SQL Server 2017 Community Driven Features

Noviembre18, 2017
Concepción, Chile
#sqlsatconce

Nombre Speaker: Javier Villegas
Cargo : DBA Manager en MSC
Twitter: @javier_vill
Email: javier.ignacio.villegas@gmail.com
Blog: sql-javier-villegas.blogspot.com.ar
SQL Server 2017 Community-driven enhancements

Sea cual sea su pasión datos – ¡hay uncapítulo virtual para usted!
24-01-2018 | SQL Saturday #684 – Concepcion, Chile3 |

Sitio de la Comunidad en Chile
chile.pass.org

Sitio de la Comunidad Global
www.pass.org

Patrocinadores del SQL Saturday

Javier Villegas
DBA Manager at Mediterranean Shipping Company (MSC)
Microsoft MVP Data Platform
Involved with the Microsoft SQL Server since SQL Server 6.5
MCP and MCTS
Blogger and MSDN Forums contributor
Specialization in SQL Server Administration, Performance Tuning and
High Availability
Frequent Speaker at NetConf , SQL Saturdays and PASS Virtual Groups
@javier_vill http://sql-javier-villegas.blogspot.com.ar/https://ar.linkedin.com/in/javiervillegas

SQL Server 2017
Community driven
enhancements

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1010
0101
0010
{ }
T-SQL
Java
C/C++
C#/VB.NET
PHP
Node.js
Python
Ruby
Heterogenous
environments
Multiple data types
Different
development languages
On-premises, cloud,
and hybrid environments

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On the platform of your choice
SQL Server 2017

What’s coming in SQL Server 2017
Supported platforms
Linux Containers
Windows
Windows Server
• RedHat Enterprise Linux (RHEL) 7.3
• SUSE Enterprise Linux (SLES) v12 SP2
• Ubuntu 16.04 & 16.10
• Possibly other Linux distributions
• Docker: Windows & Linux containers
• Windows Server / Windows 10

Agenda
• Introduction
• Smart Differential Backup
• Smart Transaction Log Backup
• SELECT INTO …. ON Filegroup
• Tempdb Setup improvements
• Tempdb monitoring and planning
• Transaction log monitoring and diagnostics
• Improved backup performance for small databases on high-end servers
• Processor information in sys.dm_os_sys_info
• Capturing Query Store runtime statistics in DBCC CLONEDATABASE
• Resumable Online Index Rebuild
• Automatic plan correction
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Introduction
Although SQL Server 2016 runs faster, SQL Server 2017 promises to run even faster and
empower customers to run smarter with intelligent database features like:
- The ability to run advanced analytics using Python in a parallelized and highly scalable
way
- The ability to store and analyze graph data
- Adaptive query processing
- Resumable Online indexing
- Allow customers to deploy it on the platform of their choice (Windows or Linux) - Docker
SQL Server is one of the most popular DBMS among the SQL community and is a preferred
choice of RDBMS among customers and ISVs owing to its strong community support
In SQL Server 2017, there are several customer delighters and community-driven
enhancements based on the learnings and feedback from customers and the community
from in-market releases of SQL Server.
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Smart Differential Backup
A new column modified_extent_page_count is introduced in sys.dm_db_file_space_usage
to track differential changes in each database file of the database.
The new column modified_extent_page_count will allow DBAs, the SQL community and
backup ISVs to build smart backup solutions, which perform differential backup only
if percentage changed pages in the database are below a threshold (say 70 to 80 percent),
otherwise full database backup is performed.
With a large number of changes in the database, cost and time to complete differential
backup is similar to that of full database backup, so there is no real benefit to differential
backup in this case; instead, it can increase database restore time.
By adding this intelligence to the backup solutions, customers can now save on restore and
recovery time while using differential backups.
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Consider a scenario where you previously had a backup plan to take full database backup on
weekends and differential backup daily.
In this case, if the database is down on Friday, you will need to restore full database backup
from Sunday, differential backups from Thursday and then T-log backups of Friday.
By leveraging modified_extent_page_count in your backup solution, you can now take full
database backup on Sunday and let’s say by Wednesday, 90 percent of pages have changed,
the backup solution takes full database backup rather than differential backup since the time
and resources consumed remain the same.
If database is down on Friday, you will restore the full database backup from Wednesday,
small differential backup from Thursday and T-log backups from Friday to restore and
recover the database much quicker compared with the previous scenario.
This feature was requested by customers and community in connect item 511305.
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USE <database-name>
GO
select CAST(ROUND((modified_extent_page_count*100.0)/allocated_extent_page_count,2) as
decimal(2,2)) from sys.dm_db_file_space_usage
GO
select
CAST(ROUND((SUM(modified_extent_page_count)*100.0)/SUM(allocated_extent_page_count),2)
as decimal(2,2)) as '% Differential Changes since last backup' from
sys.dm_db_file_space_usage
GO

Smart Transaction log Backup
In the release of SQL Server 2017, a new DMF sys.dm_db_log_stats(database_id) is
available, it exposes a new column log_since_last_log_backup_mb.
The column log_since_last_log_backup_mb empowers DBAs, the SQL community and
backup ISVs to build intelligent T-log backup solutions, which take backup based on the
transactional activity on the database.
This intelligence in the T-log backup solution will ensure the transaction log size doesn’t
grow due to a high burst of transactional activity in short time if the T-log backup frequency
is too low.
It also help avoid situations where default periodic interval for transaction log backup
creates too many T-log backup files even when there is no transactional activity on the
server adding to the storage, file management and restore overhead.
17

SELECT INTO …. ON Filegroup
One of the highly voted connect items and highly requested feature requests
from the SQL community to support loading tables into specified filegroups
while using SELECT INTO is now made available in SQL Server 2017
SELECT INTO is commonly used in data warehouse (DW) scenarios for
creating intermediate staging tables, and inability to specify filegroup was
one of the major pain points to leverage and load tables in filegroups
different from the default filegroup of the user loading the table. Starting
SQL Server 2017, SELECT INTO T-SQL syntax supports loading a table into a
filegroup other than a default filegroup of the user using the ON <Filegroup
name> keyword in TSQL syntax shown below:
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SELECT INTO …. ON Filegroup
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ALTER DATABASE [AdventureWorksDW2016] ADD FILEGROUP FG2
select * from sys.database_files
ALTER DATABASE [AdventureWorksDW2016]
ADD FILE
(
NAME= 'FG2_Data',
FILENAME = 'S:sql_dataAdventureWorksDW2016_Data1.mdf'
)
TO FILEGROUP FG2;
GO
SELECT * INTO [dbo].[FactResellerSalesXL] ON FG2 from [dbo].[FactResellerSales];

Tempdb setup improvements
One of the constant pieces of feedback, the SQL community and the field
after doing the SQL Server 2016 setup improvements is to uplift the
maximum initial file size restriction of 1 GB for tempdb in setup.
For SQL Server 2017, the setup allows initial tempdb file size up to 256 GB
(262,144 MB) per file with a warning to customers if the file size is set to a
value greater than 1 GB and if “Instant File Initialization” (IFI) is not enabled.
It is important to understand the implication of not enabling instant file
initialization (IFI) where setup time can increase exponentially depending on
the initial size of tempdb data file specified.
IFI is not applicable to transaction log size, so specifying a larger value of
transaction log can increase the setup time while starting up tempdb during
setup irrespective of the IFI setting for the SQL Server service account.

Tempdb monitoring and planning
The SQL Server Tiger team surveyed the SQL community to identify common
challenges experienced by customers with tempdb.
Tempdb space planning and monitoring were found to be top challenges
experienced by customers with tempdb.
As a first step to facilitate tempdb space planning and monitoring, a new
performant DMV sys.dm_tran_version_store_space_usage is introduced in
SQL Server 2017 to track version store usage per database.
This new DMV is useful in monitoring tempdb for version store usage for
DBAs who can proactively plan tempdb sizing based on the version store
usage requirement per database without any performance toll or overheads
of running it on production servers.

Transaction log monitoring and diagnostics
One of the highly voted connect items and highly requested requests from the
community is to expose transaction log VLF information in DMV. T-log space
issues
High VLFs and log shrink issues are some of the common challenges
experienced by DBAs. Some of the monitoring ISVs have asked for DMVs to
expose VLF information and T-log space usage for monitoring and alerting. A
new DMV sys.dm_db_log_info is introduced in SQL Server 2017 to expose the
VLF information similar to DBCC LOGINFO to monitor, alert and avert potential
T-log issues.
In addition to sys.dm_db_log_info, a new DMF sys.dm_db_log_stats(dbid) is
released , which expose aggregated transaction log information per database.

Improved backup performance for small
databases on high-end servers
After migrating an existing in-market release of SQL Server to high-end servers, customers may
experience a slowdown in backup performance when taking backups of small to medium-size
databases.
This happens as we need to iterate the buffer pool to drain the ongoing I/Os.
The backup time is not just the function of database size but also a function of active buffer pool size.
In SQL Server 2017, we have optimized the way we drain the ongoing I/Os during backup, resulting
in dramatic gains for small to medium-size databases.
We have seen more than 100x improvement when taking system database backups on a 2TB
machine.
More extensive performance testing results on various database sizes is shared below.
The performance gain reduces as the database size increases as the pages to backup and backup IO
take more time compared with iterating buffer pool.
This improvement will help improve the backup performance for customers hosting multiple small
databases on a large high-end server with large memory.

Processor information in sys.dm_os_sys_info
Another highly requested feature among customers, ISVs and the
SQL community to expose processor information in
sys.dm_os_sys_info is released in SQL Server 2017
The new columns allows to programmatically query processor
information for the servers hosting SQL Server instance, which is
useful in managing large deployments of SQL Server.
New columns exposed in sys.dm_os_sys_info DMV are
socket_count, core_count, and cores_per_socket.

Resumable Online Index Rebuild
With this feature, you can resume a paused index rebuild operation from where the rebuild
operation was paused rather than having to restart the operation at the beginning.
In addition, this feature rebuilds indexes using only a small amount of log space.
You can use the new feature in the following scenarios:
• Resume an index rebuild operation after an index rebuild failure, such as after a database
failover or after running out of disk space. There is no need to restart the operation from the
beginning. This can save a significant amount of time when rebuilding indexes for large
tables.
• Pause an ongoing index rebuild operation and resume it later. For example, you may need to
temporarily free up system resources to execute a high priority task or you may have a single
maintenance window that is too short to complete the operation for a large index. Instead of
aborting the index rebuild process, you can pause the index rebuild operation and resume it
later without losing prior progress.
• Rebuild large indexes without using a lot of log space and have a long-running transaction
that blocks other maintenance activities. This helps log truncation and avoid out-of-log errors
that are possible for long-running index rebuild operations.

Resumable Online Index Rebuild
ALTER INDEX IDX_2_5 ON [dbo].[A_Table02]
REBUILD WITH
( RESUMABLE = ON, ONLINE = ON );
SELECT total_execution_time, percent_complete, page_count,
object_id,index_id,name,sql_text,last_max_dop_used,partition_number,state
,state_desc,start_time,last_pause_time,total_execution_time
FROM sys.index_resumable_operations;

Automatic plan correction
Automatic plan correction is a new automatic tuning feature in SQL Server 2017
that identifies SQL query plans that are worse than previous one, and fix
performance issues by applying previous good plan instead of the regressed
one.
In some cases, new plan that is chosen might not be better than the previous
plans. This is rare situation and might happen if an optimal plan is not included
in a list of plans that will be considered as potential candidates. In other cases,
SQL Server might choose the best plan for the current T-SQL query, but the
selected plan might not be optimal if the same T-SQL queries is executed with
different parameter values. In that case, SQL Server might reuse the plan that is
compiled and cached after first execution even if the plan is not optimal for the
other executions. These problems are known as plan regressions.

If you identify that previous plan had better performance, you might
explicitly force SQL Server to use the plan that you identified as
optimal for some specific query using sp_force_plan procedure:
As a next step, you can let SQL Server 2017 to automatically correct
any plan that regressed.
EXEC sp_force_plan @query_id = 1223, @plan_id = 1987
ALTER DATABASE current SET AUTOMATIC_TUNING ( FORCE_LAST_GOOD_PLAN = ON )

This statement will turn-on automatic tuning in the current database and instruct SQL
Server to force last good plan if it identifies plan performance regression while the current
plan is executed.
SQL Server 2017 will continuously monitor and analyze plan performance, identify new
plans that have worse performance than the previous ones, and force last know good plan
as a corrective action.
SQL Server 2017 will keep monitoring performance of the forced plan and if it does not
help, query will be recompiled.
At the end, SQL Server 2017 will release the forced plan because query optimizer
component should find optimal plan in the future.
ALTER DATABASE current SET AUTOMATIC_TUNING ( FORCE_LAST_GOOD_PLAN = ON )

String functions
A collection of new string function in SQL Server 2017
TRIM
Removes the space character char(32) or other specified characters from the start or end of a string
TRANSLATE
Allows us to perform a one-to-one, single-character substitution in a string
CONCAT_WS()
Stands for Concatenate with Separator, and is a special form of CONCAT(). The first argument is the
separator—separates the rest of the arguments. The separator is added between the strings to be
concatenated. The separator can be a string, as can the rest of the arguments be. If the separator is NULL,
the result is NULL.
STRING_AGG
Aggregate functions compute a single result from a set of input values. With the prior versions of SQL, string
aggregation was possible using T-SQL or CLR, but there was no inbuilt function available for string
aggregation, with a separator placed in between the values.

Demostración

Other features
• Graph Data Processing with SQL Server 2017
https://blogs.technet.microsoft.com/dataplatforminsider/2017/04/20/graph-data-processing-with-sql-server-2017/
• Showplan enhancements in SQL Server 2017
https://blogs.msdn.microsoft.com/sql_server_team/sql-server-2017-showplan-enhancements/
• Adaptive Query Processing
https://www.youtube.com/watch?v=szTmo6rTUjM

Preguntas
Javier Villegas
SQL Server 2017 Community Driven Enhancements
@javier_vill
http://sql-javier-villegas.blogspot.com.ar
https://ar.linkedin.com/in/javiervillegas

Gracias por vuestra
asistencia!

SQL Server 2017 Community Driven Features

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