SQL Server 2017 features
SQL
Server 2017 Database Engine
SQL
Server 2017 includes many new Database Engine features, enhancements, and
performance improvements.
·
CLR
assemblies can now be added to a
white list, as a workaround for the clr strict security feature described in CTP 2.0. sp_add_trusted_assembly, sp_drop_trusted_assembly,
and sys.trusted_asssemblies are
added to support the white list of trusted assemblies (RC1).
·
Resumable
online index rebuild resumes an online index
rebuild operation from where it stopped after a failure (such as a failover to
a replica or insufficient disk space), or pauses and later resumes an online
index rebuild operation.
·
The IDENTITY_CACHE option
for ALTER DATABASE SCOPED CONFIGURATION allows you to avoid gaps in the values
of identity columns if a server restarts unexpectedly or fails over to a
secondary server.
·
A new generation of query processing
improvements that will adapt optimization strategies to your application
workload’s runtime conditions. For this first version of the adaptive
query processing feature family, we have three new improvements: batch
mode adaptive joins, batch mode memory grant feedback,
and interleaved execution for multi-statement table valued
functions.
·
Automatic
database tuning provides insight into
potential query performance problems, recommends solutions, and can
automatically fix identified problems.
·
New graph database
capabilities for modeling many-to-many relationships include new CREATE
TABLEsyntax for creating node and edge
tables, and the keyword MATCH for queries.
·
An sp_configure option called clr strict security is enabled by
default to enhance the security of CLR assemblies.
·
Setup now allows specifying initial
tempdb file size up to 256 GB (262,144 MB) per file, with a
warning if the file size is set greater than 1GB with IFI not enabled.
·
The modified_extent_page_count column
in sys.dm_db_file_space_usage tracks
differential changes in each database file, enabling smart backup solutions
that perform differential backup or full backup based on percentage of changed
pages in the database.
·
SELECT
INTO T-SQL syntax now supports
loading a table into a FileGroup other than the user's default by using
the ON keyword.
·
Cross database transactions are now
supported among all databases that are part of an Always On
Availability Group, including databases that are part of same instance.
·
New Availability Groups functionality
includes clusterless support, Minimum Replica Commit Availability Groups
setting, and Windows-Linux cross-OS migrations and testing.
·
New dynamic management views:
o sys.dm_db_log_stats exposes
summary level attributes and information on transaction log files, helpful for
monitoring transaction log health.
o sys.dm_tran_version_store_space_usage tracks
version store usage per database, useful for proactively planning tempdb sizing
based on the version store usage per database.
o sys.dm_db_log_info exposes VLF information to monitor, alert, and avert
potential transaction log issues.
o sys.dm_db_stats_histogram is a new dynamic management view for examining
statistics.
o sys.dm_os_host_info provides
operating system information for both Windows and Linux.
·
The Database Tuning Advisor
(DTA) has additional options and improved performance.
·
In-memory
enhancements include support for computed
columns in memory-optimized tables, full support for JSON functions in natively
compiled modules, and the CROSS APPLY operator in natively compiled modules.
·
New string functions are
CONCAT_WS, TRANSLATE, and TRIM, and WITHIN GROUP is now supported for the
STRING_AGG function.
·
There are new bulk access
options (BULK INSERT and OPENROWSET(BULK...) ) for CSV and Azure Blob
files.
·
Memory-optimized
object enhancements include sp_spaceused and
elimination of the 8 index limitation for memory-optimized tables, sp_rename
for memory-optimized tables and natively compiled T-SQL modules, and CASE and
TOP (N) WITH TIES for natively compiled T-SQL modules. Memory-optimized
filegroup files can now be stored, backed up and restored on Azure Storage.
(CTP 1.0)
·
DATABASE
SCOPED CREDENTIAL is a new class of securable,
supporting CONTROL, ALTER, REFERENCES, TAKE OWNERSHIP, and VIEW DEFINITION
permissions. ADMINISTER DATABASE BULK OPERATIONS is now visible in
sys.fn_builtin_permissions.
·
Database COMPATIBILITY_LEVEL
140 is added.
SQL
Server 2017 Integration Services (SSIS)
·
The new Scale Out feature
in SSIS has the following new and changed features.
o Scale Out Master now supports high availability.
o The failover handling of the execution logs from Scale Out
Workers is improved.
o The parameter runincluster of the stored
procedure [catalog].[create_execution] is renamed to runinscaleout for
consistency and readability.
o The SSIS Catalog has a new global property to specify the
default mode for executing SSIS packages.
·
In the new Scale Out for
SSIS feature, you can now use the Use32BitRuntime parameter
when you trigger execution.
·
SQL Server 2017 Integration Services
(SSIS) now supports SQL Server on Linux, and a new package lets you
run SSIS packages on Linux from the command line.
·
The new Scale Out for SSIS feature
makes it much easier to run SSIS on multiple machines.
·
OData Source and OData Connection
Manager now support connecting to the OData feeds of Microsoft Dynamics AX
Online and Microsoft Dynamics CRM Online.
SQL
Server 2017 Master Data Services (MDS)
·
Experience and performance are improved
when upgrading from SQL Server 2012, SQL Server 2014, and SQL Server 2016 to
SQL Server 2017 Master Data Services.
·
You can now view the sorted lists of
entities, collections and hierarchies in the Explorer page of
the Web application.
·
Performance is improved for staging
millions of records using the staging stored procedure.
·
Performance is improved when
expanding the Entities folder on the Manage Groups page
to assign model permissions. The Manage Groups page is located
in the Security section of the Web application. For more
information about the performance improvement,
SQL
Server 2017 Analysis Services (SSAS)
SQL
Server Analysis Services 2017 introduces many enhancements for tabular models.
These include:
·
Tabular mode as the default
installation option for Analysis Services.
·
Object-level security to secure the
metadata of tabular models.
·
Date relationships to easily create
relationships based on date fields.
·
New Get Data (Power
Query) data sources, and existing DirectQuery data sources support for M
queries.
·
DAX Editor for SSDT.
·
Encoding hints, an advanced feature
for optimizing data refresh of large in-memory tabular models.
·
Support for the 1400
Compatibility level for tabular models. To create new or upgrade
existing tabular model projects to the 1400 compatibility level
·
A modern Get Data experience
for tabular models at the 1400 compatibility level.
·
Hide Members property to hide blank members in ragged hierarchies.
·
New Detail Rows end-user
action to Show Details for aggregated information. SELECTCOLUMNS and DETAILROWS functions for creating
Detail Rows expressions.
·
DAX IN operator for
specifying multiple values.
SQL
Server 2017 Reporting Services (SSRS)
SQL
Server Reporting Services is no longer available to install through SQL Server
setup.
·
Comments are now available for
reports, to add perspective and collaborate with others. You can also include
attachments with comments.
·
In the latest releases of Report Builder
and SQL Server Data Tools, you can create native DAX queries against supported
SQL Server Analysis Services tabular data models by dragging and dropping
desired fields in the query designers.
·
To enable development of modern
applications and customization, SSRS now supports a fully OpenAPI compliant
RESTful API.
.
Machine
Learning in SQL Server 2017
SQL
Server R Services has been renamed SQL Server Machine Learning Services,
to reflect support for Python in addition to the R language. You can use Machine
Learning Services (In-Database) to run R or Python scripts in SQL Server, or
install Microsoft Machine Learning Server (Standalone) to
deploy and consume R and Python models that don't require SQL Server.
SQL
Server developers now have access to the extensive Python ML and AI libraries
available in the open-source ecosystem, along with the latest innovations from
Microsoft:
·
revoscalepy - This Python equivalent of RevoScaleR includes
parallel algorithms for linear and logistic regressions, decision tree, boosted
trees and random forests, as well as a rich set of APIs for data transformation
and data movement, remote compute contexts, and data sources.
·
microsoftml - This state-of-the-art package of machine learning
algorithms and transforms with Python bindings includes deep neural networks,
fast decision trees and decision forests, and optimized algorithms for linear
and logistic regressions. You also get pre-trained models based on ResNet
models that you can use for image extraction or sentiment analysis.
·
Python
operationalization with T-SQL -
Deploy Python code easily by using the stored procedure sp_execute_external_script. Get great
performance by streaming data from SQL to Python processes and using MPI ring
parallelization.
·
Python in
SQL Server compute contexts - Data
scientists and developers can execute Python code remotely from their
development environments to explore data and develop models without moving data
around.
·
Native
scoring - The PREDICT function in
Transact-SQL can be used to perform scoring in any instance of SQL Server 2017,
even if R isn't installed. All that's required is that you train the model
using one of the supported RevoScaleR and revoscalepy algorithms and save the
model in a new, compact binary format.
·
Package
management - T-SQL now supports the
CREATE EXTERNAL LIBRARY statement, to give DBAs greater management over R
packages. Use roles to control prviate or shared package access, store R
packages in the database and share them among users.
·
Performance
improvements - The stored procedure sp_execute_external_script has
been optimized to support batch mode execution for columnstore data.
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