Can Databases Be Autonomous? DAS Helps You Move Into the Future

Six Core Autonomy Features

Feature 1: Around the clock detection of anomalies: The anomalies of database workloads are being detected in real-time by using machine learning algorithms. Unlike the threshold-based alert method, this approach can detect database anomalies in a timely manner, rather than relying on crashes and faults.

  • Level-1: Basic monitoring and alert information are provided but no optimization suggestions are generated.
  • Level-2: In some scenarios, diagnosis and optimization suggestions are provided, but manual intervention is still required to decide on whether to adopt and apply the suggestions. The SQL diagnostic engine is an example.
  • Level-3: In scenarios such as SQL throttling and auto scaling, comprehensive autonomy can be implemented.
  • Level-4: Autonomous databases are provided. DAS is currently in the middle of a hard-working procedure to achieve level-4 status.

Core Innovations and Breakthroughs in Four Areas

The world’s first comprehensive database autonomy engine: DAS implements comprehensive database autonomy in various scenarios through centralized decision-making, conflict settlement, decision-making, and decision distribution in special autonomy scenarios based on root cause analysis and aggregated instance information.

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