This article deals with the basics of Semantic Search, including a complete walkthrough of Semantic Search: starting from scratch and finishing with a ready-to-use feature.
Additionally, the readers are going to learn about some of the very useful but not generally known Search features available in SQL Server like Semantic Search, which we’ll demonstrate with some basic examples.
This article also emphasizes the importance of Semantic Search for a specific form of analysis that cannot be performed with an ordinary search.
What is Semantic Search
Let’s first work out what exactly Semantic Search is and how it is different from Full-Text Search.
According to Microsoft documentation, Semantic Search provides deep insight into unstructured documents.
Semantic Search is a special search technology or feature used to perform a comprehensive search or a comparative analysis mainly in unstructured data or documents, such as MS Word documents, provided the unstructured data is stored inside the SQL Server database.
Semantic Search is only compatible with the SQL Server 2012 and later versions.
Please remember Semantic Search is not compatible with Azure SQL database or Azure data warehouse cloud solutions.
This means you have to either work with a VM on Azure or on an on-premises SQL Server instance to utilize this powerful feature.
Semantic Search vs Full-Text Search
According to Microsoft documentation, Full-Text Search lets you query the words in a document; semantic search lets you query the meaning of the document.
Semantic Search together with Full-Text Search represents one joint feature offered by Microsoft SQL Server, and you can either select to install them during the installation of your SQL Server instance or later on, by adding new features to your existing SQL instance.
Let us go through the prerequisites for the general use of Semantic Search along with some of the things required to follow the walkthrough(s) in this article.
Full-Text Search installed
It is mandatory to know how to set up Full-Text Search since Full-Text Search and Semantic Search are both offered as a joint feature.
Please refer to the article Implementing Full-Text Search in SQL Server 2016 for beginners to set up Full-Text Search, which is a prerequisite for installing Semantic Search in SQL Server.
This article expects you to have installed the Full-Text Search on your SQL Server instance.
dbForge Studio for SQL Server
The use of Semantic Search (in the walkthrough of this article) requires unstructured data to be stored in the SQL Server database, and in this article, we did this using dbForge Studio for SQL Server rather than saving directly unstructured data in SQL Server.
#sql server #full-text search #sql server #sql server 2016
SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:
1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.
2. Every database seller needs an approach to separate its item from others.
Right now, contrasts are noted where fitting.
#programming books #beginning sql pdf #commands sql #download free sql full book pdf #introduction to sql pdf #introduction to sql ppt #introduction to sql #practical sql pdf #sql commands pdf with examples free download #sql commands #sql free bool download #sql guide #sql language #sql pdf #sql ppt #sql programming language #sql tutorial for beginners #sql tutorial pdf #sql #structured query language pdf #structured query language ppt #structured query language
When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services
This is part 3 of “MS SQL Server- Zero to Hero” and in this article, we will be discussing about the SCHEMAS in SQL SERVER. Before getting into this article, please consider to visit previous articles in this series from below,
In part one, we learned the basics of data, database, database management system, and types of DBMS and SQL.
#sql server #benefits of schemas #create schema in sql #database schemas #how to create schema in sql server #schemas #schemas in sql server #sql server schemas #what is schema in sql server
We know Humans learn from their past experiences. Mean while Machines follow Instructions given by Humans. But what if Human can train Machines to learn from the past data?. In simple, this is what Machine learning is !!!. SQL Server has capabilities of Machine Learning. In this article, we will discuss about the capabilities of Machine Learning in SQL Server.
#machine learning #sql server #machine learning with sql server #ml in sql server using python #python in sql server ml #sql server machine learning services
Let’s say the chief credit and collections officer asks you to list down the names of people, their unpaid balances per month, and the current running balance and wants you to import this data array into Excel. The purpose is to analyze the data and come up with an offer making payments lighter to mitigate the effects of the COVID19 pandemic.
Do you opt to use a query and a nested subquery or a join? What decision will you make?
Before we do a deep dive into syntax, performance impact, and caveats, why not define a subquery first?
In the simplest terms, a subquery is a query within a query. While a query that embodies a subquery is the outer query, we refer to a subquery as the inner query or inner select. And parentheses enclose a subquery similar to the structure below:
SELECT col1 ,col2 ,(subquery) as col3 FROM table1 [JOIN table2 ON table1.col1 = table2.col2] WHERE col1 <operator> (subquery)
We are going to look upon the following points in this post:
As is customary, we provide examples and illustrations to enhance understanding. But bear in mind that the main focus of this post is on subqueries in SQL Server.
Now, let’s get started.
For one thing, subqueries are categorized based on their dependency on the outer query.
Let me describe what a self-contained subquery is.
Self-contained subqueries (or sometimes referred to as non-correlated or simple subqueries) are independent of the tables in the outer query. Let me illustrate this:
-- Get sales orders of customers from Southwest United States -- (TerritoryID = 4) USE [AdventureWorks] GO SELECT CustomerID, SalesOrderID FROM Sales.SalesOrderHeader WHERE CustomerID IN (SELECT [CustomerID] FROM [AdventureWorks].[Sales].[Customer] WHERE TerritoryID = 4)
As demonstrated in the above code, the subquery (enclosed in parentheses below) has no references to any column in the outer query. Additionally, you can highlight the subquery in SQL Server Management Studio and execute it without getting any runtime errors.
Which, in turn, leads to easier debugging of self-contained subqueries.
The next thing to consider is correlated subqueries. Compared to its self-contained counterpart, this one has at least one column being referenced from the outer query. To clarify, I will provide an example:
USE [AdventureWorks] GO SELECT DISTINCT a.LastName, a.FirstName, b.BusinessEntityID FROM Person.Person AS p JOIN HumanResources.Employee AS e ON p.BusinessEntityID = e.BusinessEntityID WHERE 1262000.00 IN (SELECT [SalesQuota] FROM Sales.SalesPersonQuotaHistory spq WHERE p.BusinessEntityID = spq.BusinessEntityID)
Were you attentive enough to notice the reference to BusinessEntityID from the Person table? Well done!
Once a column from the outer query is referenced in the subquery, it becomes a correlated subquery. One more point to consider: if you highlight a subquery and execute it, an error will occur.
And yes, you are absolutely right: this makes correlated subqueries pretty harder to debug.
To make debugging possible, follow these steps:
Isolating the subquery for debugging will make it look like this:
SELECT [SalesQuota] FROM Sales.SalesPersonQuotaHistory spq WHERE spq.BusinessEntityID = <constant value>
Now, let’s dig a little deeper into the output of subqueries.
Well, first, let’s think of what returned values can we expect from SQL subqueries.
In fact, there are 3 possible outcomes:
Let’s start with single-valued output. This type of subquery can appear anywhere in the outer query where an expression is expected, like the WHERE clause.
-- Output a single value which is the maximum or last TransactionID USE [AdventureWorks] GO SELECT TransactionID, ProductID, TransactionDate, Quantity FROM Production.TransactionHistory WHERE TransactionID = (SELECT MAX(t.TransactionID) FROM Production.TransactionHistory t)
When you use a MAX() function, you retrieve a single value. That’s exactly what happened to our subquery above. Using the equal (=) operator tells SQL Server that you expect a single value. Another thing: if the subquery returns multiple values using the equals (=) operator, you get an error, similar to the one below:
Msg 512, Level 16, State 1, Line 20 Subquery returned more than 1 value. This is not permitted when the subquery follows =, !=, <, <= , >, >= or when the subquery is used as an expression.
Next, we examine the multi-valued output. This kind of subquery returns a list of values with a single column. Additionally, operators like IN and NOT IN will expect one or more values.
-- Output multiple values which is a list of customers with lastnames that --- start with 'I' USE [AdventureWorks] GO SELECT [SalesOrderID], [OrderDate], [ShipDate], [CustomerID] FROM Sales.SalesOrderHeader WHERE [CustomerID] IN (SELECT c.[CustomerID] FROM Sales.Customer c INNER JOIN Person.Person p ON c.PersonID = p.BusinessEntityID WHERE p.lastname LIKE N'I%' AND p.PersonType='SC')
And last but not least, why not delve into whole table outputs.
-- Output a table of values based on sales orders USE [AdventureWorks] GO SELECT [ShipYear], COUNT(DISTINCT [CustomerID]) AS CustomerCount FROM (SELECT YEAR([ShipDate]) AS [ShipYear], [CustomerID] FROM Sales.SalesOrderHeader) AS Shipments GROUP BY [ShipYear] ORDER BY [ShipYear]
Have you noticed the FROM clause?
Instead of using a table, it used a subquery. This is called a derived table or a table subquery.
And now, let me present you some ground rules when using this sort of query:
In this case, a derived table has the benefits of a physical table. That’s why in our example, we can use COUNT() in one of the columns of the derived table.
That’s about all regarding subquery outputs. But before we get any further, you may have noticed that the logic behind the example for multiple values and others as well can also be done using a JOIN.
-- Output multiple values which is a list of customers with lastnames that start with 'I' USE [AdventureWorks] GO SELECT o.[SalesOrderID], o.[OrderDate], o.[ShipDate], o.[CustomerID] FROM Sales.SalesOrderHeader o INNER JOIN Sales.Customer c on o.CustomerID = c.CustomerID INNER JOIN Person.Person p ON c.PersonID = p.BusinessEntityID WHERE p.LastName LIKE N'I%' AND p.PersonType = 'SC'
In fact, the output will be the same. But which one performs better?
Before we get into that, let me tell you that I have dedicated a section to this hot topic. We’ll examine it with complete execution plans and have a look at illustrations.
So, bear with me for a moment. Let’s discuss another way to place your subqueries.
#sql server #sql query #sql server #sql subqueries #t-sql statements #sql