Insights, deep dives, and technical articles from the HyperQuery™ team.
By Bert Scalzo, CTO & Chief Architect, QIKR. SQL anti-patterns are common bad or inefficient coding habits that lead to severe performance degradation, high resource consumption, and system instability. Common culprits include SELECT * (retrieves every column, prevents covering indexes), leading wildcards like %abc that force full table scans, functions on indexed columns making queries non-SARGable, correlated subqueries that re-execute for every outer row, and overusing DISTINCT as a band-aid for bad join logic. ASTRO™, HyperQuery™ inference engine, is purpose-built to detect and eliminate these patterns autonomically — delivering syntactically correct PerfectSQL™ with best cost-optimized execution efficiency and 100s to 1,000s X better performance. No schema needed, no database changes, no manual tuning required.
There is a silent crisis among DBAs draining productivity and driving burnout. Research by SolarWinds revealed that over one-third of DBAs spend more than 27 hours per week on reactive firefighting instead of strategic, high-value work. We believe preactive is better than proactive, and proactive is absolutely better than reactive. It is not enough to understand a problem — you must take action on it. HyperQuery™ is the Agentic AI Query Performance Optimization Experience™: a 100% web-based, no-install service that makes it astonishingly easy for anyone to optimize any query anywhere with 1-click instant rewrites delivering syntactically correct PerfectSQL™, best cost-optimized execution plan decisioning, and 100s to 1,000s X performance — all autonomically, with zero touch, zero schema, zero tuning, zero database changes, zero hardware required.
For decades the comfortable myth in database infrastructure has been: provision more instances and run them on faster hardware. That assumption no longer holds. Databases have exploded in size, workloads are bigger and more varied, and queries are far more complex. Nothing craters query performance faster than badly coded SQL and inefficiently written, poorly executing queries — and no amount of hardware can fix that. It just busts budgets while the bad SQL problem remains. HyperQuery™ is the Agentic AI-powered query rewrite performance optimization solution that enables anyone to optimize any query anywhere, instantly and autonomically. Underpinning it is ASTRO™ — our 100% SQL-aware, 100% self-contained engine that fires the precise number of rules needed to fully optimize a given query. Better efficiency, better performance, and lower TCO are not a tradeoff — with HyperQuery™, they are the same outcome.
By Bert Scalzo. SQL is not overly complex in theory — DML consists of just INSERT, UPDATE, DELETE, and SELECT. But the SELECT command permits numerous equivalent ways to write the same query, and how a query is written has enormous impact on performance. Using a movie rental data model, this article walks through five distinct but equivalent rewrites: from non-ANSI implicit joins to explicit ANSI JOINs, from IN subqueries to EXISTS correlated subqueries, to inline views and CTEs. Each rewrite is semantically equivalent but may produce a dramatically different execution plan. This is precisely why automated, mathematically-grounded rewriting — as delivered by ASTRO™ — is essential: the first way one codes a SELECT may be correct but not necessarily optimal.
By Bert Scalzo. Building on Part 1, this installment examines the execution plan implications of five equivalent query styles using Oracle 11g R2 EXPLAIN PLAN output. Key finding: the five styles produce essentially identical performance on well-indexed, statistics-current tables — but the picture changes dramatically when statistics are stale, revealing full table scans across all five styles. The lesson: trust the optimizer to do its job, but give it the best possible query code to optimize. As ASTRO™ demonstrates, selecting the optimal rewrite from among hundreds of valid alternatives requires exhaustive, deterministic analysis that only a purpose-built inference engine can deliver consistently. Always verify: Trust but verify — as President Reagan said.