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JSON in PostgreSQL : a query tuning case
- ORMs can sometimes produce overly complex SQL with excessive joins and business logic which makes tuning queries difficult. JSON data types add another layer of complexity for DBAs to manage, often leading to full table scans and inefficient execution plans. Improving ORM-generated queries requires limiting dataset early in the query execution with INNER JOINs, adding indexing for frequent field accesses, simplifying WHERE clause conditions by eliminating functions on JSON fields, and reducing buffer operations. PostgreSQL allows creating different types of indexes for JSON and JSONB fields, such as GIN indexes or BTREE indexes.
- Avoiding unnecessary casting of JSON data is important to prevent the optimizer from degrading performance. Leveraging JSON path queries is a more advanced querying capability that PostgreSQL 12 introduced for more complex JSON structures. Views or materialized views can be used to extract JSON fields into regular columns, reducing computational burdens during query execution and allowing the database optimizer to use indexes more effectively. Best practices for JSON include using JSON data types when schema flexibility is required and storing semi-structured or unstructured data, embedding related data and aggregating diverse data. A relational schema should be used when data integrity is crucial, frequent updates on specific fields are necessary, complex queries and reporting are required, and performance optimization is paramount.
- Understanding how to use JSONB and PostgreSQL effectively can bring developers and DBAs closer to designing flexible, efficient, and scalable applications that combine the flexibility of JSON data types with the reliability of a relational database.
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