See why hundreds of industry leaders trust Secoda to unlock their data's full potential.
See why hundreds of industry leaders trust Secoda to unlock their data's full potential.
Get the guideThe PARSE_JSON function in Snowflake is utilized to convert JSON strings into VARIANT data types, which is crucial for storing and manipulating semi-structured data within Snowflake’s structured data environment. This function allows JSON data to be interpreted as a JSON document and stored in a format that facilitates advanced data analysis and operations. For those working with complex datasets, understanding Snowflake data types is essential to optimize data handling.
The syntax for the PARSE_JSON function is PARSE_JSON(<expr>), where expr is a string expression containing valid JSON. The function returns a VARIANT containing a JSON document. If the input is NULL, an empty string, or consists only of whitespace, the function returns NULL.
PARSE_JSON is commonly employed in scenarios involving semi-structured data. Here are some practical applications:
Use the PARSE_JSON function to convert JSON strings into VARIANT values and store them in a table. For optimizing performance with large datasets, consider optimizing Snowflake for large datasets.
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.
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.
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.
When using the PARSE_JSON function, users may encounter several challenges. Here are some common issues and their solutions:
The PARSE_JSON function supports input expressions up to 8 MB when compressed. This size limitation ensures that the function can handle large JSON documents efficiently while maintaining system performance. Users working with extensive datasets must be mindful of this constraint to avoid errors during JSON parsing.
When PARSE_JSON encounters empty strings or strings consisting solely of whitespace, it returns NULL. This behavior underscores the function's ability to distinguish between meaningful JSON data and empty or irrelevant input. It is crucial for users to ensure that their input data is well-formed to avoid unexpected NULL values in their analysis.
By default, JSON objects processed by PARSE_JSON cannot have duplicate keys. This constraint aligns with the JSON standard, which mandates unique keys within objects. However, Snowflake provides flexibility by allowing users to override this default behavior through a specific parameter. This option can be beneficial in scenarios where duplicate keys are necessary, although such cases should be approached with caution to maintain data integrity.
Decimal numbers are preserved with exactness in PARSE_JSON, being treated as fixed-point numbers unless scientific notation is required. This precise handling ensures that numerical data maintains its integrity during the parsing process, which is vital for applications requiring accurate numerical analysis.
Non-native JSON types such as TIMESTAMP, DATE, or BINARY must be represented as strings within PARSE_JSON. This requirement stems from JSON's lack of direct support for these data types. Users must convert these types to strings before parsing them with PARSE_JSON, ensuring compatibility and accurate data representation in Snowflake.
PARSE_JSON distinguishes between JSON null values (which are considered valid VARIANT nulls) and SQL NULLs. This differentiation is crucial, as it impacts how null values are represented and manipulated within Snowflake. Users must be aware of this distinction to handle null values appropriately in their data processing workflows.
The PARSE_JSON function works in tandem with the TO_JSON function to convert between JSON strings and VARIANTs. However, these functions differ in their handling of empty strings and whitespace, as well as potential changes in key-value pair order. Understanding these differences is essential for users who need to switch between JSON and VARIANT representations frequently. To monitor and optimize these processes, consider exploring Snowflake observability techniques.
Aspect PARSE_JSON TO_JSON Handling of Empty Strings Returns NULL Retains the empty string Key-Value Pair Order May change during parsing Preserves the original order Null Value Representation Distinguishes between JSON null and SQL NULL Represents null as JSON null
Examples provided in the context of PARSE_JSON illustrate its application in storing different data types in a VARIANT column, dealing with duplicate keys, and handling NULL values. These examples serve as practical guides for users to understand the nuances of PARSE_JSON and apply it effectively in real-world scenarios.
When using PARSE_JSON, there are several key considerations to keep in mind:
By understanding and addressing these considerations, users can maximize the utility of PARSE_JSON within Snowflake and achieve more effective data processing outcomes. For a deeper dive into task automation, explore Snowflake tasks.
Secoda is a data management platform that leverages AI to centralize and streamline data discovery, lineage tracking, governance, and monitoring. It acts as a "second brain" for data teams by providing a single source of truth, allowing users to easily find, understand, and trust their data. This is achieved through features like search, data dictionaries, and lineage visualization, which enhance data collaboration and efficiency within teams.
By offering a comprehensive view of an organization's entire data stack, Secoda enables both technical and non-technical users to access and analyze data more effectively. This results in improved data accessibility, faster data analysis, enhanced data quality, and streamlined data governance.
Secoda enhances data discovery by allowing users to search for specific data assets across their entire data ecosystem using natural language queries. This feature simplifies the process of finding relevant information, regardless of the user's technical expertise. Additionally, Secoda's data lineage tracking automatically maps the flow of data from its source to its final destination, providing complete visibility into how data is transformed and used across different systems.
The platform's AI-powered insights further enhance data understanding by extracting metadata, identifying patterns, and offering contextual information about data. This combination of features ensures that users can quickly and confidently access the data they need for analysis and decision-making.
Our cutting-edge tools simplify and enhance your operational efficiency, making it easier to manage tasks and achieve goals. With Secoda, you can streamline your data processes, reduce complexity, and increase speed by optimizing workflows.
Ready to take your data management to the next level? Get started today and experience a significant boost in productivity and efficiency.
Built for data teams, designed for everyone so you can get more from your data stack.
© 2025 Secoda, Inc. All rights reserved.
By using this website, you accept our Terms of Use and Privacy Policy.
