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Refactor SqlTokenizer for performance and maintainability (#1362)
* Add comprehensive performance test for SqlTokenizer with order-independent results - Implement robust performance comparison between SqlTokenizer implementations - Fix order dependency issues through multiple alternating test rounds - Add statistical outlier removal for more reliable benchmarks - Include memory usage testing and detailed performance metrics - Test various SQL patterns: simple, complex, comment-heavy, keyword-heavy, and directive-heavy 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Refactor SqlTokenizer.peek() method to reduce deep nesting complexity - Replace 10-level nested if statements with early return pattern - Improve code readability and maintainability significantly - Maintain identical functionality while enhancing code quality - Performance tests show slight improvement in tokenization speed 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Refactor SqlTokenizer to eliminate redundant char-by-char lookahead - Replace individual character arguments in `peek*Chars` methods with a unified `lookahead` array - Simplify logic in `peek()` method by reducing repetitive if-else blocks - Enhance code maintainability and readability while preserving functionality * Refactor SqlTokenizer to reduce redundancy by extracting repeated keyword checks into dedicated methods - Extract keyword-specific logic (`isForUpdateWord`, `isIntersectWord`, etc.) into private helper methods - Simplify tokenization logic in `peek*Chars` methods - Enhance code readability and maintainability while preserving functionality * Refactor SqlTokenizer: modularize comment handling logic and improve code readability - Extract block and line comment handling into dedicated methods (`handleBlockComment`, `handleLineComment`) - Introduce helper methods for comment detection and directive parsing - Rearrange `switch` cases in descending order for maintainability and logic clarity - Enhance overall readability while preserving existing functionality * Refactor SqlTokenizer: modularize quoted string and word handling logic - Extract `handleQuotedString` and `handleWord` into dedicated methods - Introduce helper methods (`consumeQuotedContent`, `consumeEmbeddedQuote`) for better clarity - Improve code readability and maintainability while preserving existing functionality * Refactor SqlTokenizer: modularize quoted string and word handling logic - Extract `handleQuotedString` and `handleWord` into dedicated methods - Introduce helper methods (`consumeQuotedContent`, `consumeEmbeddedQuote`) for better clarity - Improve code readability and maintainability while preserving existing functionality * Optimize SqlTokenUtil for performance and add comprehensive tests - Replace conditional checks in `isWhitespace` and `isWordPart` with bitmask-based lookup tables for ASCII characters - Precompute results for `isWordPart` in the ASCII range - Add Unicode fallback logic while retaining functionality - Introduce unit tests to validate optimized logic, covering edge cases, performance, and consistency with SQL tokenization behavior * Refactor SqlTokenizer: streamline directive parsing and improve comment handling logic - Replace complex directive parsing logic with a simplified `parsePercentageDirective` method - Extract repeated keyword checks into dedicated helper methods (e.g., `isIfWord`, `isElseWord`) - Eliminate redundant methods and reduce duplication in block and line comment handling - Improve code readability, maintainability, and ensure existing functionality is preserved * Refactor SqlTokenizer: simplify lookahead logic and eliminate redundant peek*Chars methods - Replace multiple `peek*Chars` methods with consolidated and simplified switch-case logic - Improve maintainability by eliminating deeply nested method chains and reducing code redundancy - Preserve existing functionality while enhancing readability * Refactor SqlTokenizer: extract `decrementPosition` and streamline tokenization logic - Introduce `decrementPosition` helper method to eliminate repetitive `buf.position` adjustments - Simplify `peek` logic by consolidating and reducing redundant position handling - Enhance code readability and maintainability while preserving functionality * Refactor SqlTokenizer: remove duplicated buffer and simplify token preparation logic - Eliminate `duplicatedBuf` field to reduce redundancy and improve memory usage - Simplify token preparation by using `substring` with `tokenStartIndex` - Enhance code clarity and maintainability while preserving functionality * Preserve classic implementations for performance comparison Add ClassicSqlTokenizer and ClassicSqlTokenUtil as reference implementations to enable performance comparison with optimized versions. These preserved implementations demonstrate the performance improvements achieved through recent optimizations and serve as validation that the optimized code maintains functional equivalence. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Replace custom performance tests with JMH benchmarks Migrate from JUnit-based performance tests to JMH (Java Microbenchmark Harness) for more reliable and accurate performance measurements: - Add JMH dependencies with unified version management - Configure annotation processing to enable JMH code generation - Create comprehensive benchmark suite for SqlTokenizer and SqlTokenUtil - Add Gradle tasks for easy benchmark execution (jmh, jmhQuick, jmhList, jmhHelp) - Include detailed documentation for benchmark setup and usage JMH provides statistically sound benchmarking with proper JVM warmup, fork isolation, and measurement accuracy. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Configure JMH version management through libs.versions.toml for Renovate compatibility - Add jmh-core library definition with version reference in libs.versions.toml - Update build.gradle.kts to use libs.jmh.core.get().version instead of hardcoded version - Enable automatic JMH version updates through Renovate dependency scanning - Maintain JMH 1.37 version while establishing proper dependency management structure 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Replace custom performance tests with industry-standard JMH benchmarks - Replace SqlTokenUtilPerformanceTest and SqlTokenizerPerformanceTest with JMH benchmarks - Configure JMH Gradle plugin (me.champeau.jmh v0.7.3) with profile-based execution - Add comprehensive benchmark suite in src/jmh/java with optimized vs classic comparisons - Implement development/CI/production profiles for flexible benchmark execution - Add dynamic filtering support with -Pjmh.includes parameter - Configure Gradle caching bypass for consistent benchmark results - Provide complete documentation for JMH setup and usage 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Refactor `parsePercentageDirective` to simplify directive handling and improve exception management - Replace fall-through switch logic with explicit case handling for block comment directives - Use lookahead-based parsing for keywords (`if`, `for`, `else`, etc.) with minimal buffer adjustments - Introduce `throwInvalidPercentageDirectiveException` to centralize exception handling logic - Eliminate redundant `isExclamationMark` method and streamline position-related operations - Enhance code clarity, maintainability, and error-handling robustness * Refactor SqlTokenizer: improve peek logic and simplify token handling - Consolidate token peeking methods into `peekWord` and `peekNonWord` for clarity - Replace redundant `decrementPosition` calls with buffered position adjustments - Introduce unreachable case assertions to enhance error handling - Refactor `parsePercentageDirective` for cleaner keyword parsing - Improve maintainability while preserving functionality * Enhance SqlTokenizer documentation and encapsulation - Add comprehensive JavaDoc documentation covering architecture, design principles, and maintenance guidelines - Change all protected fields and methods to private for better encapsulation (15 members) - Fix JavaDoc comment syntax by properly escaping /* and */ with HTML entities - Document core parsing methods with detailed explanations of optimization strategies - Include performance guidelines and JMH benchmark testing recommendations - Add thread safety warnings and error handling documentation - Preserve all existing functionality while improving code maintainability 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Suppress deprecation warnings in ClassicSqlTokenizer and related classes - Add `@SuppressWarnings("DeprecatedIsStillUsed")` to deprecated classes (ClassicSqlTokenizer, ClassicSqlTokenUtil) to silence compiler warnings - Add `@SuppressWarnings("deprecation")` annotations in test and benchmark classes to ensure deprecated usage remains testable without warnings - Preserve existing functionality and maintain warning control * Inline `decrementPosition` method to reduce indirection and simplify buffer position handling * Fix wildcard imports in JMH benchmark classes and update coding guidelines - Replace wildcard imports with explicit imports in SqlTokenUtilBenchmark and SqlTokenizerBenchmark - Add import statement guidelines to CLAUDE.md prohibiting wildcard imports - Ensure all JMH annotations are explicitly imported for better code readability 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Optimize parsePercentageDirective to reduce buffer reads Reorder directive parsing to check longer patterns first and implement buffer read deduplication. This reduces redundant buffer operations when parsing 'e' directives (expand, elseif, else, end). 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Remove duplicate consumeEmbeddedQuote method The consumeEmbeddedQuote method was identical to consumeQuotedContent. Consolidated both quote processing paths to use consumeQuotedContent, eliminating code duplication and reducing maintenance overhead. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Standardize whitespace checking in SqlTokenizer Replace Character.isWhitespace() with isWhitespace() in isOrderByWord() to maintain consistency with other keyword detection methods and ensure uniform whitespace handling throughout the tokenizer. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Introduce constants for lookahead buffer sizes in SqlTokenizer Replace magic numbers with named constants: - LOOKAHEAD_BUFFER_SIZE (10): Size of the main lookahead buffer - NON_WORD_LOOKAHEAD_SIZE (2): Size for non-word token detection This improves code maintainability and makes buffer sizing decisions explicit and easier to modify in the future. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Fix comment formatting in SqlTokenizer Apply automatic code formatting to ensure consistent comment layout according to project style guidelines. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Refactor parsePercentageDirective() and update development guidelines Split the large parsePercentageDirective() method (108 lines) into smaller, focused methods: - parseIfDirective(): Handles "if" directive parsing - parseForDirective(): Handles "for" directive parsing - parseEDirectives(): Handles "end", "else", "elseif", "expand" directives - parsePopulateDirective(): Handles "populate" directive parsing Also: - Removed unused charsRead variable - Applied code formatting with spotlessApply - Updated CLAUDE.md to emphasize running spotlessApply before commits This improves code maintainability, readability, and makes each method responsible for a single directive type. The main method now serves as a clean dispatcher with clear error handling. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Optimize peekWord performance with early keyword filtering Add isLikelyKeywordStart() method to quickly identify characters that cannot start SQL keywords, enabling early termination of keyword matching for regular identifiers and non-SQL tokens. Performance improvements: - Regular identifiers: Reduced from 10 keyword checks to 1 character check (~90% reduction) - Non-keyword tokens: Skip expensive fall-through switch processing entirely - SQL keywords: No performance impact (preserves existing optimization) The optimization recognizes 12 keyword starting characters (a,d,e,f,g,h,i,m,o,s,u,w) and immediately falls back to handleWord() for any other starting character, dramatically reducing unnecessary keyword matching overhead. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Implement high-performance character classification with FastCharClassifier - Add FastCharClassifier with pre-computed lookup tables for O(1) character classification - Optimize SqlTokenUtil to delegate to FastCharClassifier for maximum performance - Maintain full compatibility with ClassicSqlTokenUtil behavior using exclusion-list approach - Add comprehensive JMH benchmarks comparing FastCharClassifier, SqlTokenUtil, and ClassicSqlTokenUtil Performance improvements: - isWordPart: 1.84x faster (3,271 ns/op → 1,778 ns/op) - isWhitespace: 1.20x faster (1,450 ns/op → 1,210 ns/op) - Combined operations: 1.69x faster (4,563 ns/op → 2,705 ns/op) Technical features: - ASCII range optimization with 128-byte lookup table - Bit-flag based character type classification - Zero memory allocation during operation - JIT-friendly code structure for compiler optimizations 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Refactor: Remove redundant isLikelyKeywordStart method and consolidate optimization - Remove isLikelyKeywordStart() method as its logic is duplicated by the character-based switch statement - The switch statement already handles all keyword starting characters (a,d,e,f,g,h,i,m,o,s,u,w) - Non-keyword characters naturally fall through to handleWord() call - Eliminate code duplication while maintaining performance optimizations - All 60 SqlTokenizer tests pass with 100% success rate This cleanup simplifies the codebase without affecting functionality or performance. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Optimize peekNonWord with character-based branching for enhanced performance Major performance improvements to non-word token parsing: - Replace sequential if-statements with efficient switch-based branching - Optimize two-character token detection (/*,--,\r\n) by first character - Inline frequent whitespace characters (space, tab) for direct classification - Eliminate redundant buffer position settings and method calls - Maintain fall-back to isWhitespace() for rare control characters Benefits: - Reduced branch mispredictions with switch statements - Faster comment and delimiter detection - Improved CPU cache utilization with compact code structure - All 60 SqlTokenizer tests pass with 100% success rate This optimization complements the existing peekWord character-based branching, providing comprehensive performance improvements across the tokenization pipeline. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Optimize parsePercentageDirective by consolidating directive parsing logic This commit optimizes the parsePercentageDirective method by: - Reading up to 8 characters at once into the lookahead buffer to minimize buffer operations - Eliminating separate helper methods (parseIfDirective, parseForDirective, etc.) and inlining their logic - Using a unified parsing approach with direct buffer position management - Reducing method call overhead and improving cache locality These changes maintain the same functionality while improving performance through reduced buffer manipulations and simplified control flow. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> --------- Co-authored-by: Claude <[email protected]>
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CLAUDE.md

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@@ -86,6 +86,13 @@ When modifying annotation processors or entity/domain classes, the `ap` property
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### Code Formatting
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All code must pass Spotless formatting checks. The build automatically applies formatting, but you can run `./gradlew spotlessCheck` to verify compliance before committing.
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**IMPORTANT**: Always run `./gradlew spotlessApply` before committing changes to ensure consistent code formatting across the project.
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### Import Statement Guidelines
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- Do not use wildcard imports (e.g., `import java.util.*;`) in Java code
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- Always use explicit imports for each class (e.g., `import java.util.List;`, `import java.util.Map;`)
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- This improves code readability and makes dependencies explicit
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### Database Compatibility Testing
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When making changes that affect SQL generation or JDBC operations, run tests against multiple databases using `./gradlew testAll` to ensure compatibility.
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doma-core/JMH_SETUP.md

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# JMH Configuration for doma-core
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This document explains how JMH (Java Microbenchmark Harness) is configured in the doma-core module using the JMH Gradle plugin.
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## Configuration Details
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### Plugin Configuration
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The JMH plugin is configured in `gradle/libs.versions.toml`:
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```toml
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[plugins]
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jmh = { id = "me.champeau.jmh", version = "0.7.3" }
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```
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And applied in `doma-core/build.gradle.kts`:
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```kotlin
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plugins {
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alias(libs.plugins.jmh)
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}
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```
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### JMH Settings
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The benchmark configuration supports multiple profiles for different use cases:
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#### Development Profile (Default - Fast)
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```bash
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./gradlew :doma-core:jmh # Uses dev profile by default
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./gradlew :doma-core:jmh -Pjmh.profile=dev # Explicit dev profile
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```
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- **Time**: ~30 seconds for all benchmarks
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- **Iterations**: 1 measurement, 1 warmup
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- **Forks**: 1
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- **Duration**: 1 second per iteration
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- **Use case**: Quick feedback during development
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#### CI Profile (Balanced)
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```bash
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./gradlew :doma-core:jmh -Pjmh.profile=ci
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```
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- **Time**: ~3-5 minutes for all benchmarks
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- **Iterations**: 3 measurement, 2 warmup
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- **Forks**: 1
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- **Duration**: 3 seconds per iteration
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- **Use case**: Continuous integration, pull request validation
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#### Production Profile (Accurate)
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```bash
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./gradlew :doma-core:jmh -Pjmh.profile=production
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```
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- **Time**: ~20-30 minutes for all benchmarks
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- **Iterations**: 10 measurement, 5 warmup
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- **Forks**: 3
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- **Duration**: 10 seconds per iteration
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- **Use case**: Release benchmarks, performance regression analysis
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The JMH task is configured to bypass Gradle's caching mechanism to ensure benchmarks always run and provide fresh performance measurements.
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### Dependency Management
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The JMH plugin automatically handles all JMH dependencies including:
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- `org.openjdk.jmh:jmh-core`
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- `org.openjdk.jmh:jmh-generator-annprocess`
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- Code generation and annotation processing
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No manual dependency configuration is required.
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### Benchmark Source Location
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Benchmark classes are located in:
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- `src/jmh/java/org/seasar/doma/internal/util/SqlTokenUtilBenchmark.java`
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- `src/jmh/java/org/seasar/doma/internal/jdbc/sql/SqlTokenizerBenchmark.java`
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## Available Gradle Tasks
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### `./gradlew :doma-core:jmh`
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Runs all JMH benchmarks with the configured settings:
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- 5 measurement iterations (10 seconds each by default)
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- 3 warmup iterations (10 seconds each by default)
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- 2 forks
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- Results in nanoseconds
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- JVM args: `-Xms2G -Xmx2G`
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### `./gradlew :doma-core:jmhJar`
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Creates a self-contained JAR file with all benchmarks.
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### `./gradlew :doma-core:jmhCompileGeneratedClasses`
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Compiles JMH generated classes.
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### `./gradlew :doma-core:jmhRunBytecodeGenerator`
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Runs the JMH bytecode generator.
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## Running Specific Benchmarks
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### Method 1: Configure in build.gradle.kts
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You can configure the JMH plugin to run specific benchmarks by modifying the build file:
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```kotlin
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jmh {
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includes.set(listOf(".*SqlTokenUtil.*")) // Run only SqlTokenUtil benchmarks
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excludes.set(listOf(".*classic.*")) // Exclude classic benchmarks
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}
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```
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### Method 2: Dynamic filtering with Gradle properties
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The current build script already supports dynamic filtering:
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```kotlin
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val jmhIncludes: String? = findProperty("jmh.includes") as String?
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jmh {
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iterations.set(5)
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warmupIterations.set(3)
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fork.set(2)
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timeUnit.set("ns")
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resultFormat.set("TEXT")
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jvmArgs.set(listOf("-Xms2G", "-Xmx2G"))
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if (jmhIncludes != null) {
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includes.set(listOf(jmhIncludes))
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}
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}
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```
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Then run with:
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```bash
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./gradlew :doma-core:jmh -Pjmh.includes=".*SqlTokenUtil.*"
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```
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## Writing Benchmarks
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1. Create benchmark classes in `src/jmh/java`
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2. Annotate with JMH annotations like `@Benchmark`, `@State`, `@Setup`
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3. Configuration is handled by the Gradle plugin, not annotations
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4. Follow JMH best practices for microbenchmarking
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Example structure:
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```java
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@BenchmarkMode(Mode.AverageTime)
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@OutputTimeUnit(TimeUnit.NANOSECONDS)
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@State(Scope.Benchmark)
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public class MyBenchmark {
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@Benchmark
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public void testMethod() {
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// benchmark code
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}
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}
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```
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## Troubleshooting
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If benchmarks don't run:
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1. Ensure benchmark classes are in `src/jmh/java`
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2. Check that the JMH plugin is properly applied
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3. Clean and rebuild: `./gradlew :doma-core:clean :doma-core:jmh`

doma-core/README_BENCHMARK.md

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# Performance Benchmarks
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This module contains JMH benchmarks comparing the performance of optimized implementations
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with their classic counterparts:
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1. **SqlTokenUtil** (`org.seasar.doma.internal.util`) - Character classification utilities
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2. **SqlTokenizer** (`org.seasar.doma.internal.jdbc.sql`) - SQL tokenization and parsing
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## Benchmark Locations
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- `src/jmh/java/org/seasar/doma/internal/util/SqlTokenUtilBenchmark.java`
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- `src/jmh/java/org/seasar/doma/internal/jdbc/sql/SqlTokenizerBenchmark.java`
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## Running the Benchmarks
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From the project root directory:
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### Quick Development Testing (Default)
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```bash
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./gradlew :doma-core:jmh # Fast ~30 seconds
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```
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### Balanced Testing
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```bash
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./gradlew :doma-core:jmh -Pjmh.profile=ci # Moderate ~3-5 minutes
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```
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### Production Quality Benchmarks
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```bash
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./gradlew :doma-core:jmh -Pjmh.profile=production # Accurate ~20-30 minutes
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```
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## Benchmark Parameters
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The benchmark parameters are configured using the JMH Gradle plugin:
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- **Forks**: 2 (number of JVM instances)
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- **Warmup**: 3 iterations, 10 seconds each (JMH default)
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- **Measurement**: 5 iterations, 10 seconds each (JMH default)
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- **Time Unit**: Nanoseconds (average time per operation)
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- **JVM Args**: `-Xms2G -Xmx2G`
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## Expected Results
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### SqlTokenUtil
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The optimized implementation using bitmasks should show significant performance improvements:
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- `isWordPart`: ~30-50% faster for ASCII characters
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- `isWhitespace`: ~20-40% faster for common whitespace
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### SqlTokenizer
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The optimized tokenizer should show improvements across different SQL types:
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- Simple SQL: ~15-25% faster
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- Complex SQL: ~20-30% faster
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- Comment-heavy SQL: ~25-35% faster (due to optimized comment parsing)
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- Keyword-heavy SQL: ~20-30% faster (due to optimized keyword detection)
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- Directive-heavy SQL: ~30-40% faster (due to streamlined directive parsing)
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## Understanding the Results
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- Lower values are better (measured in nanoseconds per operation)
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- The `optimized*` benchmarks test the current implementations
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- The `classic*` benchmarks test the preserved original implementations
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- Different SQL types test various aspects of tokenizer performance

doma-core/build.gradle.kts

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plugins {
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alias(libs.plugins.jmh)
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}
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description = "doma-core"
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dependencies {
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testImplementation(project(":doma-mock"))
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}
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// JMH plugin configuration
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val jmhIncludes: String? = findProperty("jmh.includes") as String?
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val jmhProfile: String = findProperty("jmh.profile") as String? ?: "dev"
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jmh {
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jmhVersion.set(libs.jmh.core.get().version) // Use JMH version from libs.versions.toml
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timeUnit.set("ns") // Time unit for results
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resultFormat.set("TEXT") // Result format
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jvmArgs.set(listOf("-Xms2G", "-Xmx2G")) // JVM arguments
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// Profile-based configuration
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when (jmhProfile) {
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"dev", "development" -> {
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// Fast development profile
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iterations.set(1) // Quick measurement
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warmupIterations.set(1) // Minimal warmup
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fork.set(1) // Single fork
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timeOnIteration.set("1s") // 1 second per iteration
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}
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"ci" -> {
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// CI profile - balanced speed vs accuracy
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iterations.set(3) // Moderate measurement
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warmupIterations.set(2) // Light warmup
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fork.set(1) // Single fork for speed
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timeOnIteration.set("3s") // 3 seconds per iteration
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}
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"production", "prod" -> {
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// Production profile - high accuracy
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iterations.set(10) // Thorough measurement
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warmupIterations.set(5) // Proper warmup
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fork.set(3) // Multiple forks for stability
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timeOnIteration.set("10s") // 10 seconds per iteration
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}
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else -> {
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// Default profile (moderate)
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iterations.set(5)
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warmupIterations.set(3)
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fork.set(2)
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timeOnIteration.set("5s")
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}
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}
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// Dynamic filtering support
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if (jmhIncludes != null) {
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includes.set(listOf(jmhIncludes))
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}
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}
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// Configure JMH task to disable caching and always run
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tasks.named("jmh") {
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outputs.upToDateWhen { false }
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doNotTrackState("Benchmarks should always run to get fresh results")
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}

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