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PawSQL Query Rewrite uses SQL parsing, semantic analysis, and optimization rules to transform inefficient SQL into semantically equivalent forms that give the database optimizer better opportunities to produce efficient execution plans. Instead of only returning textual recommendations, PawSQL aims to generate executable optimized SQL and then validate whether the rewrite actually improves performance.

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Overview

Many SQL performance problems are caused not by missing indexes, but by SQL structures that restrict the optimizer’s available execution strategies. Common examples include:
  • Repeated execution of correlated subqueries
  • Predicates that cannot be pushed down
  • OR expressions that reduce index usability
  • Unnecessary DISTINCT or GROUP BY operations
  • Deep pagination that scans and sorts excessive rows
  • Cross-shard data movement in distributed databases
Query Rewrite targets these structural issues with equivalent transformations.

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Why Query Rewrite Matters

A database optimizer searches for the best execution plan within the structure of the SQL statement it receives. It does not always transform the business expression of that SQL into an entirely different but equivalent form. Semantically equivalent SQL expressions can expose very different optimization opportunities. PawSQL Query Rewrite expands the search space available to the optimizer.

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Key Capabilities

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Subquery rewrite

Analyze IN, EXISTS, scalar subqueries, and related forms to identify equivalent structures that may execute more efficiently on the target database.

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Predicate optimization

Optimize WHERE and JOIN predicates, including:
  • Predicate pushdown
  • Redundant predicate elimination
  • OR-condition rewrite
  • Expression simplification
  • Recovery of index-friendly predicates

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Join optimization

Identify and optimize:
  • Redundant joins
  • Join elimination
  • Structural join issues
  • Subqueries that can be decorrelated
  • Cross-shard join risks in distributed databases

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Aggregation optimization

Rewrite or simplify DISTINCT, GROUP BY, COUNT, MIN/MAX, and related aggregation patterns.

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Pagination optimization

Generate alternative forms for deep OFFSET / LIMIT pagination scenarios.

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Distributed SQL optimization

Consider distributed-database characteristics such as:
  • Distribution keys
  • Data movement
  • Cross-node joins
  • Replicated or broadcast tables
  • Global and local indexes

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Big data SQL optimization

Analyze big-data SQL patterns such as:
  • Partition pruning
  • Bucket joins
  • Data skew
  • COUNT DISTINCT
  • GROUP BY skew
  • Window-function skew
  • Global sorting

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Example

Original SQL:
Possible equivalent rewrite:
Whether this rewrite should actually be used depends on the database engine, indexes, data distribution, and execution plan. This is a key difference between PawSQL and static rewrite templates: a rewrite is a candidate; validation determines whether it is valuable.

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Semantic Safety

SQL rewriting must preserve semantics before it can improve performance. PawSQL therefore needs to account for:
  • NULL semantics
  • Aggregation semantics
  • DISTINCT behavior
  • Outer join semantics
  • Data types and implicit conversions
  • Dialect differences
  • Function and expression behavior
If semantic equivalence cannot be established reliably, a rewrite should not be forced merely for potential performance gain.

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Rewrite Categories

SQL Quality Check

Index Recommendation

Performance Validation

Execution Plan Analysis

Developer SQL Copilot

DBA Batch Slow SQL Governance

Database Migration SQL Governance

Enterprise SQL Governance Platform