Finding Correlated Rows Using EXISTS or COUNT | Redgate (2022)

Louis Davidson

13 November 2019

2 Comments

  • SQL Prompt
  • SQL Code Analysis

Should you always use EXISTS rather than COUNT when checking for the existence of any correlating rows that match your criteria? Does the former really offer "superior performance and readability". Louis Davidson investigates.

Guest post

This is a guest post from Louis Davidson.

One of SQL Prompt’s built-in “performance” code analysis rules, PE013, states (paraphrased):

Some programmers use COUNT(*) to check to see if there are any rows that match some criteria…it is recommended to use EXISTS() or NOT EXISTS() instead, for superior performance and readability.

I’d rewrite that as “…for superior readability, and performance that will always be comparable and may be better in some complex cases“. However, the superior readability bit, on its own, is worth fighting for.

The many ways to find correlating rows

As with most programming problems, there are several queries that will return the right answer, and finding correlated rows is no different. For our example, let’s say a client wants to run a special email promotion for anyone who has ever purchased an item from their shop that cost more than $500. Our requirement is simply to devise a query for the WideWorldImporters sample database that returns a list of the names and email addresses of these customers.

One developer suggests a solution that uses EXISTS with a subquery, another a solution that uses COUNT(*) with a subquery, yet another proposes one that uses just JOINs plus a DISTINCT clause in the SELECT. There are other suggestions too. They all give you the right results, but which one is “best”, or most appropriate, solution?

COUNT

Let’s start with the COUNT(*) solution:

(Video) Using Excel to calculate a correlation coefficient || interpret relationship between variables

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SELECT People.FullName, People.EmailAddress

FROM Sales.Customers

JOIN Application.People

ON People.PersonID = Customers.PrimaryContactPersonID

WHEREPeople.EmailAddress IS NOT NULL

AND(SELECT COUNT(*)

FROM Sales.Orders

JOIN Sales.OrderLines

ON OrderLines.OrderID = Orders.OrderID

WHEREOrders.CustomerID = Customers.CustomerID

ANDOrderLines.UnitPrice > 500) > 0;

SQL Prompt immediately alerts us to a possible problem, with a green wriggly line under SELECT COUNT(*)…, for a violation of performance rule PE013, but we’ll get to that shortly (you’ll also see other wavy lines indicating non-aliased tables, which I’m going to ignore in this article).

Our requirements were to return the name and email address of anyone who had registered a purchase for an item that cost more than 500. However, as written, the query more literally says “for each customer, count the number of orders they placed that cost over 500, and if that’s more than zero, give me their details.

I get the impression that the programmer was solving a slightly different problem to the one stated in the requirements. You would typically use this form of the query to find customers who have made a certain number of orders, within a range (such as 2-5 orders), rather than just to check that any order exists.

EXISTS

Here’s the EXISTS solution:

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SELECT People.FullName, People.EmailAddress

FROMSales.Customers

JOIN Application.People

ON People.PersonID = Customers.PrimaryContactPersonID

WHEREPeople.EmailAddress IS NOT NULL

ANDEXISTS

(SELECT *

FROM Sales.Orders

JOIN Sales.OrderLines

ON OrderLines.OrderID = Orders.OrderID

WHEREOrders.CustomerID = Customers.CustomerID

ANDOrderLines.UnitPrice > 500);

The use of an EXISTS operator says, “for each Customer row, does there exist even one row from in the orders table for in item with a cost of 500 or greater?” This is a precise match for the stated requirements, making it easier to read and understand for the next programmer.

DISTINCT and other solutions

Of course, there are more ways to solve this problem. In place of the subquery, you could use an IN operator:

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AND CustomerId in (SELECT CustomerId from Sales.Orders...

The query will return same correct results, but it will trigger another code analysis rule violation, PE019Consider using EXISTS instead of IN. Using EXISTS is generally preferred, here, due to the ability to test multiple columns. Also, use of NOT IN will return unexpected results when the subquery’s source data contains NULL values

Another option is just to use JOIN conditions, instead of a subquery, to get the Sales.Orders and OrderLines, and then add a DISTINCT clause to the SELECT statement, to remove the duplicate rows for customers who have ordered more than one item with a unit price of greater than 500:

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SELECT DISTINCT People.FullName, People.EmailAddress

FROM Sales.Customers

JOIN Application.People

ON People.PersonID = Customers.PrimaryContactPersonID

JOIN Sales.Orders

ON Orders.CustomerID = Customers.CustomerID

JOIN Sales.OrderLines

ON OrderLines.OrderID = Orders.OrderID

WHEREPeople.EmailAddress IS NOT NULL

ANDOrderLines.UnitPrice > 500;

I’ve seen a lot of people tackle the problem like this, believing that it is the preferred way to do it. However, it doesn’t answer the question in a straightforward way and using DISTINCT is often a code smell, indicating that more rows than necessary were processed, before removing duplicates at the end.

Another way I solved this problem was to create a temp table of all customers, then delete rows that didn’t have a qualifying order. I’d like to say that this was just as a contrived, “what’s the wackiest idea I can think of” style of solution, but I have seen it in production code more than once (and it’s not even close to being the weirdest solution I’ve seen).

(Video) Return Multiple Match Results in Excel (2 methods)

Which is better, EXISTS or COUNT (or something else)?

Each of these queries gave the same set of rows as output; they all give the correct answer. So how do we choose which is the best, or most appropriate, solution? This boils down to readability and then performance, in that order.

Readability

My guiding principle is that SQL was always intended to be as close to real, written language as possible. Whatever the problem, write the query in the simplest, set-based way possible, so that someone else can read it like a normal, declarative sentence and understand it. Most of the time, this solution will perform the best too.

Of course, this isn’t always true. Sometimes one must contort what could have been a simple query to accommodate a wonky database design. However, it holds true in enough cases that it is the best place to start. Everything after that becomes performance tuning to deal with special cases.

The EXISTS operator is the most natural way to check for the existence of rows based on some criteria and, in our example, it answers the question in the most concise way and reads most like the requirements statement. I will only choose an alternative, less readable solution if it pays back significantly in terms of performance and scalability.

Performance

Here we’ve set out our candidate solutions up front. Realistically, most programmers would stop when they found the answer that made sense to them, in the moment. If it’s not the best choice, they find that out during performance testing, and tune it. Conversely, I’ve seen overly complex queries defended on the basis that doing it that way avoids some archaic performance issue that the programmer once encountered (like on SQL Server 7.0 or earlier).

This is the value of a code analysis tool like Prompt. If the COUNT query happened to be my first solution, Prompt gives me an immediate hint that using EXISTS will be a more readable and possibly faster option.

Finding Correlated Rows Using EXISTS or COUNT | Redgate (2)

Figure 1: PE013 warning in SQL Prompt

Of course, as a diligent programmer, I now test both, rather than rely on the wisdom of built-in rules, or something I read on the Internet.

For a task such as this, I suggest two quick tests to perform: compare your version of the query, and the viable alternatives, in terms of their execution statistics and then, if necessary, their execution plans. Note that, the more realistic the data set you are using the more obvious differences may appear.

Query execution statistics

The simplest way to view the timings and other execution statistics for individual queries is to use STATISTICS IO/TIME, as follows (although STATISTICS IO introduces significant overhead in some cases, and you may prefer to use Extended Events).

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SET STATISTICS IO, TIME ON; --turn on io and time stats

--clear the procedure cache for the WideWorldImporters DB

ALTER DATABASE SCOPED CONFIGURATION CLEAR PROCEDURE_CACHE;

-- Query with EXISTS

-- Query with COUNT(*)

-- Query with DISTINCT

Execute each query a few times to get the statistics related only to executing the plan (not compiling it or caching data to memory). I also advise running each of the statements individually or some overhead in the query execution process may be added or lost from the time statistics.

I won’t reel off the statistics here but I saw no significant difference in elapsed time for any of the query variations, and for the COUNT and EXISTS queries, the execution statistics, including logical reads (from memory) and physical reads (from disk) were identical.

In more interesting cases, you might find that a less readable solution uses less CPU, less I/O (memory and disk), and takes less time. The question then becomes: is it so much less time that implementing a more cryptic way to answer the question might be worth it? This decision is not always simple. If a query runs a million times a day, saving a few milliseconds is worth it. If it runs once per day, then saving 10 seconds is almost certainly not worth it, especially if it means no one else can understand how the code works.

In this case, it’s worth considering briefly why there was no performance difference between COUNT and EXISTS. This might seem surprising because, logically, it is easy to explain why the EXISTS solution might be faster, since it stops looking for matching orders for a customer as soon as it finds the first one. The COUNT solution, and the others, as written, process all the order lines for every person who has made a purchase, then reject those that don’t meet the “greater than no orders” criteria for purchases over 500.

The execution plans reveal the answer.

(Video) SQL Query | How to identify Overlapping Date Ranges

Execution plans

Note that WideWorldImporters includes the FilterCustomersBySalesTerritoryRole security policy that I temporarily disabled so that it didn’t complicate the plans; only ever do this in development!

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ALTER SECURITY POLICY Application.FilterCustomersBySalesTerritoryRole WITH (STATE = OFF);

In my tests the execution plans for the COUNT and EXISTS queries were the same, as shown in Figure 2.

Finding Correlated Rows Using EXISTS or COUNT | Redgate (3)

Figure 2: The query plan for the COUNT(*) and EXISTS variants of the query

In almost every query example, you will likely find that the EXISTS and the COUNT queries have the same plan. Although logically the former is more efficient, in fact the query optimizer can often rewrite a query to a mathematically equivalent version that performs better, and in fact, it treats these two variants in the same way whenever it can, so the plans, and performance, are the same. Phil Factor reported similar findings in his PE019 article on the use of EXISTS instead of IN.

However, as complexity increases in a query, the optimizer may not always be able to work its magic, so you may still see some cases where the COUNT variant really is slower, as well as less readable. That said, I tested more complex versions of these queries (though still with the same predicate condition) on tables up to several hundred gigabytes in size, and still saw no differences.

However, I did find small differences if I changed the predicate condition to “greater than or equal to zero“. For example, for the COUNT(*) query:

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........ANDOrderLines.UnitPrice > 500) >= 0;

Mathematically, this query must return data. Yet, the plan for the COUNT query includes a few extra operators; a Hash Match (Aggregate) operator, to compute the COUNT(*) value, a Compute Scalar, and a Filter to filter out the rows where the COUNT(*) = 0. Collectively, they accounted for less than 2% of the work of this query.

Finding Correlated Rows Using EXISTS or COUNT | Redgate (4)

Figure 3: The query plan for the COUNT(*)>= query

Finally, I won’t show it here, but the plan for the DISTINCT query shows it to be a slightly higher costing implementation at 34% of the expected costs, to 33% each for the other two. The extra costs in the DISTINCT version of the query are mainly that it needs a Sort to implement the Distinct operator to remove duplicate values.

Whereas the previous queries use a semi-join between Customers and Orders (a semi-join is implemented as a correlated subquery, where you essentially join to the tables, but do not return any rows from one input, Orders table in this case), here we get a JOIN that will add the data to the set during processing resulting in slightly larger memory use. The resulting difference in performance is negligible, still, but this method is likely not to scale as well to large data volumes.

Considerations and false positives for code analysis rules

One of the interesting things with code analysis is that if I write the query as follows, using a variable for the COUNT filter, the results will be correct, I don’t see the PE013 warning, but I do get the poorer plan (the one in Figure 3).

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DECLARE @countvalue int = 0;

SELECT People.FullName, People.EmailAddress

FROM Sales.Customers

JOIN Application.People

ON People.PersonID = Customers.PrimaryContactPersonID

WHEREPeople.EmailAddress IS NOT NULL

AND(SELECT COUNT(1)

FROM Sales.Orders

JOIN Sales.OrderLines

ON OrderLines.OrderID = Orders.OrderID

WHEREOrders.CustomerID = Customers.CustomerID

ANDOrderLines.UnitPrice >= 500) > @countvalue;

(Video) Handling NA in R | is.na, na.omit & na.rm Functions for Missing Values

Obviously, if the value of @countvalue is always set to a literal value, this is not an ideal way to write the query, introducing what looks like a variable value to the optimizer, causing it to pick a plan that allows for a different value for @countvalue (especially if you are using this just to avoid the code analysis warning to make your team code review easier).

If the value for @countvalue is a parameter allowing the query to do more than simple or existence of a row, then this technique is the best way to provide the answer to the questions like “give me the emails of all customers who have ordered 2 or more items of unit price greater than 500” by setting the @countvalue variable to 2. And then asking for 5 or more is a simple change of a parameter.

If you’re wondering if using COUNT(1), instead of COUNT(*) makes any difference to performance: it makes no difference what the scalar expression is, unless it includes a column in the expression that could cause it to evaluate to NULL. The scalar value without a column reference is not evaluated, even if it is nonsense like 1/0:

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SELECT COUNT(1/0) FROM Sales.Orders;

This returns 73595, not Divide-By-Zero as you would expect. Any scalar expression will be ignored and counted.

Finally, note that the static code analysis bases the rule on the 0 in the magnitude Boolean expression. Ending the COUNT(*) query with any of the following will cause the same alert, even though they have very different meanings, and are hence false positives to the rule, though none of the following examples are the ideal solution to the problem.

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--First two are equivalent to NOT EXISTS

ANDOrderLines.UnitPrice >= 500) = 0;

ANDOrderLines.UnitPrice >= 500) <= 0;

--Nonsense, COUNT cannot be < 0

ANDOrderLines.UnitPrice >= 500) < 0;

--Equivalent to EXISTS

ANDOrderLines.UnitPrice >= 500) > 0;

--Will always be true

ANDOrderLines.UnitPrice >= 500) >= 0;

Summary

There are always many ways to solve a problem, and in all cases, it is best to look for the simplest solution. Simplicity, naturally, gets harder to achieve the more complex the problem you’re trying to solve. Nevertheless, I always suggest you start with the query that makes the most sense and work from there.

For finding correlated rows based in criteria that at least one row must exist, it’s clear that using EXISTS is the most appropriate solution. It is readable, answers the question in a direct and simple fashion, and will perform as least equivalently to any alternative solution. While I wasn’t able to detect any performance advantage to using EXISTS over COUNT, the readability factor is enough to warrant taking code analysis rule PE013 seriously, for the sake of your future self, and other programmers.

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FAQs

Is exists a correlated subquery? ›

EXISTS is an unary operator. It has only one operand, which is a subquery (correlated or not). If the subquery returns at least one record, then EXISTS returns TRUE . If the subquery returns no records, EXISTS returns FALSE .

How do you check if a row exists in SQL? ›

To test whether a row exists in a MySQL table or not, use exists condition. The exists condition can be used with subquery. It returns true when row exists in the table, otherwise false is returned. True is represented in the form of 1 and false is represented as 0.

How do I count rows in SQL with conditions? ›

The SQL COUNT() function returns the number of rows in a table satisfying the criteria specified in the WHERE clause. It sets the number of rows or non NULL column values. COUNT() returns 0 if there were no matching rows.

What does the exists operator check for? ›

The EXISTS operator is used to test for the existence of any record in a subquery. The EXISTS operator returns TRUE if the subquery returns one or more records.

Which is better in or exists? ›

IN works faster than the EXISTS Operator when If the sub-query result is small. If the sub-query result is larger, then EXISTS works faster than the IN Operator.

When to use exists and not exists? ›

Use EXISTS to identify the existence of a relationship without regard for the quantity. For example, EXISTS returns true if the subquery returns any rows, and [NOT] EXISTS returns true if the subquery returns no rows. The EXISTS condition is considered to be met if the subquery returns at least one row.

Is SQL better than exists? ›

The EXISTS clause is much faster than IN when the subquery results is very large. Conversely, the IN clause is faster than EXISTS when the subquery results is very small. Also, the IN clause can't compare anything with NULL values, but the EXISTS clause can compare everything with NULLs.

How do you check if a value exists in a table SQL? ›

How do I check if a record exists in SQL? Using EXISTS clause in the IF statement to check the existence of a record. Using EXISTS clause in the CASE statement to check the existence of a record. Using EXISTS clause in the WHERE clause to check the existence of a record.

Is exists faster than in SQL? ›

“EXISTS” clause is preferred when there is a need to check the existence of values in another table or when there is a need to check against more than one column. The EXISTS clause is faster than IN when the subquery results are very large. The IN clause is faster than EXISTS when the subquery results are very small.

How do you count rows with two conditions? ›

You can use the COUNTIFS function in Excel to count cells in a single range with a single condition as well as in multiple ranges with multiple conditions. If the latter, only those cells that meet all of the specified conditions are counted.

How do you count rows with multiple criteria? ›

How to Countif Multiple Criteria?
  1. Step 1: document the criteria or conditions you wish to test for.
  2. Step 2: type “=countifs(“ and select the range you want to test the first criteria on.
  3. Step 3: input the test for the criteria.
  4. Step 4: select the second range you want to test (it can be the same range again, or a new one)
31 Oct 2022

How do you count rows with a specific value? ›

On the Formulas tab, click Insert, point to Statistical, and then click one of the following functions:
  1. COUNTA: To count cells that are not empty.
  2. COUNT: To count cells that contain numbers.
  3. COUNTBLANK: To count cells that are blank.
  4. COUNTIF: To count cells that meets a specified criteria.

How do you use exists in SELECT statement? ›

The result of EXISTS is a boolean value True or False. It can be used in a SELECT, UPDATE, INSERT or DELETE statement. Syntax: SELECT column_name(s) FROM table_name WHERE EXISTS (SELECT column_name(s) FROM table_name WHERE condition);

What is the difference between exists and in operators? ›

IN operator always picks the matching values list, whereas EXISTS returns the Boolean values TRUE or FALSE. EXISTS operator can only be used with subqueries, whereas we can use the IN operator on subqueries and values both.

Can we use exists in case statement? ›

Yes, just do: SELECT CASE WHEN EXISTS(subquery) THEN... There are some situations you can't use it (e.g. in a group by clause IIRC), but SQL should tell you quite clearly in that situation.

What to use instead of exists? ›

exist
  • continue.
  • endure.
  • happen.
  • lie.
  • live.
  • occur.
  • prevail.
  • remain.

Why exists is faster than in Oracle? ›

Answers. Exist is more faster than IN because IN doesn't use indexes at the time of fetching but Exist uses Index at the time of fetching.

Is exists same as inner join? ›

Generally speaking, INNER JOIN and EXISTS are different things. The former returns duplicates and columns from both tables, the latter returns one record and, being a predicate, returns records from only one table. If you do an inner join on a UNIQUE column, they exhibit same performance.

Which is faster exists or not exists? ›

In this case, when the subquery returns even one null, NOT IN will not match any rows. Regarding performance aspects, SQL NOT EXISTS would be a better choice over SQL NOT IN. NOT EXISTS is significantly faster than NOT IN especially when the subquery result is very large.

What is the difference between exists and any in SQL? ›

Exists is same as any except for the time consumed will be less as, in ANY the query goes on executing where ever the condition is met and gives results . In case of exists it first has to check throughout the table for all the records that match and then execute it.

Why we use if not exists in SQL? ›

The NOT EXISTS in SQL Server will check the Subquery for rows existence. If there are no rows then it will return TRUE, otherwise FALSE. SQL Server Not EXISTS operator will return the results exactly opposite to the result returned by the Subquery.

Why exists () is faster than Count ()? ›

Answer: Using the T-SQL EXISTS keyword to perform an existence check is almost always faster than using COUNT(*). EXISTS can stop as soon as the logical test proves true, but COUNT(*) must count every row, even after it knows one row has passed the test.

Which is faster join or exists? ›

In cases like above the Exists statement works faster than that of Joins. Exists will give you a single record and will save the time also. In case of joins the number of records will be more and all the records must be used. Save this answer.

What can you substitute for if exists in SQL? ›

Using Joins Instead of IN or EXISTS

An alternative for IN and EXISTS is an INNER JOIN, while a LEFT OUTER JOIN with a WHERE clause checking for NULL values can be used as an alternative for NOT IN and NOT EXISTS.

How do you check or find if value EXISTS in that column? ›

You can use the MATCH() function to check if the values in column A also exist in column B. MATCH() returns the position of a cell in a row or column. The syntax for MATCH() is =MATCH(lookup_value, lookup_array, [match_type]) . Using MATCH, you can look up a value both horizontally and vertically.

How do you check if something EXISTS in a table? ›

How to check if a record exists in table in Sql Server
  1. Using EXISTS clause in the IF statement to check the existence of a record.
  2. Using EXISTS clause in the CASE statement to check the existence of a record.
  3. Using EXISTS clause in the WHERE clause to check the existence of a record.
29 Jun 2015

Which method is used to check if a value EXISTS in a set? ›

contains() method is used to check whether a specific element is present in the Set or not. So basically it is used to check if a Set contains any particular element.

Are count queries faster? ›

Less data needs to be transferred and processed.

Your COUNT query will only ever result in one row with one value so this will be quick, the other query could result in a great many rows being transferred to, and processed by, the application.

Which is faster CTE or subquery? ›

The performance of CTEs and subqueries should, in theory, be the same since both provide the same information to the query optimizer. One difference is that a CTE used more than once could be easily identified and calculated once. The results could then be stored and read multiple times.

Which is faster Isnull or coalesce? ›

For your specific case I would say isnull is clearly faster. This is not to say it is better in any other given situation. Using straight up values, or nvarchars or bits instead of int, or a column that is not a primary key, or Nesting isnull versus adding parameters to coalesce could change things.

How do I use Countif and match in Excel? ›

Match one criterion exactly -- COUNTIF
  1. Select the cell in which you want to see the count (cell A12 in this example)
  2. Type an equal sign (=) to start the formula.
  3. Type: COUNTIF(
  4. Select the cells that contain the values to check for the criterion. ...
  5. Type a comma, to separate the arguments.
  6. Type the criterion.
9 Nov 2022

Can you use Countif for 3 criteria? ›

Excel has many functions where a user needs to specify a single or multiple criteria to get the result. For example, if you want to count cells based on multiple criteria, you can use the COUNTIF or COUNTIFS functions in Excel.

How do you count based on conditions? ›

To count numbers or dates that meet a single condition (such as equal to, greater than, less than, greater than or equal to, or less than or equal to), use the COUNTIF function.

How do you use the count method in a list? ›

Python List count() method Syntax
  1. Syntax: list_name.count(object)
  2. Parameters:
  3. Returns: Returns the count of how many times object occurs in the list.
  4. Exception:
5 Aug 2022

What is difference between Countif and Countifs? ›

The difference between COUNTIF and COUNTIFS is that COUNTIF is designed for counting cells with a single condition in one range, whereas COUNTIFS can evaluate different criteria in the same or different ranges.

How do I count rows with specific text in Excel? ›

How to Count Cells with Text in Excel 365
  1. Open the “Excel spreadsheet” you wish to examine.
  2. Click on an “empty cell” to type the formula.
  3. In the empty cell, type: “ =COUNTIF (range, criteria) .” This formula counts the number of cells with text in them from within your specified cell range.
13 Jul 2022

How do I count unique rows in Excel? ›

Count the number of unique values by using a filter
  1. Select the range of cells, or make sure the active cell is in a table. ...
  2. On the Data tab, in the Sort & Filter group, click Advanced. ...
  3. Click Copy to another location.
  4. In the Copy to box, enter a cell reference. ...
  5. Select the Unique records only check box, and click OK.

What is the difference between exists and join in SQL? ›

Like EXISTS, JOIN allows one or more columns to be used to find matches. Unlike EXISTS, JOIN isn't as confusing to implement. The downside to JOIN is that if the subquery has any identical rows based on the JOIN predicate, then the main query will repeat rows which could lead to invalid query outputs.

Can we use exists with join in SQL? ›

An EXISTS join is a join in which the right side of the join needs to be probed only once for each outer row. Using such a definition, an EXISTS join does not literally use the EXISTS keyword.

Is exists a logical operator? ›

EXISTS is a logical operator that is used to check the existence, it is a logical operator that returns boolean result types as true or false only. It will return TRUE if the result of that subquery contains any rows otherwise FALSE will be returned as result.

What is a correlated subquery in SQL? ›

A correlated subquery is a subquery that refers to a column of a table that is not in its FROM clause. The column can be in the Projection clause or in the WHERE clause. In general, correlated subqueries diminish performance.

How do you use exists instead of in in Oracle? ›

IN is a clause or a condition that helps to minimize the use of multiple OR conditions in Oracle while EXISTS is a clause or a condition that is used to combine the queries and create subquery in Oracle.

How do you check if data already exists in database in C#? ›

I am using these lines of code to check if the record exists or not. SqlCommand check_User_Name = new SqlCommand("SELECT * FROM Table WHERE ([user] = '" + txtBox_UserName. Text + "') ", conn); int UserExist = (int)check_User_Name. ExecuteScalar();

Which one is a correlated subquery? ›

A correlated subquery is a subquery that refers to a column of a table that is not in its FROM clause. The column can be in the Projection clause or in the WHERE clause.

What is the best example of a correlated subquery? ›

Here is an example for a typical correlated subquery. In this example, the objective is to find all employees whose salary is above average for their department. SELECT employee_number, name FROM employees emp WHERE salary > ... In the above nested query the inner query has to be re-executed for each employee.

Which of the following is the correlated subquery? ›

Explanation: Correlated subquery references a column in the outer query and executes the subquery once for every row in the outer query while Uncorrelated subquery executes the subquery first and passes the value to the outer query.

Which one runs faster a correlated subquery or an exists? ›

On the other hand regular or non-correlated subquery return a result which is then used by the outer query. It is only executed one time and not for every row returned by the outer query, hence it is faster than a correlated subquery. Thanks for reading this article so far.

How do you find a correlated subquery? ›

To identify a correlated subquery, just look for these kinds of references. If you find at least one, you have a correlated subquery! The negative part of a data question is often solved in a SQL correlated subquery by using the NOT EXISTS operator in the WHERE clause.

What is difference between subquery and correlated query? ›

Subqueries can be categorized into two types: A noncorrelated (simple) subquery obtains its results independently of its containing (outer) statement. A correlated subquery requires values from its outer query in order to execute.

Which is better subquery or CTE? ›

CTE can be more readable: Another advantage of CTE is CTE are more readable than Subqueries. Since CTE can be reusable, you can write less code using CTE than using subquery. Also, people tend to follow the logic and ideas easier in sequence than in a nested fashion.

What is a correlated nested query in SQL give an example? ›

EXAMPLE of Correlated Subqueries : Find all the employees who earn more than the average salary in their department. SELECT last_name, salary, department_id FROM employees outer WHERE salary > (SELECT AVG(salary) FROM employees WHERE department_id = outer.department_id);

Why correlated subquery is used? ›

Correlated subquery is used in row by row processing and gets evaluated once, for each row processed by the outer query. A correlated subquery is a way to read all the rows in a table.

How does correlated query work in SQL? ›

SQL Correlated Subqueries are used to select data from a table referenced in the outer query. The subquery is known as a correlated because the subquery is related to the outer query. In this type of queries, a table alias (also called a correlation name) must be used to specify which table reference is to be used.

What is the correct syntax for exists expression? ›

The result of EXISTS is a boolean value True or False. It can be used in a SELECT, UPDATE, INSERT or DELETE statement. Syntax: SELECT column_name(s) FROM table_name WHERE EXISTS (SELECT column_name(s) FROM table_name WHERE condition);

Which two SQL statements are used most commonly with correlated subqueries? ›

Correlated subqueries are commonly used in the SELECT, WHERE, and FROM statements.

Is correlated subquery faster? ›

Answer: Correlated subqueries are usually used for EXISTS Booleans, and scalar subqueries (e.g. subqueries in the SELECT clause). Correlated subqueries and slow because the sub-query is executed ONCE for each row returned by the outer query.

How do you optimize a correlated subquery? ›

Use of Equivalent Columns

If the subquery has a column equivalent to columns in the external query, this column can be used to rewrite the correlated column in subquery. This can reduce the quantity of correlated parts and simplify the query.

Which is faster in or exists? ›

The EXISTS clause is faster than IN when the subquery results are very large. The IN clause is faster than EXISTS when the subquery results are very small.

How can you improve the performance of a correlated subquery? ›

Correlated subqueries provide an intuitive syntax for writing queries that return related data. However, they often perform poorly due to needing to execute once for every value they join on . The good news is that many correlated subqueries can be rewritten to use a derived table for improved performance.

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