SQL Server 2017 is considered a major release in the history of the SQL Server life cycle for various reasons. From my personal point of view, SQL Server 2017 is indeed an interesting release. After writing lot about it and testing various features of SQL Server 2017, I’d like to walk you through someof its interesting features.
SQL Server 2017 …
is now on the platform of or choice; SQL Server 2017 on several linux distros, that can run on Docker containers, and SQL VM in Azure, along with the good ol’ SQL Server on windows. expands its reach to support Graph database technology includes automatic database tuning supports Machine Learning by adopting python contains new dynamic management views and functions has new string functions And more…
Time to dig in
Let’s uncover the mystery behind the faster development and deployment of SQL Server. The file size of the SQL Server executable, sqlservr.exe, is ~3 MB in SQL Server 2017, compared to ~64 MB in SQL Server 2008 R2. The SQL Server team realized the importance of decoupling and modularizing the surface area code into much smaller components. The entire architecture is broken down into multiple smaller micro-services so that the deployments and integration are made on each of the respective components, and that the process is seamless.
Starting with SQL Server 2017, Microsoft is no longer using the old model of release under which cumulative updates (CUs) were released every two months and service packs (SPs) containing all the fixes from the preceding CUs were released once a year. This new model allowed shortening the release cycle and features getting added to SQL Database and SQL Server faster than ever before. Most recently, the SQL Server engineering team managed to ship SQL Server 2017 along with the new cross-platform support within 15 months after the release of SQL Server 2016. This is in contrast with the legacy three-to-five-year shipping cycles of the past.
One would feel why SQL Server2016 and 2017 are such big releases in a matter of a difference of just a few months. However, starting with 2017, Microsoftis moving to a rapid-release cadencewith enhanced functionality and new featuresbeing rolled out annually insteadof having to wait two or three years between versions.
SQL Server 2017 on LinuxClearly, two of the biggest changes with SQL Server 2017 are its support for Linux and Docker containers, both of which could have a big impact on the database trends of the future. The release of SQL Server 2017 for Linux has proven to be a step towards extending the capabilities to a high-performance platform, which has already set some TPC benchmark records.
In this release, we can look forwardto running SQL Server instances on Linux computers. Many of our previous articles show you how to get startedby creating a RedHat virtual machine in Microsoft Azure,as well as installing SQL Server on a CentOS machine.Here are some of the articles.
Installation of SQL Server vNext CTP on Linux Distribution CentOS 7 is discussed in this article: Installation of SQL Server vNext CTP on Linux Distribution CentOS 7 A Quick start Guide to Managing SQL Server 2017 on CentOS/RHEL Using the SSH Protocol is discussed in this article How to configure SQL Server 2017 on Linux with mssql-conf and other available tools is discussed in this article Python support in ML (Machine Learning)The next great feature that’s new in SQL Server 2017 is the additionof support for the Python scripting language,which is in addition to R, support for which was added in 2016, to forma new machine-learning services package. There’s no doubt that AI is now among the hottest buzzwords in IT. It seems like almost every IT product is now suddenly AI-enabled, and SQL Server is no exception in that regard. In April 2017, SQL Server was officially touted as “The first RDBMS with built-in AI!” Some of my articles that talk about these capabilities are:
Kick start Python in SQL Server is discussed in this SQL Shack article: How to use Python in SQL Server 2017 to obtain advanced data analytics Read about the basic data interpolation techniques here: Data Interpolation and Transformation using Python in SQL Server 2017 The benefits and importance it brings to DBAs are discussed in The importance of Python in SQL Server Administration Python and Powershell is two sides of the coin, according to this article: Why would a SQL Server DBA be interested in Python? Graph DatabasesThis section explores the integration of Graph database components in SQL Server 2017. The pointer at the end of this section includes a working data set of relational and graph databases, and data models. In addition, it explains how to integrate graph databases with relational databases and how to convert the relational tuples into a graph data set.
A key concept of graph databases are edges and nodes. The relationships allow entities to be linked together directly, and in many cases retrieved with one operation. In some cases, due to stringent data modeling design, the relational data model may not be a good fit as it incurs costly join operations. In the past, the lack of viable alternatives and the lack of support made the graph model a difficult alternative, but that is no more the case.
We’ll also look at the integrationof graph database support to help model many-to-many relationships as nodes and edges,rather than with the traditional relational model. The footprint of the graph database technology in the IT industry is clearly visible today, and for good reasons such as adding efficiency to social recommendations, IT network analysis, fraud detection, product recommendation, and so on.
Go ahead, look at a use case we picked to demonstrate the features of Graph databases in this article: An introduction to a SQL Server 2017 graph database
String functions One of the major capabilities of modern computers is processing the human language. Basically, the strings are transformed into the code which is then processed by a machine. The built-in string functions have always been efficient in handling string literals. It is possible to find and alter string values using various options. However, string functions and string manipulation consume most of the query execution time in decoding the various parts of the character literals. SQL Server 2017 offers various new string manipulation functions, which have been in the talk for many of its features that simplify a developer’s life; no more writing long T-SQL statements with temporary tables and complex logi