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4 Commits (feature/mq-kafka-gateway-m1)
| Author | SHA1 | Message | Date |
|---|---|---|---|
chrislu
|
f0021b1de9 |
Phase 1 Part 1: Multi-batch fetch and GZIP compression
- Implement proper record batch concatenation in fetch.go:getMultipleRecordBatches - Add batch validation with isValidRecordBatch helper - Add comprehensive tests for multi-batch concatenation scenarios - Implement actual GZIP compression in fetch_multibatch.go (replacing placeholder) - Add compression tests with different data types and ratios - Add structured logging for concatenation and compression operations Multi-batch fetch and compression features complete with full test coverage. |
4 days ago |
|
chrislu
|
342d342df3 |
security: fix clear-text logging of sensitive information in Kafka protocol
CodeQL identified several security issues where client-controlled data (topic names, protocol names, client IDs) were being logged in clear text, potentially exposing sensitive information. Fixed by sanitizing debug logging: consumer_group_metadata.go: - Remove protocol.Name from error and success logs - Remove topic list from fallback logging - Log only counts instead of actual values fetch.go: - Remove topic.Name from all Fetch debug logs - Remove topicName from schema metadata logs - Keep partition/offset info which is not sensitive fetch_multibatch.go: - Remove topicName from MultiBatch debug logs handler.go: - Remove topicName from StoreRecordBatch/GetRecordBatch logs produce.go: - Remove topicName from Produce request logs - Remove topic names from auto-creation logs - Remove topic names from partition processing logs Security Impact: - Prevents potential information disclosure through logs - Maintains debugging capability with non-sensitive data - Follows security best practice of not logging client-controlled input All functionality preserved while removing sensitive data exposure. |
4 days ago |
|
chrislu
|
ec7adc5a36 |
Update fetch_multibatch.go
|
5 days ago |
|
chrislu
|
8762a1a4af |
Phase 5: Implement multi-batch Fetch concatenation support
Multi-batch Fetch support completed: ## Core Features - **MaxBytes compliance**: Respects fetch request MaxBytes limits to prevent oversized responses - **Multi-batch concatenation**: Properly concatenates multiple record batches in single response - **Size estimation**: Pre-estimates batch sizes to optimize MaxBytes usage before construction - **Kafka-compliant behavior**: Always returns at least one batch even if it exceeds MaxBytes (first batch rule) ## Implementation Details - **MultiBatchFetcher**: New dedicated class for multi-batch operations - **Intelligent batching**: Adapts record count per batch based on available space (10-50 records) - **Proper concatenation format**: Each batch maintains independent headers and structure - **Fallback support**: Graceful fallback to single batch if multi-batch fails ## Advanced Features - **Compression ready**: Basic support for compressed record batches (GZIP placeholder) - **Size tracking**: Tracks total response size and batch count across operations - **Edge case handling**: Handles large single batches, empty responses, partial batches ## Integration & Testing - **Fetch API integration**: Seamlessly integrated with existing handleFetch pipeline - **17 comprehensive tests**: Multi-batch scenarios, size limits, concatenation format validation - **E2E compatibility**: Sarama tests pass with no regressions - **Performance validation**: Benchmarks for batch construction and multi-fetch operations ## Performance Improvements - **Better bandwidth utilization**: Fills available MaxBytes space efficiently - **Reduced round trips**: Multiple batches in single response - **Adaptive sizing**: Smaller batches when space limited, larger when space available Ready for Phase 6: Basic flexible versions support |
6 days ago |