seaweedfs/KAFKA_SCHEMA_DEVELOPMENT_PLAN.md

Kafka Schema Integration - Advanced Features Development Plan

Overview

This document outlines the development plan for implementing advanced features in the Kafka Schema Integration system for SeaweedFS Message Queue. The plan is divided into three major phases, each building upon the previous foundation.

Current State

✅ Phase A-D Completed: Basic schema integration framework

• Schema decode/encode with Avro and JSON Schema
• mq.broker integration for publish/subscribe
• Produce/Fetch handler integration
• Comprehensive unit testing

Advanced Features Development Plan

Phase E: Protobuf Support

Goal: Complete binary descriptor parsing and decoding for Protobuf messages

E1: Binary Descriptor Parsing (Week 1)

• Objective: Parse Confluent Schema Registry Protobuf binary descriptors
• Tasks:
  • Implement FileDescriptorSet parsing from binary data
  • Create ProtobufSchema struct with message type resolution
  • Add descriptor validation and caching
  • Handle nested message types and imports
• Deliverables:
  • protobuf_descriptor.go - Binary descriptor parser
  • protobuf_schema.go - Schema representation
  • Unit tests for descriptor parsing

E2: Protobuf Message Decoding (Week 2)

• Objective: Decode Protobuf messages to RecordValue format
• Tasks:
  • Implement dynamic Protobuf message decoding
  • Handle field types: scalars, repeated, maps, nested messages
  • Support for oneof fields and optional fields
  • Convert Protobuf values to schema_pb.Value format
• Deliverables:
  • Enhanced ProtobufDecoder.DecodeToRecordValue()
  • Support for all Protobuf field types
  • Comprehensive test suite

E3: Protobuf Message Encoding (Week 3)

• Objective: Encode RecordValue back to Protobuf binary format
• Tasks:
  • Implement RecordValue to Protobuf conversion
  • Handle type coercion and validation
  • Support for default values and field presence
  • Optimize encoding performance
• Deliverables:
  • ProtobufEncoder.EncodeFromRecordValue()
  • Round-trip integrity tests
  • Performance benchmarks

E4: Confluent Protobuf Integration (Week 4)

• Objective: Full Confluent Schema Registry Protobuf support
• Tasks:
  • Handle Protobuf message indexes for nested types
  • Implement Confluent Protobuf envelope parsing
  • Add schema evolution compatibility checks
  • Integration with existing schema manager
• Deliverables:
  • Complete Protobuf integration
  • End-to-end Protobuf workflow tests
  • Documentation and examples

Phase F: Compression Handling

Goal: Support for gzip/snappy/lz4/zstd in Kafka record batches

F1: Compression Detection and Framework (Week 5)

• Objective: Detect and handle compressed Kafka record batches
• Tasks:
  • Parse Kafka record batch headers for compression type
  • Create compression interface and factory pattern
  • Add compression type enumeration and validation
  • Implement compression detection logic
• Deliverables:
  • compression.go - Compression interface and types
  • record_batch_parser.go - Enhanced batch parsing
  • Compression detection tests

F2: Decompression Implementation (Week 6)

• Objective: Implement decompression for all supported formats
• Tasks:
  • GZIP: Standard library implementation
  • Snappy: github.com/golang/snappy integration
  • LZ4: github.com/pierrec/lz4 integration
  • ZSTD: github.com/klauspost/compress/zstd integration
  • Error handling and fallback mechanisms
• Deliverables:
  • gzip_decompressor.go, snappy_decompressor.go, etc.
  • Decompression performance tests
  • Memory usage optimization

F3: Record Batch Processing (Week 7)

• Objective: Extract individual records from compressed batches
• Tasks:
  • Parse decompressed record batch format (v2)
  • Handle varint encoding for record lengths and deltas
  • Extract individual records with keys, values, headers
  • Validate CRC32 checksums
• Deliverables:
  • record_batch_extractor.go - Individual record extraction
  • Support for all record batch versions (v0, v1, v2)
  • Comprehensive batch processing tests

F4: Compression Integration (Week 8)

• Objective: Integrate compression handling into schema workflow
• Tasks:
  • Update Produce handler to decompress record batches
  • Modify schema processing to handle compressed messages
  • Add compression support to Fetch handler
  • Performance optimization and memory management
• Deliverables:
  • Complete compression integration
  • Performance benchmarks vs uncompressed
  • Memory usage profiling and optimization

Phase G: Schema Evolution

Goal: Advanced schema compatibility checking and migration support

G1: Compatibility Rules Engine (Week 9)

• Objective: Implement schema compatibility checking rules
• Tasks:
  • Define compatibility types: BACKWARD, FORWARD, FULL, NONE
  • Implement Avro compatibility rules (field addition, removal, type changes)
  • Add JSON Schema compatibility validation
  • Create compatibility rule configuration
• Deliverables:
  • compatibility_checker.go - Rules engine
  • avro_compatibility.go - Avro-specific rules
  • json_compatibility.go - JSON Schema rules
  • Compatibility test suite

G2: Schema Registry Integration (Week 10)

• Objective: Enhanced Schema Registry operations for evolution
• Tasks:
  • Implement schema version management
  • Add subject compatibility level configuration
  • Support for schema deletion and soft deletion
  • Schema lineage and dependency tracking
• Deliverables:
  • Enhanced registry_client.go with version management
  • Schema evolution API integration
  • Version history and lineage tracking

G3: Migration Framework (Week 11)

• Objective: Automatic schema migration and data transformation
• Tasks:
  • Design migration strategy framework
  • Implement field mapping and transformation rules
  • Add default value handling for new fields
  • Create migration validation and rollback mechanisms
• Deliverables:
  • schema_migrator.go - Migration framework
  • field_transformer.go - Data transformation utilities
  • Migration validation and testing tools

G4: Evolution Monitoring and Management (Week 12)

• Objective: Tools for managing schema evolution in production
• Tasks:
  • Schema evolution metrics and monitoring
  • Compatibility violation detection and alerting
  • Schema usage analytics and reporting
  • Administrative tools for schema management
• Deliverables:
  • Evolution monitoring dashboard
  • Compatibility violation alerts
  • Schema usage analytics
  • Administrative CLI tools

Implementation Priorities

High Priority (Phases E1-E2)

• Protobuf binary descriptor parsing: Critical for Confluent compatibility
• Protobuf message decoding: Core functionality for Protobuf support

Medium Priority (Phases E3-F2)

• Protobuf encoding: Required for complete round-trip support
• Compression detection and decompression: Performance and compatibility

Lower Priority (Phases F3-G4)

• Advanced compression features: Optimization and edge cases
• Schema evolution: Advanced features for production environments

Technical Considerations

Dependencies

• Protobuf: google.golang.org/protobuf for descriptor parsing
• Compression: Various libraries for different compression formats
• Testing: Enhanced test infrastructure for complex scenarios

Performance Targets

• Protobuf decoding: < 1ms for typical messages
• Compression: < 10% overhead vs uncompressed
• Schema evolution: < 100ms compatibility checks

Compatibility Requirements

• Confluent Schema Registry: Full API compatibility
• Kafka Protocol: Support for all record batch versions
• Backward Compatibility: No breaking changes to existing APIs

Risk Mitigation

Technical Risks

• Protobuf complexity: Start with simple message types, add complexity gradually
• Compression performance: Implement with benchmarking from day one
• Schema evolution complexity: Begin with simple compatibility rules

Integration Risks

• Existing system impact: Comprehensive testing with existing workflows
• Performance regression: Continuous benchmarking and profiling
• Memory usage: Careful resource management and leak detection

Success Criteria

Phase E Success

• ✅ Parse all Confluent Protobuf schemas successfully
• ✅ 100% round-trip integrity for Protobuf messages
• ✅ Performance within 10% of Avro implementation

Phase F Success

• ✅ Support all Kafka compression formats
• ✅ Handle compressed batches with 1000+ records
• ✅ Memory usage < 2x uncompressed processing

Phase G Success

• ✅ Detect all schema compatibility violations
• ✅ Successful migration of 10+ schema versions
• ✅ Zero-downtime schema evolution in production

Timeline Summary

• Weeks 1-4: Protobuf Support (Phase E)
• Weeks 5-8: Compression Handling (Phase F)
• Weeks 9-12: Schema Evolution (Phase G)
• Week 13: Integration testing and documentation
• Week 14: Performance optimization and production readiness

This plan provides a structured approach to implementing the advanced features while maintaining system stability and performance.