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Time-Based Scaling

Time-based scaling is suitable for predictable, recurring workloads. For example, if business traffic regularly peaks during the day and drops at night, you can configure a scaling policy that adjusts cluster compute resources at fixed times.

A scaling policy requires at least two rules with different target vCPU values. This lets VeloDB Cloud perform periodic scale-out and scale-in actions to handle business peaks and lows.

Configure a time-based scaling policy​

To configure a time-based scaling policy, follow these steps:

  1. Log in to the VeloDB Cloud console.
  2. In the left navigation pane, under Compute, click Clusters.
  3. On the Clusters page, click the target cluster card to open the cluster Details page.
  4. In On-Demand Resources, click Scale Out/In -> Time-Based Scaling.
  5. Click + Add to create a rule.
  6. Configure the rule:
    • Execution Period: Select how often the rule runs (e.g., Every day).
    • Execution Time: Set the time when scaling occurs (e.g., 00:00).
    • Target vCPU: Enter the desired compute size.
  7. Add at least two rules with different Target vCPU values.
  8. Confirm that the rule Execution Time values do not overlap.
  9. Enable the Time-based Scaling Policy toggle.

Rules and limitations​

  • SaaS free-trial clusters do not support scaling.
  • On-demand clusters cannot have a rule with a target vCPU of 0.
  • Rules execute only while the cluster is running normally. When the cluster is paused, rebooting, or upgrading, the rule waits for a retry; if it cannot execute within 30 minutes, it is skipped.
  • If the organization does not have enough cash balance or a cloud-marketplace deduction channel, the rule is invalidated.
  • The schedule is fixed to daily; editing the period is not supported.
  • Rules must be at least one hour apart, so at most 23 rules per cluster.
  • Rule execution times cannot overlap with existing rules.
  • Scaling may cause some requests to crash or be delayed.
  • When scaling in, cache capacity shrinks in proportion to compute (vCPU). Cached data that exceeds the reduced capacity is evicted, which can noticeably slow some requests during this period.