No Index for LIKE on JSONB with Array in the Path: A Comprehensive Guide

JSONB (JSON Binary) in PostgreSQL is a powerful data type for storing semi-structured data. Its ability to be indexed makes querying JSON data efficient. However, indexing `LIKE` operations on JSONB fields, especially when arrays are involved in the path, can be tricky. This article provides a comprehensive guide to understanding why indexing `LIKE` queries with arrays in JSONB paths can be problematic and explores alternative solutions to optimize your queries.

Table of Contents

1. Introduction to JSONB in PostgreSQL
2. Understanding JSONB Indexing
3. The Challenge: LIKE Queries on JSONB with Array Paths
4. Why Indexing Fails for LIKE with Array Paths
5. Alternative Solutions and Optimizations
   1. Using JSONB Operators: @>, ?, ?|, ?&
   2. Full-Text Search with JSONB
   3. GIN Indexes with Custom Operators
   4. Preprocessing and Materialized Views
   5. Application-Level Filtering
6. Practical Examples and Code Snippets
7. Performance Considerations and Benchmarking
8. Best Practices for Querying JSONB Data
9. Common Mistakes to Avoid
10. Conclusion
11. Further Reading and Resources

1. Introduction to JSONB in PostgreSQL

JSONB is a binary format for storing JSON data in PostgreSQL. Unlike the `JSON` data type, which stores the JSON as a text string, JSONB decomposes the JSON data into a binary representation, making it faster to process. This is because the parsing overhead is only incurred during data insertion or update, not during every query.

Key features of JSONB include:

• Efficient Storage: Stored in a decomposed binary format.
• Fast Processing: Optimized for querying and data manipulation.
• Indexing Support: Allows creation of indexes to speed up queries.
• Validation: Enforces valid JSON structure.

Example of creating a table with a JSONB column:

    CREATE TABLE products (
      id SERIAL PRIMARY KEY,
      product_name VARCHAR(255),
      details JSONB
    );
  

Example of inserting data into the table:

    INSERT INTO products (product_name, details)
    VALUES (
      'Laptop',
      '{
        "brand": "Dell",
        "model": "XPS 13",
        "features": ["Intel Core i7", "16GB RAM", "512GB SSD"],
        "price": 1200
      }'::jsonb
    );
  

2. Understanding JSONB Indexing

PostgreSQL provides various indexing options for JSONB columns to optimize query performance. The most common type is a GIN (Generalized Inverted Index) index.

GIN Indexes: GIN indexes are particularly useful for indexing composite data types like arrays and JSONB. They work by creating an inverted index where each element of the JSONB document is indexed separately.

Different types of GIN indexes for JSONB include:

• jsonb_path_ops: Optimized for existence and containment operators (@>, ?, ?|, ?&). This is the recommended index for most JSONB use cases.
• jsonb_ops: Provides more general indexing capabilities, including equality and ordering comparisons. This index is less efficient than `jsonb_path_ops` for simple containment queries.

Example of creating a GIN index with `jsonb_path_ops`:

    CREATE INDEX idx_products_details ON products USING GIN (details jsonb_path_ops);
  

This index will improve the performance of queries using operators like @>, ?, ?|, and ?&.

BRIN Indexes: Block Range Indexes (BRIN) are another indexing option, particularly useful for large tables where JSONB data is naturally correlated with the physical order of the table. BRIN indexes are smaller and faster to maintain than GIN indexes, but they are only effective if the data is sorted.

3. The Challenge: LIKE Queries on JSONB with Array Paths

While GIN indexes are effective for containment and existence queries, they don’t directly support `LIKE` queries on JSONB fields, especially when the path includes arrays. This is because `LIKE` performs pattern matching on strings, and standard JSONB indexes are designed for exact matches or containment checks, not partial string matching within arrays.

Consider the following query:

    SELECT *
    FROM products
    WHERE details -> 'features' ->> 0 LIKE '%Core%';
  

This query attempts to find products where the first element of the “features” array contains the string “Core”. Even with a GIN index on the `details` column, PostgreSQL is unlikely to use the index for this `LIKE` operation. It will likely perform a sequential scan of the table, which is inefficient for large tables.

4. Why Indexing Fails for LIKE with Array Paths

The primary reason why indexing fails for `LIKE` queries with array paths in JSONB is the way GIN indexes are structured. GIN indexes break down the JSONB document into individual elements for indexing. However, they don’t index the string representations of these elements in a way that supports partial string matching using `LIKE`. Furthermore, the array structure adds complexity, as the index doesn’t inherently understand the order or position of elements within the array.

Specifically:

• GIN indexes are designed for exact matches: They are optimized for finding documents that contain a specific key-value pair or element, not for partial matches within strings.
• Array structure adds complexity: The index doesn’t maintain information about the order or position of elements within arrays, making it difficult to use for `LIKE` queries that target specific array elements.
• Data type mismatch: The `->>` operator returns a text value, but the underlying index is not designed for text-based `LIKE` operations.

Therefore, PostgreSQL typically resorts to a full table scan when executing `LIKE` queries on JSONB fields with array paths, leading to poor performance, especially on large datasets.

5. Alternative Solutions and Optimizations

While direct indexing of `LIKE` queries on JSONB with array paths is not feasible, several alternative solutions and optimizations can improve query performance.

5.1 Using JSONB Operators: @>, ?, ?|, ?&

These operators are designed for containment and existence checks within JSONB documents and are well-supported by GIN indexes.

• @> (Contains): Checks if the JSONB document on the left contains the JSONB document on the right.
• ? (Key Exists): Checks if a key exists in the JSONB document.
• ?| (Any Key Exists): Checks if any of the keys in the array exists in the JSONB document.
• ?& (All Keys Exist): Checks if all of the keys in the array exists in the JSONB document.

While these operators don’t directly support `LIKE`, you can restructure your data or query to leverage them. For example, if you need to find products with a feature containing “Core”, you could restructure your data to have a separate field indicating whether a feature contains “Core”.

Example: Adding a derived column:

    ALTER TABLE products ADD COLUMN has_core_feature BOOLEAN DEFAULT FALSE;

    UPDATE products
    SET has_core_feature = TRUE
    WHERE details -> 'features' @> '["Intel Core i5"]' OR details -> 'features' @> '["Intel Core i7"]';

    CREATE INDEX idx_products_has_core_feature ON products (has_core_feature);

    SELECT *
    FROM products
    WHERE has_core_feature = TRUE;
  

This approach requires modifying the table structure and updating the data, but it can significantly improve query performance.

5.2 Full-Text Search with JSONB

PostgreSQL’s full-text search capabilities can be used with JSONB data to perform more sophisticated pattern matching. You can convert the relevant JSONB field to a `tsvector` (text search vector) and then use full-text search operators like `@@` and `to_tsquery` to find documents that contain specific words or phrases.

Example: Using Full-Text Search:

    ALTER TABLE products ADD COLUMN features_tsvector tsvector;

    UPDATE products
    SET features_tsvector = to_tsvector('english', details -> 'features');

    CREATE INDEX idx_products_features_tsvector ON products USING GIN (features_tsvector);

    SELECT *
    FROM products
    WHERE features_tsvector @@ to_tsquery('english', 'Core & i7');
  

This approach involves creating a new column to store the text search vector and updating it whenever the JSONB data changes. It allows you to perform more complex searches than simple `LIKE` queries, but it requires more setup and maintenance.

5.3 GIN Indexes with Custom Operators

You can create custom operators and functions to extract specific parts of the JSONB data and index them using a GIN index. This approach requires more advanced PostgreSQL knowledge, but it can provide a more tailored solution for your specific use case.

Example: Creating a Custom Operator:

This is a complex solution and would require significant code to implement correctly. The basic idea involves creating a function that extracts the array elements as text, creating a custom operator that uses this function, and then creating a GIN index using this operator.

This approach is generally not recommended unless you have very specific requirements and are comfortable with advanced PostgreSQL programming.

5.4 Preprocessing and Materialized Views

If the data in your JSONB field is relatively static, you can preprocess it and store it in separate, indexed columns. This can significantly improve query performance by avoiding the need to parse the JSONB data during query execution.

Example: Using a Materialized View:

    CREATE MATERIALIZED VIEW product_features AS
    SELECT
      id,
      product_name,
      (details ->> 'brand') AS brand,
      (details -> 'features' ->> 0) AS feature1,
      (details -> 'features' ->> 1) AS feature2
    FROM products;

    CREATE INDEX idx_product_features_brand ON product_features (brand);
    CREATE INDEX idx_product_features_feature1 ON product_features (feature1);
    CREATE INDEX idx_product_features_feature2 ON product_features (feature2);

    SELECT *
    FROM product_features
    WHERE feature1 LIKE '%Core%';

    REFRESH MATERIALIZED VIEW product_features;
  

This approach involves creating a materialized view that extracts the relevant data from the JSONB field and stores it in separate columns. You can then create indexes on these columns and query the materialized view instead of the original table. The materialized view needs to be refreshed periodically to keep it up-to-date with the underlying data.

5.5 Application-Level Filtering

In some cases, the most efficient solution is to perform the filtering in your application code. This is particularly useful if you only need to retrieve a small number of records or if the filtering logic is complex and difficult to express in SQL.

Example: Filtering in Application Code:

Retrieve all products from the database and then filter the results in your application code based on the `LIKE` condition. This approach avoids the need for complex SQL queries and indexing, but it can be inefficient if you need to retrieve a large number of records.

6. Practical Examples and Code Snippets

This section provides more detailed examples of how to implement the alternative solutions discussed above.

Example 1: Using @> operator:

    -- Original query (inefficient):
    SELECT *
    FROM products
    WHERE details -> 'features' ->> 0 LIKE '%Core%';

    -- Alternative using @> operator:
    SELECT *
    FROM products
    WHERE details @> '{"features": ["Intel Core i7"]}';
  

Example 2: Using Full-Text Search:

    -- Add a tsvector column:
    ALTER TABLE products ADD COLUMN features_tsvector tsvector;

    -- Update the tsvector column:
    UPDATE products
    SET features_tsvector = to_tsvector('english', details -> 'features');

    -- Create a GIN index on the tsvector column:
    CREATE INDEX idx_products_features_tsvector ON products USING GIN (features_tsvector);

    -- Query using full-text search:
    SELECT *
    FROM products
    WHERE features_tsvector @@ to_tsquery('english', 'Core & i7');
  

Example 3: Using a Materialized View:

    -- Create a materialized view:
    CREATE MATERIALIZED VIEW product_features AS
    SELECT
      id,
      product_name,
      (details ->> 'brand') AS brand,
      (details -> 'features' ->> 0) AS feature1,
      (details -> 'features' ->> 1) AS feature2
    FROM products;

    -- Create indexes on the materialized view:
    CREATE INDEX idx_product_features_brand ON product_features (brand);
    CREATE INDEX idx_product_features_feature1 ON product_features (feature1);
    CREATE INDEX idx_product_features_feature2 ON product_features (feature2);

    -- Query the materialized view:
    SELECT *
    FROM product_features
    WHERE feature1 LIKE '%Core%';

    -- Refresh the materialized view:
    REFRESH MATERIALIZED VIEW product_features;
  

7. Performance Considerations and Benchmarking

The performance of each alternative solution depends on various factors, including the size of your data, the complexity of your queries, and the hardware resources available to your PostgreSQL server. It’s essential to benchmark different approaches to determine which one provides the best performance for your specific use case.

Benchmarking Tips:

• Use a realistic dataset: Test with a dataset that reflects the size and structure of your production data.
• Run multiple iterations: Run each query multiple times to get a stable average execution time.
• Use EXPLAIN ANALYZE: Use the `EXPLAIN ANALYZE` command to understand how PostgreSQL is executing your queries and identify potential bottlenecks.
• Consider different index types: Experiment with different index types (e.g., GIN, BRIN) to see which one performs best.
• Monitor resource usage: Monitor CPU, memory, and disk I/O usage to identify potential resource constraints.

General Performance Guidelines:

• @> operator: Generally performs well with GIN indexes.
• Full-Text Search: Can be slower than @> for simple queries, but it’s more flexible for complex pattern matching.
• Materialized Views: Can provide excellent performance for read-heavy workloads, but they require maintenance to keep them up-to-date.

8. Best Practices for Querying JSONB Data

Following these best practices can help you optimize your queries and avoid common pitfalls.

• Use the right operators: Choose the appropriate JSONB operators for your queries (@>, ?, ?|, ?&) instead of relying on `LIKE`.
• Create appropriate indexes: Create GIN indexes on JSONB columns to speed up queries. Use `jsonb_path_ops` for containment and existence queries.
• Normalize your data: Consider denormalizing your data and storing frequently queried fields in separate columns.
• Use materialized views: Create materialized views to precompute frequently used query results.
• Monitor query performance: Regularly monitor query performance and identify potential bottlenecks.

9. Common Mistakes to Avoid

• Using LIKE without understanding index limitations: Don’t assume that `LIKE` queries on JSONB fields will automatically use indexes.
• Over-indexing: Creating too many indexes can slow down data modification operations.
• Ignoring data distribution: Consider the distribution of your data when choosing indexing strategies.
• Not refreshing materialized views: Materialized views need to be refreshed regularly to stay up-to-date.
• Using the wrong JSONB operators: Choose the appropriate operators for your specific use case.

10. Conclusion

Indexing `LIKE` queries on JSONB fields with array paths is challenging due to the way GIN indexes are structured and the nature of `LIKE` operations. However, by understanding the limitations of indexing and exploring alternative solutions like using JSONB operators, full-text search, materialized views, and application-level filtering, you can significantly improve the performance of your queries and effectively work with JSONB data in PostgreSQL.

11. Further Reading and Resources

• PostgreSQL JSONB Documentation
• PostgreSQL Indexing Documentation
• EnterpriseDB Blog on JSONB Indexing
• Cybertec PostgreSQL Blog on JSONB Indexing