import common.individualPerformanceTestArtifactRules
import common.killProcessStep
import common.skipConditionally
import jetbrains.buildServer.configs.kotlin.BuildSteps
import model.CIBuildModel
import model.Stage

class TestPerformanceTest(model: CIBuildModel, stage: Stage) : BaseGradleBuildType(stage, init = {
    val os = Os.LINUX
    val buildTypeThis = this
    val testProject = "smallJavaMultiProject"

# Extra Models

Continuing with the previous example, it will be common to have more than one related model.

This is especially the case for user models, because:

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

!!! danger
    Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.

import jetbrains.buildServer.configs.kotlin.BuildStep
import jetbrains.buildServer.configs.kotlin.buildSteps.script
import model.CIBuildModel
import model.Stage
import model.StageName

class FlakyTestQuarantine(model: CIBuildModel, stage: Stage, os: Os, arch: Arch = Arch.AMD64) : BaseGradleBuildType(stage = stage, init = {
    id("${model.projectId}_FlakyQuarantine_${os.asName()}_${arch.asName()}")

import common.functionalTestExtraParameters
import jetbrains.buildServer.configs.kotlin.BuildSteps
import jetbrains.buildServer.configs.kotlin.buildFeatures.perfmon
import model.CIBuildModel
import model.Stage
import model.StageName
import model.TestCoverage

const val functionalTestTag = "FunctionalTest"

sealed class ParallelizationMethod {
    open val extraBuildParameters: String
        @JSONField(serialize = false)

import jetbrains.buildServer.configs.kotlin.ReuseBuilds
import model.CIBuildModel
import model.PerformanceTestType
import projects.PerformanceTestProject

class PerformanceTestsPass(model: CIBuildModel, performanceTestProject: PerformanceTestProject) : BaseGradleBuildType(
    failStage = performanceTestProject.spec.failsStage,
    init = {
        id("${performanceTestProject.spec.asConfigurationId(model)}_Trigger")

import gradlebuild.binarycompatibility.jApiClass
import gradlebuild.binarycompatibility.simpleName
import japicmp.model.JApiClass
import japicmp.model.JApiCompatibility
import japicmp.model.JApiConstructor
import japicmp.model.JApiField
import japicmp.model.JApiMethod


private
typealias PredicateVisitor = GenericVisitorAdapter<Boolean, Unit?>


internal
object JavaSourceQueries {

### Resource allocation

A denial of service caused by one model could bring down the entire server, but
we don't consider this as a vulnerability, given that models can exhaust
resources in many different ways and solutions exist to prevent this from
happening (e.g., rate limits, ACLs, monitors to restart broken servers).

### Model sharing

If the multitenant design allows sharing models, make sure that tenants and

The same way, this database wouldn't receive a Pydantic model (an object with attributes), only a `dict`.

You can use `jsonable_encoder` for that.

It receives an object, like a Pydantic model, and returns a JSON compatible version:

=== "Python 3.10+"

    ```Python hl_lines="4  21"
    {!> ../../../docs_src/encoder/tutorial001_py310.py!}

数据类的和运作方式与 Pydantic 模型相同。实际上，它的底层使用的也是 Pydantic。

!!! info "说明"

    注意，数据类不支持 Pydantic 模型的所有功能。

    因此，开发时仍需要使用 Pydantic 模型。

    但如果数据类很多，这一技巧能给 FastAPI 开发 Web API 增添不少助力。🤓

## `response_model` 使用数据类

在 `response_model` 参数中使用 `dataclasses`：

```Python hl_lines="1  7-13  19"
{!../../../docs_src/dataclasses/tutorial002.py!}
```

本例把数据类自动转换为 Pydantic 数据类。

API 文档中也会显示相关概图：

import jetbrains.buildServer.configs.kotlin.DslContext
import jetbrains.buildServer.configs.kotlin.buildSteps.GradleBuildStep
import model.CIBuildModel
import model.JsonBasedGradleSubprojectProvider
import model.PerformanceTestCoverage
import model.PerformanceTestType
import model.Stage
import model.StageName
import org.junit.jupiter.api.Assertions.assertEquals
import org.junit.jupiter.api.Test
import java.io.File