Loki, a Prometheus-inspired logging service launched by Grafana Labs, is approaching general availability as it optimizes the storage, search, and aggregation of logs, making them easily accessible in Grafana. The service supports both microservices and monolithic applications and aims to correlate logs and metrics efficiently. The first installment of a multi-part series focuses on query optimization efforts to enhance Loki's CPU and memory usage by improving how log data is processed. Loki stores log data in compressed "chunks," which can contain over 100,000 lines, leading to challenges in resource consumption when handling numerous chunks. The optimization process involves techniques such as using the pprof tool and go benchmarks to address memory and CPU usage issues, including dealing with Go's garbage collector and optimizing memory allocation through object pooling. The series also discusses reducing unnecessary allocations during regex filtering and strategies for identifying and resolving memory leaks. Future installments will delve into iterators, ingestion retention, and further enhancements to Loki's capabilities.