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Top 100 SQL Interview Questions You Must Prepare In 2023

What is the difference between clustered and non-clustered index in SQL?

In SQL, indexes are used to improve the performance of database queries by allowing faster data retrieval. The two main types of indexes are clustered indexes and non-clustered indexes, which differ in their structure and how they organize and store data.

Here are the key differences between clustered and non-clustered indexes:

Clustered Index:

  1. Structure: A clustered index determines the physical order of data in a table. It reorganizes the data in the table to match the order of the index key values. Each table can have only one clustered index.

  2. Storage: The data rows are physically stored in the order of the clustered index. This means that a table can be physically sorted and stored based on the clustered index key.

  3. Data Access: When a query uses the clustered index, it can directly retrieve the desired data from the table because the data is stored in the same order as the index. This makes clustered indexes efficient for retrieving large ranges of data.

  4. Performance Impact: Modifying data in a table with a clustered index can be slower because inserting, updating, or deleting a row may require reordering the data to maintain the physical order specified by the index.

  5. Unique Constraint: A clustered index can enforce a unique constraint on a column or a set of columns, ensuring that each value in the indexed column(s) is unique.

Non-Clustered Index:

  1. Structure: A non-clustered index is a separate structure from the actual data. It contains a copy of the indexed columns along with a pointer to the corresponding data rows.

  2. Storage: The data rows are not physically sorted or stored based on the non-clustered index. Instead, the index provides a quick lookup mechanism to locate the desired data.

  3. Data Access: When a query uses a non-clustered index, it first searches the index to find the location of the data and then retrieves the data from the table using the pointer. Non-clustered indexes are efficient for searching specific values or retrieving a small set of data.

  4. Performance Impact: Modifying data in a table with non-clustered indexes is generally faster than with clustered indexes because the indexes are separate from the actual data and do not require reordering of the data.

  5. Multiple Indexes: A table can have multiple non-clustered indexes, allowing for efficient access to different subsets of data based on different columns or combinations of columns.

In summary, a clustered index determines the physical order of data in a table, while a non-clustered index provides a separate structure for quick data lookup. Clustered indexes are efficient for retrieving large ranges of data, while non-clustered indexes are useful for searching specific values or retrieving smaller data subsets. The choice between clustered and non-clustered indexes depends on the specific requirements of the database and the types of queries being performed.

What are diffrent data type in MS SQL server?

Microsoft SQL Server provides several data types that you can use to define columns in database tables or variables in SQL queries. Here are some commonly used data types in MS SQL Server:

  1. Numeric Data Types:

    • INT: Integer values (-2,147,483,648 to 2,147,483,647).

    • DECIMAL: Fixed-point numeric values with a specified precision and scale.

    • FLOAT: Floating-point approximate numeric values.

  2. Character Data Types:

    • CHAR: Fixed-length character strings.

    • VARCHAR: Variable-length character strings.

    • NVARCHAR: Variable-length Unicode character strings.

    • TEXT: Variable-length non-Unicode character data (deprecated, use VARCHAR(MAX) instead).

  3. Date and Time Data Types:

    • DATE: Date values without time components.

    • TIME: Time values without date components.

    • DATETIME: Date and time values (with millisecond precision).

    • DATETIME2: Date and time values with greater precision (up to 100 nanoseconds).

  4. Boolean Data Type:

    • BIT: Represents boolean values (0 for false, 1 for true).

  5. Binary Data Types:

    • BINARY: Fixed-length binary data.

    • VARBINARY: Variable-length binary data.

    • IMAGE: Variable-length binary data (deprecated, use VARBINARY(MAX) instead).

  6. Large Object Data Types:

    • VARCHAR(MAX): Variable-length character data (up to 2 GB).

    • NVARCHAR(MAX): Variable-length Unicode character data (up to 2 GB).

    • VARBINARY(MAX): Variable-length binary data (up to 2 GB).

  7. Other Data Types:

    • XML: XML data.

    • UNIQUEIDENTIFIER: Unique identifier (GUID).

    • SQL_VARIANT: Stores values of various SQL Server-supported data types.

What are different types of date functions in MS SQL server? Explain with example

Microsoft SQL Server provides various date functions that you can use to manipulate and extract information from date and time values. Here are some commonly used date functions in MS SQL Server, along with examples of their usage:

  1. GETDATE():

    • Syntax: GETDATE()

    • Example: SELECT GETDATE() AS CurrentDateTime;

    • Description: Returns the current date and time.

  2. SYSDATETIME():

    • Syntax: SYSDATETIME()

    • Example: SELECT SYSDATETIME() AS CurrentDateTime;

    • Description: Returns the current date, time, and fractional seconds.

  3. DATEADD():

    • Syntax: DATEADD(interval, number, date)

    • Example: SELECT DATEADD(DAY, 7, GETDATE()) AS NextWeek;

    • Description: Adds or subtracts a specified time interval (e.g., days, months, years) to a given date.

  4. DATEDIFF():

    • Syntax: DATEDIFF(interval, startdate, enddate)

    • Example: SELECT DATEDIFF(DAY, '2022-01-01', '2022-01-15') AS DaysDiff;

    • Description: Calculates the difference between two dates in the specified interval.

  5. DATEPART():

    • Syntax: DATEPART(datepart, date)

    • Example: SELECT DATEPART(YEAR, GETDATE()) AS CurrentYear;

    • Description: Extracts a specific part (e.g., year, month, day) from a date.

  6. YEAR():

    • Syntax: YEAR(date)

    • Example: SELECT YEAR(GETDATE()) AS CurrentYear;

    • Description: Returns the year part of a date.

  7. MONTH():

    • Syntax: MONTH(date)

    • Example: SELECT MONTH(GETDATE()) AS CurrentMonth;

    • Description: Returns the month part of a date.

  8. DAY():

    • Syntax: DAY(date)

    • Example: SELECT DAY(GETDATE()) AS CurrentDay;

    • Description: Returns the day part of a date.

  9. GETUTCDATE():

    • Syntax: GETUTCDATE()

    • Example: SELECT GETUTCDATE() AS CurrentUTCDateTime;

    • Description: Returns the current UTC date and time.

  10. CONVERT():

    • Syntax: CONVERT(datatype, expression, style)

    • Example: SELECT CONVERT(VARCHAR(10), GETDATE(), 101) AS FormattedDate;

    • Description: Converts a date from one data type to another or formats a date as a string according to the specified style.

What is Normalization and what are the advantages of it?

Normalization is a process in database design that aims to eliminate data redundancy and improve data integrity by organizing data into separate tables and defining relationships between them. The goal of normalization is to minimize data duplication and ensure that each piece of data is stored in one place only.

Advantages of normalization include:

  1. Data Integrity: By eliminating data redundancy, normalization helps maintain data integrity by ensuring consistency and accuracy of data. Updates, deletions, and insertions can be performed without introducing anomalies or inconsistencies.

  2. Efficient Storage: Normalized tables reduce data redundancy and optimize storage space. Storing data in a structured and efficient manner can lead to improved performance in terms of storage requirements and query execution.

  3. Simplified Updates: With normalized tables, updates only need to be made in one place. This simplifies the updating process and reduces the chances of inconsistencies or errors resulting from updating data in multiple locations.

  4. Improved Query Performance: Normalization allows for efficient querying and retrieval of data. By breaking down data into logical and smaller tables, queries can be more targeted and specific, leading to improved performance.

  5. Scalability and Flexibility: Normalized tables provide a flexible foundation for future changes and expansions. New data can be easily added without impacting the existing structure or causing data duplication.

  6. Easier Maintenance: Maintaining a normalized database is generally easier and less prone to errors. Modifications, additions, and deletions can be performed in a more straightforward manner, and the overall database structure remains consistent.

What is Normalization in SQL?

Normalization in SQL refers to the process of organizing and structuring a database's tables and relationships to eliminate data redundancy and improve data integrity. It involves breaking down large, complex tables into smaller, more manageable ones and establishing relationships between them using keys. The goal is to reduce data duplication and ensure that each piece of data is stored in one place only.

Normalization in SQL typically involves the following normal forms:

  1. First Normal Form (1NF): Ensures that each column in a table contains atomic values (indivisible) and there are no repeating groups. Each column must have a unique name, and every row should be uniquely identifiable.

  2. Second Normal Form (2NF): Builds upon 1NF and ensures that all non-key attributes depend on the entire key, rather than just part of it. In other words, each column in a table should be functionally dependent on the primary key.

  3. Third Normal Form (3NF): Extends 2NF by removing transitive dependencies. Transitive dependencies occur when a non-key attribute depends on another non-key attribute instead of directly on the primary key. To achieve 3NF, non-key attributes should depend only on the primary key and not on other non-key attributes.

Further normalization forms like Boyce-Codd Normal Form (BCNF), Fourth Normal Form (4NF), and Fifth Normal Form (5NF) can be applied to address specific cases of data redundancy and dependencies.

By normalizing a database, several benefits can be achieved, including:

  1. Improved data integrity and accuracy.

  2. Reduced data redundancy and storage space requirements.

  3. Simplified data updates, inserts, and deletions.

  4. Better query performance through efficient indexing and optimized table structures.

  5. Scalability and flexibility to accommodate future changes and expansions.

  6. Easier maintenance and data management.

Normalization is an essential concept in database design and plays a crucial role in creating well-structured, efficient, and maintainable databases.

What is join in SQL? What are different types of join in SQL with example?

In SQL, a join is a mechanism used to combine rows from two or more tables based on a related column between them. It allows you to retrieve data from multiple tables as a single result set. Joins are fundamental in relational databases for establishing relationships and retrieving data based on those relationships.

Here are the different types of joins in SQL:

  1. INNER JOIN:

    • Syntax: SELECT * FROM table1 INNER JOIN table2 ON table1.column = table2.column;

    • Description: Returns only the matching rows from both tables based on the specified join condition.

  2. LEFT JOIN (or LEFT OUTER JOIN):

    • Syntax: SELECT * FROM table1 LEFT JOIN table2 ON table1.column = table2.column;

    • Description: Returns all rows from the left table (table1) and the matching rows from the right table (table2). If there is no match, NULL values are included for the columns of the right table.

  3. RIGHT JOIN (or RIGHT OUTER JOIN):

    • Syntax: SELECT * FROM table1 RIGHT JOIN table2 ON table1.column = table2.column;

    • Description: Returns all rows from the right table (table2) and the matching rows from the left table (table1). If there is no match, NULL values are included for the columns of the left table.

  4. FULL JOIN (or FULL OUTER JOIN):

    • Syntax: SELECT * FROM table1 FULL JOIN table2 ON table1.column = table2.column;

    • Description: Returns all rows from both tables. If there is a match, the result set includes the matched rows. If there is no match, NULL values are included for the columns of the non-matching table.

  5. CROSS JOIN (or Cartesian Join):

    • Syntax: SELECT * FROM table1 CROSS JOIN table2;

    • Description: Returns the Cartesian product of both tables, resulting in all possible combinations of rows from both tables.

  6. SELF JOIN:

    • Syntax: SELECT * FROM table1 t1 INNER JOIN table1 t2 ON t1.column = t2.column;

    • Description: Performs a join on a table with itself, typically used when there is a need to compare rows within the same table.

These are the main types of joins in SQL. Each join type serves a specific purpose and can be used based on the desired result set and the relationships between the tables.

How do we avoid getting duplicate entries in a query?

To avoid getting duplicate entries in a query result, you can use the DISTINCT keyword or apply appropriate grouping and aggregation techniques. Here are two common approaches:

  1. Using the DISTINCT keyword:

    • Syntax: SELECT DISTINCT column1, column2, ... FROM table;

    • Description: The DISTINCT keyword eliminates duplicate rows from the query result, considering all selected columns collectively. Only unique rows will be returned.

    Example: Consider a "customers" table with columns "customer_id" and "customer_name". To retrieve a list of unique customer names, you can use the DISTINCT keyword as follows:

    sql

    SELECT DISTINCT customer_name FROM customers;

  2. Using GROUP BY:

    • Syntax: SELECT column1, column2, ... FROM table GROUP BY column1, column2, ...;

    • Description: The GROUP BY clause groups rows based on specified columns, and you can use aggregate functions (e.g., COUNT, SUM, AVG) to perform calculations on each group. This approach is useful when you need to perform aggregations while avoiding duplicate entries.

    Example: Suppose you have an "orders" table with columns "order_id", "customer_id", and "order_date". To retrieve a list of unique customer IDs along with the count of orders for each customer, you can use the GROUP BY clause as follows:

    sql

    SELECT customer_id, COUNT(*) AS order_count FROM orders GROUP BY customer_id;

By applying either the DISTINCT keyword or appropriate grouping techniques, you can eliminate duplicate entries in your query results and retrieve the desired information in a concise and accurate manner.

How to find duplicate records in columns of two tables with same column names?

To find duplicate records in columns of two tables with the same column names, you can use the JOIN clause along with the appropriate comparison condition. Here's an example query that demonstrates this:

sql

SELECT table1.column_name FROM table1 JOIN table2 ON table1.column_name = table2.column_name

In this query, replace table1 and table2 with the names of your two tables, and replace column_name with the actual column name you want to compare.

The query performs an inner join between the two tables based on the column_name, which is assumed to be the common column in both tables. It returns the duplicate values from that column.

If you want to find duplicate records across multiple columns, you can modify the query by adding additional comparison conditions in the JOIN clause:

sql

SELECT table1.column1, table1.column2 FROM table1 JOIN table2 ON table1.column1 = table2.column1 AND table1.column2 = table2.column2

Again, replace table1, table2, column1, and column2 with the appropriate names from your tables and columns.

By using the JOIN clause with the appropriate comparison conditions, you can identify and retrieve the duplicate records based on the specified column or columns in the two tables.

What is subquery in SQL?

In SQL, a subquery (also known as an inner query or nested query) is a query that is embedded within another query. It allows you to use the result of one query (the subquery) as a part of another query (the main query). Subqueries are enclosed within parentheses and can be used in various parts of a SQL statement, such as the SELECT, FROM, WHERE, and HAVING clauses.

The subquery can be seen as a query that is executed first and its result is then used by the outer query to perform further operations. The result of a subquery can be a single value, a single row, a single column, or a result set with multiple rows and columns.

Here's an example to illustrate the usage of a subquery:

sql

SELECT column1, column2 FROM table1 WHERE column1 IN (SELECT column1 FROM table2 WHERE column3 = 'value');

In this example, the subquery (SELECT column1 FROM table2 WHERE column3 = 'value') is embedded within the main query. It retrieves the values from column1 of table2 where column3 equals 'value'. The result of the subquery is then used in the main query's WHERE clause to filter the rows from table1 where column1 matches the values obtained from the subquery.

Subqueries provide flexibility and allow you to perform complex queries by breaking them down into smaller, more manageable parts. They can be used to retrieve specific subsets of data, perform calculations, filter results, or even join multiple tables. Subqueries are a powerful tool in SQL for writing more sophisticated queries and working with data from multiple sources.

How to create empty tables with the same structure with another new table?

To create an empty table with the same structure as an existing table, you can use the CREATE TABLE statement with the SELECT INTO clause. Here's an example:

sql

CREATE TABLE new_table AS SELECT * FROM existing_table WHERE 1 = 0;

In this example:

  • new_table is the name of the new table you want to create.

  • existing_table is the name of the table whose structure you want to replicate.

The SELECT * FROM existing_table WHERE 1 = 0 part of the statement selects no rows (1 = 0 condition is always false), but it fetches the structure (columns and their data types) of the existing table. By using the SELECT INTO clause, the result of this query is used to create a new table (new_table) with the same structure as existing_table, but without any data.

After executing this SQL statement, you will have an empty table (new_table) with the same columns and data types as existing_table. It's worth noting that the new table will not have any constraints, indexes, or triggers defined on the original table.

What is the usage of the NVL() or null function?

The NVL() function, or its equivalent in some database systems, is used to handle NULL values in SQL queries. It allows you to substitute a specific value for NULL, providing a default value or alternative expression when encountering NULL.

The basic syntax of the NVL() function is as follows:

sql

NVL(expression, default_value)

Here's an explanation of its usage:

  • expression: This is the value or column that you want to evaluate. If the expression evaluates to NULL, the NVL() function replaces it with the default value.

  • default_value: This is the value that will be returned if the expression is NULL.

The NVL() function is commonly used in scenarios where you want to handle NULL values and replace them with a meaningful or desired value. Some use cases include:

  1. Replacing NULL values with a specific default value:

    sql

    SELECT column1, NVL(column2, 'Unknown') AS column2 FROM table;

    In this example, if column2 contains NULL values, the NVL() function substitutes them with the string 'Unknown'.

  2. Performing calculations or expressions on columns that may contain NULL:

    sql

    SELECT column1, NVL(column2, 0) * column3 AS result FROM table;

    Here, if column2 has NULL values, the NVL() function replaces them with the value 0 before performing the multiplication with column3.

  3. Handling NULL values in conditional statements:

    sql

    SELECT column1, CASE WHEN NVL(column2, 0) > 10 THEN 'Greater than 10' ELSE 'Less than or equal to 10' END AS result FROM table;

    In this case, the NVL() function is used within a CASE statement to handle NULL values in the comparison.

The specific function name for handling NULL values may vary depending on the database system. For example, Oracle uses NVL(), while SQL Server uses ISNULL(), and PostgreSQL uses COALESCE(). However, they all serve a similar purpose of providing a way to handle NULL values in SQL queries.

 Explain character-manipulation functions? Explains its different types in SQL.

Character-manipulation functions in SQL are used to perform various operations on string or character data. These functions allow you to modify, extract, or manipulate individual characters or portions of a string. Here are some commonly used character-manipulation functions in SQL:

  1. CONCAT or || (Concatenation): The CONCAT or || operator is used to concatenate two or more strings together. It combines the specified strings into a single string. The basic syntax is:

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    SELECT CONCAT(string1, string2, ...) FROM table;

    Example:

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    SELECT CONCAT('Hello', ' ', 'World') AS result; -- Output: Hello World

  2. SUBSTRING or SUBSTR: The SUBSTRING or SUBSTR function is used to extract a substring from a string. It allows you to retrieve a portion of a string based on the starting position and length. The basic syntax is:

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    SELECT SUBSTRING(string, start_position, length) FROM table;

    Example:

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    SELECT SUBSTRING('Hello World', 7, 5) AS result; -- Output: World

  3. LENGTH or LEN: The LENGTH or LEN function is used to determine the length of a string. It returns the number of characters in a string. The basic syntax is:

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    SELECT LENGTH(string) FROM table;

    Example:

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    SELECT LENGTH('Hello') AS result; -- Output: 5

  4. UPPER: The UPPER function is used to convert a string to uppercase. It transforms all characters in the string to their uppercase equivalents. The basic syntax is:

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    SELECT UPPER(string) FROM table;

    Example:

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    SELECT UPPER('Hello World') AS result; -- Output: HELLO WORLD

  5. LOWER: The LOWER function is used to convert a string to lowercase. It transforms all characters in the string to their lowercase equivalents. The basic syntax is:

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    SELECT LOWER(string) FROM table;

    Example:

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    SELECT LOWER('Hello World') AS result; -- Output: hello world

These are just a few examples of character-manipulation functions in SQL. Different database systems may have additional or slightly different functions, but these functions are widely supported and commonly used across many SQL implementations.

Write code find Nth highest salary from salary table?

SELECT DISTINCT Salary FROM SalaryTable ORDER BY Salary DESC LIMIT N-1, 1;

n this query, replace SalaryTable with the name of your salary table, and N with the desired rank of the salary you want to retrieve. For example, if you want to find the 3rd highest salary, replace N with 3.

Explanation of the query:

  1. SELECT DISTINCT Salary: This selects the distinct salary values from the table. If there are multiple rows with the same salary, using DISTINCT ensures that only unique salary values are considered.

  2. ORDER BY Salary DESC: This orders the salary values in descending order, from highest to lowest. This step is crucial to determine the Nth highest salary.

  3. LIMIT N-1, 1: This limits the result set to one row, starting from the (N-1)th position. Since the result is ordered in descending order, the (N-1)th row will correspond to the Nth highest salary. By selecting only one row with LIMIT 1, we retrieve the Nth highest salary.

Please note that this query assumes that the salary values are stored as numerical data in the "Salary" column of the "SalaryTable". Adjust the table and column names accordingly based on your specific database schema.

Write code find Nth highest salary from salary table with rank fuction

SELECT Salary
FROM (
  SELECT Salary, RANK() OVER (ORDER BY Salary DESC) AS SalaryRank
  FROM SalaryTable
) AS RankedSalaries
WHERE SalaryRank = N;

In this query, replace SalaryTable with the name of your salary table, and N with the desired rank of the salary you want to retrieve. For example, if you want to find the 3rd highest salary, replace N with 3.

Explanation of the query:

  1. The inner query SELECT Salary, RANK() OVER (ORDER BY Salary DESC) AS SalaryRank FROM SalaryTable assigns a rank to each salary in the SalaryTable, ordering them in descending order.

  2. The outer query selects the salaries from the result of the inner query, filtering for the row where the SalaryRank matches the desired Nth rank.

This query utilizes the RANK() function along with the OVER clause to generate ranks for the salary values. The RANK() function assigns a unique rank to each distinct salary value, with higher salaries receiving lower ranks. By filtering for the row with the desired rank in the outer query, you can retrieve the Nth highest salary.

Please note that this query assumes that the salary values are stored as numerical data in the "Salary" column of the "SalaryTable". Adjust the table and column names accordingly based on your specific database schema.

Write code find Nth highest salary from salary table without rank function without limit

If you want to find the Nth highest salary from a salary table without using the RANK() function or the LIMIT clause, you can achieve it using a combination of subqueries and the ORDER BY clause. Here's an example:

SELECT Salary
FROM SalaryTable
WHERE Salary = (
  SELECT DISTINCT Salary
  FROM SalaryTable
  ORDER BY Salary DESC
  OFFSET N-1 ROWS FETCH NEXT 1 ROW ONLY
);
 

In this query, replace SalaryTable with the name of your salary table, and N with the desired rank of the salary you want to retrieve. For example, if you want to find the 3rd highest salary, replace N with 3.

Explanation of the query:

  1. The inner subquery SELECT DISTINCT Salary FROM SalaryTable ORDER BY Salary DESC OFFSET N-1 ROWS FETCH NEXT 1 ROW ONLY selects the distinct salary values from the SalaryTable, orders them in descending order, and skips the first N-1 rows. Then it fetches the next 1 row only, which corresponds to the Nth highest salary.

  2. The outer query selects the salaries from the SalaryTable where the salary matches the result of the inner subquery. This ensures that only the Nth highest salary is returned.

By using the OFFSET and FETCH NEXT clauses, you can skip a certain number of rows and fetch a specific number of rows, achieving similar functionality to the LIMIT clause.

Please note that this query assumes that the salary values are stored as numerical data in the "Salary" column of the "SalaryTable". Adjust the table and column names accordingly based on your specific database schema. Additionally, keep in mind that the OFFSET and FETCH NEXT clauses are supported in certain database systems like SQL Server, PostgreSQL, and Oracle, but may not be available in others.

What is the difference between the RANK() and DENSE_RANK() functions?

The RANK() and DENSE_RANK() functions are both window functions in SQL that assign a ranking to rows based on a specified ordering. However, there is a key difference in how they handle ties or rows with the same values. Here's an explanation of each function:

 

RANK() Function:

 

The RANK() function assigns a unique rank to each row based on the specified ordering.

If two or more rows have the same values and are assigned the same rank, the next rank(s) are skipped, resulting in gaps in the ranking sequence.

The subsequent ranks are adjusted to account for the skipped ranks. For example, if two rows have a rank of 2, the next row will have a rank of 4 (skipping rank 3).

Example:

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RANK() OVER (ORDER BY Salary DESC) AS SalaryRank

DENSE_RANK() Function:

 

The DENSE_RANK() function assigns a unique rank to each row based on the specified ordering.

If two or more rows have the same values and are assigned the same rank, the subsequent rank(s) are not skipped. There are no gaps in the ranking sequence.

The subsequent ranks are not adjusted. If two rows have a rank of 2, the next row will have a rank of 3 (without skipping rank 3).

Example:

sql

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DENSE_RANK() OVER (ORDER BY Salary DESC) AS SalaryRank

Here's an example to illustrate the difference between RANK() and DENSE_RANK() functions:

 

Consider the following dataset of salaries:

 

Employee            Salary

A             5000

B             4000

C             4000

D             3000

E              2000

RANK() function ranking:

 

Employee            Salary    SalaryRank

A             5000     1

B             4000     2

C             4000     2

D             3000     4

E              2000     5

DENSE_RANK() function ranking:

 

Employee            Salary    SalaryRank

A             5000     1

B             4000     2

C             4000     2

D             3000     3

E              2000     4

In summary, the RANK() function may have gaps in the ranking sequence if there are ties, whereas the DENSE_RANK() function does not have gaps and maintains a continuous ranking sequence for tied rows. The choice between RANK() and DENSE_RANK() depends on the specific requirements of your ranking calculation and how you want to handle ties in the ranking order.

what is constraint in SQL?

In SQL, a constraint is a rule or condition that is applied to a table or column to enforce data integrity and maintain the consistency and validity of the data. Constraints define certain limits or restrictions on the data that can be inserted, updated, or deleted in a table. They help ensure that the data conforms to specified rules and requirements. Here are some common types of constraints in SQL:

  1. Primary Key Constraint: A primary key constraint is used to uniquely identify each row in a table. It ensures that the specified column or combination of columns has a unique value for each row and does not contain null values.

  2. Foreign Key Constraint: A foreign key constraint establishes a relationship between two tables. It ensures that the values in a column (foreign key) of one table match the values in a primary key column (referenced key) of another table.

  3. Unique Constraint: A unique constraint ensures that the values in a column or a combination of columns are unique across the table. It prevents duplicate values from being inserted.

  4. Not Null Constraint: A not null constraint ensures that a column does not contain null values. It enforces that a value must be provided for the column during data insertion.

  5. Check Constraint: A check constraint allows you to define a condition that must be satisfied for the data in a column. It ensures that the values inserted or updated in the column meet the specified condition.

These are just a few examples of constraints in SQL. Constraints play a crucial role in maintaining data integrity, enforcing business rules, and preventing inconsistencies or errors in the database. By applying constraints, you can establish a set of rules and restrictions that govern the behavior of your data and ensure its accuracy and reliability.

What are SQL clauses used with SELECT query in SQL?

When using the SELECT query in SQL, there are several clauses that can be used to modify the behavior and results of the query. Here are some of the commonly used clauses with the SELECT query:

  1. SELECT: Specifies the columns to retrieve from the table. It is used to define the projection of the query and determines the data that will be returned.

  2. FROM: Specifies the table or tables from which to retrieve the data. It defines the source of the data for the query.

  3. WHERE: Filters the rows based on a specified condition. It allows you to retrieve only the rows that satisfy the given condition.

  4. GROUP BY: Groups the rows based on one or more columns. It is used in conjunction with aggregate functions to perform calculations on groups of rows.

  5. HAVING: Filters the groups of rows generated by the GROUP BY clause based on a condition. It allows you to filter groups using aggregate functions.

  6. ORDER BY: Sorts the result set based on one or more columns in ascending or descending order. It allows you to control the order in which the rows are returned.

  7. DISTINCT: Removes duplicate rows from the result set. It ensures that only unique values are returned for the specified columns.

  8. LIMIT/OFFSET: Limits the number of rows returned or skips a specified number of rows. The LIMIT clause specifies the maximum number of rows to return, while the OFFSET clause specifies the number of rows to skip before starting to return rows.

  9. JOIN: Combines rows from two or more tables based on a related column between them. It allows you to retrieve data from multiple tables based on a common relationship.

  10. UNION: Combines the result sets of two or more SELECT queries into a single result set. It is used to combine rows from different tables or queries.

These clauses provide flexibility and control over the data retrieval process in SQL. By using these clauses appropriately, you can filter, sort, group, and manipulate data to retrieve the desired information from the database.

What is Cursor? How to use a Cursor?

In SQL, a cursor is a database object that allows you to retrieve and manipulate rows from a result set returned by a query. A cursor provides a way to traverse the rows of the result set one at a time and perform operations on each row. Here's an overview of how to use a cursor:

  1. Declare a Cursor: You need to declare a cursor before using it. The declaration specifies the SELECT query that will be used to retrieve the result set.

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    DECLARE cursor_name CURSOR FOR SELECT column1, column2 FROM table_name;

  2. Open the Cursor: After declaring the cursor, you need to open it to start retrieving rows from the result set.

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    OPEN cursor_name;

  3. Fetch Rows: Use the FETCH statement to retrieve rows from the cursor one at a time. The FETCH statement assigns the column values of the current row to variables.

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    FETCH NEXT FROM cursor_name INTO @variable1, @variable2;

    This statement fetches the next row from the cursor and assigns the values of column1 and column2 to the @variable1 and @variable2 variables, respectively.

  4. Process the Row: Once you have fetched a row, you can process it as needed. You can perform calculations, apply logic, or update values based on the row's data.

  5. Repeat Fetching: Continue fetching rows using the FETCH statement until there are no more rows in the result set.

  6. Close the Cursor: After you have finished fetching rows, close the cursor to release the resources associated with it.

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    CLOSE cursor_name;

  7. Deallocate the Cursor: Finally, deallocate the cursor to remove it from memory.

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    DEALLOCATE cursor_name;

Using a cursor allows you to perform row-level operations and iterate through the result set in a procedural manner. It can be useful when you need to perform complex operations on individual rows or when you want to process data row by row.

However, it's important to note that using a cursor in SQL should be considered carefully, as it may have performance implications, especially when dealing with large result sets. In many cases, it's recommended to use set-based operations instead of cursors to achieve better performance.

what is PL SQL?

PL/SQL stands for Procedural Language/Structured Query Language. It is Oracle Corporation's procedural extension to the SQL language. PL/SQL is a procedural programming language that integrates seamlessly with SQL, allowing you to write powerful and flexible database applications.

PL/SQL is primarily used to create stored procedures, functions, triggers, and packages within Oracle Database. It provides procedural constructs such as variables, loops, conditionals, and exception handling, which enable you to write complex business logic and manipulate data within the database.

Here are some key features and characteristics of PL/SQL:

  1. Block Structure: PL/SQL code is organized into blocks, which are logical units of code. A block consists of declarations, executable statements, and exception handlers. Blocks can be nested within other blocks.

  2. Variables and Data Types: PL/SQL supports a wide range of data types, including scalar types (such as number, varchar2, date), composite types (such as records and tables), and reference types. You can declare variables and constants to hold data within a PL/SQL block.

  3. Procedural Constructs: PL/SQL provides various procedural constructs, including loops (such as FOR, WHILE, and LOOP), conditional statements (such as IF-THEN-ELSE), and branching statements (such as GOTO and CASE). These constructs allow you to control the flow of execution and implement complex business logic.

  4. Exception Handling: PL/SQL has robust exception handling mechanisms that allow you to catch and handle errors that may occur during execution. You can define exception handlers to gracefully handle errors, log error information, and perform recovery actions.

  5. Stored Procedures and Functions: PL/SQL allows you to create stored procedures and functions, which are named blocks of code that can be invoked and executed within the database. Stored procedures and functions encapsulate business logic and can be reused and shared among multiple applications.

  6. Triggers: PL/SQL enables you to create triggers, which are special types of stored procedures that automatically execute in response to specific database events (such as insert, update, or delete operations).

  7. Packages: PL/SQL supports the creation of packages, which are logical containers that encapsulate related procedures, functions, variables, and types. Packages provide a way to organize and modularize PL/SQL code, improving code reusability and maintainability.

PL/SQL is widely used in Oracle Database environments for developing database-centric applications, implementing business rules, and enhancing the functionality of the database. It provides a powerful and flexible programming language for working with data and performing operations within the Oracle ecosystem.

What is Stored Procedures ?

A stored procedure is a named set of SQL statements that are stored and executed on a database server. It is a type of database object that encapsulates a sequence of operations or queries as a single unit. Stored procedures are typically created and stored within the database, and they can be invoked and executed by applications or other database objects.

Here are some key characteristics and benefits of stored procedures:

  1. Reusability: Stored procedures promote code reusability. Once created, they can be called and executed multiple times from different applications or parts of the database. This avoids code duplication and improves maintenance.

  2. Modularity: Stored procedures allow you to modularize your database logic by breaking it into smaller, manageable units. Each stored procedure can perform a specific task or operation, which can be called independently or combined with other procedures to form complex business logic.

  3. Security and Access Control: Stored procedures provide a security layer by allowing controlled access to data. Application users can be granted permissions to execute specific stored procedures, while direct access to underlying tables or data can be restricted.

  4. Performance Optimization: Stored procedures can enhance performance by reducing network traffic. Since the entire procedure is executed on the server side, only the results are transmitted back to the client. This can minimize the back-and-forth communication and improve overall system performance.

  5. Encapsulation and Abstraction: Stored procedures encapsulate the database logic, hiding the underlying implementation details. This abstraction makes it easier to maintain and update the database logic without affecting the applications or clients that use the procedures.

  6. Transaction Management: Stored procedures support transaction management. Multiple SQL statements within a procedure can be executed as part of a single transaction, ensuring atomicity, consistency, isolation, and durability (ACID properties) for complex database operations.

  7. Reduced Network Latency: By executing complex operations on the database server, stored procedures can reduce network latency. Instead of transmitting large datasets back and forth, only the necessary results or output are transmitted, reducing the data transfer overhead.

Stored procedures are supported by various database management systems, including Oracle, SQL Server, MySQL, and PostgreSQL. They provide a powerful mechanism for implementing business logic, data manipulation, and data access within the database server, enhancing performance, security, and maintainability of database-driven applications.

what is use of stored procedure ? Explain with example

Stored procedures have several uses and benefits in database applications. Here are some common use cases for stored procedures along with examples:

  1. Business Logic Implementation: Stored procedures are often used to implement complex business logic within the database. They can encapsulate a series of operations or calculations that need to be performed on the data. For example, consider a stored procedure that calculates the total sales amount for a given product:

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    CREATE PROCEDURE CalculateTotalSales AS BEGIN SELECT SUM(SalesAmount) AS TotalSales FROM Sales WHERE ProductId = @ProductId; END;

    This stored procedure calculates the total sales amount for a specific product by summing the sales amounts from the "Sales" table. The input parameter @ProductId allows the procedure to calculate the sales for a specific product.

  2. Data Access and Retrieval: Stored procedures can be used to retrieve data from the database based on specific criteria. They can include complex queries or joins and return the desired result set. For example, consider a stored procedure that retrieves all customers who have placed an order in the last month:

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    CREATE PROCEDURE GetActiveCustomers AS BEGIN SELECT * FROM Customers WHERE LastOrderDate >= DATEADD(MONTH, -1, GETDATE()); END;

    This stored procedure retrieves all customer records from the "Customers" table where the LastOrderDate is within the last month. This allows you to easily retrieve a specific set of data without writing the query logic repeatedly in different parts of your application.

  3. Data Manipulation and Updates: Stored procedures can perform data manipulation tasks, such as inserting, updating, or deleting records in the database. They can enforce data validation rules and maintain data integrity. For example, consider a stored procedure that inserts a new order into the "Orders" table:

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    CREATE PROCEDURE InsertOrder @OrderDate DATETIME, @CustomerId INT, @TotalAmount DECIMAL(10, 2) AS BEGIN INSERT INTO Orders (OrderDate, CustomerId, TotalAmount) VALUES (@OrderDate, @CustomerId, @TotalAmount); END;

    This stored procedure takes input parameters for the order date, customer ID, and total amount and inserts a new record into the "Orders" table. The procedure ensures that the data is inserted correctly and follows any necessary validation rules.

  4. Performance Optimization: Stored procedures can improve performance by reducing network traffic and optimizing query execution. By executing a series of SQL statements on the database server, stored procedures can reduce the overhead of transmitting data back and forth between the client and the server. Additionally, stored procedures can be compiled and cached by the database engine, resulting in faster execution times for subsequent invocations.

These are just a few examples of how stored procedures can be used in database applications. The actual usage of stored procedures may vary depending on the specific requirements and design of your application. Stored procedures provide a flexible and powerful mechanism for implementing complex database logic, improving performance, security, and maintainability.

What is Functions in PL SQL?

In PL/SQL, a function is a named program unit that performs a specific task and returns a value. Functions are similar to stored procedures, but they differ in the way they are invoked and the result they produce. Functions are designed to return a single value, whereas procedures can return multiple values or no value at all.

Here are some key features and characteristics of functions in PL/SQL:

  1. Return Value: Functions are defined to return a value of a specific data type. The return value can be any valid PL/SQL data type, such as number, string, date, or a user-defined type.

  2. Input Parameters: Functions can accept input parameters, which are used to pass values to the function for processing. Input parameters are optional, and a function can have zero or more parameters.

  3. Logic and Operations: Like stored procedures, functions can contain SQL statements, PL/SQL procedural constructs (such as loops and conditional statements), and other function calls. They can perform calculations, manipulate data, and execute database operations.

  4. Encapsulation: Functions encapsulate a specific set of operations or calculations as a separate unit. They allow you to modularize and reuse code, similar to stored procedures.

  5. Function Invocation: Functions are invoked by calling their name and providing the required input parameters. The returned value can be assigned to a variable or used directly in an expression.

  6. Usage in SQL Statements: Functions can be used in SQL statements, such as SELECT, INSERT, UPDATE, and DELETE, to perform calculations or retrieve values. They can be used in the SELECT list, WHERE clause, or any other part of the SQL statement where an expression is allowed.

Here's an example of a simple function in PL/SQL that calculates the square of a given number:

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CREATE FUNCTION CalculateSquare(p_number NUMBER) RETURN NUMBER IS v_result NUMBER; BEGIN v_result := p_number * p_number; RETURN v_result; END;

In this example, the function CalculateSquare accepts a single input parameter p_number of type NUMBER. It calculates the square of the input number and assigns the result to the local variable v_result. Finally, the function returns the value of v_result as the result of the function.

You can invoke this function in PL/SQL code or use it within SQL statements, as shown below:

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DECLARE v_input NUMBER := 5; v_output NUMBER; BEGIN v_output := CalculateSquare(v_input); DBMS_OUTPUT.PUT_LINE('Square of ' || v_input || ' is ' || v_output); END;

The function CalculateSquare is invoked with the input value 5, and the returned result is assigned to the variable v_output. The output is then displayed using the DBMS_OUTPUT.PUT_LINE procedure.

Functions in PL/SQL provide a powerful way to encapsulate and reuse logic, perform calculations, and retrieve values within database applications. They can be utilized for a wide range of purposes, such as data transformation, validation, and computation.

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