JSON Functions in SQL

JSON has become the popular lightweight data interchange format, and it is used. The relational databases, like SQL, have become increasingly sensitive towards support of JSON. Develo

pers can store JSON data within SQL queries and then query and manipulate them right there in SQL queries. This article covers the following topics: SQL JSON Functions, How to use JSON functions effectively within SQL, JSON common operations, and SQL Server JSON Support. We would discuss them with examples and table support, so that becomes easy.

JSON Functions in SQL Introduction

JSON became highly popular with regard to data representation in web applications and APIs because it is straightforward and easy to read. Many SQL databases now have built-in functions that allow for the management of data in JSON within relational databases, and developers can integrate the power of SQL with the flexibility of JSON structures, which gives the best of both worlds.

Why Use JSON in SQL?

While relational databases remain conventionally structured on tables and rows, many applications would require much more flexible data storage. JSON provides a semi-structured format that allows for varying schema with nested data models. Among the advantages in using JSON functions in SQL is the following benefit:

• Flexibility: JSON supports the use of more flexible data structures than traditional relational tables.
• Compatibility: JSON is widely used in a number of APIs and modern application. As such, it’s relatively easier to integrate with other external systems using JSON.
• Simplicity: JSON is easy to read and interpretable, thus a good choice when transferring data between systems.

JSON in SQL Data Storage

Before we look at JSON functions in SQL, some context is in order regarding how JSON data are stored. In the new-fangled databases used by SQL Server, MySQL, and PostgreSQL, JSON data are stored in a column as a JSON or VARCHAR type, depending on the database system.

Example: JSON Data in a SQL Table

Let’s consider a table Product with product information including name, price, and details with specification in JSON.

Product Table:

ProductID	ProductName	ProductDetails (JSON)
1	Laptop	{“brand”: “Dell”, “price”: 1200, “specs”: {“RAM”: “16GB”, “SSD”: “512GB”}}
2	Smartphone	{“brand”: “Apple”, “price”: 999, “specs”: {“RAM”: “8GB”, “SSD”: “128GB”}}
3	Tablet	{“brand”: “Samsung”, “price”: 600, “specs”: {“RAM”: “6GB”, “SSD”: “256GB”}}

Here, the ProductDetails column holds product specifications in JSON format. Using SQL JSON functions, you can retrieve, manipulate, and update this JSON data.

Common SQL JSON functions

Many SQL databases support a set of JSON functions for manipulating JSON data. The names for each function vary by product, but their general functionality is identical.

1. JSON_VALUE()

The JSON_VALUE() function “exposes” a scalar value from a JSON string. This means that you can choose selected values out of a JSON object with the help of a specified path.

Syntax:

JSON_VALUE (expression, path)

Example: To extract the brand of a product from the ProductDetails column, you can use the JSON_VALUE() function as follows:

SELECT ProductName, JSON_VALUE(ProductDetails, '$.brand') AS Brand
FROM Products;

Output:

ProductName	Brand
Laptop	Dell
Smartphone	Apple
Tablet	Samsung

2. JSON_QUERY()

JSON_QUERY() retrieves an entire JSON object or array. It is used when you want to extract a complete section of the JSON data.

Syntax:

JSON_QUERY (expression, path)

Example: Suppose we want to retrieve the specs (a nested JSON object) for each product:

SELECT ProductName, JSON_QUERY(ProductDetails, '$.specs') AS Specifications
FROM Products;

Output:

ProductName	Specifications
Laptop	{“RAM”: “16GB”, “SSD”: “512GB”}
Smartphone	{“RAM”: “8GB”, “SSD”: “128GB”}
Tablet	{“RAM”: “6GB”, “SSD”: “256GB”}

3. JSON_MODIFY()

JSON_MODIFY() allows you to update an existing JSON object by modifying the value at a specified path. This is helpful when you want to update individual properties within a JSON object.

Syntax:

JSON_MODIFY (expression, path, new_value)

Example: Let’s say we want to update the price of the laptop in the Products table. We can do this using the JSON_MODIFY() function:

UPDATE Products
SET ProductDetails = JSON_MODIFY(ProductDetails, '$.price', 1100)
WHERE ProductID = 1;

After the update, the price of the laptop will change to 1100.

4. JSON_OBJECT()

JSON_OBJECT() creates a new JSON object from a set of key-value pairs. It is useful when you need to generate JSON data within a query.

Syntax:

JSON_OBJECT (key, value [, key, value]...)

Example: Let’s create a new JSON object that combines the ProductName and ProductDetails:

SELECT JSON_OBJECT('name', ProductName, 'details', ProductDetails) AS ProductInfo
FROM Products;

Output:

ProductInfo
{“name”: “Laptop”, “details”: {“brand”: “Dell”, “price”: 1200, “specs”: {“RAM”: “16GB”, “SSD”: “512GB”}}}
{“name”: “Smartphone”, “details”: {“brand”: “Apple”, “price”: 999, “specs”: {“RAM”: “8GB”, “SSD”: “128GB”}}}
{“name”: “Tablet”, “details”: {“brand”: “Samsung”, “price”: 600, “specs”: {“RAM”: “6GB”, “SSD”: “256GB”}}}

5. ISJSON()

ISJSON() checks whether a given expression is valid JSON. It returns 1 if the input is valid JSON and 0 if not.

Syntax:

ISJSON (expression)

Example: Let’s check if the ProductDetails column contains valid JSON:

SELECT ProductName, ISJSON(ProductDetails) AS IsValidJSON
FROM Products;

Output:

ProductName	IsValidJSON
Laptop	1
Smartphone	1
Tablet	1

SQL Server JSON Support

JSON functionality in SQL Server has significantly enhanced the capabilities of relational databases by enabling the storage and manipulation of JSON data alongside traditional relational data. With JSON functionality in SQL Server introduced in SQL Server 2016, users gain access to a suite of built-in functions designed for querying, modifying, and validating JSON documents. Functions such as JSON_VALUE() allow users to extract scalar values from JSON strings, while JSON_QUERY() can retrieve entire JSON objects or arrays. Additionally, the OPENJSON() function is a key feature of JSON functionality in SQL Server, parsing JSON text into a tabular format, which makes it easier to integrate with SQL queries. The ability to utilize these functions means that developers can benefit from JSON functionality in SQL Server to seamlessly combine NoSQL and relational concepts within the same database environment. Understanding JSON functionality in SQL Server is essential for modern applications that require efficient handling of semi-structured data and for leveraging the full potential of this powerful feature in SQL Server

SQL Server provides full JSON support since SQL Server 2016. That means developers can work easily with JSON data in the SQL world. SQL Server’s JSON functions, namely JSON_VALUE(), JSON_QUERY(), and JSON_MODIFY(), make extracting, querying, and manipulation of JSON data without any additional transformations needed.

Storing JSON Data in SQL Server

Generally, in SQL Server, JSON data is stored in NVARCHAR columns. Although SQL Server doesn’t have a specific JSON type, the ISJSON() function prevents improper data from being stored as valid JSON.

Example: Inserting JSON Data

You can actually import JSON data directly into a table of SQL Server by using an insert statement. For instance:

INSERT INTO Products (ProductName, ProductDetails)
VALUES 
('Smartwatch', '{"brand": "Fitbit", "price": 200, "specs": {"RAM": "1GB", "SSD": "8GB"}}');

This inserts a new row with JSON data for the ProductDetails column.

Using Functions on JSON in SQL Queries

One good feature of using JSON functions in SQL queries is that you can readily integrate relational and JSON data by using functions to manipulate JSON objects so that you make SQL much more versatile in handling the administration of modern data formats.

Combining JSON Data with Relational Queries

Let’s retrieve the ProductName with the price from the JSON ProductDetails. We can include both JSON and relational data in a single result set by combining the standard SQL queries with JSON data.

SELECT ProductName, JSON_VALUE(ProductDetails, '$.price') AS Price
FROM Products
WHERE JSON_VALUE(ProductDetails, '$.price') > 800;

This query filters out products that have a price more than 800 by extracting the price from the JSON object with the help of the JSON_VALUE() function.

Example: JSON Aggregation

Sometimes you will want to aggregate into a single JSON object. SQL Server provides for returning results as JSON arrays through the FOR JSON clause.

SELECT ProductName, ProductDetails
FROM Products
FOR JSON AUTO;

Output:

[
    {"ProductName": "Laptop", "ProductDetails": {"brand": "Dell", "price": 1200, "specs": {"RAM": "16GB", "SSD": "512GB"}}},
    {"ProductName": "Smartphone", "ProductDetails": {"brand": "Apple", "price": 999, "specs": {"RAM": "8GB", "SSD": "128GB"}}},
    {"ProductName": "Tablet", "ProductDetails": {"brand": "Samsung", "price": 600, "specs": {"RAM": "6GB", "SSD": "256GB"}}}
]

JSON Data Manipulation in SQL

JSON data manipulation in SQL has become increasingly important as databases evolve to support semi-structured data formats. Effective JSON data manipulation in SQL allows users to store, query, and manipulate JSON objects and arrays seamlessly. SQL provides a variety of functions specifically designed for this purpose, such as JSON_VALUE(), which enables the extraction of scalar values from JSON documents, and JSON_QUERY(), which allows for retrieving entire JSON objects or arrays. Additionally, the OPENJSON() function can be used for JSON data manipulation in SQL, parsing JSON text and returning it as a set of rows and columns, making it easier to integrate JSON data into relational structures. By leveraging these capabilities, developers can efficiently handle complex data types and perform advanced queries through JSON data manipulation in SQL. Understanding JSON data manipulation in SQL is essential for modern applications that rely on flexible data structures and require efficient access to both structured and unstructured information.

SQL provides the possibility of manipulating JSON data, acting as if it were native to SQL. Let’s look at some typical tasks in manipulating JSON data in SQL:

Updating JSON Data

To update a nested value in a JSON object, you might use the function JSON_MODIFY().

UPDATE Products
SET ProductDetails = JSON_MODIFY(ProductDetails, '$.specs.SSD', '1TB')
WHERE ProductID = 1;

This updates the SSD specification of the first product to 1TB.

Inserting JSON Data into SQL Tables

When dealing with JSON data from external sources, you can parse and insert it into SQL tables. SQL Server’s OPENJSON() function allows you to parse JSON strings and return them as rows.

DECLARE @json NVARCHAR(MAX) = 
'[
    {"ProductName": "Headphones", "ProductDetails": {"brand": "Sony", "price": 300, "specs": {"RAM": "N/A", "SSD": "N/A"}}},
    {"ProductName": "Camera", "ProductDetails": {"brand": "Canon", "price": 800, "specs": {"RAM": "N/A", "SSD": "N/A"}}}
]';

INSERT INTO Products (ProductName, ProductDetails)
SELECT ProductName, ProductDetails
FROM OPENJSON(@json)
WITH (
    ProductName NVARCHAR(100) '$.ProductName',
    ProductDetails NVARCHAR(MAX) '$.ProductDetails'
);

Advantages of JSON Functions in SQL

JSON supports SQL functions that allow for the strong integration of storing, querying, and manipulating JSON data natively in relational databases. They prove to be useful for modern web and mobile applications using JSON to exchange data. In summary, the main benefits for using JSON functions in SQL are as follows:

1. Interoperation between structured and semi-structured data

JSON functions in the SQL databases enable the storage of not only structured or relational but also semi-structured data. This thus allows flexible storage of data, not with strict schema enforcement, hence allowing for support to the need for the storage of ever-growing amounts of unstructured data common in modern applications by combining traditional relational with JSON data in SQL databases.

2. Easier Data Exchange

JSON has become one of the most widely used data interchange formats across web APIs. The functions for JSON provide native functions for SQL databases for storing and querying JSON. Such an integration into web services and even external systems will henceforth require no data transforms and make interactions between databases and the rest of the application ecosystem more streamlined.

3. Querying and Manipulating JSON Data

SQL’s JSON functions make rich querying and manipulation of JSON data stored in columns possible. You can extract particular values, update parts of a JSON document, or even validate the structure of JSON data. You are able to manipulate JSON data efficiently within SQL queries using SQL Server’s JSON_VALUE(), JSON_QUERY(), and JSON_MODIFY() functions without needing external tools or any coding languages.

4. Better Performance with JSON Operations

The main performance advantage of utilizing JSON functions within SQL is that JSON operations can be carried out faster than when handled and parsed outside the database because the database engine natively supports JSON operations. That way, queries against or modifying JSON data are optimized, whereas additional application-layer logic is minimized.

5. Schema Flexibility in Evolution

JSON functions allow the developers to store schema-less data in the relational database and hence permits flexibility in schema changes. Since the structure of the JSON data does not have a fixed structure at all, it becomes easier to modify along with changing business requirements, thereby avoiding complex migrations and schema changes in the database. This will be very helpful in agile development environments, where the features of applications change rapidly.

6. Readability and Simplicity Improved

With JSON functions, you can store and retrieve JSON data natively: it is easier to work with, from the perspective of application development. You can directly query and update JSON columns without complicated joins or data mapping logic-this improves readability and maintainability of SQL queries in dealing with semi-structured data.

7. Reduced Data Redundancy

When using hierarchical or nested data, JSON functions allow for the storage of related attributes within a single JSON column so you don’t necessarily require extra tables or complex relationships. This would reduce redundancy of data, in addition to simplifying the database schema, since developers would no longer have to come up with multiple tables just to represent hierarchical structures.

8. Compatibility with Modern Applications

Modern applications, especially those that are developed using the JavaScript frameworks, rely on JSON for data interchange. SQL databases with JSON support make things easier to handle JSON data directly in the database rather than intermediate processing. This makes it easier to integrate SQL databases with front-end frameworks, APIs, and NoSQL-like use cases.

9. Interoperability with NoSQL Features

The native support for structures like JSON or other formats by some RDBMS makes it possible for the SQL databases to support data in JSON format, with the maintaining flexibility of NoSQL, at the same time ensuring reliability and consistency of relational systems. This hybrid approach allows organizations to have the best of both worlds; therefore, SQL is viable for applications traditionally reliant on NoSQL solutions.

10. Native Support Across Major RDBMS

The JSON functions are natively supported in most modern relational database systems, including PostgreSQL, MySQL, SQL Server, and Oracle. Good support from most of the major database systems means that developers can use JSON data and functions without depending on any external tool or libraries, thus helping to avoid cross-platform development hassles and frequent changeover between databases.

Disadvantages of JSON Functions in SQL

While JSON functions in SQL have a plethora of advantages, they do possess some disadvantages and limitations. Awareness of these disadvantages is important to smart decisions regarding when or whether JSON in SQL databases makes sense for a specific application. To that end, the primary disadvantages of JSON functions in SQL are:

1. Overhead of complex queries for both exection of performance.

Although JSON functions are optimized within most relational databases, complex queries involving multiple JSON functions do come with a performance overhead. This mainly occurs when handling very large JSON objects or performing frequent nested queries, significantly slowing down database operations compared to classical relational querying.

2. Data Integrity and Constraints

Unlike the relational data, strictly validated by the constraints in the database (for example, using a foreign key, unique constraint, check constraint), JSON does not enforce strict validation rules in SQL. This can result in inconsistent or, rather, invalid data appearing in the fields that contain JSON; after all, JSON data does not gain the same level of integrity enforcement as an SQL column.

3. Less Indexing for Fields Containing JSON

Indexing JSON can be very difficult and is actually often more restricted than indexing standard columns in SQL databases. Although many databases offer partial or full indexing for JSON fields, the indexes generated by those are usually not as efficient as standard column indexes. In particular, querying JSON may be slower where large datasets must be scanned, or there are lots of frequent lookups on deeply nested JSON attributes.

4. Higher Complexity in Data Management

JSON functionality can quickly add unnecessary complexity to data management and maintenance tasks in SQL databases. In comparison, JSON might be a much more varied structure from record to record, meaning it is significantly more difficult to enforce strict data models and maintain consistency across records. That flexibility creates challenges when trying to validate data or in schema changes because updating the format of the JSON might require code- or data-migration processes to be rewritten in numerous parts of the application.

5. Complexity with Traditional SQL Tools

Many existing SQL solutions like reporting tools, BI tools, or ETL systems are optimized for structured relational data. Using JSON functions, those tools may have trouble processing or extracting data from fields containing JSON data, so tasks such as analysis, reporting, or integration with other systems become much more complicated. Therefore, the customer might need extra custom coding or additional tools to process JSON data.

6. Latency because of Data Redundancy

JSON’s flexibility to store nested and hierarchical data also brings in redundant data in case the same data is copied and pasted in multiple JSON files. In relational models, normalization is a technique that minimizes redundancy; however in case of JSON-based storage, it may lead to duplicated values in a record, which may increase the size of the database and makes maintenance tasks complex.

7. Complexity in Query Optimization

JSON data is harder to query optimize. Relational databases are optimized for structured data where, mostly based on the indexes and query plans, queries can be dynamically fine-tuned. Because of this, the unstructured nature of JSON data complicates query optimization. As demonstrated, extractions of value from deep levels of nesting or operations involving aggregate functions on JSON data prove to be inefficient in comparison with queries performed over regular columns and lead at times to suboptimal performance.

8. Greater Requirements for Storage Capacity

when using JSON with SQL databases, it will probably consume more of the required storage capacity for loading that data, considering a structured kind would be more compact. The fields of the JSON are, in many cases, stored as text or binary but could obviously be far larger than field types of traditional data. Furthermore, the flexibility of JSON will produce more significant amounts of data-more especially for complicated or highly nested structures where extra storage space may be needed over time.

9. Challenge of Maintaining Relationships

Since foreign keys are implemented explicitly in a traditional relational database, relations between these tables are strictly defined. However, it becomes very difficult using JSON to enforce relationships since JSON data is self-contained, as well as being much lax than a relational database to enforce the same level of integrity when conducting relations. In some sense, these may enforce inconsistencies concerning referential integrity, which may lead to erroneous data based on how one tries to attempt to relate complex data structures to each other.

10. Less mature tooling and ecosystem

Though JSON functions are gaining support in SQL databases, they have not yet matured to the full extent that is seen for the traditional relational data ecosystem. Tools, libraries, and frameworks for SQL supporting JSON are still very new compared with a very experienced relational data processing ecosystem. This can mean lower support for troubleshooting, performance tuning, or best practices about handling JSON data in SQL databases.