val fileNamePostfix = "$testProject-performance-scenarios.csv"
    val performanceTestSplitDirectoryName = "performance-test-splits"
    val unixScript = """
mkdir -p $performanceTestSplitDirectoryName
rm -rf $performanceTestSplitDirectoryName/*-$fileNamePostfix
cat > $performanceTestSplitDirectoryName/$action-$fileNamePostfix << EOL
${csvLines.joinToString("\n")}
EOL

echo "Performance tests to be ${action}d in this build"

        params {
            text(
                "performance.baselines",
                type.defaultBaselines,
                display = ParameterDisplay.PROMPT,
                allowEmpty = true,
                description = "The baselines you want to run performance tests against. Empty means default baseline."
            )
            param("performance.channel", performanceTestBuildSpec.channel())

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

The simpler the problem solved by the tool, the better performance it will get. And most of the benchmarks don't test the additional features provided by the tool.

The hierarchy is like:

* **Uvicorn**: an ASGI server
    * **Starlette**: (uses Uvicorn) a web microframework

package util

import jetbrains.buildServer.configs.kotlin.Project

object UtilPerformanceProject : Project({
    id("Util_Performance")
    name = "Performance"

    buildType(AdHocPerformanceScenarioLinux)
    buildType(AdHocPerformanceScenarioWindows)
    buildType(AdHocPerformanceScenarioMacOS)
    buildType(AdHocPerformanceScenarioMacAppleSilicon)

    params {
        param("env.GRADLE_ENTERPRISE_ACCESS_KEY", "%ge.gradle.org.access.key%")

    Ele claramente inspirou Uvicorn e Starlette, que são atualmente mais rápidos que o Sanic em testes de performance abertos.

!!! check "**FastAPI** inspirado para"
    Achar um jeito de ter uma performance insana.

    É por isso que o **FastAPI** é baseado em Starlette, para que ele seja o framework mais rápido disponível (performance testada por terceiros).

                buildReportTab("Incubating APIs Report", "incubation-reports/all-incubating.html")
            }
            if (stage.performanceTests.isNotEmpty()) {
                buildReportTab("Performance", "performance-test-results.zip!report/index.html")
            }
        }

        specificBuildTypes = stage.specificBuilds.map {
            it.create(model, stage)
        }

      env:
        # Exclude some projects and configurations that should not contribute to the dependency graph
        DEPENDENCY_GRAPH_EXCLUDE_PROJECTS: ':docs|:internal-performance-testing|:enterprise-plugin-performance|:performance|:internal-integ-testing'

    ```

    </div>

    !!! tip
        By adding the `standard`, Uvicorn will install and use some recommended extra dependencies.

        That including `uvloop`, the high-performance drop-in replacement for `asyncio`, that provides the big concurrency performance boost.

=== "Hypercorn"

    * <a href="https://gitlab.com/pgjones/hypercorn" class="external-link" target="_blank">Hypercorn</a>, an ASGI server also compatible with HTTP/2.

// 4. buildScanPerformance test, which doesn't depend on compileAll
    isInBuild(
        "Check_CompileAllBuild",
        "Check_BuildDistributions",
        "Component_GradlePlugin_Performance_PerformanceLatestMaster",
        "Component_GradlePlugin_Performance_PerformanceLatestReleased"
    )

fun Project.isExpectedCompileCacheMiss() =
// Expected cache-miss:
// 1. CompileAll is the seed build

Especificamente, observa-se Uvicorn, Starlette e FastAPI comparados juntos (entre muitas outras ferramentas).

Quanto mais simples o problema resolvido pela ferramenta, melhor a performance que ela terá. E a maioria dos _benchmarks_ não testam as características adicionais fornecidas pela ferramenta.

A hierarquia segue assim:

* **Uvicorn**: um servidor ASGI