SQL Server Always ON Availability Group is a solution meant to achieve high availability and disaster recovery for SQL Server databases. We can configure this functionality between the Windows-based SQL Server installations, Linux-based SQL Server installations, and even between Linux and Windows-based SQL Server installations together.
Typical queries in the SELECT * FROM table format are sometimes not enough. When the data for a query is not in one table, but in several, or when it is necessary to specify several selection parameters at once, you will need more sophisticated queries.
This article will explain how to build such queries and provide examples of complex SQL queries.
In the previous articles, we covered sequential steps necessary to build a virtual machine, install Ubuntu 18.04 operating system, and configure SQL Server 2019 on that Ubuntu machine. This is 3rd part of this series. Our goal is to demonstrate the step-by-step process of installing SQL Server Tools and its components on the Ubuntu 18.04 Linux system.
The main idea around the SQL Server function called STUFF is concatenating multiple columns into a single column with more flexibility than the CONCAT function would provide. Besides, STUFF can be combined with other techniques for some interesting effects.
In this article, we’ll explore the possibilities that the STUFF command provides for SQL Database specialists.
Recently we encountered an interesting performance problem on one of our SQL Server databases that process transactions at a serious rate. The transaction table used to capture these transactions became a hot table. As a result, the problem showed up in the application layer. It was an intermittent timeout of the session seeking to post transactions.
This happened because a session would typically “hold on” to the table and cause a series of spurious locks in the database.
Analytic functions are special kinds of pre-built functions that come with PostgreSQL by default. They allow you to execute a variety of analytical workloads on your datasets and prepare results.
In the world of cloud computing, where we need to provide insights to customers in a swift and meaningful way, understanding these analytical functions solves a lot of challenges.
This article will talk about the most common analytical functions used in PostgreSQL.
Hash indexes are an integral part of databases. If you’ve ever used a database, chances are that you have seen them in action without even realizing it.
Hash indexes differ in work from other types of indexes because they store values rather than pointers to records located on a disk. This ensures faster searching and insertion into the index. That’s why hash indexes are often used as primary keys or unique identifiers.
Imagine you have a business-critical MS SQL Server serving requests for both OLTP and some batch processing tasks, as data gets pulled by another team. That might be reporting services or extracting data to push to another data warehousing environment, or else. Under this scenario, your SQL Server is treating numerous requests from multiple applications. This in turn affects overall performance and can impact higher priority tasks.
Twitter Activity provides loads of information for analysis. You can learn your target audience better, determine the most popular content, check the right time when your visitors are more active, etc. By getting the Twitter Activity data and putting it into the advanced analytical systems, you can correlate that data with other essential information from different sources. The prospects are tremendous.
In this article, we’ll explore the case of importing the Twitter data with the help of SQL Developer Web into an Autonomous Database. The imported Twitter dataset is in JSON format. Thus, you’ll need to work on how to import JSON data into Autonomous Database.
SQL Server instances house databases containing the data for the backend stack of a business model, or configuration data for particular applications. Regardless of the use case, an instance has a set of values/settings that should be tuned to follow best practices.
The purpose of the Stored Procedure that I’m showcasing in this article is to present the DBA a set of important settings/values that shouldn’t be overlooked. Moreover, I will share a cool feature that help DBAs keep control of any particular setting/value that has been recently changed/modified.
