Log Analysis System - Scaling Considerations
Horizontal Scaling
Ingestion Scaling
- Multiple Kafka partitions (1000+)
- Logstash pipeline workers
- Auto-scaling based on lag
- Load balancing
Storage Scaling
- Elasticsearch sharding
- Add data nodes for capacity
- Replication for availability
- Index lifecycle management
Query Scaling
- Dedicated coordinator nodes
- Query result caching
- Shard allocation awareness
- Read replicas
Performance Optimization
Ingestion
- Batch indexing (1000 docs)
- Async replication
- Bulk API usage
- Compression
Search
- Query caching
- Field data caching
- Index sorting
- Doc values
Storage
- Best compression codec
- Force merge segments
- Shrink old indexes
- Tiered storage
Bottleneck Mitigation
High Ingestion Rate
- Increase Kafka partitions
- Scale Logstash workers
- Tune bulk size
- Disable replicas during bulk
Slow Queries
- Query timeout
- Result size limits
- Shard request cache
- Aggregation optimization
Storage Growth
- Index lifecycle policies
- Automatic rollover
- Snapshot to S3
- Delete old indexes
Cost Optimization
Compute
- Spot instances
- Right-sizing
- Auto-scaling
- Reserved capacity
Storage
- Tiered storage
- Compression
- Lifecycle policies
- S3 for archives
This scaling strategy ensures the log system can handle massive volumes efficiently.