Concurrency
The capacity of various components or units of a program, algorithm, or problem to be executed partially or out of sequence without changing the result is known as concurrency in computer science.

Transaction
A transaction is a collection of operations that performs a single logical function in a database application. Multiple transactions execute at the same time can cause the concurrency issue.

Transaction Properties
A database management system (DBMS) is considered a relational database management system (RDBMS) if it follows the transactional properties, ACID, a set of properties of database transactions intended to guarantee data validity..

• A: Atomicity
  • Transactions are often composed of multiple statements. Atomicity guarantees that each transaction is treated as a single "unit", which either succeeds completely or fails completely:
• C: Consistency
  • Consistency ensures that a transaction can only bring the database from one consistent state to another, preserving database invariants: any data written to the database must be valid according to all defined rules, including constraints, cascades, triggers, and any combination thereof.
• I: Isolation
  • Transactions are often executed concurrently (e.g., multiple transactions reading and writing to a table at the same time). Isolation ensures that concurrent execution of transactions leaves the database in the same state that would have been obtained if the transactions were executed sequentially.
• D: Durability
  • Durability guarantees that once a transaction has been committed, it will remain committed even in the case of a system failure (e.g., power outage or crash)

Concurrency Problem Types
The concurrency problem mostly arises when both the users try to write the same data, or when one is writing and the other is reading. Apart from this logic, there are some common types of concurrency problems:

• Dirty Reads
  • This problem occurs when another process reads the changed, but uncommitted data. For instance, if one process has changed data but not committed it yet, another process is able to read the same data. This leads to the inconsistent state for the reader.
• Lost Updates
  • This problem occurs when two processes try to manipulate the same data simultaneously. This problem can lead to data loss, or the second process might overwrite the first processs change.
• Non-repeatable Reads
  • This problem occurs when one process is reading the data, and another process is writing the data. In non-repeatable reads, the first process reading the value might get two different values, as the changed data is read a second time because the second process changes the data.
• Phantom Reads
  • If two same queries executed by two users show different output, then it would be a Phantom Read problem. For instance, If user A select a query to read some data, at the same time the user B insert some new data but the user A only get able to read the old data at the first attempt, but when user A re-query the same statement then he/she gets a different set of data.

Solve Concurrency Problems
SQL Server provides 5 different levels of transaction isolation to overcome these Concurrency problems. These 5 isolation levels work on two major concurrency models:

• Pessimistic model -
  • Readers can block writers, and
  • Writers can block readers.
• Optimistic model -
  • Readers cannot block writers, and
  • Writers cannot block readers, but
  • Writer can block another writer.

Note that readers are users are performing the SELECT operations. Writers are users are performing INSERT, ALTER, UPDATE, S.E.T. operations.

5 Isolation Level
In a SQL server, a query can have five different isolation levels:

• Read Uncommitted --- pessimistic
  • One transaction is allowed to read the data that is about to be changed by the commit of another process.
    • Allows the dirty read problem.
• Read Committed --- pessimistic ---  This option is the SQL Server default.
  • Only allowed to read data that is committed,
    • Eliminates the dirty read problem.
      • In reading data if there are concurrent transactions that can delete or write data, the read is blocked until other work is complete.
• Repeatable Read --- pessimistic
  • All Transactions but Insert has to wait till another transaction's update or read query is complete.
    • Eliminates the Non-Repeatable Read problem, but
    • Phantom data exists
• Serializable --- pessimistic
  • Prevent the Phantom Read problem.
    • The highest level of isolation in the pessimistic model.
    • Can ask any transaction to wait until the current transaction completes.
• Snapshot  --- optimistic
  • Snapshot follows the optimistic model of concurrency, and this level of isolation takes a snapshot of the current data and uses it as a copy for the different transactions. Here each transaction has its copy of data, so if a user tries to perform a transaction like an update or insert, it asks him to re-verify all the operation before the process gets started executing.

These levels are ordered in terms of the separation of work by two different processes, from minimal separation to maximal.

HostForLIFEASP.NET SQL Server 2022 Hosting

When working with SQL Server, there may be instances where keeping column synchronization across two or more tables is required. If you have two tables, Member and MemberProfiles, each containing User data, it is imperative that any changes made in one table are reflected in the other. You can accomplish this with the help of a few trustworthy and effective approaches. This article will cover the three most widely used techniques: employing triggers, writing SQL scripts that use the INSERT, UPDATE, and DELETE commands, and utilizing the MERGE statement.

1. Making Use of Triggers
Triggers are distinct SQL statements that execute automatically each time a table is changed. Tables' columns can be synchronized with triggers to ensure that modifications made in one table are reflected in another. To keep the Name field in the Members table and the MemberProfiles table in sync, for example, you may use an AFTER UPDATE trigger.

CREATE TRIGGER trgUpdateMemberProfiles
ON Members
AFTER UPDATE
AS
BEGIN
    UPDATE MemberProfiles
    SET Name = u.Name
    FROM MemberProfiles p
    JOIN inserted u ON p.UserId = u.UserId
END;

Whenever the Members table is updated, this trigger is triggered automatically. It synchronizes the changed Name in the Members table with the Name column in the MemberProfiles table using the inserted table, which temporarily stores updated rows. Although this approach is effective for basic synchronization tasks, as the system grows larger, it may become challenging to manage intricate logic using triggers.

2. SQL Scripts
Writing SQL queries to perform the INSERT, UPDATE, and DELETE actions is a more difficult method of maintaining column consistency between tables. Developers have complete control over the timing and mechanism of synchronization when using this method. You can manually create queries that insert the same data into the MemberProfiles table when you insert a new row into the Members table.
CREATE PROCEDURE AddMemberAndProfile
    @UserId INT,
    @Name NVARCHAR(100),
    @Email NVARCHAR(100),
    @Age INT
AS
BEGIN
    BEGIN TRANSACTION;
    BEGIN TRY
        INSERT INTO Members(UserId, Name, Email)
        VALUES (@UserId, @Name, @Email);
        INSERT INTO MemberProfiles (UserId, Name, Age)
        VALUES (@UserId, @Name, @Age);
        COMMIT;
    END TRY
    BEGIN CATCH
        ROLLBACK;
        THROW;
    END CATCH;
END;

The two insertions are handled as a single atomic transaction when BEGIN TRANSACTION is used. To ensure consistency, if one fails, the other gets rolled back. This method gives you flexibility and control, but it also necessitates writing and updating manual queries, which can get difficult as your application gets bigger.

3. Using the MERGE Statement (Best for Automation)
An effective method for automating table synchronization is the MERGE statement. It's a great option for quickly and easily maintaining table synchronization because it integrates INSERT, UPDATE, and DELETE actions into a single query. A row is checked to see if it already exists in both tables-> if it does-> it is updated. If not then its inserted.
MERGE INTO MemberProfiles AS Target
USING (SELECT UserId, Name FROM Members) AS Source
ON Target.UserId = Source.UserId
WHEN MATCHED THEN
    UPDATE SET Target.Name = Source.Name
WHEN NOT MATCHED BY TARGET THEN
    INSERT (UserId, Name)
    VALUES (Source.UserId, Source.Name);

Here, the MemberProfiles table is the Target and the Members table is the Source. The Name column in the MemberProfiles table is updated if a matching UserId is discovered. A new row is introduced if no match is discovered. Because it eliminates the need to write several queries, this solution is perfect for developers searching for a simplified, automated approach to data synchronization.
Which Method Should You Apply?

Triggers: Ideal for basic synchronization if you want the second table to automatically maintain synchronization at all times.
SQL Scripts: Excellent if you want complete control over the timing and method of data synchronization, but they involve more manual effort.
MERGE Statement: The most effective way to handle complex situations with a single, clear query and automate the procedure.

Conclusion
Data consistency requires that columns in SQL Server tables be consistent. Every approach has its own benefits, whether it is using the potent MERGE command for efficiency, manual SQL scripts for control, or triggers for automation. Select the method that assures reliable synchronization and best suits the requirements of your application.

HostForLIFEASP.NET SQL Server 2022 Hosting

Subqueries are a fundamental component of SQL that let you execute intricate queries within of more comprehensive queries. They are an effective tool in SQL for carrying out intricate data analysis and manipulation. There are three primary categories of subqueries: scalar subqueries, correlated subqueries, and EXISTS/NOT EXISTS subqueries.

Correlated Subqueries
Correlated subqueries are subqueries that reference a column from the outer query. This makes them dependent on the outer query's results, meaning the subquery is executed once for each row of the outer query.

Example. Finding employees whose salary is greater than the average salary of their department.

SELECT employee_name, salary
FROM employees
WHERE salary > (
  SELECT AVG(salary)
  FROM employees AS dept_avg
  WHERE dept_avg.department = employees.department
);

In this example, the subquery calculates the average salary for each department, and the outer query compares each employee's salary against that average. The subquery depends on the DepartmentID from the outer query, making it correlated.

When to Use?
When you need to compare each row in a table against a calculated value from related data.
For filtering data based on conditions that vary with each row.

Scalar Subqueries
A scalar subquery returns a single value, making it ideal for use in expressions where a single value is required. Scalar subqueries can be used in SELECT, WHERE, and ORDER BY clauses, among others.

Example. Finding the average salary in the company.
SELECT employee.name, (SELECT AVG(salary) FROM employee) AS average_salary
FROM employee;

Here, the subquery calculates the average salary for all employees, and the result is assigned to the average_salary column in the outer query.

When to Use?
When you need to include a single value in your main query, such as a count, average, or specific calculation.
For calculating values dynamically based on related data.

EXISTS and NOT EXISTS

EXISTS and NOT EXISTS subqueries are used to check for the existence or non-existence of rows in a subquery. These operators return a boolean value (TRUE or FALSE), making them useful for conditional queries.

Example. Finding departments that have employees earning more than 100,000.
SELECT department
FROM departments
WHERE EXISTS (
  SELECT 1
  FROM employees
  WHERE employees.department = departments.department
    AND employees.salary > 100000
);

In this query, the outer query returns departments. The subquery checks if there are any employees for that department who have a salary greater than 100,000.

When to Use?
Use EXISTS when you need to check if a related record exists.
Use NOT EXISTS to filter out records that have a related record.

Conclusion

Subqueries are a fundamental tool in SQL for performing complex data manipulations and retrieving specific subsets of data. By understanding the different types of subqueries, you can effectively leverage them to solve a wide range of data-related problems.

HostForLIFEASP.NET SQL Server 2022 Hosting