PawSQL

Identify the problematic slow query from application performance monitoring (APM) tools or database slow query logs.

Input the raw SQL query and the associated table schema (DDL) into the PawSQL Advisor.

Review the automated index recommendations and SQL syntactical rewrite suggestions provided by the engine.

Apply the provided index creation DDL or rewritten query to the database and verify the reduction in execution latency.

Extract complex legacy SQL queries containing multiple nested subqueries or outdated JOIN syntax.

Paste the query into PawSQL and initiate rule-based query rewriting.

Compare the visual execution plan and estimated cost of the original query against the optimized version.

Replace the legacy query within the application codebase with the semantically equivalent, optimized SQL.

Configure PawSQL integration within the CI/CD pipeline or enforce usage of the IDE plugin.

Run automated analysis on newly committed SQL scripts or MyBatis XML mapper files.

Review pipeline warnings regarding missing indexes or suboptimal join operations flagged by the engine.

Implement required indexes or rewrite queries to pass the performance gate before merging code into the main branch.

Import the Java project's MyBatis mapper XML files into the PawSQL IDE plugin.

Allow PawSQL to parse and evaluate dynamic SQL tags (e.g., <if>, <where>, <foreach>).

Analyze the statically generated permutations of the SQL against the target database schema.

Implement the recommended XML structure adjustments or apply suggested database indexes.

Extract the existing SQL workload from the source database system.

Input the queries into PawSQL with the target database dialect (e.g., Oracle to PostgreSQL) selected.

Identify queries that suffer from performance degradation due to the target database's optimizer characteristics.

Apply dialect-specific rewrite rules and indexing strategies provided by PawSQL to restore optimal performance.