Best Practice for Defining Composite Metrics Without Excessive Schema Overhead

**Problem**

Hi,

I’m looking for advice on the best way to define composite metrics based on existing metrics. For example, I want to define a new metric C as the sum of metrics A and B, and then be able to query metric C via a REST API.

Currently, I’m considering redefining a new metric C in the schema. However, this approach leads to an excessive number of schemas, which I’ve observed can decrease API query performance. Additionally, each new schema requires a recompilation process, which becomes more time-consuming as the number of schemas increases.

Is there a recommended approach to handle such composite metrics without the drawbacks of having too many schemas? How can I efficiently define and query composite metrics without negatively impacting performance and compile times?

Thank you for your insights!

**Related Cube.js schema**
```javascript
cube(`users`, {
sql_table: `users`,

measures: {
m_a: {
type: `sum`,
sql: `m_a`,
},
m_b: {
type: `sum`,
sql: `m_c`,
},
m_c: {
type: `sum`,
sql: `${m_a} + ${m_b}`,
},
},

dimensions: {
organization_id: {
sql: `organization_id`,
type: `number`,
primary_key: true,
},

created_at: {
sql: `created_at`,
type: `time`,
},

country: {
sql: `country`,
type: `string`,
},
},
});
```