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Description

MySQL Error 3908 indicates an attempt to store a non-scalar data type, such as an array or an object, into a key part of an index that is defined to expect a scalar (single, atomic) value. This typically occurs when indexing JSON data directly without extracting scalar components, or when a multi-valued index (MVI) part expects a scalar.

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Error Message

Cannot store an array or an object in a scalar key part of the index '%s'.

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Known Causes

3 known causes

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Indexing Non-Scalar JSON Data

Attempting to create or update an index on a column that contains JSON arrays or objects directly, where the index key part expects a single, atomic value.

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Mismatched Data Types

The data being inserted or updated into an indexed column is an array or object, but the index definition or column type expects a scalar value, leading to a type mismatch.

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Misconfigured Multi-Valued Index (MVI)

When using MySQL's multi-valued index features, a specific part of the index explicitly designated as scalar receives an array or object instead of an atomic value.

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Solutions

4 solutions available

1. Normalize Data into Separate Columns medium

Deconstruct complex data types into individual, scalar columns for indexing.

1

Identify the column containing the non-scalar value (array or object) that is causing the indexing error. Let's assume it's a JSON column named `complex_data` in a table named `my_table`.

2

Determine which specific parts of the JSON you need to index. For example, if `complex_data` is `{'id': 123, 'tags': ['sql', 'database']}`, and you want to index `id` and the first tag.

3

Add new scalar columns to your table to store these extracted values. For instance, add an `item_id` (e.g., INT) and `first_tag` (e.g., VARCHAR).

ALTER TABLE my_table ADD COLUMN item_id INT;
ALTER TABLE my_table ADD COLUMN first_tag VARCHAR(255);

4

Populate these new columns by extracting data from the original non-scalar column. Use JSON functions like `JSON_EXTRACT` (or `->` operator) for JSON data.

UPDATE my_table SET item_id = JSON_EXTRACT(complex_data, '$.id'), first_tag = JSON_EXTRACT(complex_data, '$.tags[0]');

5

Create indexes on the newly created scalar columns.

CREATE INDEX idx_item_id ON my_table (item_id);
CREATE INDEX idx_first_tag ON my_table (first_tag);

6

Optionally, consider removing or altering the original non-scalar column if it's no longer needed or if you've fully migrated its indexed data.

2. Leverage JSON Indexing Features (MySQL 5.7+) medium

Use MySQL's built-in JSON indexing capabilities for specific JSON document paths.

1

Ensure you are using MySQL version 5.7 or later, which supports JSON data types and indexing.

2

Identify the JSON column (e.g., `complex_data` in `my_table`) and the specific path within the JSON document you need to index. For example, to index the value at `$.id`.

3

Create a functional index on the JSON column, specifying the path to the value you want to index. This creates an index on the extracted scalar value.

CREATE INDEX idx_complex_data_id ON my_table ((CAST(JSON_EXTRACT(complex_data, '$.id') AS UNSIGNED)));

4

If you need to index multiple paths within the JSON, create separate functional indexes for each path. For indexing an array element, specify its index. For example, to index the first element of a 'tags' array:

CREATE INDEX idx_complex_data_first_tag ON my_table ((CAST(JSON_EXTRACT(complex_data, '$.tags[0]') AS CHAR(255))));

5

Ensure the `CAST` function uses an appropriate data type for the indexed value (e.g., `UNSIGNED` for integers, `CHAR(255)` for strings).

3. Use Full-Text Indexing for Textual Arrays/Objects medium

Employ full-text indexing for searching within large text-based arrays or object properties.

1

This solution is primarily for searching within text content stored in arrays or object properties, not for exact value matching or range queries.

2

Ensure your MySQL server is configured with full-text search capabilities and that the appropriate plugin is installed and enabled.

3

Alter your table to include a `FULLTEXT` index on the column containing the non-scalar data. You might need to extract relevant text content into a separate column first if the original column is not already a text type.

ALTER TABLE my_table ADD FULLTEXT INDEX ft_complex_data (complex_data);

4

Use `MATCH AGAINST` syntax for querying. For example, to find rows where `complex_data` contains the word 'database':

SELECT * FROM my_table WHERE MATCH(complex_data) AGAINST('database' IN NATURAL LANGUAGE MODE);

5

Consider performance implications and the types of queries you will be running. Full-text indexing is optimized for text search, not for structured data retrieval.

4. Refactor Schema to Avoid Non-Scalar Indexing advanced

Redesign the database schema to adhere to scalar indexing principles.

1

This is a more fundamental solution that involves rethinking how your data is structured.

2

Analyze the relationships and data types involved. If you're storing lists of items or nested structures, consider creating separate tables for these entities.

3

For example, if you have a `products` table with a `tags` JSON array, create a `product_tags` table with `product_id` and `tag_name` columns. Each tag would then be a separate row, allowing for standard scalar indexing on `tag_name`.

CREATE TABLE product_tags (
    product_id INT,
    tag_name VARCHAR(255),
    PRIMARY KEY (product_id, tag_name),
    FOREIGN KEY (product_id) REFERENCES products(id)
);

4

Implement foreign keys and relationships to maintain data integrity.

5

Migrate existing data to the new, normalized schema. This may involve complex data transformation scripts.

6

This approach provides the most robust and performant solution for complex data structures by aligning with relational database design principles.

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Related Errors

5 related errors

MySQL Error 3908: Indexing Non-Scalar Values