In the beginning, there were files. Later there were navigational databases based on structured files. Then there were IMS and CODASYL, and around 40 years ago we had some of the first relational databases. Throughout much of the 1980s and 1990s “database” strictly meant “relational database.” SQL ruled.
Then with the growing popularity of object-oriented programming languages, some thought the solution to the “impedance mismatch” of object-oriented languages and relational databases was to map objects in the database. Thus we ended up with “object-oriented databases.” The funny thing about object databases was that in many cases they were basically a normal database with an object mapper built-in. These waned in popularity and the next real mass-market attempt was “NoSQL” in the 2010s.
NoSQL attacked both relational databases and SQL in the same vein. The main problem this time was that the Internet had destroyed the underlying premise of the 40-year-old relational database management system (RDBMS) architecture. These databases were designed to conserve precious disk space and scale vertically. There were now way too many users and way too much for one fat server to handle. NoSQL databases said that if you had a database with no joins, no standard query language (because implementing SQL takes time), and no data integrity then you could scale horizontally and handle that volume. This solved the issue of vertical scale but introduced new problems.
Developed in parallel with these online transaction processing systems (OLTP) was another type of mainly relational database called an online analytical processing system (OLAP). These databases supported the relational structure but executed queries with the understanding that they would return massive amounts of data. Businesses in the 1980s and 1990s were still largely driven by batch processing. In addition, OLAP systems developed the ability for developers and analysts to imagine and store data as n-dimensional cubes. If you imagine a two-dimensional array and lookups based on two indices so that you are basically as efficient as constant time but then take that and add another dimension or another so that you can do what are essentially lookups of three or more factors (say supply, demand, and the number of competitors)—you could more efficiently analyze and forecast things. Constructing these, however, is laborious and a very batch-oriented effort.
Around the same time as scale-out NoSQL, graph databases emerged. Many things are not “relational” per se, or not based on set theory and relational algebra, but instead on parent-child or friend-of-a-friend relationships. A classic example is product line to product brand to model to components in the model. If you want to know “what motherboard is in my laptop,” you find out that manufacturers have complicated sourcing and the brand or model number may not be enough. If you want to know what-all motherboards are used in a product line, in classic (non-CTE or Common Table Expression) SQL you have to walk tables and issue queries in multiple steps. Initially, most graph databases didn’t shard at all. In truth, many types of graph analysis can be done without actually storing the data as a graph.
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.
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At the Distributed SQL Summit 2020, Lianghong Xu – Engineering Manager & Tech Lead, Pinterest – presented the talk “Pinterest’s Exploration of Distributed SQL”. In the talk he covered the evolution of storage at Pinterest, the role that the HBase ecosystem plays within the company, current challenges and opportunities for innovation, and finally, their exploration of Distributed SQL as a viable solution to some of these challenges.
In 2012, Pinterest started out with a sharded MySQL deployment which handled pins, boards, and users. The following year they introduced HBase to their stack. HBase was leveraged as a columnar store and provides graph service capabilities. In 2015, RocksDB was brought into the mix as a key-value store and to assist with machine learning styled workloads. As the popularity of Pinterest grew and new use cases began to evolve, new capabilities like distributed transactions and support for secondary indexes meant that some additional middleware had to be developed to work in concert with HBase. Fast forward to 2020, and Pinterest now finds itself at a stage in their evolution to start exploring distributed SQL. Why? Because many use cases are now requiring NoSQL scalability with SQL capabilities.
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When you develop large chunks of T-SQL code with the help of the SQL Server Management Studio tool, it is essential to test the “Live” behavior of your code by making sure that each small piece of code works fine and being able to allocate any error message that may cause a failure within that code.
The easiest way to perform that would be to use the T-SQL debugger feature, which used to be built-in over the SQL Server Management Studio tool. But since the T-SQL debugger feature was removed completely from SQL Server Management Studio 18 and later editions, we need a replacement for that feature. This is because we cannot keep using the old versions of SSMS just to support the T-SQL Debugger feature without “enjoying” the new features and bug fixes that are released in the new SSMS versions.
If you plan to wait for SSMS to bring back the T-SQL Debugger feature, vote in the Put Debugger back into SSMS 18 to ask Microsoft to reintroduce it.
As for me, I searched for an alternative tool for a T-SQL Debugger SSMS built-in feature and found that Devart company rolled out a new T-SQL Debugger feature to version 6.4 of SQL – Complete tool. SQL Complete is an add-in for Visual Studio and SSMS that offers scripts autocompletion capabilities, which help develop and debug your SQL database project.
The SQL Debugger feature of SQL Complete allows you to check the execution of your scripts, procedures, functions, and triggers step by step by adding breakpoints to the lines where you plan to start, suspend, evaluate, step through, and then to continue the execution of your script.
You can download SQL Complete from the dbForge Download page and install it on your machine using a straight-forward installation wizard. The wizard will ask you to specify the installation path for the SQL Complete tool and the versions of SSMS and Visual Studio that you plan to install the SQL Complete on, as an add-in, from the versions that are installed on your machine, as shown below:
Once SQL Complete is fully installed on your machine, the dbForge SQL Complete installation wizard will notify you of whether the installation was completed successfully or the wizard faced any specific issue that you can troubleshoot and fix easily. If there are no issues, the wizard will provide you with an option to open the SSMS tool and start using the SQL Complete tool, as displayed below:
When you open SSMS, you will see a new “Debug” tools menu, under which you can navigate the SQL Debugger feature options. Besides, you will see a list of icons that will be used to control the debug mode of the T-SQL query at the leftmost side of the SSMS tool. If you cannot see the list, you can go to View -> Toolbars -> Debugger to make these icons visible.
During the debugging session, the SQL Debugger icons will be as follows:
The functionality of these icons within the SQL Debugger can be summarized as:
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Databases are evolving. For the past decade, we’ve read thinkpiece after thinkpiece taking firm stances on the “SQL vs. NoSQL” debate — some of which declaratively pronounce the death of SQL or the death of NoSQL. In DZone’s annual report on SQL v NoSQL database usage, we’re excited to see that we’re moving beyond that paradigm. What we’re seeing in this report is more nuanced, and a lot more exciting: the death of the “SQL vs. NoSQL” binary altogether.
This is happening for a couple reasons. For one, more and more companies are using a combination of both for their business needs. They do not have one monolithic database — they run specific applications on the tools that best suit that workload. 58% of the companies surveyed in the report are using a combination of both.
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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.
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