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Topics

Introduction to SQL

What is SQL?SQL HistorySQL StandardsSQL vs NoSQLSQL Database Management Systems

SQL Basics

SQL SyntaxSQL CommentsSQL Data TypesSQL OperatorsSQL ExpressionsSQL LiteralsSQL IdentifiersSQL KeywordsSQL Case Sensitivity

SQL Database Fundamentals

Database ConceptsTables and FieldsPrimary KeysForeign KeysIndexesConstraintsNormalizationDenormalizationSQL Schema Design

SQL Data Manipulation

SQL SELECT StatementSQL INSERT StatementSQL UPDATE StatementSQL DELETE StatementSQL MERGE StatementSQL AliasesSQL WildcardsSQL LIKE OperatorSQL IN OperatorSQL BETWEEN OperatorSQL NULL ValuesSQL ORDER BY ClauseSQL LIMIT ClauseSQL OFFSET Clause

SQL Filtering and Sorting

SQL WHERE ClauseSQL AND, OR, NOT OperatorsSQL GROUP BY ClauseSQL HAVING ClauseSQL DISTINCT KeywordSQL TOP Clause

SQL Joins

SQL INNER JOINSQL LEFT JOINSQL RIGHT JOINSQL FULL OUTER JOINSQL CROSS JOINSQL Self JOINSQL Natural JOINSQL Multiple JOINs

SQL Functions

SQL Aggregate FunctionsSQL Scalar FunctionsSQL String FunctionsSQL Numeric FunctionsSQL Date FunctionsSQL Conversion FunctionsSQL System FunctionsSQL User-Defined Functions

SQL Subqueries

SQL Subquery BasicsCorrelated SubqueriesNested SubqueriesSQL EXISTS OperatorSQL ANY and ALL OperatorsSubqueries in SELECTSubqueries in FROMSubqueries in WHERE

SQL Views

Creating SQL ViewsAltering SQL ViewsDropping SQL ViewsUpdatable ViewsMaterialized ViewsView Limitations

SQL Transactions

SQL Transaction BasicsACID PropertiesBEGIN TRANSACTIONCOMMITROLLBACKSAVEPOINTTransaction Isolation Levels

SQL Data Definition

SQL CREATE TABLESQL ALTER TABLESQL DROP TABLESQL TRUNCATE TABLESQL CREATE INDEXSQL DROP INDEXSQL CREATE DATABASESQL ALTER DATABASESQL DROP DATABASE

SQL Data Control

SQL GRANTSQL REVOKESQL RolesSQL UsersSQL Privileges

SQL Performance Optimization

SQL Query OptimizationSQL Execution PlansSQL Index OptimizationSQL Query TuningSQL CachingSQL Partitioning

Advanced SQL Concepts

SQL Stored ProceduresSQL TriggersSQL CursorsSQL Common Table ExpressionsSQL Window FunctionsSQL Pivot and UnpivotSQL JSON OperationsSQL XML OperationsSQL Full-Text Search

SQL Best Practices

SQL Naming ConventionsSQL Code OrganizationSQL Error HandlingSQL Security Best PracticesSQL Backup and RecoverySQL Version Control

SQL JSON Operations

JSON (JavaScript Object Notation) has become a popular data format for storing and exchanging information. Modern SQL databases now offer robust support for JSON operations, allowing developers to work with structured and semi-structured data efficiently.

Understanding JSON in SQL

JSON in SQL provides a flexible way to store complex data structures within relational databases. It bridges the gap between traditional tabular data and hierarchical, document-style information.

Key Features:

  • Store JSON data in dedicated column types
  • Query and manipulate JSON data using SQL
  • Index JSON fields for improved performance
  • Combine relational and JSON data in queries

JSON Data Types

Different database management systems offer various JSON data types. For example:

  • PostgreSQL: JSONB (binary JSON)
  • MySQL: JSON
  • SQL Server: NVARCHAR(MAX) with IS JSON check constraint

Querying JSON Data

SQL provides functions and operators to extract and manipulate JSON data. Here's an example using PostgreSQL:

SELECT data->'name' AS name,
       data->'age' AS age
FROM users
WHERE (data->>'age')::int > 30;

This query extracts the 'name' and 'age' fields from a JSON column and filters users older than 30.

Modifying JSON Data

You can update JSON fields using SQL statements. Here's an example in MySQL:

UPDATE products
SET details = JSON_SET(details,
    '$.color', 'red',
    '$.size', 'large')
WHERE id = 1;

This statement updates the 'color' and 'size' properties of a JSON object stored in the 'details' column.

Indexing JSON Fields

To improve query performance, you can create indexes on specific JSON fields. For instance, in PostgreSQL:

CREATE INDEX idx_user_email
ON users ((data->>'email'));

This creates an index on the 'email' field within the JSON data, optimizing queries that filter or join on this field.

Best Practices

  • Use JSON for flexible, schema-less data that doesn't fit well into traditional relational structures
  • Normalize frequently queried or updated JSON fields into separate columns for better performance
  • Utilize JSON validation constraints to ensure data integrity
  • Be mindful of the trade-offs between flexibility and query performance when working with JSON data

Related Concepts

To further enhance your understanding of SQL and database management, explore these related topics:

By mastering SQL JSON operations, you'll be well-equipped to handle modern data requirements in your applications, combining the strengths of relational databases with the flexibility of JSON structures.