How to Parse JSON in Amazon Redshift

How can I parse a JSON string into a usable data type in Amazon Redshift?

json_parse converts a JSON-formatted VARCHAR into the SUPER data type so you can query nested keys with dot and bracket notation.

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Description

What does json_parse do in Redshift?

json_parse converts a valid JSON string into the SUPER type, letting you query semi-structured data with dot and bracket operators and PartiQL functions.

How do I parse a JSON column when loading data?

Call json_parse inside COPY, INSERT, or UPDATE to transform raw VARCHAR JSON into SUPER as the data lands—saving storage and enabling instant analytics.

Example during INSERT

INSERT INTO products(id,name,price,stock,attributes)SELECT id,name,price,stock,json_parse(raw_json)FROM staging_products;

What is the basic syntax?

Use json_parse(value) for conversion; pair with is_valid_json to avoid errors. For legacy clusters, fall back to json_extract_path_text on VARCHAR JSON.

How can I query fields after parsing?

Dot syntax (attributes.color) accesses objects, while brackets (attributes['sizes'][0]) traverse arrays. Combine with standard SQL filters for fast analytics.

How do I extract values without converting to SUPER?

json_extract_path_text(varchar_json,'key','subkey') returns TEXT directly—handy when you only need a single attribute and SUPER support is unavailable.

Best practices for parsing JSON

Validate with is_valid_json; store parsed data in dedicated SUPER columns; create late-binding views to shield BI tools; monitor storage because SUPER is compressed but still larger than pure columns.

Practical e-commerce use case

Store dynamic product attributes (color, size, tags) in a SUPER column so front-end teams can add new keys without schema changes, while analysts still query them efficiently.

Why How to Parse JSON in Amazon Redshift is important

How to Parse JSON in Amazon Redshift Example Usage


--Report product colors and quantities sold last month
WITH item_attrs AS (
  SELECT oi.quantity,
         p.id,
         json_parse(p.attributes_json) AS attrs
  FROM   orderitems oi
  JOIN   products    p ON p.id = oi.product_id
  WHERE  oi.order_id IN (
          SELECT id FROM orders WHERE order_date >= date_trunc('month', current_date) - interval '1 month')
)
SELECT attrs.color   AS color,
       SUM(quantity) AS total_sold
FROM   item_attrs
GROUP  BY color
ORDER  BY total_sold DESC;

How to Parse JSON in Amazon Redshift Syntax


--Convert JSON VARCHAR to SUPER
json_parse ( json_string ) RETURNS SUPER

--Validate JSON before parsing
is_valid_json ( json_string ) RETURNS BOOLEAN

--Legacy extraction without SUPER
a. json_extract_path_text ( json_string , 'key' [, 'subkey', ...] ) RETURNS TEXT

--Example with e-commerce tables
SELECT id,
       json_parse(details_json)      AS details_super
FROM   products;

Common Mistakes

Using json_extract_path_text on a SUPER column. The function expects VARCHAR JSON and fails on SUPER. Fix by accessing keys with dot or bracket notation once the data is in SUPER.
Skipping validation of incoming JSON. Invalid strings cause runtime errors when json_parse runs. Always wrap the call with is_valid_json or filter bad rows in staging tables.