Exploding JSON Arrays in SQL Server 2019: A Step-by-Step Guide

Understanding the Problem and the Proposed Solution

As a developer, working with JSON data can be both exciting and challenging. In this article, we’ll explore how to explode JSON arrays in a SQL Server 2019 column. We’ll delve into the proposed solution provided by Stack Overflow user, which uses a combination of OPENJSON and CROSS APPLY to achieve this.

Background: Understanding JSON Data in SQL Server

Before we dive into the solution, let’s quickly review how JSON data is stored in SQL Server. When you insert a JSON value into a column, it’s essentially converted into a string that contains the JSON data. This means you can’t directly query the individual elements of the array without using specialized functions like OPENJSON.

The Problem: Exploding JSON Arrays

Let’s consider an example table named Schema.table with two columns: id and json_col. The json_col column contains a JSON value that represents an array of objects, where each object has two properties: name and age. Our goal is to create a new table that contains individual rows for each name-age pair.

Using `OPENJSON` with a `WITH` Clause

The proposed solution uses OPENJSON with a WITH clause to read the JSON array into two separate variables: names and ages. This allows us to work with individual elements of the array without having to use complex filtering logic later on.

CROSS APPLY OPENJSON(t.json_col) 
            WITH(names NVARCHAR(MAX) AS JSON
                ,ages  NVARCHAR(MAX) AS JSON)

In this code snippet, we’re using OPENJSON to parse the JSON value in the json_col column. The WITH clause specifies that we want to read two separate variables: names and ages. These variables will contain the individual elements of the JSON array.

Exploding the Names Array

Now that we have the names variable, we can use another instance of OPENJSON to “explode” it into individual rows. This allows us to access each name individually and retrieve its corresponding age value from the ages variable.

CROSS APPLY OPENJSON(A.names) B;

Here, we’re applying OPENJSON again to the names variable. The result is a new table with individual rows for each name in the array.

Combining Data Using `JSON_VALUE` and `JSON_TABLE`

To combine data from both the ages variable and the exploded B table, we use the JSON_VALUE function to retrieve the age value corresponding to each name. We also create a new table using the JSON_TABLE function to simplify the resulting data.

SELECT t.id
      ,B.[key] As ValueIndex
      ,B.[value] AS PersonNam
      ,JSON_VALUE(A.ages,CONCAT('$[',B.[key],']')) AS PersonAge
FROM @tbl t
CROSS APPLY OPENJSON(t.json_col) 
            WITH(names NVARCHAR(MAX) AS JSON
                ,ages  NVARCHAR(MAX) AS JSON) A
CROSS APPLY OPENJSON(A.names) B;

In this code snippet, we’re using JSON_VALUE to retrieve the age value corresponding to each name. The CONCAT function is used to specify the position of the age value within the array.

Using a Sample Table

To illustrate the proposed solution in action, let’s create a sample table with some JSON data:

DECLARE @tbl TABLE(id INT,json_col NVARCHAR(MAX));
INSERT INTO @tbl VALUES(1,N'{"names":["John","Peter"],"ages":["31","40"]}')
                      ,(2,N'{"names":["Jim"],"ages":["17"]}');

SELECT t.id
      ,B.[key] As ValueIndex
      ,B.[value] AS PersonNam
      ,JSON_VALUE(A.ages,CONCAT('$[',B.[key],']')) AS PersonAge
FROM @tbl t
CROSS APPLY OPENJSON(t.json_col) 
            WITH(names NVARCHAR(MAX) AS JSON
                ,ages  NVARCHAR(MAX) AS JSON) A
CROSS APPLY OPENJSON(A.names) B;

This code snippet creates a sample table with two rows of JSON data. When executed, it produces the following result:

id  ValueIndex PersonNam PersonAge
1   John       31
1   Peter      40
2   Jim        17

Best Practices and Considerations

While the proposed solution works well for this specific problem, there are some best practices to keep in mind when working with JSON data:

Entity-centered approach: When storing JSON data, consider using an entity-centered approach (array of objects) instead of a position-dependent storage. This can help reduce errors and improve maintainability.
JSON validation: Always validate user input data to prevent potential security vulnerabilities or unexpected behavior.
Performance considerations: Be mindful of performance implications when working with large JSON datasets.

Conclusion

Exploding JSON arrays in SQL Server involves using specialized functions like OPENJSON and CROSS APPLY. By following the proposed solution outlined in this article, you can create a new table that contains individual rows for each name-age pair. Remember to consider best practices and potential performance implications when working with large JSON datasets.

Last modified on 2023-05-23