Kafka Log Compaction Monitoring
Monitor and optimize Kafka log compaction processes with comprehensive segment analysis, cleanup policy tracking, and storage efficiency monitoring for optimal topic performance.
Complete Compaction Visibility
Monitor every aspect of Kafka log compaction with real-time metrics and intelligent optimization.
Compaction Progress
Track log compaction progress and identify segments awaiting cleanup processing.
Storage Efficiency
Monitor disk usage reduction and storage optimization through compaction.
Segment Analysis
Analyze log segments, their sizes, and compaction eligibility status.
Advanced Compaction Analytics
Deep insights into log compaction performance and storage optimization patterns.
Smart Compaction Alerts
Intelligent alerting system that identifies compaction issues and optimization opportunities:
- Stalled compaction process detection
- High dirty ratio alerts
- Inefficient compaction patterns
- Storage bloat notifications
Real-time Compaction Dashboard
Comprehensive visualization of log compaction health and performance metrics:
- Live compaction thread status
- Segment size distribution
- Cleanup policy effectiveness
- Key deduplication metrics
Critical Log Compaction Scenarios
Essential use cases where log compaction monitoring optimizes storage and performance.
State Store Topics
Monitor compaction for topics used as state stores in Kafka Streams applications.
Configuration Management
Optimize compaction for topics storing configuration data and system metadata.
Event Sourcing
Ensure efficient compaction for event sourcing patterns and snapshot topics.
Comprehensive Compaction Metrics
Monitor every aspect of Kafka log compaction with detailed metrics and optimization insights.
Compaction Health Metrics
Storage Optimization
Frequently Asked Questions
Log compaction is a cleanup policy that retains only the most recent value for each key in a topic. Instead of deleting messages by time, it keeps the latest state of each record, making it ideal for state stores and changelog topics.
Use log compaction for topics that represent the current state of a key (like database changelogs, configuration data, or Kafka Streams state stores). Use time-based retention for event streams where you need to keep all events for a period.
The dirty ratio is the percentage of log data that contains duplicate keys and is eligible for compaction. A high dirty ratio means more storage is being used than necessary and compaction may be falling behind.
KLogic tracks compaction thread activity, dirty ratios, segment sizes, and compaction throughput. It alerts when compaction falls behind or when topics are using excessive storage due to compaction issues.
Common causes include insufficient compaction threads, large segment sizes, high write throughput, or broker resource constraints. KLogic identifies bottlenecks and provides optimization recommendations.
Yes, KLogic allows you to monitor compaction metrics for individual topics or groups of topics. You can set up alerts specific to critical compacted topics like state stores or configuration topics.
Optimize Storage with Log Compaction Monitoring
Start monitoring your Kafka log compaction today and maximize storage efficiency.