Testing Tips

We run several kinds of tests at Sentry as part of our CI process. This section aims to document some of the sentry specific helpers and give guidelines on what kinds of tests you should consider including when building new features.

Getting Setup

The acceptance and python tests require a functioning set of devservices. It is recommended you use devservices to ensure you have the required services running. If you also use your local environment for local testing you will want to use the --project flag to separate local testing volumes from test suite volumes:

# Turn off services for local testing.
sentry devservices down

# Turn on services with a test prefix to use separate containers and volumes
sentry devservices up --project test

# Verify that test containers came up correctly
docker ps --format '{{.Names}}'

# Later when you're done running tests and want to run local servers again
sentry devservices down --project test && sentry devservices up

When using the --project option you can confirm which containers are running docker ps. Each running container should be prefixed with test_. See the devservices docs section for more information on managing services.

Python Tests

For python tests we use pytest and testing tools provided by Django. On top of this foundation we've added a few base test cases (in sentry.testutils.cases).

Endpoint integration tests is where the bulk of our test suite is focused. These tests help us ensure that the APIs our customers, integrations and front-end application continue to work in expected ways. You should endeavour to include tests that cover the various user roles, and cross organization/team access scenarios, as well as invalid data scenarios as those are often overlooked when manually testing.

Running pytest

You can use pytest to run a single directory, single file, or single test depending on the scope of your changes:

# Run tests for an entire directory
pytest tests/sentry/api/endpoints/

# Run tests for all files matching a pattern in a directory
pytest tests/sentry/api/endpoints/test_organization_*.py

# Run test from a single file
pytest tests/sentry/api/endpoints/test_organization_group_index.py

# Run a single test
pytest tests/snuba/api/endpoints/test_organization_events_distribution.py::OrganizationEventsDistributionEndpointTest::test_this_thing

# Run all tests in a file that match a substring
pytest tests/snuba/api/endpoints/test_organization_events_distribution.py -k method_name

Some frequently used options for pytest are:

• -k Filter test methods/classes by a substring.
• -s Don't capture stdout when running tests.

Refer to the pytest docs for more usage options.

Creating data in tests

Sentry has also added a suite of factory helper methods that help you build data to write your tests against. The factory methods in sentry.testutils.factories are available on all our test suite classes. Use these methods to build up the required organization, projects and other postgres based state.

You should also use store_event() to store events in a similar way that the application does in production. Storing events requires your test to inherit from SnubaTestCase. When using store_event() take care to set a timestamp in the past on the event. When omitted, the timestamp is uses 'now' which can result in events not being picked due to timestamp boundaries.

from sentry.testutils.helpers.datetime import before_now
from sentry.utils.samples import load_data

def test_query(self):
    data = load_data("python", timestamp=before_now(minutes=1))
    event = self.store_event(data, project_id=self.project.id)

Setting options and feature flags

If your tests are for feature flagged endpoints, or require specific options to be set. You can use helper methods to mutate the configuration data into the right state:

def test_success(self):
    with self.feature('organization:new-thing'):
        with self.options({'option': 'value'}):
            # Run test logic with features and options set.

    # Disable the new-thing feature.
    with self.feature({'organization:new-thing': False}):
        # Run you logic with a feature off.

External Services

Use the responses library to add stub responses for an outbound API requests your code is making. This will help you simulate success and failure scenarios with relative ease.

Working with time reliably

When writing tests related to ingesting events we have to operate within the constraint of events cannot be older than 30 days. Because all events must be recent, we cannot use traditional time freezing strategies to get consistent data in tests. Instead of choosing arbitrary points in time we work backwards from the present, and have a few helper functions to do so:

from sentry.testutils.helpers.datetime import before_now, iso_format

five_min_ago = before_now(minutes=5)
iso_timestamp = iso_format(five_min_ago)

These functions generate datetime objects, and ISO 8601 formatted datetime strings relative to the present enabling you to have events at known time offsets without violating the 30 day constraint that relay imposes.

Inspecting SQL queries in tests

Add the following to conftest.py in the project root:

import itertools
from django.conf import settings
from django.db import connection, connections, reset_queries
from django.template import Template, Context

@pytest.fixture(scope="function", autouse=True)
def log_sql():
    reset_queries()
    settings.DEBUG = True

    yield

    time = sum([float(q["time"]) for q in connection.queries])
    t = Template(
        "{% for sql in sqllog %}{{sql.sql|safe}}{% if not forloop.last %}\n\n{% endif %}{% endfor %}"
    )
    queries = list(itertools.chain.from_iterable([conn.queries for conn in connections.all()]))
    log = t.render(Context({"sqllog": queries, "count": len(queries), "time": time}))
    print(log)

Now all SQL executed during tests will be printed to stdout. It's recommended to narrow down the output by using pytest's -k selector. Also note that you'll need to pass -s to see stdout.

Acceptance Tests

Our acceptance tests leverage selenium and chromedriver to simulate a user using the front-end application and the entire backend stack. We use acceptance tests for two purposes at Sentry:

1. To cover workflows that are not possible to cover with just endpoint tests or with Jest alone.
2. To prepare snapshots for visual regression tests via our visual regression GitHub Actions.

Acceptance tests can be found in tests/acceptance and run locally with pytest.

Running acceptance tests

When you run acceptance tests, webpack will be run automatically to build static assets. If you change Javascript files while creating or modifying acceptance tests, you'll need to rm .webpack.meta after each change to trigger a rebuild on static assets.

# Run a single acceptance test.
pytest tests/acceptance/test_organization_group_index.py \
    -k test_with_onboarding

# Run the browser with a head so you can watch it.
pytest tests/acceptance/test_organization_group_index.py \
    --no-headless=true \
    -k test_with_onboarding

# Open each snapshot image
SENTRY_SCREENSHOT=1 VISUAL_SNAPSHOT_ENABLE=1 \
    pytest tests/acceptance/test_organization_group_index.py \
    -k test_with_onboarding

Locating Elements

Because we use emotion our classnames are generally not useful to browser automation. Instead we liberally use data-test-id attributes to define hook points for browser automation and Jest tests.

Dealing with Asynchronous actions

All of our data is loaded asynchronously into the front-end and acceptance tests need to account for various latencies and response times. We favour using selenium's wait_until* features to poll the DOM until elements are present or visible. We do not use sleep().

Visual Regression

Pixels Matter and because of that we use visual regressions to help catch unintended changes to how Sentry is rendered. During acceptance tests we capture screenshots and compare the screenshots in your pull request to approved baselines.

While we have fairly wide coverage with visual regressions there are a few important blind spots:

• Hover cards and hover states
• Modal windows
• Charts and data visualizations

All of these components and interactions are generally not included in visual snapshots, and you should take care when working on any of them.

Dealing with always changing data

Because visual regression compares image snapshots, and a significant portion of our data deals with timeseries data we often need to replace time based content with 'fixed' data. You can use the getDynamicText helper to provide fixed content for components/data that is dependent on the current time or varies too frequently to be included in a visual snapshot.

Jest Tests

Our Jest suite covers providing functional and unit testing for frontend components. We favour writing functional tests that interact with components and observe outcomes (navigation, API calls) over checking prop passing and state mutations. See the Frontend Handbook for more Jest testing tips.

# Run jest in interactive mode
yarn test

# Run a single test
yarn test tests/js/spec/views/issueList/overview.spec.js

API Fixtures

Because our Jest tests run without an API we have a variety of fixture builders available to help generate API response payloads. The TestStubs global includes all the fixture functions in tests/js/sentry-test/fixtures/.

You should also use MockApiClient.addMockResponse() to set responses for API calls your components will make. Failing to mock an endpoint will result in tests failing.