When working with JSON data in programming languages like JavaScript or TypeScript we might encounter an error message stating “Could Not Identify an Equality Operator for Type JSON.” This error occurs because the JSON objects are not natively comparable using equality operators like == or === in many programming languages.

Problem Statement

Consider a scenario where we have JSON objects and we want to compare them for equality in the code. However, when attempting to do so the error:

This error typically arises because JSON objects are not directly comparable using the standard equality operators due to their structure and properties.

Understanding the Issue

PostgreSQL provides two data types for storing JSON data: json and jsonb. The json data type stores an exact copy of the input text, which preserves whitespace, order of keys, and duplicates. However, it does not support indexing and lacks an equality operator. On the other hand, jsonb datatype stores JSON data in a decomposed binary format, supports indexing, and provides operators for equality comparison.

Scenarios Causing the Error

1. Using json Type in a Unique Constraint or Primary Key: PostgreSQL cannot enforce uniqueness without an equality operator, leading to errors.
2. Using json Data Type in JOIN Clauses: Without an equality operator, it’s impossible to perform JOIN operations on columns of type json.

Solutions to Fix the “Could Not Identify an Equality Operator for Type Python JSON” Error

Here are several approaches to resolve this error, focusing on transitioning to the jsonb data type or redefining how data is used and stored.

1. Convert json Columns to jsonb

The most straightforward solution to this problem is converting existing json columns to jsonb. The jsonb data type supports all operations that require an equality operator.

SQL Command to Alter Column Type

ALTER TABLE your_table_name
ALTER COLUMN your_column_name TYPE jsonb USING your_column_name::jsonb;

This command converts a column from json to jsonb safely, ensuring all current functionalities remain intact while gaining new capabilities.

2. Use JSON Data Wisely in Schemas

If converting to jsonb is not feasible, reconsider how JSON data is used within your database schema:

• Avoid using JSON for Primary Keys: Store essential identifiers in separate, type-appropriate columns.
• Avoid JSON in JOIN Clauses: Extract key information into separate columns that can be indexed and joined efficiently.

3. Query Adjustment in Python

When working with PostgreSQL and JSON data in Python, particularly using libraries like psycopg2, adjust your queries to cast json to jsonb where necessary, or ensure all operations on JSON columns do not require an equality comparison.

Example Python Code

import psycopg2

# Connect to PostgreSQL database
conn = psycopg2.connect(database="testdb", user='postgres',
                        password='password', host='127.0.0.1', port='5432')
cur = conn.cursor()

# Using jsonb in query to leverage equality operators
cur.execute("SELECT * FROM your_table WHERE your_jsonb_column @> '{"key": "value"}'::jsonb;")
results = cur.fetchall()

# Process results
for row in results:
    print(row)

# Close connection
cur.close()
conn.close()

4. Data Normalization

Consider normalizing data where possible:

• Extract keys: If you frequently access specific keys within JSON, consider extracting these into separate columns.
• Table transformations: Transform JSON fields into multiple columns or even separate tables, depending on the complexity and usage of the data.

5. Custom Equality Function

Implement a function that compares two JSON objects for the equality. This function will recursively traverse the objects and compare each key-value pair.

function isEqual(obj1, obj2) {
    // Check if both objects are JSON
    if (typeof obj1 !== 'object' || typeof obj2 !== 'object' || obj1 === null || obj2 === null) {
        return obj1 === obj2;
    }
    // Get the keys of both objects
    const keys1 = Object.keys(obj1);
    const keys2 = Object.keys(obj2);
    // Check if number of keys are same
    if (keys1.length !== keys2.length) {
        return false;
    }
    // Compare key-value pairs
    for (const key of keys1) {
        if (!isEqual(obj1[key], obj2[key])) {
            return false;
        }
    }
    return true;
}

6. Using a Library (Lodash)

If we prefer using a library, Lodash provides a utility function _.isEqual() that can compare two JSON objects deeply.

const _ = require('lodash');
const obj1 = { a: 1, b: { c: 2 } };
const obj2 = { a: 1, b: { c: 2 } };
console.log(_.isEqual(obj1, obj2)); // Outputs: true

Code Example

Let’s illustrate how the problem is resolved with the code example:

Setup PostgreSQL Table with jsonb

You can set this up directly in your PostgreSQL client or use a script:

-- Connect to your PostgreSQL server and run this SQL script.

CREATE TABLE if not exists jsonb_example (
    id serial PRIMARY KEY,
    data jsonb NOT NULL
);

-- Insert some sample data
INSERT INTO jsonb_example (data) VALUES
('{"name": "John", "age": 28, "city": "New York"}'::jsonb),
('{"name": "Jane", "age": 32, "city": "Los Angeles"}'::jsonb);

Step 2: Python Script to Interact with PostgreSQL

This script will connect to the PostgreSQL database, insert a new jsonb record, and then perform a query to retrieve records where the JSON data matches certain criteria.

import psycopg2

def connect():
    """ Connect to the PostgreSQL database server """
    conn = None
    try:
        # Connect to your PostgreSQL DB
        conn = psycopg2.connect(
            host="localhost",
            database="testdb",
            user="postgres",
            password="yourpassword")

        # Create a cursor object
        cur = conn.cursor()
        
        # Insert JSON data into jsonb column
        json_data = '{"name": "Alice", "age": 24, "city": "Boston"}'
        cur.execute("INSERT INTO jsonb_example (data) VALUES (%s::jsonb)", (json_data,))
        conn.commit()

        # Query data using an equality operator on jsonb
        cur.execute("SELECT * FROM jsonb_example WHERE data @> '{\"city\": \"Boston\"}'::jsonb;")
        rows = cur.fetchall()
        for row in rows:
            print(row)

        # Close communication with the database
        cur.close()
    except (Exception, psycopg2.DatabaseError) as error:
        print(error)
    finally:
        if conn is not null:
            conn.close()
            print('Database connection closed.')

if __name__ == '__main__':
    connect()

Output :

(1, {'name': 'Alice', 'age': 24, 'city': 'Boston'})

By utilizing jsonb and the associated operators, you can perform complex queries on JSON data stored in PostgreSQL, overcoming limitations that arise with the json data type.

Conclusion

By implementing a custom equality check function or using the library like Lodash we can effectively compare JSON objects for the equality in the applications resolving the “Could Not Identify an Equality Operator for Type JSON” error. This approach ensures robust handling of the JSON data comparisons in the code.