pkg maps, func Clone[$0 interface{ ~map[$1]$2 }, $1 comparable, $2 interface{}]($0) $0 #57436
pkg maps, func Copy[$0 interface{ ~map[$2]$3 }, $1 interface{ ~map[$2]$3 }, $2 comparable, $3 interface{}]($0, $1) #57436
pkg maps, func DeleteFunc[$0 interface{ ~map[$1]$2 }, $1 comparable, $2 interface{}]($0, func($1, $2) bool) #57436
pkg maps, func Equal[$0 interface{ ~map[$2]$3 }, $1 interface{ ~map[$2]$3 }, $2 comparable, $3 comparable]($0, $1) bool #57436

        for (projection in typeArguments) {
            // E.g., Test : Comparable<Test>
            val type = (projection as? ConeKotlinTypeProjection)?.type ?: continue
            // E.g., Comparable<Test>
            val newType = substituteOrNull(type) ?: continue
            // Visit new type: e.g., Test, as a type argument, is substituted with Comparable<Test>, again.
            if (newType.hasRecursiveTypeArgument(visited)) return true

 */
private
val mappedTypeStrings =
    mapOf(
        // Built-ins
        "java.lang.Cloneable" to "kotlin.Cloneable",
        "java.lang.Comparable" to "kotlin.Comparable",
        "java.lang.Enum" to "kotlin.Enum",
        "java.lang.annotation.Annotation" to "kotlin.Annotation",
        "java.lang.Deprecated" to "kotlin.Deprecated",

But all this functionality of using asynchronous code with `async` and `await` is many times summarized as using "coroutines". It is comparable to the main key feature of Go, the "Goroutines".

## Conclusion

Let's see the same phrase from above:

A container image is comparable to the **program** file and contents, e.g. `python` and some file `main.py`.

Pero toda esta funcionalidad de usar código asincrónico con `async` y `await` se resume muchas veces como usar "coroutines". Es comparable a la característica principal de Go, las "Goroutines".

## Conclusión

Veamos la misma frase de arriba:

pkg os, func Unsetenv(string) error
pkg syscall, func Unsetenv(string) error

# CL 144020043 reflect: add Type.Comparable, Russ Cox <******@****.***>
pkg reflect, type Type interface, Comparable() bool

# CL 153670043 runtime: add PauseEnd array to MemStats and GCStats, Jens Frederich <******@****.***>
pkg runtime, type MemStats struct, PauseEnd [256]uint64

An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

### Examples of Replication Tools and Strategies

Pydantic is a library to define data validation, serialization and documentation (using JSON Schema) based on Python type hints.

That makes it extremely intuitive.

It is comparable to Marshmallow. Although it's faster than Marshmallow in benchmarks. And as it is based on the same Python type hints, the editor support is great.

!!! check "**FastAPI** uses it to"

Pydantic est une bibliothèque permettant de définir la validation, la sérialisation et la documentation des données (à l'aide de JSON Schema) en se basant sur les Python type hints.

Cela le rend extrêmement intuitif.

Il est comparable à Marshmallow. Bien qu'il soit plus rapide que Marshmallow dans les benchmarks. Et comme il est
basé sur les mêmes type hints Python, le support de l'éditeur est grand.

!!! check "**FastAPI** l'utilise pour"