[ {
  "scenario" : "org.gradle.performance.experiment.declarativedsl.DeclarativeDslConfigurationPerformanceTest.configure",
  "durations" : [ {
    "testProject" : "largeEmptyMultiProjectDeclarativeDsl",
    "linux" : 350,
    "windows" : 828,
    "macOs" : 229
  } ]
}, {
  "scenario" : "org.gradle.performance.experiment.declarativedsl.DeclarativeDslFirstUsePerformanceTest.clean checkout",
  "durations" : [ {

      }
    } ]
  }, {
    "testId" : "org.gradle.performance.experiment.declarativedsl.DeclarativeDslFirstUsePerformanceTest.cold daemon",
    "groups" : [ {
      "testProject" : "largeEmptyMultiProjectDeclarativeDsl",
      "coverage" : {
        "per_day" : [ "linux", "macOs", "windows" ]
      }
    } ]
  }, {
    "testId" : "org.gradle.performance.experiment.declarativedsl.DeclarativeDslFirstUsePerformanceTest.first use",

name: Cleanup stale performance test data

on:
  schedule:
    - cron: '0 4 * * 1'  # Run every Monday at 4:00 AM UTC
  workflow_dispatch:

permissions:
  contents: read
  id-token: write

jobs:
  cleanup-stale-performance-data:
    runs-on: ubuntu-latest

    if: github.repository == 'gradle/gradle'

    steps:
      - name: Checkout repository
        uses: actions/checkout@v6
      

      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'

closed, up to a conservative limit, to allow better connection reuse. For most
programs, this change should be a no-op, or result in a performance improvement.
In rare cases, programs that do not benefit from connection reuse might
experience performance degradation if they had been improperly allowing an
excessive amount of idle connections to linger; usually by setting

      - name: Update performance-test-durations.json
        run: |
          ./gradlew performance:writePerformanceTimes -Porg.gradle.performance.db.url=${{ env.PERFORMANCE_DB_URL }} -Porg.gradle.performance.db.username=${{ env.PERFORMANCE_DB_USERNAME }}
      - name: Create Pull Request
        uses: peter-evans/create-pull-request@v8
        with:
          commit-message: Update performance test durations

        name = "AdHoc Performance Scenario - ${os.asName()} ${arch.asName()}"
        id(id)

        applyPerformanceTestSettings(os = os, arch = arch, timeout = 420)
        setArtifactRules(INDIVIDUAL_PERFORAMCE_TEST_ARTIFACT_RULES)

        params {
            text(
                "performance.baselines",
                "",
                display = ParameterDisplay.PROMPT,

There were a couple of custom FastAPI response classes that were intended to optimize JSON performance.

However, they are now deprecated as you will now get better performance by using a [Response Model - Return Type](https://fastapi.tiangolo.com/tutorial/response-model/).

That way, Pydantic will serialize the data into JSON bytes on the Rust side, which will achieve better performance than these custom JSON responses.

                )
                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, FlakyTestStrategy.EXCLUDE)

However, the Java code we have is not fully annotated, which causes several consequences:
* IDE warnings can be misleading
* Redundant null checks may increase binary size, hurt performance, and increase cognitive load for readers
* Missing null checks may cause NullPointerException
* Bridging with Kotlin code is polluted with platform types