The New Astro Runtime

Astronomer announces major performance and reliability enhancements to its managed Astro platform, re-engineering key Apache Airflow scheduling, execution, scaling, and recovery components while retaining Airflow compatibility without requiring customer changes. The platform is designed for modern data and AI orchestration workloads, including ETL, dbt transformations, model training and evaluation, and event-driven pipelines, and is reported to support up to 500,000 concurrent tasks. In load tests, Astro achieved p95 task-start latency of about 230 milliseconds at 100,000 concurrent tasks and roughly 300 milliseconds at 300,000 tasks, compared with Celery’s reported 23.582-second latency at 50,000 tasks. Astro attributes these results to event-driven scheduling, an Airflow-aware scaling and healing layer, high-availability components, and multi-region database recovery capabilities, while claiming 85% fewer failures under load than open-source Airflow.

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