When a DBA is working with the SQL Server, he/she might sometimes say that the execution plan is like a black hole. As a matter of fact, though, you have to look at execution plans as weather forecasts.
In this article, I will be discussing various aspects of executions plans, types and common options.
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The SQL Server Query Store is a relatively new feature introduced in SQL Server 2016. It is basically a SQL Server “flight recorder” or “black box”, capturing a history of executed queries, query runtime execution statistics, execution plans etc. against a specific database. This information helps in identifying performance problems caused by query plan changes and troubleshooting by quickly finding performance differences, even after SQL Server restart or upgrade. All data that SQL Server Query Store capture are stored on disk.
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As all other database engines that require a GUI too to interact with, Microsoft built SQL Server Management Studio as a tool that provides an environment to manage, configure, monitor and administrate the SQL Server infrastructure for different kind of instances, that are installed locally at your machine, on a remote server with Windows or Linux operating systems or hosted in the cloud such as SQL Azure or Azure Data warehouse.
Recall that the SQL Server Management Studio consists of the Object Explorer, which is used to view and manage all objects within the SQL Server instance, the Template Explorer, that is used to deal with the scripting text files and the Solution Explorer, that is responsible for building the projects that contain list of T-SQL scripts in SQL Server.
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One of the main tasks of a SQL Server database administrator is performance tuning. Sometimes, though, coders or developers don’t always prioritize database performance or query optimization. Here is a typical scenario
The user’s read requests in SQL Server are managed and controlled by the SQL Server Relational Engine, that is responsible for determining the most optimized access method, such as index scan or table scan, to retrieve the requested data. These read requests are also optimized internally by the SQL Server Storage Engine, the buffer manager components specifically, that is responsible for determining the general read pattern to be performed.
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In the first part of this article, we will discuss about parallelism in the SQL Server Engine. Parallel processing is, simply put, dividing a big task into multiple processors. This model is meant to reduce processing time.
Execution plans are the equivalent to highways and traffic signs of T-SQL queries. They tell us how a query is executed.
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We recently inherited a database environment where we’re facing significant data growth with limits on the sizes we can allow our databases to grow. Since we maintain multiple development, QA and production environments and these environments must be sized appropriately. We set a few standards about tables that exceed certain sizes – rows, data or both, or have certain growth patterns and our standards force compression at thresholds we set for our different environments (including using page, row, or clustered columnstore index compression) We’re looking for how we can get information about data and compression in tables and options we have so that we can quickly determine candidates that don’t currently match our best practices design we’ve set for our environments.
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Quite often developers put the responsibility of the server performance on the shoulders of DBAs. But to be honest, as a developer, it is your responsibility to write code which is efficient and optimal. However, when writing code, the developer often lacks the knowledge and/or experience about SQL Server to meet this requirement.
In this series, I will be discussing various aspects of query designing, keeping performance and scalability in mind. We will be addressing the most common concepts which we often see that negatively impact SQL code written by developers.
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R is a very popular data programing language. R is especially used in data analysis, statistics calculations, predictions, data mining and machine learning. R is used by data scientist, economist, genetic scientists, and statisticians. R has very wide usage in real life. Healthcare, finance, marketing, and manufacturing are some of them.
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In SQL Server, internal latch architecture protects memory during SQL operations. It ensures the consistency of memory structures with read and write operation on pages. Rudimentarily, it has two classes, buffer latches, and non-buffer latches which perform lightweight synchronization in the SQL Engine.
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In the article on Deadlock Analysis and Prevention, we saw how deadlock occurs. We saw that SQL Server selects one of the processes involved in the deadlock as deadlock victim. In this article, we will look at the criteria on which SQL server selects a deadlock victim. Why one process is selected as deadlock victim and not the other.
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Monitoring a SQL Server database is a critical component of database administration. Ninety percent of the organizations expect the number of databases to increase over the next twelve months. An increase in data volumes can have negative effects on the availability of databases. Hence, SQL Server database monitoring is considered a critical responsibility of a database administrator. Organizations tend to spend a lot of their funds towards enterprise solutions. And due to the sensitive and growing nature of business and user needs, application availability is very important nowadays.
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Without a doubt, few technologies in SQL Server cause as much confusion and the spread of misinformation as indexes. This article looks at some of the most asked questions and a few that should be asked but often are not. We’ll be using SQL Server 2016 for the examples and a tool, for SQL Server query execution plan analysis, ApexSQL Plan, to explore the effects of indexes on a typical business problem: A table of customers.
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When you define SQL Server database tables, local variables, expressions or parameters, you should specify what kind of data will be stored in those objects, such as text data, numbers, money or dates. This attribute is called the SQL Server Data Type. SQL Server provides us with a big library of system data types that define all types of data that can be used with SQL Server, from which we can choose the SQL Server data type that is suitable for the data we will store in that object. You can also define your own customized user defined data type using T-SQL script. SQL Server data types can be categorized into seven main categories:
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In a previous article entitled Hands on Full-Text Search in SQL Server, we had an overview on the Full-Text feature as it’s implemented in SQL Server. We saw how to create Full-Text indexes and that they were stored inside a container called a Full-Text catalog. We’ve also seen that, by design, this kind of index will generate a fragmentation.
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SQL Server Maintenance Plans is a SQL Server Management Studio built-in feature that helps in creating a workflow of variant database administration tasks, which can be run automatically using a predefined schedule or manually triggered by the user.
SQL Server Maintenance Plans allow you to use typical database maintenance tasks or customize your own task using a T-SQL script that runs on the local server or group of SQL Servers, providing more flexibility to the database administration tasks.
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It is a common misconception that you need real production data, or production like data, to effectively tune queries in SQL Server. I am going to explain how you can compile the same execution plans as what your production environment would compile, so that you can tune them in a non-production environment, gaining these benefits.
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Availability groups can no longer be considered new. The feature was released in SQL Server 2012 but I often notice that SQL Server Failover Cluster Instances (FCIs) are much better understood. For a very long time, when people thought about SQL Server they thought about the entire instance as a unit. Microsoft has been implementing new features and services which begin to segment the concept of an instance from the concept of a database.
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Before going through the main concern of this article, indexing the foreign key columns, let’s take a small trip back to review the SQL Server Indexes and Foreign Key concepts.
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Indexes are used to speed-up query process in SQL Server, resulting in high performance. They are similar to textbook indexes. In textbooks, if you need to go to a particular chapter, you go to the index, find the page number of the chapter and go directly to that page. Without indexes, the process of finding your desired chapter would have been very slow.
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Understanding indexing needs allows us to ensure that important processes run efficiently and that our server hardware is not being over-taxed by poorly performing queries.
Collecting metrics on SQL Server index usage and missing index needs is critical to making smart decisions. To be truly proactive, though, we need to create a framework that allows us to quickly, efficiently, and regularly report on changes in indexing needs. With a system in place that can let us know when changes are needed, we can stay ahead of the optimization game and keep our customers happy!
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As discussed in the article All about latches in SQL Server, spinlocks are also a special form of locks that SQL Server uses to protect data. Spinlocks are lightweight objects used by the SQL Server Operating System (SQLOS) to protect data structure access. To understand, properly, the difference between latches and spinlocks and why the spinlock is an important SQL Server’ object type, it is vital to understand the cycle of query execution in SQL Server
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SQL Server In-Memory OLTP, also known as Hekaton when it was introduced in SQL Server 2014, provides us with the ability to move specific database tables and suitable stored procedures into memory and compile the stored procedures into native x86 code. As a result, you can easily query these database objects directly from memory with the best performance and the least possible data access latency. In addition to that, the SQL Server Engine will no longer use the old latching and locking mechanism to control the data access concurrency. Instead, a high performance row versioning mechanism will be used to control the concurrency. This optimistic concurrency mechanism is 5 times to 20 times faster than the normal disk-based processing, due to reading the data from the memory directly.
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There are many situations in a DBA’s life that lead him or her to monitor space growth over time in SQL Server. In short, we often want to know which tables are growing the most and how much they grow.
For that reason, it’s preferable to develop a solution that will help us in that particular task and this is exactly what this article is about. The main components will include
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SQL Server locks, discussed in the article All about locking in SQL Server, which is applied on data for the duration of the logical operation to preserve logical transaction consistency. SQL Server latches, however, are a special type of low-level system locks which are held as long as the physical operation lasts on the memory page in order to protect memory consistency
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