Optimize your compute usage

The compute optimizer tool provides actionable insights to help you manage compute spend and implement automated compute controls. This means you can identify inefficient areas, receive clear recommendations for optimization, and ultimately achieve potential compute savings, improved query efficiency, and a more streamlined New Relic experience.

Access compute optimizer

You'll find the compute optimizer in the administration panel within New Relic.

1. Log in to New Relic.
2. Open your personal settings by clicking your user name in the bottom-left corner of the New Relic interface. Go to the administration panel.
3. Click compute optimizer to access the dashboard.

The compute optimizer dashboard

The compute optimizer dashboard presents two crucial metrics:

• Inefficient usage: This provides an overview several of inefficient compute areas identified in your account.
• Potential savings: This metric quantifies the estimated savings you can achieve by implementing the recommendations provided by the tool.

Recommendations

The core of the compute optimizer is the recommendations table, which breaks down the identified optimization opportunities:

• Component name: Identifies the specific item or instance that has an optimization opportunity (for example, the name of a particular alert condition).
• Product capability: Identifies the broader New Relic product capability associated with the optimization opportunity. The initial release focuses on alert conditions.
• CCU usage: Provides an understanding of the current compute consumption (CCU usage) for each listed component, helping you understand its current impact.
• Recommendation: Offers actionable insights for optimizing your compute. For example, you might see a recommendation to "Add a WHERE clause outside the SELECT statement," which could save a significant percentage of CCUs for a specific alert condition.
• CCU savings estimate: This quantifies the potential compute reduction for each individual recommendation listed in the table. For example, if a recommendation is to optimize alert X, this column shows the specific CCU savings you could achieve just for alert X. All these individual CCU savings estimate values suggested in each row are then added together to calculate the total potential savings metric.

All insights presented in the compute optimizer are dynamic and based on your time picker settings, allowing you to analyze data relevant to specific periods.

The action menu for each recommendation offers an optimized query wherever applicable, making it easy to implement the suggested changes directly.