership through her presentations, keynotes, webinars, publications and engaging with her on social media presence as DBAKevlar or her blog.

    Browse publications by this author
  • Allan Hirt

    SQLHA, LLC founder, consultant, trainer, author, and business continuity, infrastructure, and virtualization expert Allan Hirt has been working with SQL Server since 1992 when it was still a Sybase product as well as clustering in Windows Server since the late 1990s when it was known as Wolfpack. Currently a dual Microsoft MVP (Data Platform; Cloud and Datacenter Management) as well as a VMware vExpert, Allan works with all sizes of customers no matter if they are on premises or in the public cloud and delivers training and speaks at events over the world.

    Browse publications by this author
  • Dave Noderer

    Dave Noderer is a software developer and the CEO / President and founder of Computer Ways, Inc., a software development company since 1994. He is the leader of FlaDotNet holding monthly developer meetup, was a Microsoft MVP for 16 years, and is a co-founder of the Microsoft Cloud South Florida User Group. In 2005 he held the first South Florida Code Camp. This annual, free event now called the South Florida Software Developer Conference attracts over 1000 developers.

    Browse publications by this author
  • Mitchell Pearson

    Mitchell Pearson has worked as a Data Platform Consultant and Trainer for the last 8 years. Mitchell has authored books on SQL Server, Power BI and the Power Platform. Data Platform experience includes designing and implementing enterprise level Business Intelligence solutions with the Microsoft SQL Server stack (T-SQL, SSIS, SSAS, SSRS), the Power Pla

    Browse publications by this author
  • James Rowland-Jones

    James Rowland-Jones is a principal consultant for The Big Bang Data Company. His focus and passion is to architect and deliver highly scalable analytical platforms that are creative, simple, and elegant in their design. James specializes in big data warehouse solutions that leverage both SQL Server PDW and Hadoop ecosystems. James is a keen advocate for the SQL Server community, both internationally and in the United Kingdom. He currently serves on the board of directors for PASS and sits on the organizing committee for SQLBits (Europe's largest event for the Microsoft Data Platform). James has been awarded Microsoft's MVP accreditation since 2008 for his services to the community.

    Browse publications by this author
  • Dustin Ryan

    Dustin Ryan is a Senior Cloud Solution Architect at Microsoft. He has worked in the business intelligence and data warehousing field since 2008, has spoken at community events such as SQL Saturday, SQL Rally, and PASS Summit, and has a wide range of experience designing and building data solutions using SQL Server and Azure. Prior to his time at Microsoft, Dustin worked as a business intelligence consultant and trainer for Pragmatic Works. He is also an author, contributor and technical editor of books. Dustin resides outside Jacksonville, Florida with his wife, three children, and three-legged cat and enjoys spending time with his family and serving at his local church.

    Browse publications by this author
  • Arun Sirpal

    Arun Sirpal is a Microsoft MVP specialized within the Microsoft Data Platform which includes SQL Server 2008 R2-2019 (performance tuning, HA/DR, T-SQL, security, backups and general DBA tasks) and Microsoft Azure based technologies such as SQL Server within Azure virtual machines (IaaS), Azure SQL Database (PaaS), SQL database elastic pools, managed instances, Azure SQL DW, Azure Synapse, ADFv2, Cosmos DB (NoSQL), and Azure Cloud Shell. He is a frequent writer, blogger, speaker and technical reviewer for subjects based on SQL Server and Microsoft Azure - Data Platform. He is also known as BlobEater.

    Browse publications by this author
  • Buck Woody

    Buck Woody works on the Azure Data Services team at Microsoft, and uses data and technology to solve business and science problems. With over 35 years of professional and practical experience in computer technology, he is also a popular speaker at conferences around the world; author of over 700 articles and eight books on databases, machine learning, and R, he also sits on various Data Science Boards at two US Universities, and specializes in advanced data analysis techniques. He is passionate about mentoring and growing the next generation of data professionals.

    Browse publications by this author
Latest Reviews (1 reviews total)
I purchased both a hard copy and electronic copies of the Learn SQL Database Programming. I have to admit the books is reasonably priced, a good read and examples should help individuals learn SQL. Unfortunately, the continuing errors are making it difficult to learn SQL. Currently, I am looking for an alternative book about SQL Database Programming. Many of the codes are incomplete, inaccurate, and just do not work as advertised. I followed the instructions to create the Manager and Teams tables, the codes did not work. I kept receiving errors. Additionally, with I tried to import the data from the csv files to the Manager and Teams tables, the imports did not work. I received error in manager table import indicating the year was out of date range 1871 to 2155. The manager.csv file has dates from 1871 to 2018. I finally managed to get the import to work, however, only files with 1901 and higher dates imported (2974 of 3504 - 530 records did not upload). Same problem with the teams file and only 49 records downloaded out of 2895. Until these issues are resolved, I cannot continue because the rest of the learning will be negatively impacted by the incomplete data imports.
Introducing Microsoft SQL Server 2019
Unlock this book and the full library FREE for 7 days
Start now