In this post, we are going to take a deeper look at the cardinality estimation process. We will use SQL Server 2014, the main concepts might also be applied to the earlier versions, however, the process details are different.
The algorithms responsible for performing the cardinality estimation in SQL Server 2014 are implemented in the classes called Calculators.
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In my previous article, Introduction to data science, data understanding and preparation, I showed how to make an overview of the distribution of a discrete SQL variable. I analyzed the NumberCarsOwned variable from the dbo.vTargetMail view that you can find in the AdventureWorksDW2016 demo database. The data science graphs I created in R and Python and the histogram created with T-SQL were all very nice. Now let me try to create a histogram for another SQL variable from that view, for the Education variable. I am starting with R, as you can see from the following code.
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In this blog post we are going to talk about the principles and the main concepts which are used by the optimizer to perform an estimation, and also, we will do a little bit math, so be prepared.
A cardinality estimation mechanism, as a first step, usually uses base statistics to estimate the expected number of rows that should be returned from the base table. You may look at these statistics using DBCC command – DBCC SHOW_STATISTICS.
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This article is part of the SQL Server Tools series, aimed at giving you an idea of the available tools and techniques to build applications using SQL Server tools. The first article of the series covers the rudimentary DevOps concepts and discusses on a high level, the overview of SQL Server tools that can be piped for DevOps operations.
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In this blog post, I’m going to look at the place of the Cardinality Estimation Process in the whole Optimization Process. We’ll see some internals, that will show us, why the Query Optimizer is so sensitive to the cardinality estimation. To understand that we should observe the main steps that a server performs when the query is sent for execution.
Below you may see the picture of the general plan-building process in SQL Server from the moment when a server receives a query till the storage engine is ready to retrieve the data. Take a quick look first, and next I’ll provide explanations.
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This document is intended for application developers and database administrators who plan to migrate an Oracle Database to Microsoft SQL Server.
Nowadays, most IT professionals are aware that Oracle Database is a very good database solution, but it’s not inexpensive, to say the least. If you want to partition a table in Oracle, it’s an option that you must pay for, which is the same for advanced security options like Database Encryption or Dynamic Data Masking. In contrast, SQL Server comes with these options out of the box when you use the Enterprise Edition.
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This post opens a series of blog posts dedicated to my observations of the new cardinality estimator in SQL Server 2014. But, before we jump to the new features, I’d like to provide some background, to make the next posts clearer.
We’ll start by discussing the role of Cardinality Estimation in SQL Server, trying to answer – what is it and why it is needed.
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In this article, we will discuss the SQL Server Reporting Service Report Builder and look at how to handle common client, end user requests using Report Builder. Report Builder is a very powerful tool to create a report for the SQL Server Reporting Service.
Advantages of the Report Builder is that it:
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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Sequence objects are used to sequentially generate numeric values. They were introduced in SQL Server 2012.
Sequence objects are similar to the IDENTITY column in any SQL table. However, unlike the IDENTITY column, they are independent and are not attached to any table. Sequence objects are used both independently and within the DML statements i.e. INSERT, UPDATE and DELETE.
This article will take a detailed look at sequence objects.
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This is the second article in the “Backup and Restore (or Recovery) in SQL Server” stairway series (see the full TOC below). This article deals with the different phases of data management life cycle and it encompasses the following topics:
In the analytical world, time is an important slicer. The ability to view data over time helps analyze measures in actionable steps. An Analysis Service (SSAS) database can help accomplish this important step. Business users are going to want the ability to see measures a like year to date as well as the period over period comparisons. SSAS enables Time Intelligence with 2 features: the date table and DAX functions.
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In my previous article, SQL Server Table Structure Overview, we described, in detail, the difference between Heap table structure, in which the data pages are not sorted in any ordering criteria and the pages itself are not sorted or linked between each other, and Clustered tables, in which the data is sorted within the data pages and the pages will be also linked in a double linked list, based on the index key. In this article, we will go through the structure of the SQL Server index, itself.
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This article gives the overview of the Database Migration Assistant Tool to access, plan and migrate the SQL Server from an old version.
Database migration is part of the DBA job. We can’t avoid it, and there are multiple options that can be taken:
Data science, machine learning, data mining, advanced analytics, or however you want to name it, is a hot topic these days. Many people would like to start some project in this area. However, very soon after the start you realize you have a huge problem: your data. Your data might come from your line of business applications, data warehouses, or even external sources. Typically, it is not prepared for applying advanced analytical algorithms on it straight out of the source. In addition, you have to understand your data thoroughly, otherwise you might feed the algorithms with inappropriate variables. Soon you learn the fact that is well known to seasoned data scientists: you spend around 70-80% of the time dedicated to a data science project on data preparation and understanding.
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There has been an ever-growing discomfort on documenting things especially when it’s very dynamic in nature and I was at one time undergoing the same. We have been developing SQL Server Analysis Tabular Model’s, which were quite a many in numbers and documenting 50 or 60 models manually is a big effort. In addition, development always demands change or enhancements that incurs changes on the documentation that is a continuous process. Adding the changes on the documentation is a time consuming process and sometimes loses track or remains inconsistent with the code built which is a critical issue when the system is in production for years.
I decided to automate the documentation of the SSAS model, to ease the manual effort here as well as save time. In addition, help the end users to see and review the latest change on the models quickly without even bothering the development team.
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SQL Server Agent can be used to run a wide variety of tasks within SQL Server. The built-in monitoring tools, though, are not well-suited for environments with many servers or many databases.
Removing reliance on default notifications and building our own processes can allow for greater flexibility, less alerting noise, and the ability to track failure conditions that are not typically tracked by SQL Server!
At the heart of the SQL Server Agent service is the ability to create, schedule, and customize jobs. These jobs can be given schedules that determine at what times of day a task should execute. Jobs can also be given triggers, such as a server restart or alert to respond to. Jobs can also be called via TSQL from anywhere that has the appropriate access and permissions to SQL Server Agent.
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Of late, there’s been a lot of noise around the term, GDPR. Chances are, some of us even had to go through learning sessions targeted at IT professionals to learn about what this new standard of data protection means. GDPR is primarily a European privacy law which sets a new bar, globally, on privacy rights, compliance, and security. GDPR is mainly about protecting the rights of every individual, providing the individual with more control over his personal data. It dictates how data should be handled, managed and protected going forward, the individual’s choice being the prime focus.
Today, data is widespread; many corporations handle part of the data on the cloud and part of it on premises. Our focus being SQL Server, we shall talk about what capabilities Microsoft gives us in order to be compliant with these laws that come into effect on the 25th of May, 2018. We would have to modify our data handling procedures keeping the focus on the security of the data processing.
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This collection of content will provide an overview of SQL formatter tools, with product page links for each of the tools. If you noticed a SQL formatter tool we missed, please let us know in the comments below.
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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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Microsoft SQL Server is a relational database management systems (RDBMS) that, at its fundamental level, stores the data in tables. The tables are the database objects that behave as containers for the data, in which the data will be logically organized in rows and columns format. Each row is considered as an entity that is described by the columns that hold the attributes of the entity. For example, the customers table contains one row for each customer, and each customer is described by the table columns that hold the customer information, such as the CustomerName and CustomerAddress. The table rows have no predefined order, so that, to display the data in a specific order, you would need to specify the order that the rows will be returned in. Tables can be also used as a security boundary/mechanism, where database users can be granted permissions at the table level.
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Sometimes we need to store information including photos in our database. For example, the photo of the product, the photo of the team members. But how can we store images in SQL Server?
We could create an application in .NET or Java, but if we do not have experience in those programming languages, we could use SQL Server tools to do it.
In this new article, we will learn the following tips that will help us to work with images including how to:
We have discussed how to created estimated execution plans and actual execution plans in various formats in my previous article SQL Server Query Execution Plan for beginners – Types and Options.
In this article we will continue discussing the various execution plan operators related to clustered indexes, and what they do, when do they appear and what happens when they do.
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In a manner of speaking, planning and implementing a SQL Server backup design is an art. Backup, Restoration, Recovery, Business Continuity Plans (BCP), and Disaster Recovery (DR) are different phases of data revolving around the discussions involving data backup. In other words, it’s about how we ensure to retain the business data through any sort of situation that’s thrown at us.
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