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Database Management System
Difference between DELETE,DROP and TRUNCATE.
DELETE, DROP, and TRUNCATE are SQL commands used for different purposes in managing database objects and data. Here’s a breakdown of their differences:
DELETE:
DELETE is a Data Manipulation Language (DML) command used to remove specific rows from a table based on specified conditions.
DELETE affects the data within a table and can be executed with or without a WHERE clause to specify the condition for row deletion.
DELETE is a row-level operation and can be rolled back using the transaction control statements.
DROP:
DROP is a Data Definition Language (DDL) command used to remove database objects entirely from the database, such as tables, views, indexes, or schemas.
DROP is used to delete entire objects and their associated data from the database.
DROP is a structural operation and cannot be rolled back. Once an object is dropped, it is permanently removed from the database.
TRUNCATE:
TRUNCATE is a DDL command used to remove all data from a table while preserving the structure of the table.
TRUNCATE is faster than DELETE as it removes all rows from the table in a single operation, rather than deleting them one by one.
TRUNCATE is a structural operation and cannot be rolled back. Once a table is truncated, all data within the table is permanently removed.
TRUNCATE resets the table to an empty state, but it does not reset the auto-incrementing sequence or reclaim space within the database file.
In summary, DELETE is used to remove specific rows from a table, DROP is used to remove entire database objects, and TRUNCATE is used to remove all data from a table while maintaining the table structure. DELETE and TRUNCATE are DML commands, while DROP is a DDL command. It’s important to exercise caution while using DROP and TRUNCATE commands, as they can result in permanent data loss.
Difference between Nested subquery,correlated subquery and joins.
Nested subquery, correlated subquery, and joins are all techniques used in SQL to combine data from multiple tables or perform complex queries. Here’s a breakdown of their differences:
Nested Subquery:
A nested subquery is a query that is embedded within another query. The inner subquery is executed first and its result is then used by the outer query.
The inner subquery can be written within the WHERE clause, FROM clause, or HAVING clause of the outer query.
The inner subquery operates independently of the outer query and is executed only once, providing a result set that is then used by the outer query to perform further operations or filtering.
The result of the nested subquery is not directly correlated to the outer query.
Correlated Subquery:
A correlated subquery is a subquery that refers to the columns of the outer query, creating a relationship or correlation between the subquery and the outer query.
The subquery is executed repeatedly, once for each row processed by the outer query, using the values from the current row of the outer query in its execution.
The correlated subquery is evaluated for each row of the outer query and its result is used to influence or filter the result of the outer query.
The result of the correlated subquery is dependent on the current row being processed in the outer query.
Joins:
Joins are used to combine rows from two or more tables based on a related column between them.
Joins can be performed using different types such as INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN, each determining the behavior of the combination.
Joins are used to retrieve data from multiple tables based on specified join conditions, combining columns and matching rows between tables.
Joins provide a way to establish relationships between tables and access related data from multiple tables in a single result set.
In summary, nested subqueries and correlated subqueries are techniques used to perform complex queries within a single query. Nested subqueries are independent and executed first, providing a result set used by the outer query. Correlated subqueries are evaluated for each row of the outer query and depend on the current row being processed. Joins, on the other hand, combine rows from multiple tables based on specified join conditions and establish relationships between tables. Joins allow you to retrieve data from multiple tables simultaneously.
What is the use of IN and not IN keyword? How is statement executed having IN or not IN keyword?
The IN and NOT IN keywords in SQL are used as comparison operators to match a value against a list of values. These operators allow you to check if a value exists or does not exist within a specified list. Here’s how they work:
IN:
The IN keyword checks if a value matches any value within a specified list.
It is commonly used in the WHERE clause of a query to filter rows based on multiple possible values.
Syntax:
value IN (value1, value2, ...)Example:
SELECT * FROM customers WHERE country IN ('USA', 'Canada', 'UK')The above example will retrieve rows from the “customers” table where the country column value is either ‘USA’, ‘Canada’, or ‘UK’.
NOT IN:
The NOT IN keyword checks if a value does not match any value within a specified list.
It is used to exclude rows that have values present in the specified list.
Syntax:
value NOT IN (value1, value2, ...)Example:
SELECT * FROM customers WHERE country NOT IN ('USA', 'Canada', 'UK')The above example will retrieve rows from the “customers” table where the country column value is not ‘USA’, ‘Canada’, or ‘UK’.
When executing a statement that includes the IN or NOT IN keywords:
The database engine evaluates the list of values specified within parentheses.
It compares the value being checked against each value in the list.
If a match is found (in the case of IN) or no match is found (in the case of NOT IN), the corresponding row is included in the result set.
The statement is executed, and the result set is returned based on the matching or non-matching values.
The IN and NOT IN operators are useful when you want to filter rows based on multiple values rather than using multiple OR conditions. They provide a concise way to match or exclude values from a given list.
Where is EXIST or not EXIST keyword used and how is it executed?
The EXISTS and NOT EXISTS keywords in SQL are used to check for the existence or non-existence of rows in a subquery. These keywords are often used in conjunction with a correlated subquery to conditionally filter or include rows based on the result of the subquery. Here’s how they work:
EXISTS:
The EXISTS keyword checks if the specified subquery returns any rows.
It is commonly used in the WHERE clause of a query to conditionally include rows if the subquery returns any result.
Syntax:
EXISTS (subquery)Example:
SELECT * FROM customers WHERE EXISTS (SELECT * FROM orders WHERE customers.customer_id = orders.customer_id)The above example will retrieve rows from the “customers” table if there is at least one matching row in the “orders” table for the same customer_id.
NOT EXISTS:
The NOT EXISTS keyword checks if the specified subquery does not return any rows.
It is used to conditionally exclude rows if the subquery returns any result.
Syntax:
NOT EXISTS (subquery)Example:
SELECT * FROM customers WHERE NOT EXISTS (SELECT * FROM orders WHERE customers.customer_id = orders.customer_id)The above example will retrieve rows from the “customers” table if there is no matching row in the “orders” table for the same customer_id.
When executing a statement that includes the EXISTS or NOT EXISTS keywords:
The database engine evaluates the specified subquery.
It checks if the subquery returns any rows (EXISTS) or does not return any rows (NOT EXISTS).
If the result matches the specified condition, the corresponding row is included in the result set.
The statement is executed, and the result set is returned based on the existence or non-existence of rows in the subquery.
The EXISTS and NOT EXISTS keywords are powerful tools for conditional filtering and determining the existence of related rows. They are particularly useful when using correlated subqueries, where the subquery references columns from the outer query. By checking for the existence or non-existence of rows in the subquery, you can make decisions on including or excluding rows in the final result set.
What are the five aggregate functions?
The five commonly used aggregate functions in SQL are:
COUNT: The COUNT function is used to count the number of rows in a result set or the number of occurrences of a specific column. It can be used with the asterisk (*) to count all rows or with a specific column to count non-null values.
SUM: The SUM function calculates the sum of the values in a numeric column. It is used to obtain the total or cumulative value of a specific column.
AVG: The AVG function calculates the average (mean) value of a numeric column. It is used to obtain the average value of a specific column.
MAX: The MAX function returns the maximum value of a column. It is used to find the highest value in a specific column.
MIN: The MIN function returns the minimum value of a column. It is used to find the lowest value in a specific column.
These aggregate functions are often used in combination with the GROUP BY clause to perform calculations on subsets of data grouped by one or more columns. They help summarize and analyze data in a meaningful way by providing statistical information about the dataset.
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