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SQL performance problems rarely appear out of nowhere in production. Inefficient query patterns, missing or unsuitable indexes, and execution-plan changes after a database migration are usually introduced during development and only surface later as production incidents. PawSQL is a SQL engineering and governance platform for developers, testers, database administrators, and enterprise governance teams. It brings SQL quality check, query rewriting, index recommendation, performance validation, and execution plan analysis into one platform covering the full SQL lifecycle.
Make SQL quality and performance part of the software engineering lifecycle.

Use Cases

End-to-end workflows for development, CI/CD, DBA, and enterprise governance.

Quickstart

Run your first review, rewrite, and index recommendation on a single query.

Supported Databases

Database, version, and capability support matrix.

Choose How to Use PawSQL

Public cloud, private deployment, or IDE / API / MCP access.

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The Full SQL Lifecycle

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Core Capabilities

PawSQL does not only detect SQL problems — it produces actionable optimization plans.

SQL Quality Check

Detect risks against development standards and database best practices before release.

Query Rewrite

Restructure queries under semantic-equivalence constraints to give the optimizer better options.

Index Recommendation

Design candidate indexes from filters, joins, ordering, and statistics.

Performance Validation

Use the database optimizer to compare cost, access paths, and plans before and after.

Execution Plan Analysis

Turn text plans into a visual execution tree to locate bottlenecks quickly.

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Rewrite example

For more complex queries, PawSQL also analyzes subqueries, joins, aggregation, and predicate pushdown, generating candidate plans while preserving semantics.

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What each capability checks

  • SQL coding standards
  • DDL and DML risks
  • Query performance risks
  • Index usage issues
  • Database best practices
  • Custom enterprise rules
  • Subquery and join rewrites
  • Aggregation and grouping optimization
  • Predicate pushdown
  • Structural rewrites such as OR and UNION
  • Semantic equivalence checks
  • Filter and join conditions
  • Sort and grouping columns
  • Existing and redundant indexes
  • Column selectivity
  • Composite index design
  • Database optimizer behavior
  • Optimizer cost
  • Access paths and join methods
  • Estimated row counts
  • Index usage
  • Plans before and after optimization
  • Full table scans
  • High-cost operators
  • Join strategies
  • Index usage
  • Cardinality estimates and cost distribution

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One Product, Several Delivery Forms

PawSQL is one product. Cloud denotes the public deployment form; Engine, Optimizer, Auditor, Advisor, and Patroller are components or delivery forms of the same product — not five separate products. Start from the task you need to accomplish, then check the environment requirements for the access method. See Choose How to Use PawSQL.

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Fits Into Your Existing Workflow

SQL does not live only in a database client. It appears in IDEs, repositories, CI/CD pipelines, review platforms, migration projects, slow-query logs, and internal developer platforms. PawSQL integrates through IDE extensions, APIs, webhooks, MCP, or PawSQL Server. See Choose How to Use PawSQL.

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Start by Role or Scenario

Get feedback and optimization suggestions while writing SQL.Start with Developer SQL Copilot.

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Built for Multi-Database Environments

Real enterprise environments run relational, distributed, and domestic databases and data warehouses side by side, each with its own dialect, optimizer, indexing model, and plan format. PawSQL was designed for multi-database SQL governance from the start: one analysis entry point for every database, while keeping database-specific optimization logic. View supported databases →

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From SQL Optimization to SQL Engineering

Traditional SQL optimization happens after production problems appear. PawSQL moves governance earlier and keeps it continuous. SQL is therefore no longer just a string in an application, but an engineering asset that can be analyzed, validated, governed, and continuously improved.

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Get Started in Three Steps

1

Choose how to use PawSQL

Decide where the service runs and how your team connects to it. See Choose How to Use PawSQL.
2

Create a workspace

Specify the database engine and version, then provide context through DDL or a database connection. See Workspaces & Context.
3

Run your first optimization

Submit a query and review findings, rewrite suggestions, index recommendations, and validation results. See Quickstart.

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Find documentation by goal

Supported Databases

Confirm database, version, and capability coverage.

Choose How to Use PawSQL

Installation, deployment, and access methods.

API Reference

Integrate PawSQL through the API.
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Optimize Your First SQL Query

Create a workspace, submit a query, and review rewrite, index, and performance recommendations in PawSQL.

Choose How to Use PawSQL

Choose between PawSQL Cloud and private deployment, then connect through the web console, an IDE, automation, or MCP.

Supported Databases

Databases PawSQL supports (relational, Chinese enterprise, distributed, and big-data), with a per-database capability matrix.