This document summarizes the implementation of comprehensive monitoring and observability for Apache Iceberg integration in Mini Data Cloud, completed as part of task 9 in the Iceberg integration specification.
- Added
micrometer-registry-prometheusdependency to control plane - Updated application configuration to expose Prometheus metrics endpoint
- Configured management endpoints to include
/actuator/prometheus
File: minicloud-control-plane/src/main/java/com/minicloud/controlplane/service/IcebergCatalogMetricsService.java
Key Metrics:
iceberg.catalog.tables.created.total- Total tables creatediceberg.catalog.tables.dropped.total- Total tables droppediceberg.catalog.schema.evolutions.total- Schema evolution operationsiceberg.catalog.operations.total- All catalog operationsiceberg.catalog.tables.active- Currently active tables (gauge)iceberg.catalog.table.creation.time- Table creation durationiceberg.catalog.schema.evolution.time- Schema evolution durationiceberg.catalog.lookup.time- Catalog lookup durationiceberg.catalog.cache.hits.total- Cache hit counticeberg.catalog.cache.misses.total- Cache miss count
Features:
- Multi-level caching metrics (L1/L2/L3)
- Table lifecycle tracking
- Schema evolution performance monitoring
- Cache hit rate calculation
File: minicloud-control-plane/src/main/java/com/minicloud/controlplane/service/NYCDatasetMetricsService.java
Key Metrics:
nyc.queries.total- Total NYC dataset queriesnyc.rows.processed.total- Total rows processednyc.bytes.scanned.total- Total bytes scannednyc.query.execution.time- Query execution durationnyc.data.loading.time- Data loading durationnyc.partitions.pruned.total- Partitions pruned countnyc.files.skipped.total- Files skipped countnyc.dataset.queries.total- Per-dataset query counts (tagged)
Dataset-Specific Tracking:
- Yellow Taxi queries
- Green Taxi queries
- For-Hire Vehicle (FHV) queries
- 311 Service Request queries
- Weather data queries
Features:
- Partition pruning efficiency tracking
- File skip efficiency monitoring
- Query pattern analysis
- Data loading performance metrics
File: minicloud-control-plane/src/main/java/com/minicloud/controlplane/controller/IcebergMetricsController.java
New Endpoints:
GET /api/iceberg/metrics/catalog/performance- Catalog performance metricsGET /api/iceberg/metrics/nyc/performance- NYC dataset performance metricsGET /api/iceberg/metrics/nyc/dataset/{dataset}- Individual dataset metricsGET /api/iceberg/metrics/comprehensive- All metrics combined
Enhanced Features:
- Comprehensive metrics aggregation
- RESTful API for metrics access
- Health check integration
- Performance monitoring endpoints
File: tools/monitoring/grafana/dashboards/iceberg-overview.json
Panels:
- Query and transaction rates (time series)
- Active queries gauge
- Query execution time percentiles
- File processing rates
- Partition pruning efficiency gauge
- Time travel & transaction performance
Features:
- Real-time monitoring with 5-second refresh
- Percentile-based performance analysis
- Efficiency metrics visualization
- Dark theme optimized
File: tools/monitoring/grafana/dashboards/iceberg-table-statistics.json
Panels:
- Active Iceberg tables count
- Total tables created count
- Schema evolutions count
- Catalog cache hit rate gauge
- Catalog operation rates
- Catalog operation performance
- Cache performance metrics
Features:
- Table lifecycle monitoring
- Schema evolution tracking
- Cache performance analysis
- Operation timing metrics
File: tools/monitoring/grafana/dashboards/iceberg-transactions.json
Panels:
- Active transactions count
- Total transactions count
- Transaction time percentiles
- Transaction rate monitoring
- Transaction duration analysis
- Transaction distribution pie chart
- Active transactions over time
Features:
- ACID transaction monitoring
- Performance percentile analysis
- Transaction lifecycle tracking
- Real-time transaction status
File: tools/monitoring/grafana/dashboards/nyc-dataset-analytics.json
Panels:
- Total NYC queries, bytes scanned, rows processed
- Partition pruning efficiency gauge
- Query distribution by dataset (pie chart)
- Query rates by dataset (time series)
- NYC query execution time percentiles
- Partition pruning performance
- Data loading performance
Features:
- Dataset-specific analytics
- Query pattern analysis
- Performance optimization insights
- Data processing efficiency metrics
File: tools/monitoring/grafana/dashboards/dashboard-provisioning.yml
Configuration:
- Automatic dashboard loading
- Iceberg folder organization
- Update interval configuration
- UI update permissions
The existing Docker Compose configuration already includes:
- Prometheus service on port 9091
- Grafana service on port 3000
- Volume mounts for dashboards and datasources
- Network connectivity between services
- Query Performance: Execution times, throughput, success rates
- Catalog Operations: Table lifecycle, schema evolution, cache performance
- Transaction Monitoring: ACID transaction performance and status
- NYC Dataset Analytics: Dataset-specific performance and usage patterns
- Partition Pruning: Efficiency metrics for query optimization
- Prometheus Metrics: Time-series data collection
- Grafana Dashboards: Visual monitoring and alerting
- REST API Endpoints: Programmatic access to metrics
- Health Checks: Service status monitoring
- File Pruning Efficiency: Tracks partition and file skipping effectiveness
- Cache Performance: Hit rates and lookup times
- Query Optimization: Execution time percentiles and bottleneck identification
- Resource Utilization: Active queries and transactions monitoring
- Real-time Monitoring: 5-second refresh intervals
- Historical Analysis: Time-series data retention
- Multi-Dataset Support: NYC dataset-specific tracking
- Scalability Metrics: Performance under load
docker-compose up -d- Grafana: http://localhost:3000 (admin/admin)
- Prometheus: http://localhost:9091
- Control Plane Metrics: http://localhost:8080/actuator/prometheus
- Iceberg Overview - General performance monitoring
- Iceberg Table Statistics - Catalog and table metrics
- Iceberg Transactions - ACID transaction monitoring
- NYC Dataset Analytics - Dataset-specific analytics
GET /api/iceberg/metrics/performance- Overall performanceGET /api/iceberg/metrics/catalog/performance- Catalog metricsGET /api/iceberg/metrics/nyc/performance- NYC dataset metricsGET /api/iceberg/metrics/comprehensive- All metrics combined
- Proactive Monitoring: Early detection of performance issues
- Capacity Planning: Resource utilization insights
- Performance Optimization: Query and transaction tuning
- Troubleshooting: Detailed metrics for issue resolution
- Usage Analytics: Dataset popularity and query patterns
- Performance Benchmarking: Comparative analysis across datasets
- Efficiency Metrics: Partition pruning and cache effectiveness
- Trend Analysis: Historical performance tracking
- Performance Testing: Metrics for load testing validation
- Feature Impact: Before/after performance comparison
- Optimization Guidance: Bottleneck identification
- Quality Assurance: Performance regression detection
- Performance threshold alerts
- Error rate monitoring
- Capacity utilization warnings
- SLA compliance tracking
- Machine learning-based anomaly detection
- Predictive performance modeling
- Query optimization recommendations
- Resource allocation suggestions
- External monitoring system integration
- Custom metric exporters
- Third-party dashboard templates
- API gateway metrics
This implementation provides a comprehensive monitoring and observability solution for Apache Iceberg integration, enabling operational excellence and performance optimization in the Mini Data Cloud platform.