def mock_stats(mock_config):
'''
Mock Datadog authentication and stats management
'''
from static_analysis_bot import stats
# Configure Datadog with a dummy token
# and an ultra fast flushing cycle
stats.auth('test_token')
stats.api.stop()
stats.api.start(flush_interval=0.001)
assert not stats.api._disabled
assert stats.api._is_auto_flushing
class MemoryReporter(object):
'''
A reporting class that reports to memory for testing.
Used in datadog unit tests:
https://github.com/DataDog/datadogpy/blob/master/tests/unit/threadstats/test_threadstats.py
'''
def __init__(self, api):
self.metrics = []
self.events = []
self.api = api
def flush_metrics(self, metrics):
self.metrics += metrics
def flush_events(self, events):
self.events += events
def flush(self):
# Helper for unit tests to force flush
self.api.flush(time.time() + 20)
def get_metrics(self, metric_name):
return list(itertools.chain(*[
[
[t, point * m['interval']]
for t, point in m['points']
]
for m in self.metrics
if m['metric'] == metric_name
]))
# Gives reporter access to unit tests to access metrics
stats.api.reporter = MemoryReporter(stats.api)
yield stats.api.reporter
def mock_stats(mock_config):
'''
Mock Datadog authentication and stats management
'''
from static_analysis_bot import stats
# Configure Datadog with a dummy token
# and an ultra fast flushing cycle
stats.auth('test_token')
stats.api.stop()
stats.api.start(flush_interval=0.001)
assert not stats.api._disabled
assert stats.api._is_auto_flushing
class MemoryReporter(object):
'''
A reporting class that reports to memory for testing.
Used in datadog unit tests:
https://github.com/DataDog/datadogpy/blob/master/tests/unit/threadstats/test_threadstats.py
'''
def __init__(self, api):
self.metrics = []
self.events = []
self.api = api
def flush_metrics(self, metrics):
self.metrics += metrics
def flush_events(self, events):
self.events += events
def flush(self):
# Helper for unit tests to force flush
self.api.flush(time.time() + 20)
def get_metrics(self, metric_name):
return list(itertools.chain(*[
[
[t, point * m['interval']]
for t, point in m['points']
]
for m in self.metrics
if m['metric'] == metric_name
]))
# Gives reporter access to unit tests to access metrics
stats.api.reporter = MemoryReporter(stats.api)
yield stats.api.reporter
def main(source,
id,
cache_root,
mozreview_diffset,
mozreview_revision,
taskcluster_secret,
taskcluster_client_id,
taskcluster_access_token,
):
secrets = get_secrets(taskcluster_secret,
config.PROJECT_NAME,
required=(
'APP_CHANNEL',
'REPORTERS',
'ANALYZERS',
'PHABRICATOR',
),
existing={
'APP_CHANNEL': 'development',
'REPORTERS': [],
'ANALYZERS': ['clang-tidy', ],
'PUBLICATION': 'IN_PATCH',
},
taskcluster_client_id=taskcluster_client_id,
taskcluster_access_token=taskcluster_access_token,
)
init_logger(config.PROJECT_NAME,
PAPERTRAIL_HOST=secrets.get('PAPERTRAIL_HOST'),
PAPERTRAIL_PORT=secrets.get('PAPERTRAIL_PORT'),
SENTRY_DSN=secrets.get('SENTRY_DSN'),
MOZDEF=secrets.get('MOZDEF'),
)
# Setup settings before stats
settings.setup(secrets['APP_CHANNEL'], cache_root, secrets['PUBLICATION'])
# Setup statistics
datadog_api_key = secrets.get('DATADOG_API_KEY')
if datadog_api_key:
stats.auth(datadog_api_key)
# Load reporters
reporters = get_reporters(
secrets['REPORTERS'],
taskcluster_client_id,
taskcluster_access_token,
)
# Load index service
index_service = get_service(
'index',
taskcluster_client_id,
taskcluster_access_token,
)
# Load Phabricator API
phabricator_api = PhabricatorAPI(**secrets['PHABRICATOR'])
if 'phabricator' in reporters:
reporters['phabricator'].setup_api(phabricator_api)
# Load unique revision
if source == 'phabricator':
revision = PhabricatorRevision(id, phabricator_api)
elif source == 'mozreview':
revision = MozReviewRevision(id, mozreview_revision, mozreview_diffset)
else:
raise Exception('Unsupported analysis source: {}'.format(source))
w = Workflow(reporters, secrets['ANALYZERS'], index_service, phabricator_api)
try:
w.run(revision)
except Exception as e:
# Log errors to papertrail
logger.error(
'Static analysis failure',
revision=revision,
error=e,
)
# Index analysis state
w.index(revision, state='error')
# Then raise to mark task as erroneous
raise
def main(taskcluster_secret,
taskcluster_client_id,
taskcluster_access_token,
):
secrets = get_secrets(taskcluster_secret,
config.PROJECT_NAME,
required=(
'APP_CHANNEL',
'REPORTERS',
'PHABRICATOR',
'ALLOWED_PATHS',
),
existing={
'APP_CHANNEL': 'development',
'REPORTERS': [],
'PUBLICATION': 'IN_PATCH',
'ALLOWED_PATHS': ['*', ],
},
taskcluster_client_id=taskcluster_client_id,
taskcluster_access_token=taskcluster_access_token,
)
init_logger(config.PROJECT_NAME,
PAPERTRAIL_HOST=secrets.get('PAPERTRAIL_HOST'),
PAPERTRAIL_PORT=secrets.get('PAPERTRAIL_PORT'),
SENTRY_DSN=secrets.get('SENTRY_DSN'),
MOZDEF=secrets.get('MOZDEF'),
timestamp=True,
)
# Setup settings before stats
phabricator = secrets['PHABRICATOR']
settings.setup(
secrets['APP_CHANNEL'],
secrets['PUBLICATION'],
secrets['ALLOWED_PATHS'],
)
# Setup statistics
datadog_api_key = secrets.get('DATADOG_API_KEY')
if datadog_api_key:
stats.auth(datadog_api_key)
# Load reporters
reporters = get_reporters(
secrets['REPORTERS'],
taskcluster_client_id,
taskcluster_access_token,
)
# Load index service
index_service = get_service(
'index',
taskcluster_client_id,
taskcluster_access_token,
)
# Load queue service
queue_service = get_service(
'queue',
taskcluster_client_id,
taskcluster_access_token,
)
# Load Phabricator API
phabricator_reporting_enabled = 'phabricator' in reporters
phabricator_api = PhabricatorAPI(phabricator['api_key'], phabricator['url'])
if phabricator_reporting_enabled:
reporters['phabricator'].setup_api(phabricator_api)
# Load unique revision
revision = Revision(
phabricator_api,
try_task=queue_service.task(settings.try_task_id),
# Update build status only when phabricator reporting is enabled
update_build=phabricator_reporting_enabled,
)
# Run workflow according to source
w = Workflow(reporters, index_service, queue_service, phabricator_api)
try:
w.run(revision)
except Exception as e:
# Log errors to papertrail
logger.error(
'Static analysis failure',
revision=revision,
error=e,
)
# Index analysis state
extras = {}
if isinstance(e, AnalysisException):
extras['error_code'] = e.code
extras['error_message'] = str(e)
w.index(revision, state='error', **extras)
# Update Harbormaster status
revision.update_status(state=BuildState.Fail)
# Then raise to mark task as erroneous
raise
def main(
id,
work_dir,
taskcluster_secret,
taskcluster_client_id,
taskcluster_access_token,
):
secrets = get_secrets(
taskcluster_secret,
config.PROJECT_NAME,
required=(
'APP_CHANNEL',
'REPORTERS',
'ANALYZERS',
'PHABRICATOR',
'ALLOWED_PATHS',
'MAX_CLONE_RUNTIME',
),
existing={
'APP_CHANNEL': 'development',
'REPORTERS': [],
'ANALYZERS': [
'clang-tidy',
],
'PUBLICATION': 'IN_PATCH',
'ALLOWED_PATHS': [
'*',
],
'MAX_CLONE_RUNTIME': 15 * 60,
},
taskcluster_client_id=taskcluster_client_id,
taskcluster_access_token=taskcluster_access_token,
)
init_logger(
config.PROJECT_NAME,
PAPERTRAIL_HOST=secrets.get('PAPERTRAIL_HOST'),
PAPERTRAIL_PORT=secrets.get('PAPERTRAIL_PORT'),
SENTRY_DSN=secrets.get('SENTRY_DSN'),
MOZDEF=secrets.get('MOZDEF'),
timestamp=True,
)
# Setup settings before stats
phabricator = secrets['PHABRICATOR']
settings.setup(
secrets['APP_CHANNEL'],
work_dir,
secrets['PUBLICATION'],
secrets['ALLOWED_PATHS'],
secrets.get('COVERITY_CONFIG'),
secrets['MAX_CLONE_RUNTIME'],
phabricator.get('build_plan'),
)
# Setup statistics
datadog_api_key = secrets.get('DATADOG_API_KEY')
if datadog_api_key:
stats.auth(datadog_api_key)
# Load reporters
reporters = get_reporters(
secrets['REPORTERS'],
taskcluster_client_id,
taskcluster_access_token,
)
# Load index service
index_service = get_service(
'index',
taskcluster_client_id,
taskcluster_access_token,
)
# Load queue service
queue_service = get_service(
'queue',
taskcluster_client_id,
taskcluster_access_token,
)
# Load Phabricator API
phabricator_api = PhabricatorAPI(phabricator['api_key'],
phabricator['url'])
if 'phabricator' in reporters:
reporters['phabricator'].setup_api(phabricator_api)
# Load unique revision
if settings.source == SOURCE_PHABRICATOR:
revision = PhabricatorRevision(phabricator_api, diff_phid=id)
elif settings.source == SOURCE_TRY:
revision = PhabricatorRevision(phabricator_api,
try_task=queue_service.task(
settings.try_task_id))
else:
raise Exception('Unsupported source {}'.format(settings.source))
# Run workflow according to source
w = Workflow(reporters, secrets['ANALYZERS'], index_service, queue_service,
phabricator_api)
try:
w.run(revision)
except Exception as e:
# Log errors to papertrail
logger.error(
'Static analysis failure',
revision=revision,
error=e,
)
# Index analysis state
extras = {}
if isinstance(e, AnalysisException):
extras['error_code'] = e.code
extras['error_message'] = str(e)
w.index(revision, state='error', **extras)
# Update Harbormaster status
revision.update_status(state=BuildState.Fail)
# Then raise to mark task as erroneous
raise
