What Is parse_json in Snowflake?

The PARSE_JSON function in Snowflake is a specialized function designed to interpret an input string as a JSON document and convert it into a value of type VARIANT. This conversion allows the JSON data to be stored and manipulated within Snowflake’s data structures, facilitating advanced data analysis and operations.

What is the PARSE_JSON function in Snowflake?

The PARSE_JSON function in Snowflake is used to convert JSON strings into VARIANT data types. This is essential for storing and manipulating semi-structured data within Snowflake’s structured data environment.

PARSE_JSON(<expr>)

In this syntax, expr represents a string expression holding valid JSON information. The function returns a VARIANT containing a JSON document. If the input is NULL, an empty string, or a string with only whitespace, the function returns NULL.

How is PARSE_JSON used in practice?

PARSE_JSON is commonly used in scenarios involving semi-structured data. Here are some practical examples:

• Storing Different Data Types in a VARIANT Column: The function can handle various data types, converting JSON strings into corresponding VARIANT values, which is crucial for storing semi-structured data within a structured table format.
• Handling NULL Values: PARSE_JSON returns NULL for any input that is NULL, an empty string, or contains only whitespace, ensuring data integrity and accuracy.
• Comparing PARSE_JSON and TO_JSON: While PARSE_JSON and TO_JSON are nearly reciprocal, they differ in handling whitespace and the order of key-value pairs, which can affect the output.

How to Use the PARSE_JSON Function in Snowflake?

1. Storing Different Data Types in a VARIANT Column

Use the PARSE_JSON function to convert JSON strings into VARIANT values and store them in a table.

CREATE OR REPLACE TABLE vartab (n NUMBER(2), v VARIANT);
INSERT INTO vartab
SELECT column1 AS n, PARSE_JSON(column2) AS v
FROM VALUES
(1, 'null'), (2, null), (3, 'true'), (4, '-17'), (5, '123.12'), (6, '1.912e2'),
(7, '"Om ara pa ca na dhih"'), (8, '[-1, 12, 289, 2188, false,]'),
(9, '{"x": "abc", "y": false, "z": 10}') AS vals;

This example demonstrates how to store various data types in a VARIANT column using the PARSE_JSON function.

2. Handling NULL Values

Handle NULL values effectively using the PARSE_JSON function.

SELECT TO_JSON(NULL), TO_JSON('null'::VARIANT), PARSE_JSON(NULL), PARSE_JSON('null');

This query shows how PARSE_JSON returns NULL for NULL inputs, empty strings, or strings with only whitespace.

3. Comparing PARSE_JSON and TO_JSON

Understand the differences between PARSE_JSON and TO_JSON functions.

CREATE OR REPLACE TABLE jdemo2 (varchar1 VARCHAR, variant1 VARIANT);
INSERT INTO jdemo2 (varchar1) VALUES ('{"PI":3.14}');
UPDATE jdemo2 SET variant1 = PARSE_JSON(varchar1);

SELECT varchar1,
PARSE_JSON(varchar1),
variant1,
TO_JSON(variant1),
PARSE_JSON(varchar1) = variant1,
TO_JSON(variant1) = varchar1
FROM jdemo2;

This example illustrates the near reciprocity between PARSE_JSON and TO_JSON, highlighting differences in whitespace handling and key-value pair ordering.

Common Challenges and Solutions

When using the PARSE_JSON function, users may encounter several challenges. Here are some common issues and their solutions:

• Input Size: The maximum input size for PARSE_JSON is 8 MB (compressed). Ensure your JSON documents do not exceed this limit to avoid errors.
• Numeric Precision: PARSE_JSON preserves exact representation for decimal numbers. Numbers in scientific notation or those that can’t be stored as fixed-point decimals are stored as DOUBLE.
• Non-native JSON Types: Types like TIMESTAMP, DATE, TIME, and BINARY must be represented as strings within the JSON document.

Recap of using the PARSE_JSON in Snowflake

The PARSE_JSON function in Snowflake is a powerful tool for interpreting JSON strings and converting them into VARIANT types. This functionality is crucial for handling semi-structured data, performing advanced data manipulations, and ensuring data integrity. By understanding the nuances and best practices associated with PARSE_JSON, users can leverage its capabilities to enhance their data operations within Snowflake.

• Facilitates Handling of Semi-structured Data: PARSE_JSON allows for the seamless integration of semi-structured data within structured data environments.
• Enhances Data Manipulation Capabilities: Users can easily convert JSON data into a format that can be manipulated using Snowflake’s powerful SQL capabilities.
• Ensures Data Integrity: By preserving numeric precision and gracefully handling NULL values, PARSE_JSON ensures the integrity of the data being processed.