In a previous article, Functions vs stored procedures in SQL Server, we compared Functions vs stored procedures across various attributes. In this article, we will continue the discussion. We will talk also about Table-valued functions and compare performance with stored procedures with table valued functions and scalar functions.
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Usually DBAs prefer stored procedures in SQL instead of functions in SQL Server. Is this a good practice?
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Test databases should be processed with unit tests
In many shops code is unit tested at the point of commit. For databases, I prefer running all unit tests at once and in sequence against a QA database, vs development, as part of a Test step, in my continuous integration workflow pipeline. Yes, issues would be caught later than at check-in, but continuous integration largely solves this with frequent iterations, including at a commit itself. So the difference between on-check in, unit testing and unit testing a build created on-commit, is simply that the unit tests will be run against a fully re-constituted QA database, vs Development. The previous article in this series is SQL Server database continuous integration (CI) Best practices and how to implement them – Source control. Read more »
This article provides for a roadmap to continuous integration and delivery best practices, and along the way demonstrates how to apply these with ApexSQL tools and technologies. In some sections this article is aspirational, as no solution yet exists, but demonstrates our plan, direction and roadmap. As the tools that apply these best practices are released this article will be updated accordingly.
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Before we delve into these SQL concepts, note that I like to do all my development in SQL Management Studio. So if you want to follow along go ahead and open that up.
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In this article, we will talk about generating random values for testing purposes.
I once had a customer with software that worked fine in the demo with 30 rows, but after some months, the software had more than a million rows and it became very slow. The problem was not SQL Server, the problem was the application, which was not designed for tables with millions of rows. The customer sued to the software provider and lawyers were needed to create a resolution. If the provider had tested the software with millions of rows, this problem would have never happened.
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Troubleshooting SQL Server is all about gathering the right evidence. Ordinarily we utilise a variety of different methods and analyse their output to look for specific areas where we would focus our diagnostic efforts. We could for example, use the results of various DMVs to look at wait statistic and resource information to help us focus our investigation in a particular area of SQL Server.
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Static data (aka Code, Lookup, List or Reference data), in the context of a relational database management system, is generally data that represents fixed data, that generally doesn’t change, or if it does, changes infrequently, such as abbreviations for US states for example e.g. ME, MA, NC. This data is typically referenced, extensively, by transactional type data. For example, an customer table would have references to static table for City name, State or province, Country, Payment terms e.g. NET 30 etc.
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Visual Studio Code (Code), have you heard of this product yet? This is an open-source, cross-platform and extremely light weight code editor from Microsoft. You may see some folks explain this editor as the little brother to Visual Studio Community Edition (VS Community), but it is more compared to editors like Atom, Sublime Text or even Notepad++. It is not something you can use to compile program code, so it is for the less complex coding projects. I utilize Code as my editor of choice now with PowerShell, and even T-SQL at times. In this article, I wanted to walk you through using Code and note some specific extensions I use for PowerShell and SQL Server.
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In this article, I compiled a list of FAQs and Answers about dates.
SQL Server 2016 SP1 is released as announced by Microsoft. It comes with a bunch of new features and enhancements as a result of customer and community feedback. In this article, I will introduce you to these new features and enhancements.
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Basic extraction of the SQL Server database data is usually achieved by querying the databases and creating stored procedure to automate the extraction process. Unfortunately, extracting the information in this way will not yield high-end reports, and only basic table-shaped reports are available when extracting the information via SQL Server Management Studio or similar tools. In order to create high-end reports that will include additional projections of the data, such are graphs, lists, charts… SQL Server offers powerful reporting options within the SQL Server Reporting service.
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In many of our past “get togethers” we have discussed pivoting raw data in order to best represent corporate data within our business reporting. As we may remember, in many instances we pivoted the data within the report query.
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For the purpose of this article, Bitbucket is chosen as a Git hosting service. In order to prepare the “communication” between Git hosting service and your application, the following steps need to be performed:
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To make the first steps into the BI world easier, you just need to build one SSAS Tabular model database. In this article, we will learn some basics of querying a SSAS Tabular model database with simple DAX queries, starting with a theoretical approach, and then retrieve data and analyze it.
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Running totals have long been the core of most financial systems, be statements or even balance calculations at a given point in time. Now it’s not the hardest thing to do in SQL Server but it is definitely not the fastest thing in the world either as each record has to be evaluated separately. Prior to SQL Server 2012, you have to manually define the window/subset in which you want to calculate you running total, normally we would define a row number with a window on a specific order or a customer depending on the requirements at hand.
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Since we have presented a way to review the history of committed changes using Git, Team Foundation Server and Subversion, let’s check how the same can be achieved when working with the Mercurial source control system.
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In this article, I continue to review the exciting features available in SQL Server 2016. One such feature is the long awaited T-SQL support for JSON formatted data. In this article we take a look at how JSON support will impact data warehouse solutions.
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A common activity when writing T-SQL queries is connecting to local databases and processing data directly. But there will be situations in which you need to connect to a remote database that is located in a different instance in the same server or in a different physical server, and process its data in parallel with the local data processing.
SQL Server provides us with four useful methods to connect to the remote database servers, even other database server types, and query its data within your T-SQL statement. In this article, we will discuss these four methods and how to use it to query remote SQL Server databases.
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In previous articles, I have already covered the revision history for Git and Team Foundation Server. Similarly, this article covers the revision history of committed changesets using Subversion as the source control system.
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Similarly, as described previously in this article, where the revision history is covered for the Git source control system, we’ll present the workflow of reviewing the history of committed SQL database objects using Team Foundation Server (TFS) source control system. In order to use TFS and have SQL database objects being version controlled, Visual Studio is required, as well as TFS server, either installed on a machine or TFS through Team Services, which is actually TFS “in the cloud”.
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Team City is a Java based continuous integration and deployment server shipped as commercial software but free to use for up to 20 build configurations. It polls the source control repository for any changes providing build reports and statistics to the team.
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The need to search through database schema for specific words or phrases is commonplace for any DBA. The ability to do so quickly, accurately, and completely is critical when developing new features, modifying existing code, or cleaning up the vestiges from an application’s ancient history.
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The need to search through database schema for specific words or phrases is commonplace for any DBA. The ability to do so quickly, accurately, and completely is critical when developing new features, modifying existing code, or cleaning up the vestiges from an application’s ancient history.
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In my previous article, we went over the basics of Mercurial, as well as some arguments why using it is critical for database administrators. Among many reasons, it allows us to easily track history and changes to our scripts, which in turn makes it easier for us to experiment and enhance our toolkit, since we can do so safely without fear of permanently causing damage. In this installment, we are going to go into more depth on the specifics of two feature of Mercurial that, once harnessed, can add significant efficiency to our coding workflows.
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