chart-tests

Use ao escrever, editar, revisar ou executar testes de chart Helm para o repositório Astronomer APC. Abrange padrões pytest, uso de render_chart(), sub-chart…

npx skills add https://github.com/astronomer/astronomer --skill chart-tests

Chart Test Writing Guide

Critical Rules

  1. Always run tests with uv run — never python3 -m pytest or python bin/...
  2. Sub-chart values MUST be nested under the sub-chart name (see Values Nesting)
  3. No helm unittest plugin — all tests are pytest-based using render_chart()
  4. One render_chart() call per test function — never call it multiple times in the same function to cover different value combinations (see One Condition Per Test)

Test Organization

tests/
├── chart_tests/              # Helm template rendering tests (main focus)
│   ├── test_<component>.py   # One file per component
│   ├── conftest.py           # Shared fixtures
│   └── test_data/            # Feature configs, expected outputs
├── functional/               # End-to-end cluster tests
├── k8s_schema/               # Cached Kubernetes API schemas
└── utils/
    ├── chart.py              # render_chart() and helpers
    ├── fixtures.py           # Common fixtures
    └── __init__.py           # get_containers_by_name(), get_all_features(), etc.

Writing Tests

Basic Pattern

import pytest
from tests import supported_k8s_versions
from tests.utils.chart import render_chart

DEPLOYMENT_FILE = "charts/grafana/templates/grafana-deployment.yaml"


@pytest.mark.parametrize("kube_version", supported_k8s_versions)
def test_some_feature(kube_version):
    """Brief description of what is being tested."""
    docs = render_chart(
        kube_version=kube_version,
        show_only=[DEPLOYMENT_FILE],
        values={"grafana": {"enabled": True}},
    )
    assert len(docs) == 1
    assert docs[0]["kind"] == "Deployment"

Sub-Chart Values Nesting — CRITICAL

Templates in charts/<subchart>/templates/ belong to a sub-chart. Values for those templates must be nested under the sub-chart's top-level key:

# ❌ WRONG — will not override sub-chart values
values = {"houston": {"replicas": 3}}

# ✅ CORRECT — nest under the sub-chart name
values = {"astronomer": {"houston": {"replicas": 3}}}

# ✅ EXAMPLE — disable a feature in the astronomer sub-chart
docs = render_chart(
    values={"astronomer": {"dpLink": {"enabled": False}}},
    show_only=["charts/astronomer/templates/dp-link/dp-link-deployment.yaml"],
)
assert len(docs) == 0

Top-level charts (e.g. nginx, grafana, prometheus) use their chart name directly:

values = {"nginx": {"serviceType": "LoadBalancer"}}
values = {"grafana": {"extraEnvVars": [...]}}

Using show_only

Always use show_only to target the specific template being tested:

docs = render_chart(
    show_only=[
        "charts/nginx/templates/controlplane/nginx-cp-service.yaml",
        "charts/nginx/templates/dataplane/nginx-dp-service.yaml",
    ]
)

Parametrized Tests

Always parametrize over supported_k8s_versions and over relevant values axes:

@pytest.mark.parametrize("kube_version", supported_k8s_versions)
@pytest.mark.parametrize("plane_mode,docs_count", [("control", 1), ("unified", 1), ("data", 0)])
def test_deployment_should_render(kube_version, plane_mode, docs_count):
    docs = render_chart(
        kube_version=kube_version,
        show_only=[DEPLOYMENT_FILE],
        values={"global": {"plane": {"mode": plane_mode}}},
    )
    assert len(docs) == docs_count

One Condition Per Test

Never call render_chart() more than once inside the same test function to check several value combinations (e.g. default, feature-enabled, feature-disabled). Each render_chart() call is its own test condition — hiding several behind one function name makes failures ambiguous (which call failed?) and hides the test matrix from pytest --collect-only / -k filtering. Use parametrization when the calls only differ by a value/expectation, or separate test functions when they differ conceptually:

# ❌ WRONG — three renders buried in one function
def test_secretstore_rule(kube_version):
    docs = render_chart(kube_version=kube_version, show_only=[ROLE_FILE])
    assert absent(docs[0])

    docs = render_chart(
        kube_version=kube_version, values={"global": {"dataPlaneFailover": {"enabled": True}}}, show_only=[ROLE_FILE]
    )
    assert present(docs[0])

    docs = render_chart(
        kube_version=kube_version, values={"global": {"dataPlaneFailover": {"enabled": False}}}, show_only=[ROLE_FILE]
    )
    assert absent(docs[0])
# ✅ CORRECT — parametrize when only the value/expectation changes
@pytest.mark.parametrize("kube_version", supported_k8s_versions)
@pytest.mark.parametrize(
    "dataplane_failover_enabled,rule_expected",
    [(None, False), (True, True), (False, False)],
    ids=["default", "enabled", "disabled"],
)
def test_secretstore_rule(kube_version, dataplane_failover_enabled, rule_expected):
    values = {}
    if dataplane_failover_enabled is not None:
        values = {"global": {"dataPlaneFailover": {"enabled": dataplane_failover_enabled}}}
    docs = render_chart(kube_version=kube_version, values=values, show_only=[ROLE_FILE])
    assert (expected_rule in docs[0]["rules"]) == rule_expected

Or, if an existing test function already renders with the exact values you need (e.g. a test for dataPlaneFailover.enabled: True that targets a related template), prefer adding your assertion to that template's doc in the existing show_only list over writing a new render_chart() call.

Testing with All Features Enabled

get_all_features() enables as many compatible features as possible. Not all features can be enabled simultaneously due to incompatibilities.

from tests.utils import get_all_features


def test_with_all_features():
    docs = render_chart(values=get_all_features())
    kinds = [doc["kind"] for doc in docs]
    assert "Deployment" in kinds

Testing Probe Customization

Every container must support customizable livenessProbe and readinessProbe. When adding a new component:

  1. Add its probes to tests/chart_tests/test_data/enable_all_probes.yaml
  2. Run tests with that file to verify probes are rendered correctly

Cross-cutting invariants — ALWAYS guard with a cross-cutting test

Some requirements must hold for every container/pod/object the cluster renders, not just one component — typically because an admission controller (e.g. a Gatekeeper/OPA constraint) rejects the whole install if a single object violates them. Examples: every container must define a startupProbe (allow-with-probes, PINF-691), allowPrivilegeEscalation: false (PINF-585/713), no forbidden Service fields (PINF-692).

For requirements like these, a per-component test is not enough — a new component added later silently reintroduces the violation. Always add a single cross-cutting test that:

  1. Renders with get_all_features() (what a real install presents to the admission controller), and
  2. Iterates every container across every pod-manager kind and asserts the invariant, so any new component that violates it fails the suite automatically.

Use get_chart_containers() or get_containers_by_name() over the filtered doc list. test_probes.py (TestStartupProbes, TestCustomProbes) and test_security_context_override.py are the reference patterns. Write this test first (TDD): it should fail (red) listing every offending container before you implement the fix, then pass (green) once coverage is complete.


ConfigMap Scripts

Scripts embedded in ConfigMaps must follow these conventions:

  1. Static content only — scripts must not use Helm templating to conditionally modify their content based on chart values. The rendered output must be identical regardless of what values are passed.

  2. Environment variable inputs — all runtime configuration must be passed as environment variables defined in the container spec (via env or envFrom), not baked into the script at render time.

  3. Stored as files on disk — scripts must be committed as real files in the repository (e.g. under charts/<subchart>/files/) so they can be linted and reviewed like any other source file.

  4. Included via .Files.Get — scripts must be included in ConfigMap templates using .Files.Get, not inline Helm template blocks:

    # ✅ CORRECT
    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: {{ include "chart.fullname" . }}-scripts
    data:
      my-script.sh: {{ .Files.Get "files/my-script.sh" | quote }}
    
    # ❌ WRONG — inline script with template logic
    data:
      my-script.sh: |
        #!/bin/sh
        {{- if .Values.someFlag }}
        do_something
        {{- end }}
    

Test Utilities

render_chart(values, show_only, kube_version, validate_objects)

Renders the chart via helm template and returns parsed YAML documents.

ParameterTypeDescription
valuesdictValues merged with chart defaults
show_onlylist[str]Templates to render (filters output)
kube_versionstrK8s version for schema validation
validate_objectsboolValidate against K8s schemas (default True)
from tests.utils.chart import render_chart

docs = render_chart(
    values={"nginx": {"enabled": True}},
    show_only=["charts/nginx/templates/controlplane/nginx-cp-service.yaml"],
    kube_version="1.31.0",
)

get_containers_by_name(doc, *, include_init_containers=False)

Returns {name: container_dict} for all containers in a pod manager doc (Deployment, StatefulSet, DaemonSet, Job, CronJob). Pass include_init_containers=True to also include init containers.

from tests.utils import get_containers_by_name

c_by_name = get_containers_by_name(doc, include_init_containers=True)
assert c_by_name["grafana"]["securityContext"] == {"readOnlyRootFilesystem": True}
assert c_by_name["bootstrapper"]["securityContext"] == {"readOnlyRootFilesystem": True}

get_all_features()

Returns a values dict with most components enabled.

from tests.utils import get_all_features

Other utilities in tests/utils/__init__.py

  • get_env_vars_dict(container_env) — converts env list to {name: value} dict
  • get_service_ports_by_name(doc) — returns service ports keyed by name
  • get_pod_template(doc) — extracts pod template from any pod manager
  • get_service_account_name_from_doc(doc) — returns the serviceAccountName
  • dot_notation_to_dict(dotted_string, default_value) — builds nested dict from dot notation

Running Tests

Correct examples

# Full suite in parallel (fastest — use for full runs)
uv run pytest tests/chart_tests/ -n auto --quiet

# Full suite, verbose
uv run pytest tests/chart_tests/ --verbose

# Single file
uv run pytest tests/chart_tests/test_grafana.py --verbose

# Tests matching a pattern
uv run pytest tests/chart_tests/ -k "test_service" --verbose

# Single test
uv run pytest tests/chart_tests/test_grafana.py::test_deployment_should_render --verbose

# Verbose output, stop on first failure
uv run pytest tests/chart_tests/ -vv --capture=no --maxfail=1

# Iterate on failures: re-run only last-failed tests
uv run pytest tests/chart_tests/ --maxfail=1 --lf

Tip: -n auto uses all CPU cores. Omit it when running a single file to avoid subprocess overhead.

Incorrect examples

# ❌ WRONG — we do not need to activate the venv manually, and we do not run pytest without `uv run`
.venv/bin/activate && pytest tests/chart_tests/

# ❌ WRONG — do not create virtual environment with python, do not use pip install. Use `uv run` to run all python files in the repo.
python3 -m venv .venv && .venv/bin/pip install -r requirements-dev.txt && .venv/bin/python3 -m pytest tests/chart_tests/

Kubernetes Schema Validation

Tests validate rendered manifests against cached K8s OpenAPI schemas in tests/k8s_schema/v<version>-standalone/. Validation runs by default; disable with validate_objects=False.

def test_custom_resource():
    docs = render_chart(
        show_only=["charts/airflow-operator/templates/crds/airflow.yaml"],
        validate_objects=True,
    )
    for doc in docs:
        assert doc["kind"] == "CustomResourceDefinition"

Mais skills de astronomer

airflow-state-store
astronomer
Persists task and asset state across retries and DAG runs using Airflow 3.3's AIP-103 key/value stores (`task_state_store`, `asset_state_store`) and the…
creating-openlineage-extractors
astronomer
Extratores OpenLineage personalizados para operadores Airflow não suportados e cenários complexos de linhagem. Duas abordagens: adicionar métodos OpenLineage diretamente aos operadores que você possui (recomendado), ou criar extratores personalizados para operadores de terceiros que você não pode modificar. Os extratores interceptam a execução do operador em três pontos: antes da execução para linhagem estática, após o sucesso para saídas determinadas em tempo de execução e, opcionalmente, após falha para linhagem parcial. Registre os extratores via airflow.cfg ou ambiente...
debugging-dags
astronomer
Análise sistemática de causa raiz e remediação para DAGs do Airflow com falhas, utilizando fluxos de investigação estruturados. Orienta por um processo de diagnóstico em quatro etapas: identificar a falha, extrair detalhes do erro, reunir informações contextuais e fornecer etapas de remediação acionáveis. Classifica as falhas em quatro tipos (dados, código, infraestrutura, dependência) para focar a investigação e sugerir correções apropriadas. Fornece comandos CLI prontos para recuperação de logs, comparação de execuções, limpeza de tarefas e DAG...
delegating-to-otto
astronomer
Direciona o agente Otto da
deploying-airflow
astronomer
Implantar DAGs e projetos do Airflow. Use quando o usuário quiser implantar código, enviar DAGs, configurar CI/CD, implantar em produção ou perguntar sobre estratégias de implantação…
deploying-go-sdk-bundles
astronomer
Compila, empacota e implanta pacotes compilados do Airflow Go SDK para que o ExecutableCoordinator possa executá-los. Use quando o usuário quiser compilar um pacote de tarefas Go, pedir…
testing-dags
astronomer
Ciclos iterativos de teste-depuração-correção para DAGs do Airflow com diagnóstico abrangente de falhas. Comece com af runs trigger-wait <dag_id> para executar um DAG e aguardar a conclusão; não são necessárias verificações prévias. Em caso de falha, use af runs diagnose para um resumo abrangente de falhas e af tasks logs para inspecionar detalhes de erros de tarefas específicas. Suporta configuração personalizada, timeouts e tentativas de repetição; lida com cenários de sucesso, falha e timeout com interpretação clara da resposta. Validação rápida disponível...
tracing-downstream-lineage
astronomer
Rastreie a linhagem de dados downstream para avaliar o impacto de alterações antes de modificar tabelas ou DAGs. Identifica consumidores diretos de uma tabela ou DAG alvo por meio de busca em código-fonte, dependências de views e conexões com ferramentas de BI. Constrói uma árvore de dependências completa mapeando todos os impactos downstream, desde tabelas até dashboards e modelos de ML. Categoriza dependências por criticidade (crítica, alta, média, baixa) para priorizar comunicação com stakeholders e testes. Gera um relatório de impacto com avaliação de risco, afetados...