The SEQUENCE object in SQL Server is an effective tool for producing distinct numerical numbers in a predetermined order. It was first included in SQL Server 2012 to offer features akin to those of IDENTITY columns with more adaptability. Because SEQUENCE is table-independent, developers can utilize it in a variety of scenarios or tables. The fundamentals of SEQUENCE will be covered in this article, along with comparisons to other options like IDENTITY and a fresh real-world example to show how to use it.

What is SEQUENCE in SQL Server?
A SEQUENCE is a user-defined schema-bound object that generates a sequence of numeric values. Unlike IDENTITY, which is tied to a specific table column, SEQUENCE exists independently and can be used across multiple tables or queries.

Key Features of SEQUENCE

• Independent Object: Not tied to a single table or column.
• Customizable: Allows control over the starting value, increment step, minimum, maximum, and cycle behavior.
• Reusable: Can be used in multiple tables or even in calculations.
• Flexible Usage: Values can be retrieved using the NEXT VALUE FOR function.

Syntax
CREATE SEQUENCE schema_name.sequence_name
    AS data_type
    START WITH <initial_value>
    INCREMENT BY <step>
    [MINVALUE <min_value>]
    [MAXVALUE <max_value>]
    [CYCLE | NO CYCLE]
    [CACHE <cache_size> | NO CACHE]

-- START WITH: Specifies the starting value.
-- INCREMENT BY: Specifies the increment between each value (positive or negative).
-- MINVALUE/MAXVALUE: Specifies the minimum and maximum allowed values.
-- CYCLE: Restarts the sequence when it reaches the maximum or minimum value.
-- CACHE: Improves performance by caching sequence values in memory.

Example
We will create a use case for managing order numbers in an e-commerce system. The goal is to assign unique order IDs to transactions using a SEQUENCE object.

Step 1. We will create a SEQUENCE object named order_sequence to generate unique order IDs starting from 1000 and incrementing by 10.
USE Hostforlife
GO

CREATE SEQUENCE dbo.order_sequence
    AS INT
    START WITH 1000
    INCREMENT BY 10

Step 2. Next, we will create a table customer_orders to store customer order details. The order_id column will use the SEQUENCE object to generate unique IDs automatically.
USE Hostforlife
GO

CREATE TABLE dbo.customer_orders (
    order_id INT NOT NULL DEFAULT (NEXT VALUE FOR order_sequence),
    customer_name VARCHAR(100) NOT NULL,
    product_name VARCHAR(100) NOT NULL,
    order_date DATE DEFAULT GETDATE()
)

Step 3. Insert a few sample records into the customer_orders table. The order_id column will automatically get its value from the SEQUENCE object.
USE Hostforlife
GO

INSERT INTO dbo.customer_orders (customer_name, product_name)
VALUES
    ('Peter', 'Smartphone'),
    ('leon', 'Laptop'),
    ('Michael', 'Tablet');

Step 4. Retrieve the data to see the order_id values generated by the SEQUENCE.
USE Hostforlife
GO

SELECT * FROM dbo.customer_orders

Step 5. Use the sys.sequences catalog view to check the properties of the SEQUENCE object. This query will provide details such as the current value, increment, and start value of the SEQUENCE.

SELECT * FROM sys.sequences WHERE name = 'order_sequence';

Output

Step 6. When more records are inserted, the SEQUENCE continues generating unique values.
USE Hostforlife
GO

INSERT INTO customer_orders (customer_name, product_name)
VALUES ('John', 'Headphones');

Advantages

• Greater Control: SEQUENCE provides more control compared to IDENTITY, such as restarting, cycling, and specifying custom increments.
• Reusability: It can be used across multiple tables or in ad hoc queries.
• Predictability: Developers can predict the next value without inserting a record (unlike IDENTITY).
• Performance: Caching values improve performance for high-volume applications.

SEQUENCE vs IDENTITY

Conclusion
The SEQUENCE object in SQL Server is a versatile tool for generating sequential numbers, offering greater flexibility and control than IDENTITY. Whether you're building a multi-table system or need precise control over numbering, SEQUENCE is a valuable addition to your SQL Server toolkit. By leveraging SEQUENCE, you can design robust, scalable, and reusable numbering systems tailored to your application’s needs.

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SQL Server has two types function system or built-in function and user-defined function. In today’s article, we shall cover User Defined Functions (UDF). UDFs are the custom functions that developers need to create. SQL Server supports two types of User-Defined Functions.

• Table-Valued Functions
• Scalar Valued Functions

Now, let’s look into how to create them. To create User-Defined Functions, let’s consider the table.

Table-Valued Function
When we run a function and return a table as a result, then it’s called a table-valued function.
CREATE FUNCTION [dbo].[Function_Name]()
RETURNS TABLE
AS
RETURN (
    Statement
)

Now, let's create a Table-Valued function.
CREATE FUNCTION [dbo].[udf_ProductDetailsQuantitywise] (@product_quantity INT)
RETURNS TABLE
AS
RETURN (
    SELECT *
    FROM Products
    WHERE product_quantity >= @product_quantity
);

Below is the Output.

As we can see in the above screenshot, the Table-Valued function is created, we took input parameter as @product_quantity which will return table as a result. Now, let’s call the function [dbo].[udf_ProductDetailsQuantitywise] and pass the value as 12 for the input parameter.
SELECT * FROM [dbo].[udf_ProductDetailsQuantitywise] (12)

We can see [dbo].[udf_ProductDetailsQuantitywise] function is used to get the quantity-wise product table. Hence, the table-valued function is useful for returning the table based on business logic.

Scaler Valued Function
Unlike the table-valued function, this function returns a single scaler value based on the input parameter we passed.
CREATE FUNCTION [dbo].[Function_Name] (ParameterList)
RETURNS DataType
AS
BEGIN
    RETURN Return_DataType
END

Now, let's create a Scaler-Valued function.
CREATE FUNCTION [dbo].[udf_ProductPriceTotal]
    (@unit_price INT, @product_quantity INT)
RETURNS INT
AS
BEGIN
    RETURN @unit_price * @product_quantity
END

Below is the Output.

As we can see in the above screenshot, scaler-valued function is created, it took two input parameters @unit_price and @product_quantity which will return a single scaler value as a result.

Now, let’s call the function using [dbo].[udf_ProductPriceTotal].
SELECT
    product_name,
    unit_price,
    product_quantity,
    [dbo].[udf_ProductPriceTotal](unit_price, product_quantity) AS TotalPrice
FROM Products;

We can see the udf_ProductPriceTotal function is used to get the total price of each product in the table. Hence, the scaler function is useful for returning the single scaler value.

Summary
UDFs are a great way to customize the function based on specific needs. In the article, we covered how to create them by taking simple examples. I hope you liked the article, and I am looking forward to your comments and suggestions.

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One of the most popular relational database management systems (RDBMS), SQL Server, has several strong features. The SELECT statement is one of the most important and commonly used commands among them. It is the foundation of the majority of SQL operations since it enables users to get data from a database. This article will examine SQL Server's SELECT capability, including its fundamental syntax, more complex applications, and some best practices for maximizing its use.

We demonstrate the results of these claims using a sample Employee table to help put this idea into concrete form.

SELECT Statement
To fetch all records from the Employees table.
SELECT * FROM Employees;

In this query

• SELECT * tells SQL Server to return all columns.
• FROM Employees specifies the table from which to retrieve the data.

While using * to select all columns can be convenient, it is generally better practice to specify the exact columns you need to retrieve. This reduces unnecessary data retrieval and improves performance, especially when dealing with large tables. At its core, the SELECT statement allows you to query data from one or more tables. The basic syntax for a SELECT query looks like this.

SELECT FirstName, LastName FROM Employees;

Filtering Data with the WHERE Clause
The real power of SELECT comes when you start using the WHERE clause to filter the data returned. This allows you to specify conditions that must be met for rows to be included in the result set.
SELECT FirstName, LastName FROM Employees
WHERE Department = 'Sales';

The query fetches only the employees working in the "Sales" department.

You can also use operators like =, >, <, BETWEEN, IN, LIKE, and IS NULL to create complex conditions. For example.
SELECT FirstName, LastName, Salary FROM Employees
WHERE Salary > 50000 AND Department IN ('HR', 'Sales');

This retrieves the employees in the HR or Sales departments with a salary greater than $50,000.

Sorting Data with ORDER BY
To arrange the result set in a specific order, you can use the ORDER BY clause. By default, it sorts in ascending order (A to Z, lowest to highest), but you can also use DESC to sort in descending order.
SELECT FirstName, LastName, Salary FROM Employees
ORDER BY Salary DESC;

This query returns the employees sorted by salary, from the highest to the lowest.

Aggregating Data with GROUP BY
In many cases, you'll want to group rows together based on certain columns and then perform aggregate functions like COUNT, SUM, AVG, MIN, or MAX on those groups. The GROUP BY clause allows you to do this.
SELECT Department, COUNT(*) AS EmployeeCount
FROM Employees
GROUP BY Department;

This query returns the number of employees in each department. The COUNT(*) function counts the number of rows in each group formed by the Department column.

Using DISTINCT to Remove Duplicates
If you want to eliminate duplicate rows from your result set, you can use the DISTINCT keyword. This is particularly useful when querying data that might contain repeated values.

SELECT DISTINCT Department FROM Employees;

This query returns only the unique departments from the Employees table.

Limiting the Number of Results with TOP
When working with large datasets, it’s often useful to limit the number of rows returned by a query. SQL Server allows you to do this using the TOP keyword.
SELECT TOP 5 FirstName, LastName FROM Employees;

This query returns only the first 5 rows from the Employees table. You can also use PERCENT to return a percentage of the rows.
SELECT TOP 10 PERCENT FirstName, LastName FROM Employees;

Subqueries and Nested Queries
SQL Server allows you to nest queries within other queries. A subquery is a query that is embedded inside a larger query. Subqueries can be used in the SELECT, FROM, and WHERE clauses.
SELECT FirstName, LastName, Salary
FROM Employees
WHERE Salary > (SELECT AVG(Salary) FROM Employees);

This query returns the names and salaries of employees who earn more than the average salary.

Best Practices for Using SELECT in SQL Server

• Be Specific with Columns: Avoid using SELECT * in production code, as it can lead to unnecessary data being returned and can negatively impact performance.
• Use Indexes Efficiently: Proper indexing on tables can significantly speed up SELECT queries, especially those with WHERE conditions or JOIN operations.
• Avoid N+1 Query Problem: When using JOIN or subqueries, ensure your queries are optimized to prevent fetching data multiple times unnecessarily.
• Limit Data Retrieval: When testing queries or working with large datasets, always limit the number of rows returned using TOP, especially if you don’t need the entire dataset.

Conclusion
The SELECT statement is a versatile and powerful feature in SQL Server that allows developers to retrieve and manipulate data effectively. By mastering its basic and advanced features, you can write efficient queries that meet your application’s needs. Whether you're filtering data, aggregating results, or joining multiple tables, understanding how to use SELECT to its full potential is crucial for any SQL Server user. By following best practices and leveraging the various clauses and functions available, you can optimize the performance of your queries and ensure that your database operations are both effective and efficient.

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The terms "distributed SQL" and "monolithic SQL" refer to different architectures for SQL databases, each with unique characteristics in terms of performance, scalability, resilience, and operational complexity. Let's dive into both in detail.

Monolithic SQL Databases

Monolithic SQL databases, like SQL Server and Oracle, are traditional relational databases typically designed to run on a single server or a single cluster (in a primary replica configuration).

Characteristics of Monolithic SQL Databases

• Single Node or Primary-Replica Architecture
  • The core database instance often runs on a single node (server), which handles most of the read and write operations.
  • Some monolithic databases can be configured with a primary-replica setup, where the primary server handles all writes and propagates these changes to one or more replicas. However, replicas are primarily read-only and require special configurations to handle writes.
• Vertical Scalability (Scale-Up)
  • These databases rely on scaling vertically, meaning that performance is improved by upgrading the existing server with more powerful hardware (CPU, memory, storage).
  • Vertical scaling has limits; there’s only so much you can upgrade a single machine, and it can be very costly.
• Centralized Storage
  • Data is stored centrally on a single machine or cluster. This centralized approach can make the system easier to manage and maintain, but it also introduces a single point of failure unless high availability mechanisms like clustering are in place.
• Replication and Failover
  • High availability is achieved through replication and clustering, where replicas can take over if the primary instance fails. However, failover to replicas isn’t instant and often requires downtime.
• Latency and Geographic Constraints
  • Since the database runs on a single server or data center, latency is often low for local applications but can be high for users located far from the data center. This can make monolithic databases less ideal for applications needing global reach and low latency in multiple regions.

Pros and Cons of Monolithic SQL Databases
Pros

• Simplicity: Centralized management is often easier to configure, secure, and monitor.
• ACID Transactions: Strong ACID compliance makes it reliable for applications that need consistent transactions.
• Reliability: Mature ecosystem and extensive support for enterprise use cases.

Cons

• Limited Scalability: Scaling up has physical and cost limitations.
• Single Point of Failure: Even with replication, failover can introduce downtime.
• Performance Bottlenecks: Since all operations go through a single machine, heavy workloads can create bottlenecks.
• Latency for Global Applications: Users outside the data center’s region may experience high latency, affecting the user experience.

Distributed SQL Databases
Distributed SQL databases, such as CockroachDB, Google Spanner, and YugabyteDB, are designed to operate across multiple nodes, often located in different geographic regions, in a single logical database system.

Characteristics of Distributed SQL Databases

• Distributed Nodes (Multi-Node Architecture)
  • The database consists of multiple nodes across various locations (data centers or regions). Each node can handle read and write requests, spreading the workload evenly across the cluster.
  • Nodes communicate through consensus algorithms (like Raft or Paxos) to ensure data consistency and reliability.
• Horizontal Scalability (Scale-Out)
  • Distributed SQL databases can scale horizontally by adding more nodes to the cluster, which automatically increases the capacity for reads and writes.
  • This “scale-out” architecture allows the database to handle very large workloads and datasets with minimal operational overhead.
• Data Replication and Sharding
  • Data is automatically sharded (split) across multiple nodes and replicated for high availability. If a node fails, replicas on other nodes take over, providing seamless continuity with minimal downtime.
  • Some distributed databases allow data placement across specific regions or data centers to minimize latency for specific user bases, an approach sometimes called “multi-region awareness.”
• Geographic Flexibility and Low Latency
  • By placing nodes close to where data is being accessed, distributed SQL databases can reduce latency for global applications. This is especially beneficial for applications with users in multiple regions who require quick access to data.
  • Distributed SQL databases support “geo-partitioning,” where data can be stored close to users, reducing latency and ensuring that laws and regulations (such as GDPR) about data residency are adhered to.
• High Availability and Fault Tolerance
  • Built-in mechanisms provide fault tolerance so the database continues to operate even if some nodes go offline. Automatic failover and load balancing ensure that the system is highly available.
  • Distributed SQL databases are resilient by design, as they can lose nodes and still maintain consistent data availability.

Pros and Cons of Distributed SQL Databases

Pros

• Scalability: Easily scale out by adding more nodes without downtime or costly hardware upgrades.
• High Availability: Fault tolerance and automatic failover make them resilient to failures.
• Global Low Latency: Geographically distributed nodes help reduce latency for users around the world.
• Consistency: Strong consistency models are built-in, often matching the ACID compliance found in monolithic databases.

Cons

• Complexity: Distributed databases are inherently more complex to manage and troubleshoot than centralized ones.
• Networking Costs: Communication between nodes in different locations can incur network costs and add some latency.
• Consistency Trade-offs: Though they aim to be ACID-compliant, certain configurations may need to sacrifice some consistency for availability, depending on the specific CAP requirements.

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