Arc is a high-performance time-series database capable of ingesting 2.4 million metrics per second, along with logs, traces, and events using a unified MessagePack columnar protocol. Currently in alpha release, it features a stable core with ongoing developments, including advanced SQL analytics via DuckDB, flexible storage options, and built-in token-based authentication, making it suitable for development and testing environments. The system is designed for high-throughput ingestion, low latency, and efficient data management, aiming to support observability across various telemetry types.

The article discusses the implementation of a time series engine using Rust, focusing on optimizing performance under heavy load conditions. It highlights various techniques such as throttling to manage resource allocation effectively and ensure system stability during peak usage. Key challenges and solutions in developing a robust time series database are also addressed.