We support auto-instrumentation for the following CI providers:

If you are using auto-instrumentation for one of these providers, you can skip the rest of the setup steps below.

GitHub Actions나 CircleCI와 같이 기본 작업자 노드에 액세스하지 않고 클라우드 CI 공급자를 사용할 경우, 라이브러리를 구성해 에이전트리스 모드로 사용하세요. 이 모드를 이용하려면 다음 환경 변수를 설정하세요.

DD_CIVISIBILITY_AGENTLESS_ENABLED=true (필수)

에이전트리스 모드 활성화 또는 비활성화
기본값: false

DD_API_KEY (필수)

테스트 결과를 업로드하는 데 사용되는 Datadog API 키
기본값: (empty)

추가로 데이터를 보낼 Datadog 사이트를 구성하세요.

DD_SITE (필수)

결과를 업로드할 Datadog 사이트
기본값: datadoghq.com

Jenkins 또는 자체 관리형 GitLab CI와 같은 온프레미스 CI 공급자에서 테스트를 실행하는 경우, 에이전트 설치 지침에 따라 각 작업자 노드에 Datadog 에이전트를 설치합니다. 자동으로 테스트 결과를 로그와 기본 호트스 메트릭과 연결할 수 있기 때문에 이 옵션을 추천합니다.

쿠버네티스 실행기를 사용하는 경우 Datadog에서는 Datadog 연산자를 사용할 것을 권고합니다. 연산자에는 Datadog 허용 제어기가 포함되어 있어 빌드 파드에 자동으로 추적기 라이브러리를 삽입합니다. 참고: Datadog 연산자를 사용할 경우 허용 제어기가 작업을 해주기 때문에 추적기 라이브러리를 다운로드 받고 삽입할 필요가 없습니다. 따라서 아래 해당 단계를 건너뛰어도 됩니다. 그러나 테스트 가시화 기능을 사용할 때 필요한 파드의 환경 변수나 명령줄 파라미터는 설정해야 합니다.

쿠버네티스를 사용하지 않거나 Datadog 허용 제어기를 사용할 수 없고 CI 공급자가 컨테이너 기반 실행기를 사용하는 경우, 추적기를 실행하는 빌드 컨테이너에서 환경 변수 DD_TRACE_AGENT_URL(기본값 http://localhost:8126)를 해당 컨테이너 내에서 액세스할 수 있는 엔드포인트로 설정합니다. 참고: 빌드 내에서 localhost를 사용하면 기본 작업자 노드나 에이전트를 실행하는 컨테이너를 참조하지 않고 컨테이너 자체를 참조합니다.

DD_TRACE_AGENT_URL 은 프로토콜과 포트(예: http://localhost:8126)를 포함하고 DD_AGENT_HOST과 DD_TRACE_AGENT_PORT보다 우선하며, CI Visibility를 위해 Datadog 에이전트의 URL을 설정하는 데 권장되는 설정 파라미터입니다.

Datdog 에이전트에 연결하는 데 아직 문제가 있다면 에이전트리스 모드를 사용해 보세요. 참고: 이 방법을 사용할 경우 테스트가 로그 및 인프라스트럭처 메트릭과 상관 관계를 수립하지 않습니다.

To enable instrumentation of pytest tests, add the --ddtrace option when running pytest, specifying the name of the service or library under test in the DD_SERVICE environment variable, and the environment where tests are being run (for example, local when running tests on a developer workstation, or ci when running them on a CI provider) in the DD_ENV environment variable:

DD_SERVICE=my-python-app DD_ENV=ci pytest --ddtrace

If you also want to enable the rest of the APM integrations to get more information in your flamegraph, add the --ddtrace-patch-all option:

DD_SERVICE=my-python-app DD_ENV=ci pytest --ddtrace --ddtrace-patch-all

Adding custom tags to tests

To add custom tags to your tests, declare ddspan as an argument in your test:

from ddtrace import tracer

# Declare `ddspan` as argument to your test
def test_simple_case(ddspan):
    # Set your tags
    ddspan.set_tag("test_owner", "my_team")
    # test continues normally
    # ...

To create filters or group by fields for these tags, you must first create facets. For more information about adding tags, see the Adding Tags section of the Python custom instrumentation documentation.

Adding custom measures to tests

Just like tags, to add custom measures to your tests, use the current active span:

from ddtrace import tracer

# Declare `ddspan` as an argument to your test
def test_simple_case(ddspan):
    # Set your tags
    ddspan.set_tag("memory_allocations", 16)
    # test continues normally
    # ...

Read more about custom measures in the Add Custom Measures Guide.

To instrument your benchmark tests with pytest-benchmark, run your benchmark tests with the --ddtrace option when running pytest, and Datadog detects metrics from pytest-benchmark automatically:

def square_value(value):
    return value * value


def test_square_value(benchmark):
    result = benchmark(square_value, 5)
    assert result == 25

To enable instrumentation of unittest tests, run your tests by appending ddtrace-run to the beginning of your unittest command.

Make sure to specify the name of the service or library under test in the DD_SERVICE environment variable. Additionally, you may declare the environment where tests are being run in the DD_ENV environment variable:

DD_SERVICE=my-python-app DD_ENV=ci ddtrace-run python -m unittest

Alternatively, if you wish to enable unittest instrumentation manually, use patch() to enable the integration:

from ddtrace import patch
import unittest
patch(unittest=True)

class MyTest(unittest.TestCase):
def test_will_pass(self):
assert True

Manual testing API

As of version 2.13.0, the Datadog Python tracer provides the Test Optimization API (ddtrace.ext.test_visibility) to submit test optimization results as needed.

API execution

The API uses classes to provide namespaced methods to submit test optimization events.

Test execution has two phases:

• Discovery: inform the API what items to expect
• Execution: submit results (using start and finish calls)

The distinct discovery and execution phases allow for a gap between the test runner process collecting the tests and the tests starting.

API users must provide consistent identifiers (described below) that are used as references for Test Optimization items within the API’s state storage.

Enable test_visibility

You must call the ddtrace.ext.test_visibility.api.enable_test_visibility() function before using the Test Optimization API.

Call the ddtrace.ext.test_visibility.api.disable_test_visibility() function before process shutdown to ensure proper flushing of data.

Domain model

The API is based around four concepts: test session, test module, test suite, and test.

Modules, suites, and tests form a hierarchy in the Python Test Optimization API, represented by the item identifier’s parent relationship.

Test session

A test session represents a project’s test execution, typically corresponding to the execution of a test command. Only one session can be discovered, started, and finished in the execution of Test Optimization program.

Call ddtrace.ext.test_visibility.api.TestSession.discover() to discover the session, passing the test command, a given framework name, and version.

Call ddtrace.ext.test_visibility.api.TestSession.start() to start the session.

When tests have completed, call ddtrace.ext.test_visibility.api.TestSession.finish() .

Test module

A test module represents a smaller unit of work within a project’s tests run (a directory, for example).

Call ddtrace.ext.test_visibility.api.TestModuleId(), providing the module name as a parameter, to create a TestModuleId.

Call ddtrace.ext.test_visibility.api.TestModule.discover(), passing the TestModuleId object as an argument, to discover the module.

Call ddtrace.ext.test_visibility.api.TestModule.start(), passing the TestModuleId object as an argument, to start the module.

After all the children items within the module have completed, call ddtrace.ext.test_visibility.api.TestModule.finish(), passing the TestModuleId object as an argument.

Test suite

A test suite represents a subset of tests within a project’s modules (.py file, for example).

Call ddtrace.ext.test_visibility.api.TestSuiteId(), providing the parent module’s TestModuleId and the suite’s name as arguments, to create a TestSuiteId.

Call ddtrace.ext.test_visibility.api.TestSuite.discover(), passing the TestSuiteId object as an argument, to discover the suite.

Call ddtrace.ext.test_visibility.api.TestSuite.start(), passing the TestSuiteId object as an argument, to start the suite.

After all the child items within the suite have completed, call ddtrace.ext.test_visibility.api.TestSuite.finish(), passing the TestSuiteId object as an argument.

Test

A test represents a single test case that is executed as part of a test suite.

Call ddtrace.ext.test_visibility.api.TestId(), providing the parent suite’s TestSuiteId and the test’s name as arguments, to create a TestId. The TestId() method accepts a JSON-parseable string as the optional parameters argument. The parameters argument can be used to distinguish parametrized tests that have the same name, but different parameter values.

Call ddtrace.ext.test_visibility.api.Test.discover(), passing the TestId object as an argument, to discover the test. The Test.discover() classmethod accepts a string as the optional resource parameter, which defaults to the TestId’s name.

Call ddtrace.ext.test_visibility.api.Test.start(), passing the TestId object as an argument, to start the test.

Call ddtrace.ext.test_visibility.api.Test.mark_pass(), passing the TestId object as an argument, to mark that the test has passed successfully. Call ddtrace.ext.test_visibility.api.Test.mark_fail(), passing the TestId object as an argument, to mark that the test has failed. mark_fail() accepts an optional TestExcInfo object as the exc_info parameter. Call ddtrace.ext.test_visibility.api.Test.mark_skip(), passing the TestId object as an argument, to mark that the test was skipped. mark_skip() accepts an optional string as the skip_reason parameter.

Exception information

The ddtrace.ext.test_visibility.api.Test.mark_fail() classmethod holds information about exceptions encountered during a test’s failure.

The ddtrace.ext.test_visibility.api.TestExcInfo() method takes three positional parameters:

• exc_type: the type of the exception encountered
• exc_value: the BaseException object for the exception
• exc_traceback: the Traceback object for the exception

Codeowner information

The ddtrace.ext.test_visibility.api.Test.discover() classmethod accepts an optional list of strings as the codeowners parameter.

Test source file information

The ddtrace.ext.test_visibility.api.Test.discover() classmethod accepts an optional TestSourceFileInfo object as the source_file_info parameter. A TestSourceFileInfo object represents the path and optionally, the start and end lines for a given test.

The ddtrace.ext.test_visibility.api.TestSourceFileInfo() method accepts three positional parameters:

• path: a pathlib.Path object (made relative to the repo root by the Test Optimization API)
• start_line: an optional integer representing the start line of the test in the file
• end_line: an optional integer representing the end line of the test in the file

Setting parameters after test discovery

The ddtrace.ext.test_visibility.api.Test.set_parameters() classmethod accepts a TestId object as an argument, and a JSON-parseable string, to set the parameters for the test.

Note: this overwrites the parameters associated with the test, but does not modify the TestId object’s parameters field.

Setting parameters after a test has been discovered requires that the TestId object be unique even without the parameters field being set.

Code example

from ddtrace.ext.test_visibility import api
import pathlib
import sys

if __name__ == "__main__":
    # Enable the Test Optimization service
    api.enable_test_visibility()

    # Discover items
    api.TestSession.discover("manual_test_api_example", "my_manual_framework", "1.0.0")
    test_module_1_id = api.TestModuleId("module_1")
    api.TestModule.discover(test_module_1_id)

    test_suite_1_id = api.TestSuiteId(test_module_1_id, "suite_1")
    api.TestSuite.discover(test_suite_1_id)

    test_1_id = api.TestId(test_suite_1_id, "test_1")
    api.Test.discover(test_1_id)

    # A parameterized test with codeowners and a source file
    test_2_codeowners = ["team_1", "team_2"]
    test_2_source_info = api.TestSourceFileInfo(pathlib.Path("/path/to_my/tests.py"), 16, 35)

    parametrized_test_2_a_id = api.TestId(
        test_suite_1_id,
        "test_2",
        parameters='{"parameter_1": "value_is_a"}'
    )
    api.Test.discover(
        parametrized_test_2_a_id,
        codeowners=test_2_codeowners,
        source_file_info=test_2_source_info,
        resource="overriden resource name A",
    )

    parametrized_test_2_b_id = api.TestId(
        test_suite_1_id,
        "test_2",
        parameters='{"parameter_1": "value_is_b"}'
    )
    api.Test.discover(
      parametrized_test_2_b_id,
      codeowners=test_2_codeowners,
      source_file_info=test_2_source_info,
      resource="overriden resource name B"
    )

    test_3_id = api.TestId(test_suite_1_id, "test_3")
    api.Test.discover(test_3_id)

    test_4_id = api.TestId(test_suite_1_id, "test_4")
    api.Test.discover(test_4_id)


    # Start and execute items
    api.TestSession.start()

    api.TestModule.start(test_module_1_id)
    api.TestSuite.start(test_suite_1_id)

    # test_1 passes successfully
    api.Test.start(test_1_id)
    api.Test.mark_pass(test_1_id)

    # test_2's first parametrized test succeeds, but the second fails without attaching exception info
    api.Test.start(parametrized_test_2_a_id)
    api.Test.mark_pass(parametrized_test_2_a_id)

    api.Test.start(parametrized_test_2_b_id)
    api.Test.mark_fail(parametrized_test_2_b_id)

    # test_3 is skipped
    api.Test.start(test_3_id)
    api.Test.mark_skip(test_3_id, skip_reason="example skipped test")

    # test_4 fails, and attaches exception info
    api.Test.start(test_4_id)
    try:
      raise(ValueError("this test failed"))
    except:
      api.Test.mark_fail(test_4_id, exc_info=api.TestExcInfo(*sys.exc_info()))

    # Finish suites and modules
    api.TestSuite.finish(test_suite_1_id)
    api.TestModule.finish(test_module_1_id)
    api.TestSession.finish()

Plugins for pytest that alter test execution may cause unexpected behavior.

Parallelization

Plugins that introduce parallelization to pytest (such as pytest-xdist or pytest-forked) create one session event for each parallelized instance. Multiple module or suite events may be created if tests from the same package or module execute in different processes.

The overall count of test events (and their correctness) remain unaffected. Individual session, module, or suite events may have inconsistent results with other events in the same pytest run.

Test ordering

Plugins that change the ordering of test execution (such as pytest-randomly) can create multiple module or suite events. The duration and results of module or suite events may also be inconsistent with the results reported by pytest.

The overall count of test events (and their correctness) remain unaffected.

In some cases, if your unittest test execution is run in a parallel manner, this may break the instrumentation and affect test optimization.

Datadog recommends you use up to one process at a time to prevent affecting test optimization.