Inefficient querying of JSONB complex operations

# Pain Point: Inefficient Querying of JSONB Complex Operations Every time a developer needs to filter, compare, or search within PostgreSQL's JSONB columns, they hit a wall of sluggish queries that should take milliseconds but instead crawl through seconds—or worse, timeout entirely. Teams waste hours writing convoluted workarounds: extracting JSON into temporary tables, denormalizing data back into rigid schemas, or building custom application-layer filtering logic that bleeds computational burden away from the database where it belongs. As one frustrated developer described it: "Efficiently querying JSON data with operations like arithmetic comparison (<, >, etc) and substring match" becomes an odyssey when your tables have arbitrary nesting and your query planner can't optimize what it doesn't understand. The workarounds fail catastrophically at scale—denormalization bloats your schema and creates sync nightmares, while pushing logic to the application layer transforms a single database call into thousands of in-memory operations, killing performance and burning through cloud infrastructure budgets. For teams managing customer-provided, dynamically-structured data, this inefficiency isn't a minor inconvenience; it's a silent tax on every feature release, every report generation, every real-time dashboard that depends on flexible data structures.