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Release v0.7.0: Complete Job Batching & Bulk Operations
This release completes the comprehensive Job Batching & Bulk Operations feature and prepares for publication to crates.io. ## Release Updates ### Version - Bumped version from 0.6.0 to 0.7.0 in Cargo.toml - Updated README installation examples to reflect v0.7.0 ### Documentation - **NEW**: Added comprehensive batch operations documentation (docs/batch-operations.md) - Complete API reference with examples - Best practices and performance considerations - Troubleshooting guide for common issues - Database-specific optimization details - Updated README to include batch operations in documentation links - Moved completed feature from ROADMAP.md to implemented features ### CHANGELOG - Added detailed v0.7.0 release notes highlighting: - JobBatch struct and configuration options - High-performance bulk database operations (PostgreSQL UNNEST, MySQL chunking) - Enhanced worker batch processing with statistics - Comprehensive API for batch management - 18 new tests and examples - Complete backward compatibility ### ROADMAP - Removed completed Job Batching & Bulk Operations from Phase 1 - Updated implementation priority list - Next focus: Job Result Storage & Retrieval ## Feature Completion Summary The Job Batching & Bulk Operations feature is now complete with: ✅ JobBatch struct with flexible configuration ✅ Optimized bulk database operations (PostgreSQL & MySQL) ✅ Enhanced worker batch processing ✅ Comprehensive API for batch management ✅ Full test coverage and examples ✅ Complete documentation Ready for crates.io publication and production use. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
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CHANGELOG.md

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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [0.7.0] - 2025-06-27
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### Added
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- **📦 Comprehensive Job Batching & Bulk Operations**
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- `JobBatch` struct for creating and managing job batches with configurable batch sizes
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- Three partial failure modes: `ContinueOnError`, `FailFast`, and `CollectErrors`
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- Batch validation ensuring all jobs in a batch belong to the same queue
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- Automatic chunking for large batches to respect database limits (10,000 jobs max)
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- Batch metadata support for tracking and categorization
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- **🚀 High-Performance Bulk Database Operations**
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- PostgreSQL implementation using `UNNEST` for optimal bulk insertions
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- MySQL implementation using multi-row `VALUES` with automatic 100-job chunking
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- Atomic batch operations with transaction support
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- New database tables: `hammerwork_batches` for batch metadata and tracking
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- Batch status tracking with job counts (pending, completed, failed, total)
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- **👷 Enhanced Worker Batch Processing**
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- `with_batch_processing_enabled()` for optimized batch job handling
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- `BatchProcessingStats` for comprehensive batch processing metrics
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- Automatic batch completion detection and status updates
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- Batch-aware job processing with enhanced monitoring
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- Success rate tracking with configurable thresholds (>95% for successful batches)
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- **📊 Batch Operations API**
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- `enqueue_batch()` - Bulk enqueue jobs with optimized database operations
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- `get_batch_status()` - Real-time batch progress and statistics
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- `get_batch_jobs()` - Retrieve all jobs belonging to a batch
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- `delete_batch()` - Clean up completed batches
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- `BatchResult` with success/failure rates and job error tracking
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- **🧪 Comprehensive Testing & Examples**
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- 18 new batch-specific tests covering all functionality
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- `batch_example.rs` demonstrating bulk job operations
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- `worker_batch_example.rs` showcasing worker batch processing features
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- Integration tests for both PostgreSQL and MySQL batch operations
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- Edge case testing for large batches and failure scenarios
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- **📖 Documentation**
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- Complete batch operations documentation at `docs/batch-operations.md`
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- Best practices for batch size selection and failure handling
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- Performance considerations and database-specific optimizations
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- Troubleshooting guide for common batch processing issues
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### Enhanced
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- Extended `DatabaseQueue` trait with batch operation methods
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- Worker event recording now tracks batch-specific metrics
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- Job struct enhanced with optional `batch_id` field
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- Statistics integration for batch job monitoring
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### Technical Implementation
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- **Memory Efficient**: Configurable batch sizes to manage memory usage
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- **Network Optimized**: Reduces database round trips from N to 1-10 for N jobs
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- **Type Safe**: Strongly typed batch operations with comprehensive validation
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- **Backward Compatible**: All existing functionality preserved, batching is opt-in
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## [0.6.0] - 2025-06-26
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### Added
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---
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## Release Links
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- [0.7.0](https://github.com/CodingAnarchy/hammerwork/releases/tag/v0.7.0)
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- [0.6.0](https://github.com/CodingAnarchy/hammerwork/releases/tag/v0.6.0)
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- [0.5.0](https://github.com/CodingAnarchy/hammerwork/releases/tag/v0.5.0)
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- [0.4.0](https://github.com/CodingAnarchy/hammerwork/releases/tag/v0.4.0)
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- [0.3.0](https://github.com/CodingAnarchy/hammerwork/releases/tag/v0.3.0)
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- [0.2.2](https://github.com/CodingAnarchy/hammerwork/releases/tag/v0.2.2)
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- [0.2.1](https://github.com/CodingAnarchy/hammerwork/releases/tag/v0.2.1)
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- [0.2.0](https://github.com/CodingAnarchy/hammerwork/releases/tag/v0.2.0)

Cargo.toml

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[package]
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name = "hammerwork"
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version = "0.6.0"
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version = "0.7.0"
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edition = "2021"
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description = "A high-performance, database-driven job queue for Rust with PostgreSQL and MySQL support, featuring job prioritization, cron scheduling, timeouts, rate limiting, Prometheus metrics, alerting, and comprehensive statistics collection"
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license = "MIT"

README.md

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```toml
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[dependencies]
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# Default features include metrics and alerting
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hammerwork = { version = "0.6", features = ["postgres"] }
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hammerwork = { version = "0.7", features = ["postgres"] }
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# or
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hammerwork = { version = "0.6", features = ["mysql"] }
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hammerwork = { version = "0.7", features = ["mysql"] }
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# Minimal installation
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hammerwork = { version = "0.6", features = ["postgres"], default-features = false }
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hammerwork = { version = "0.7", features = ["postgres"], default-features = false }
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```
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**Feature Flags**: `postgres`, `mysql`, `metrics` (default), `alerting` (default)
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- **[Worker Configuration](docs/worker-configuration.md)** - Worker setup, rate limiting, statistics
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- **[Cron Scheduling](docs/cron-scheduling.md)** - Recurring jobs with timezone support
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- **[Priority System](docs/priority-system.md)** - Five-level priority system with weighted scheduling
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- **[Batch Operations](docs/batch-operations.md)** - High-performance bulk job processing
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- **[Monitoring & Alerting](docs/monitoring.md)** - Prometheus metrics and notification systems
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## Basic Example

ROADMAP.md

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## Phase 1: High Impact, Low-Medium Complexity
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*Essential features that provide significant value with reasonable implementation effort*
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### 📦 Job Batching & Bulk Operations
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**Impact: High** | **Complexity: Low-Medium** | **Priority: High**
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Significant performance improvement for high-throughput scenarios.
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```rust
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// Process multiple jobs as a single unit
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let batch = JobBatch::new("email_batch")
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.with_jobs(email_jobs)
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.with_batch_size(100)
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.with_partial_failure_handling(PartialFailureMode::ContinueOnError);
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// Bulk enqueue operations
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queue.enqueue_batch(jobs).await?;
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```
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### 🔄 Job Result Storage & Retrieval
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**Impact: High** | **Complexity: Medium** | **Priority: High**
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Features are ordered within each phase by priority and should generally be implemented in the following sequence:
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**Phase 1 (Foundation)**
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1. Job Batching & Bulk Operations
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2. Job Result Storage & Retrieval
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3. Advanced Scheduling Patterns
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1. Job Result Storage & Retrieval
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2. Advanced Scheduling Patterns
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**Phase 2 (Advanced Features)**
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1. Job Dependencies & Workflows

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