def __init__(self, *args, **kwargs):
"""
:API: public
"""
super(RunTracker, self).__init__(*args, **kwargs)
self._run_timestamp = time.time()
self._cmd_line = ' '.join(['pants'] + sys.argv[1:])
# Initialized in `initialize()`.
self.run_info_dir = None
self.run_info = None
self.cumulative_timings = None
self.self_timings = None
self.artifact_cache_stats = None
self.pantsd_stats = None
# Initialized in `start()`.
self.report = None
self._main_root_workunit = None
# A lock to ensure that adding to stats at the end of a workunit
# operates thread-safely.
self._stats_lock = threading.Lock()
# Log of success/failure/aborted for each workunit.
self.outcomes = {}
# Number of threads for foreground work.
self._num_foreground_workers = self.get_options().num_foreground_workers
# Number of threads for background work.
self._num_background_workers = self.get_options().num_background_workers
# self._threadlocal.current_workunit contains the current workunit for the calling thread.
# Note that multiple threads may share a name (e.g., all the threads in a pool).
self._threadlocal = threading.local()
# For background work. Created lazily if needed.
self._background_worker_pool = None
self._background_root_workunit = None
# Trigger subproc pool init while our memory image is still clean (see SubprocPool docstring).
SubprocPool.set_num_processes(self._num_foreground_workers)
SubprocPool.foreground()
self._aborted = False
# Data will be organized first by target and then scope.
# Eg:
# {
# 'target/address:name': {
# 'running_scope': {
# 'run_duration': 356.09
# },
# 'GLOBAL': {
# 'target_type': 'pants.test'
# }
# }
# }
self._target_to_data = {}
def __init__(self, *args, **kwargs):
"""
:API: public
"""
super(RunTracker, self).__init__(*args, **kwargs)
self._run_timestamp = time.time()
self._cmd_line = ' '.join(['pants'] + sys.argv[1:])
# Initialized in `initialize()`.
self.run_info_dir = None
self.run_info = None
self.cumulative_timings = None
self.self_timings = None
self.artifact_cache_stats = None
# Initialized in `start()`.
self.report = None
self._main_root_workunit = None
# A lock to ensure that adding to stats at the end of a workunit
# operates thread-safely.
self._stats_lock = threading.Lock()
# Log of success/failure/aborted for each workunit.
self.outcomes = {}
# Number of threads for foreground work.
self._num_foreground_workers = self.get_options().num_foreground_workers
# Number of threads for background work.
self._num_background_workers = self.get_options().num_background_workers
# self._threadlocal.current_workunit contains the current workunit for the calling thread.
# Note that multiple threads may share a name (e.g., all the threads in a pool).
self._threadlocal = threading.local()
# For background work. Created lazily if needed.
self._background_worker_pool = None
self._background_root_workunit = None
# Trigger subproc pool init while our memory image is still clean (see SubprocPool docstring).
SubprocPool.set_num_processes(self._num_foreground_workers)
SubprocPool.foreground()
self._aborted = False
# Data will be organized first by target and then scope.
# Eg:
# {
# 'target/address:name': {
# 'running_scope': {
# 'run_duration': 356.09
# },
# 'GLOBAL': {
# 'target_type': 'pants.test'
# }
# }
# }
self._target_to_data = {}
def __init__(self, *args, **kwargs):
"""
:API: public
"""
super().__init__(*args, **kwargs)
self._run_timestamp = time.time()
self._cmd_line = " ".join(["pants"] + sys.argv[1:])
self._v2_goal_rule_names: Tuple[str, ...] = tuple()
self.run_uuid = uuid.uuid4().hex
# Select a globally unique ID for the run, that sorts by time.
millis = int((self._run_timestamp * 1000) % 1000)
# run_uuid is used as a part of run_id and also as a trace_id for Zipkin tracing
str_time = time.strftime("%Y_%m_%d_%H_%M_%S",
time.localtime(self._run_timestamp))
self.run_id = f"pants_run_{str_time}_{millis}_{self.run_uuid}"
# Initialized in `initialize()`.
self.run_info_dir = None
self.run_info = None
self.cumulative_timings = None
self.self_timings = None
# Initialized in `start()`.
self.report = None
self._main_root_workunit = None
self._all_options = None
# A lock to ensure that adding to stats at the end of a workunit
# operates thread-safely.
self._stats_lock = threading.Lock()
# Log of success/failure/aborted for each workunit.
self.outcomes = {}
# Number of threads for foreground work.
self._num_foreground_workers = self.options.num_foreground_workers
# Number of threads for background work.
self._num_background_workers = self.options.num_background_workers
# self._threadlocal.current_workunit contains the current workunit for the calling thread.
# Note that multiple threads may share a name (e.g., all the threads in a pool).
self._threadlocal = threading.local()
# For background work. Created lazily if needed.
self._background_root_workunit = None
# Trigger subproc pool init while our memory image is still clean (see SubprocPool docstring).
SubprocPool.set_num_processes(self._num_foreground_workers)
SubprocPool.foreground()
self._aborted = False
self._end_memoized_result: Optional[ExitCode] = None
self.native = Native()
self.run_logs_file: Optional[Path] = None
def __init__(self, *args, **kwargs):
super(RunTracker, self).__init__(*args, **kwargs)
run_timestamp = time.time()
cmd_line = ' '.join(['pants'] + sys.argv[1:])
# run_id is safe for use in paths.
millis = int((run_timestamp * 1000) % 1000)
run_id = 'pants_run_{}_{}_{}'.format(
time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime(run_timestamp)), millis,
uuid.uuid4().hex)
info_dir = os.path.join(self.get_options().pants_workdir, self.options_scope)
self.run_info_dir = os.path.join(info_dir, run_id)
self.run_info = RunInfo(os.path.join(self.run_info_dir, 'info'))
self.run_info.add_basic_info(run_id, run_timestamp)
self.run_info.add_info('cmd_line', cmd_line)
# Create a 'latest' symlink, after we add_infos, so we're guaranteed that the file exists.
link_to_latest = os.path.join(os.path.dirname(self.run_info_dir), 'latest')
relative_symlink(self.run_info_dir, link_to_latest)
# Time spent in a workunit, including its children.
self.cumulative_timings = AggregatedTimings(os.path.join(self.run_info_dir,
'cumulative_timings'))
# Time spent in a workunit, not including its children.
self.self_timings = AggregatedTimings(os.path.join(self.run_info_dir, 'self_timings'))
# Hit/miss stats for the artifact cache.
self.artifact_cache_stats = \
ArtifactCacheStats(os.path.join(self.run_info_dir, 'artifact_cache_stats'))
# Number of threads for foreground work.
self._num_foreground_workers = self.get_options().num_foreground_workers
# Number of threads for background work.
self._num_background_workers = self.get_options().num_background_workers
# We report to this Report.
self.report = None
# self._threadlocal.current_workunit contains the current workunit for the calling thread.
# Note that multiple threads may share a name (e.g., all the threads in a pool).
self._threadlocal = threading.local()
# For main thread work. Created on start().
self._main_root_workunit = None
# For background work. Created lazily if needed.
self._background_worker_pool = None
self._background_root_workunit = None
# Trigger subproc pool init while our memory image is still clean (see SubprocPool docstring).
SubprocPool.set_num_processes(self._num_foreground_workers)
SubprocPool.foreground()
self._aborted = False
def __init__(self, *args, **kwargs):
super(RunTracker, self).__init__(*args, **kwargs)
run_timestamp = time.time()
cmd_line = ' '.join(['pants'] + sys.argv[1:])
# run_id is safe for use in paths.
millis = int((run_timestamp * 1000) % 1000)
run_id = 'pants_run_{}_{}_{}'.format(
time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime(run_timestamp)), millis,
uuid.uuid4().hex)
info_dir = os.path.join(self.get_options().pants_workdir, self.options_scope)
self.run_info_dir = os.path.join(info_dir, run_id)
self.run_info = RunInfo(os.path.join(self.run_info_dir, 'info'))
self.run_info.add_basic_info(run_id, run_timestamp)
self.run_info.add_info('cmd_line', cmd_line)
# Create a 'latest' symlink, after we add_infos, so we're guaranteed that the file exists.
link_to_latest = os.path.join(os.path.dirname(self.run_info_dir), 'latest')
relative_symlink(self.run_info_dir, link_to_latest)
# Time spent in a workunit, including its children.
self.cumulative_timings = AggregatedTimings(os.path.join(self.run_info_dir,
'cumulative_timings'))
# Time spent in a workunit, not including its children.
self.self_timings = AggregatedTimings(os.path.join(self.run_info_dir, 'self_timings'))
# Hit/miss stats for the artifact cache.
self.artifact_cache_stats = \
ArtifactCacheStats(os.path.join(self.run_info_dir, 'artifact_cache_stats'))
# Number of threads for foreground work.
self._num_foreground_workers = self.get_options().num_foreground_workers
# Number of threads for background work.
self._num_background_workers = self.get_options().num_background_workers
# We report to this Report.
self.report = None
# self._threadlocal.current_workunit contains the current workunit for the calling thread.
# Note that multiple threads may share a name (e.g., all the threads in a pool).
self._threadlocal = threading.local()
# For main thread work. Created on start().
self._main_root_workunit = None
# For background work. Created lazily if needed.
self._background_worker_pool = None
self._background_root_workunit = None
# Trigger subproc pool init while our memory image is still clean (see SubprocPool docstring).
SubprocPool.set_num_processes(self._num_foreground_workers)
SubprocPool.foreground()
self._aborted = False
def __init__(self, *args, **kwargs):
"""
:API: public
"""
super().__init__(*args, **kwargs)
self._run_timestamp = time.time()
self._cmd_line = ' '.join(['pants'] + sys.argv[1:])
self._sorted_goal_infos = tuple()
self._v2_goal_rule_names = tuple()
self.run_uuid = uuid.uuid4().hex
# Select a globally unique ID for the run, that sorts by time.
millis = int((self._run_timestamp * 1000) % 1000)
# run_uuid is used as a part of run_id and also as a trace_id for Zipkin tracing
str_time = time.strftime('%Y_%m_%d_%H_%M_%S',
time.localtime(self._run_timestamp))
self.run_id = f'pants_run_{str_time}_{millis}_{self.run_uuid}'
# Initialized in `initialize()`.
self.run_info_dir = None
self.run_info = None
self.cumulative_timings = None
self.self_timings = None
self.artifact_cache_stats = None
self.pantsd_stats = None
# Initialized in `start()`.
self.report = None
self.json_reporter = None
self._main_root_workunit = None
self._all_options = None
# A lock to ensure that adding to stats at the end of a workunit
# operates thread-safely.
self._stats_lock = threading.Lock()
# Log of success/failure/aborted for each workunit.
self.outcomes = {}
# Number of threads for foreground work.
self._num_foreground_workers = self.get_options(
).num_foreground_workers
# Number of threads for background work.
self._num_background_workers = self.get_options(
).num_background_workers
# self._threadlocal.current_workunit contains the current workunit for the calling thread.
# Note that multiple threads may share a name (e.g., all the threads in a pool).
self._threadlocal = threading.local()
# A logger facade that logs into this RunTracker.
self._logger = RunTrackerLogger(self)
# For background work. Created lazily if needed.
self._background_worker_pool = None
self._background_root_workunit = None
# Trigger subproc pool init while our memory image is still clean (see SubprocPool docstring).
SubprocPool.set_num_processes(self._num_foreground_workers)
SubprocPool.foreground()
self._aborted = False
# Data will be organized first by target and then scope.
# Eg:
# {
# 'target/address:name': {
# 'running_scope': {
# 'run_duration': 356.09
# },
# 'GLOBAL': {
# 'target_type': 'pants.test'
# }
# }
# }
self._target_to_data = {}
self._end_memoized_result = None
