SQL Server 2017

Dmitry Piliugin

SQL Server 2017: How to Get a Parallel Plan

April 28, 2018 by

SQL Server chooses parallel plans based on the costing (there are also some other factors that should be met for the plan that it can go parallel). Sometimes serial plan is slightly cheaper than a parallel, so it is assumed to be faster and picked by the optimizer, however, because the costing model is just a model it is not always true (for a number of reasons, enlisted in Paul’s article below) and parallel plan runs much faster.

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Dmitry Piliugin

SQL Server 2017: Statistics to Compile a Query Plan

April 28, 2018 by

While preparing the post about Adaptive Joins, I’d like to share a quick post about the hidden gem in SQL Server 2017 CTP 2.0, discovered recently. In this short post, we will look at how you can determine what statistics are used by the optimizer during a plan compilation in SQL Server 2017.

Prior to SQL Server 2017, there were two ways how you could do it, both undocumented and involving undocumented trace flags.

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Dmitry Piliugin

SQL Server 2017: Sort, Spill, Memory and Adaptive Memory Grant Feedback

April 27, 2018 by

Sorting is one of the key operations in query processing. SQL Server can achieve sorting by either reading data in an ordered fashion, for example, performing ordered Rowstore index scan or performing an explicit sort. If we want to get sorted data from a Columnstore index, the only option is to perform a sort explicitly with a Sort operator in a query plan, because a Columnstore index has no particular order, at least at the moment of writing this post.

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Dmitry Piliugin

SQL Server 2017: Interleaved Execution for mTVF

April 27, 2018 by

In this post, we are going to look at the new feature in SQL Server 2017 – interleaved execution. You need to install SQL Server 2017 CTP 1.3 to try it, if you are ready, let’s start.

Now, when a CTP 2.0 of SQL Server 2017 is out, you don’t need to turn on the undocumented TF described further, and the plans are also different, so the examples from this post use CTP.1.3, probably not actual at the moment (I was asked to hold this post, until the public CTP 2 is out, and interleaved execution is officially announced). However, the post demonstrates Interleaved execution details and might be still interesting.

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Dmitry Piliugin

SQL Server 2017: Scalar Subquery Simplification

April 26, 2018 by

Nowadays a lot of developers use Object-Relational Mapping (ORM) frameworks. ORM is a programming technique that maps data from an object-oriented to a relational format, i.e. it allows a developer to abstract from a relational database (SQL Server, for example), use object-oriented language (C#, for example) and let an ORM to do all the “talks” to a database engine by generating query texts automatically. ORMs are not perfect, especially if they are used in a wrong way. Sometimes they generate inefficient queries, e.g. a query with redundant expressions. SQL Server has a mechanism to struggle with that inefficiency called a query simplification.

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Dmitry Piliugin

SQL Server 2017: Columnstore in-place updates

April 26, 2018 by

In this post, I continue the exploration of SQL Server 2017 and we will look at the nonclustered columnstore index updates.

Columnstore index has some internal structures to support updates. In 2014 it was a Delta Store – to accept newly inserted rows (when there will be enough rows in delta store, server compresses it and switches to Columnstore row groups) and a Deleted Bitmap to handle deleted rows. In 2016 there are more internal structures, Mapping Index for a clustered Columnstore index to maintain secondary nonclustered indexes and a deleted buffer to speed up deletes from a nonclustered Columnstore index.

Updates were always split into insert + delete. But that is now changed, if a row locates in a delta store, now inplace updates are possible. Another change is that it is now possible to have a per row (narrow) plan instead of per index (wide) plan.

Let’s make some experiments.

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Dmitry Piliugin

SQL Server 2017: Columnstore Indexes and Trivial Plan

April 25, 2018 by

Some time ago, SQL Server 2017 was released and issued as CTP. The most exciting release in that CTP was that SQL Server now supports Linux! This is awesome and I consider it to be great news for many people.

I am personally interested in the new features of query processing, and finally I had some time to install the SQL Server 2017 and dig a little bit into it. Currently, it is CTP 1.2 available, and I will use this version for my experiments.

While exploring new extended events, I’ve found an interesting event compilation_stage_statistics and one of the columns of this event was trivial_plan_scanning_cs_index_discarded with the following description “Number of trivial plans discarded or could have been discarded which scan Columnstore index”. That pushed me to do some investigations of the topic.

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Sifiso W. Ndlovu

How to migrate SQL Server 2017 Master Data Services Models into another server

March 23, 2018 by

Often as consultants, we don’t get to work onsite alongside our clients instead we are given copies of clients’ production environment and work on proposed solutions back at our offices. Once development has been completed, we then deploy and integrate our solution back to the client’s production environment. I’ve recently had to adopt a similar offsite development approach whilst working on a project that included development and configuration of master data services. In this article, I will demonstrate how a SQL Server 2017 Master Data Services (MDS) model can be exported from one environment (i.e. MDS Dev) and deployed into another environment (i.e. MDS Prod).

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Sifiso W. Ndlovu

Replace bridge tables in a Data Warehouse with SQL Server 2017 graph database

March 8, 2018 by

Just like in Santa’s Bag of Goodies, every release of SQL Server often has something for everyone – be it enhancements to DMVs for the DBAs, new functions for T-SQL developers or new SSIS control tasks for ETL developers. Likewise, the ability to effectively support many-to-many relationships type in SQL Graph has ensured that there is indeed something in it for the data warehouse developers in SQL Server 2017. In this article, we take you through the challenges of modelling many-to-many relationships in relational data warehouse environments and later demonstrate how data warehouse teams can take advantage of the many-to-many relationship feature in SQL Server 2017 Graph Database to effectively model and support their data warehouse solutions.

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Sifiso W. Ndlovu

Impact of CLR Strict Security configuration setting in SQL Server 2017

February 13, 2018 by

Every seasoned SQL Server developer will tell you that no matter how hard you try, there are just operations in SQL Server better implemented elsewhere than relying on native Transact-SQL language (T-SQL). Operations such as performing complex calculations, implementing regular expression checks and accessing external web service applications can easily lead to your SQL Server instance incurring significant performance overhead. Thankfully, through its common language runtime (CLR) feature, SQL Server provides developers with a platform to address some of the inconveniences of native T-SQL by supporting an import of assembly files produced from projects written in. Net programming languages (i.e. C#, VB.NET). I have personally found CLR to be very useful when it comes to splitting string characters into multiple delimited lines.

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Daniel Calbimonte

The history of SQL Server – the evolution of SQL Server features

February 2, 2018 by

Introduction

This article will explain the main features in SQL Server 2017, 2016, 2015, 2014, 2012, 2008, 2005, 2000, 7, 6.5, 6.0, 4.2, 1.1 and 1.0.

In the past, the first SQL Server versions supported OS/2 (an operative system created by Microsoft and IBM) and Windows.

Now, the new versions of SQL Server (vNext and SQL Server 2017) can be installed in Linux. 15 years ago, it was impossible to think that. Linux and Microsoft were just like oil in water and now, Microsoft loves Linux.

Also, we now enjoy full integration with Azure, Tabular Databases, SSIS, SSAS and more. In this article, we will talk about all these changes and improvements.

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Marko Zivkovic

How to install and configure SQL Operations Studio for Windows

February 1, 2018 by

SQL Operations Studio is free, lightweight database development and operations cross-platform tool for private and commercial usage, that can be installed on Windows, macOS, and Linux for SQL Server, Azure SQL Database and Azure SQL Data Warehouse.

SQL Operations Studio is built to simplify work of database developers, database administrators, and system administrators. SQL Operations Studio boosts your productivity with smart code snippets, keyword completion, IntelliSense, source control integration, the ability to view and save results in CSV, Excel, JSON format, and the capability to organize and manage favorite database connections, etc. The first version of SQL Operations Studio was released in November 2017.

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Prashanth Jayaram

Top 8 new (or enhanced) SQL Server 2017 DMVs and DMFs for DBAs

January 17, 2018 by

Dynamic management views (DMVs) and dynamic management functions (DMFs) are system views and system functions that return metadata of the system state. On querying the related system objects, database administrators can understand the internals of SQL Server. It allows us to monitor the performance of the SQL Server instance, and diagnose issues with it.

SQL Server 2017 ships with a number of new and enhanced dynamic management views and dynamic management functions that will help DBAs monitor the health and performance of SQL Server instances. A few existing DMV’s such as sys.dm_os_sys_info and sys.dm_db_file_space_usage have been enhanced. Some have also been newly built and available only for SQL Server 2017.

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Sifiso W. Ndlovu

How to plot a SQL Server 2017 graph database using PowerBI

January 9, 2018 by

In the article How to plot a SQL Server 2017 graph database using SQL Server R, I highlighted the lack of built-in graph data visualisation as one major limitation of the SQL Server 2017 graph database feature. In the same article, I went on to suggest making use of SQL Server R as one workaround that could be utilised in order to successfully plot and visualise diagrams out of SQL Server 2017 graph database objects. However, whilst 3rd party graph database vendors such as Neo4j provide an interactive and hyperlinked graph diagrams that allows you to – amongst other things – easily drilldown and identify node-relationships as indicated in Figure 1, the graph plotted using SQL Server R is not very interactive in fact it is simply a static image file as shown in Figure 2.

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Gerald Britton

Get more out of Python on SQL Server 2017

January 5, 2018 by

Introduction

One of the new features announced with SQL Server 2017 is support for the Python language. This is big! In SQL Server 2016, Microsoft announced support for the R language – an open source language ideally suited for statistical analysis and machine learning (ML). Recognizing that many data scientists use Python with ML libraries, the easy-to-learn-hard-to-forget language has now been added to the SQL Server ML suite.

There’s a big difference between R and Python though: R is a domain-specific language while Python is general purpose. That means that the full power of Python is available within SQL Server. This article leaves ML aside for the moment and explores a few of the other possibilities.

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Sifiso W. Ndlovu

How to plot a SQL Server 2017 graph database using SQL Server R

January 3, 2018 by

A few years ago, one common business case I came across in my professional career that required modelling of data into a many-to-many entity relationship type was the representation of a consultants and their projects. Such a business case became a many-to-many entity relationship type because whilst each project can be undertaken by several consultants, consultants can in turn be involved in many different projects. When it came to storing such data in a relational database engine, it meant that we had to make use of bridging tables and also make use of several self-joins to successfully query the data.

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Sifiso W. Ndlovu

How to enable and disable the Identity Cache in SQL Server 2017

December 20, 2017 by

Every data warehouse developer is likely to appreciate the significance of having surrogate keys as part of derived fields in your facts and dimension tables. Surrogate keys make it easy to define constraints, create and maintain indexes, as well as define relationships between tables. This is where the Identity property in SQL Server becomes very useful because it allows us to automatically generate and increment our surrogate key values in data warehouse tables. Unfortunately, the generating and incrementing of surrogate keys in versions of SQL Server prior to SQL Server 2017 was at times challenging and inconsistent by causing huge gaps between identity values. In this article, we take a look at one improvement made in SQL Server 2017 to reduce the creation of gaps between identity values.

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