All Products
Search
Document Center

Tair (Redis® OSS-Compatible):Prediction-based auto scaling

Last Updated:Jun 10, 2026

Database Autonomy Service (DAS) provides a prediction-based auto scaling policy for Tair (Redis OSS-compatible). This policy uses instance data from the past 10 days to predict performance metrics for the next 24 hours. When a predicted value meets or exceeds the target, a scale-out is recommended.

Note

This feature only provides scale-out recommendations. You must scale out manually. For automatic scale-out, see:

  • Automatic scale-out: Automatically scales out instances in increments based on preset thresholds. Manual scale-in required. Suitable for traffic bursts and dynamic workloads.

  • Scheduled auto scaling: Scales out instances at scheduled times based on preset policies. Automatic scale-in supported. Suitable for known periodic high workloads.

Procedure

  1. Log on to the console and go to the Instances page. In the top navigation bar, select the region in which the instance that you want to manage resides. Then, find the instance and click the instance ID.

  2. In the navigation pane on the left, click CloudDBA > Performance Trends.

  3. On the Performance Trends page, click Autonomy Service Settings.

    Important

    For proxy mode instances, switch to a Proxy Node first, then click Autonomy Service Settings.

  4. On the Auto Scaling tab of the Autonomous Function Settings panel, click Add Policy. On the Add Policy page, set the following parameters:

    Parameter

    Description

    Policy Name

    The name of the policy.

    Mode

    Select Prediction-based Auto Scaling.

    Note

    Currently, this mode only provides scale-out recommendations. It does not perform automatic scale-out.

    Engine Type

    Database engine. Default: Redis.

    Metric Type

    Prediction metric. SelectMemory Usage (%).

    Destination Value

    Target metric threshold (%). A scale-out is recommended when the predicted value meets or exceeds this threshold.

    Example: If set to 70, a scale-out is recommended when predicted memory usage reaches 70% or higher.

  5. Click OK.

  6. In the Recommended Policies section, find your policy and click Apply in the Actions column.

    Note
    • To modify a policy, click Modify in the Actions column and update settings on the Update Policy tab.

    • To remove a policy, find it in the Applied Policies section and click Cancel in the Actions column.

View prediction-based auto scaling results

  1. Log on to the DAS console.

  2. In the navigation pane on the left, click Instance Monitoring.

  3. Find the instance and click its ID to open the details page.

  4. In the navigation pane on the left, click Autonomy Center.

  5. On the Autonomy Center page, view Auto Scaling events within the selected time range.

  6. Click Details for a Database Workload Prediction event to view prediction-based auto scaling details.

    image