def __init__(self, pywren_config):
self.config = pywren_config
self.rabbitmq_monitor = self.config['pywren'].get(
'rabbitmq_monitor', False)
self.store_status = strtobool(os.environ.get('STORE_STATUS', 'True'))
storage_config = extract_storage_config(self.config)
self.internal_storage = InternalStorage(storage_config)
self.response = {'exception': False}
def delete_runtime(name, config=None):
config = default_config(config)
storage_config = extract_storage_config(config)
internal_storage = InternalStorage(storage_config)
compute_config = extract_compute_config(config)
compute_handler = Compute(compute_config)
runtimes = compute_handler.list_runtimes(name)
for runtime in runtimes:
compute_handler.delete_runtime(runtime[0], runtime[1])
runtime_key = compute_handler.get_runtime_key(runtime[0], runtime[1])
internal_storage.delete_runtime_meta(runtime_key)
def clean_all(config=None):
logger.info('Cleaning all PyWren information')
config = default_config(config)
storage_config = extract_storage_config(config)
internal_storage = InternalStorage(storage_config)
compute_config = extract_compute_config(config)
compute_handler = Compute(compute_config)
# Clean object storage temp dirs
sh = internal_storage.storage_handler
runtimes = sh.list_keys(storage_config['bucket'], RUNTIMES_PREFIX)
if runtimes:
sh.delete_objects(storage_config['bucket'], runtimes)
compute_handler.delete_all_runtimes()
clean_os_bucket(storage_config['bucket'],
JOBS_PREFIX,
internal_storage,
sleep=1)
# Clean local runtime_meta cache
if os.path.exists(CACHE_DIR):
shutil.rmtree(CACHE_DIR)
# Clean localhost temp dirs
localhost_jobs_path = os.path.join(TEMP, JOBS_PREFIX)
if os.path.exists(localhost_jobs_path):
shutil.rmtree(localhost_jobs_path)
localhost_runtimes_path = os.path.join(TEMP, RUNTIMES_PREFIX)
if os.path.exists(localhost_runtimes_path):
shutil.rmtree(localhost_runtimes_path)
def create_runtime(name, memory=None, config=None):
config = default_config(config)
storage_config = extract_storage_config(config)
internal_storage = InternalStorage(storage_config)
compute_config = extract_compute_config(config)
compute_handler = Compute(compute_config)
memory = config['pywren']['runtime_memory'] if not memory else memory
timeout = config['pywren']['runtime_timeout']
logger.info('Creating runtime: {}, memory: {}'.format(name, memory))
runtime_key = compute_handler.get_runtime_key(name, memory)
runtime_meta = compute_handler.create_runtime(name, memory, timeout=timeout)
try:
internal_storage.put_runtime_meta(runtime_key, runtime_meta)
except Exception:
raise("Unable to upload 'preinstalled-modules' file into {}".format(internal_storage.backend))
def update_runtime(name, config=None):
config = default_config(config)
storage_config = extract_storage_config(config)
internal_storage = InternalStorage(storage_config)
compute_config = extract_compute_config(config)
compute_handler = Compute(compute_config)
timeout = config['pywren']['runtime_timeout']
logger.info('Updating runtime: {}'.format(name))
runtimes = compute_handler.list_runtimes(name)
for runtime in runtimes:
runtime_key = compute_handler.get_runtime_key(runtime[0], runtime[1])
runtime_meta = compute_handler.create_runtime(runtime[0], runtime[1], timeout)
try:
internal_storage.put_runtime_meta(runtime_key, runtime_meta)
except Exception:
raise("Unable to upload 'preinstalled-modules' file into {}".format(internal_storage.backend))
def clean_runtimes(config=None):
logger.info('Cleaning all runtimes and cache information')
config = default_config(config)
storage_config = extract_storage_config(config)
internal_storage = InternalStorage(storage_config)
compute_config = extract_compute_config(config)
compute_handler = Compute(compute_config)
# Clean local runtime_meta cache
if os.path.exists(CACHE_DIR):
shutil.rmtree(CACHE_DIR)
sh = internal_storage.storage_handler
runtimes = sh.list_keys(storage_config['bucket'], 'runtime')
if runtimes:
sh.delete_objects(storage_config['bucket'], runtimes)
compute_handler.delete_all_runtimes()
def __init__(self, config = None, runtime=None, runtime_memory=None, compute_backend=None,
compute_backend_region=None, storage_backend=None, storage_backend_region=None,
workers=None, rabbitmq_monitor=None, remote_invoker=None, log_level=None):
self.start_time = time.time()
self._state = self.State.New
self.is_pywren_function = is_pywren_function()
# Log level Configuration
self.log_level = log_level
if not self.log_level:
if(logger.getEffectiveLevel() != logging.WARNING):
self.log_level = logging.getLevelName(logger.getEffectiveLevel())
if self.log_level:
os.environ["PYWREN_LOGLEVEL"] = self.log_level
if not self.is_pywren_function:
default_logging_config(self.log_level)
# Overwrite pywren config parameters
pw_config_ow = {}
if runtime is not None:
pw_config_ow['runtime'] = runtime
if runtime_memory is not None:
pw_config_ow['runtime_memory'] = runtime_memory
if compute_backend is not None:
pw_config_ow['compute_backend'] = compute_backend
if compute_backend_region is not None:
pw_config_ow['compute_backend_region'] = compute_backend_region
if storage_backend is not None:
pw_config_ow['storage_backend'] = storage_backend
if storage_backend_region is not None:
pw_config_ow['storage_backend_region'] = storage_backend_region
if workers is not None:
pw_config_ow['workers'] = workers
if rabbitmq_monitor is not None:
pw_config_ow['rabbitmq_monitor'] = rabbitmq_monitor
if remote_invoker is not None:
pw_config_ow['remote_invoker'] = remote_invoker
self.config = default_config(copy.deepcopy(config), pw_config_ow)
self.executor_id = create_executor_id()
logger.debug('FunctionExecutor created with ID: {}'.format(self.executor_id))
self.data_cleaner = self.config['pywren'].get('data_cleaner', True)
self.rabbitmq_monitor = self.config['pywren'].get('rabbitmq_monitor', False)
if self.rabbitmq_monitor:
if 'rabbitmq' in self.config and 'amqp_url' in self.config['rabbitmq']:
self.rabbit_amqp_url = self.config['rabbitmq'].get('amqp_url')
else:
raise Exception("You cannot use rabbitmq_mnonitor since 'amqp_url'"
" is not present in configuration")
storage_config = extract_storage_config(self.config)
self.internal_storage = InternalStorage(storage_config)
self.invoker = FunctionInvoker(self.config, self.executor_id, self.internal_storage)
self.futures = []
self.total_jobs = 0
self.cleaned_jobs = set()
class FunctionExecutor:
class State:
New = 'New'
Running = 'Running'
Ready = 'Ready'
Done = 'Done'
Error = 'Error'
def __init__(self, config = None, runtime=None, runtime_memory=None, compute_backend=None,
compute_backend_region=None, storage_backend=None, storage_backend_region=None,
workers=None, rabbitmq_monitor=None, remote_invoker=None, log_level=None):
self.start_time = time.time()
self._state = self.State.New
self.is_pywren_function = is_pywren_function()
# Log level Configuration
self.log_level = log_level
if not self.log_level:
if(logger.getEffectiveLevel() != logging.WARNING):
self.log_level = logging.getLevelName(logger.getEffectiveLevel())
if self.log_level:
os.environ["PYWREN_LOGLEVEL"] = self.log_level
if not self.is_pywren_function:
default_logging_config(self.log_level)
# Overwrite pywren config parameters
pw_config_ow = {}
if runtime is not None:
pw_config_ow['runtime'] = runtime
if runtime_memory is not None:
pw_config_ow['runtime_memory'] = runtime_memory
if compute_backend is not None:
pw_config_ow['compute_backend'] = compute_backend
if compute_backend_region is not None:
pw_config_ow['compute_backend_region'] = compute_backend_region
if storage_backend is not None:
pw_config_ow['storage_backend'] = storage_backend
if storage_backend_region is not None:
pw_config_ow['storage_backend_region'] = storage_backend_region
if workers is not None:
pw_config_ow['workers'] = workers
if rabbitmq_monitor is not None:
pw_config_ow['rabbitmq_monitor'] = rabbitmq_monitor
if remote_invoker is not None:
pw_config_ow['remote_invoker'] = remote_invoker
self.config = default_config(copy.deepcopy(config), pw_config_ow)
self.executor_id = create_executor_id()
logger.debug('FunctionExecutor created with ID: {}'.format(self.executor_id))
self.data_cleaner = self.config['pywren'].get('data_cleaner', True)
self.rabbitmq_monitor = self.config['pywren'].get('rabbitmq_monitor', False)
if self.rabbitmq_monitor:
if 'rabbitmq' in self.config and 'amqp_url' in self.config['rabbitmq']:
self.rabbit_amqp_url = self.config['rabbitmq'].get('amqp_url')
else:
raise Exception("You cannot use rabbitmq_mnonitor since 'amqp_url'"
" is not present in configuration")
storage_config = extract_storage_config(self.config)
self.internal_storage = InternalStorage(storage_config)
self.invoker = FunctionInvoker(self.config, self.executor_id, self.internal_storage)
self.futures = []
self.total_jobs = 0
self.cleaned_jobs = set()
def _create_job_id(self, call_type):
job_id = str(self.total_jobs).zfill(3)
self.total_jobs += 1
return '{}{}'.format(call_type, job_id)
def lidar_call_async(self, func, data, extra_env=None, runtime_memory=None,
timeout=EXECUTION_TIMEOUT, include_modules=[], exclude_modules=[]):
"""
For running one function execution asynchronously
:param func: the function to map over the data
:param data: input data
:param extra_data: Additional data to pass to action. Default None.
:param extra_env: Additional environment variables for action environment. Default None.
:param runtime_memory: Memory to use to run the function. Default None (loaded from config).
:param timeout: Time that the functions have to complete their execution before raising a timeout.
:param include_modules: Explicitly pickle these dependencies.
:param exclude_modules: Explicitly keep these modules from pickled dependencies.
:return: future object.
"""
job_id = self._create_job_id('A')
runtime_meta = self.invoker.select_runtime(job_id, runtime_memory)
job = create_map_job(self.config, self.internal_storage,
self.executor_id, job_id,
map_function=func,
iterdata=[data],
runtime_meta=runtime_meta,
runtime_memory=runtime_memory,
extra_env=extra_env,
include_modules=include_modules,
exclude_modules=exclude_modules,
execution_timeout=timeout)
futures = self.invoker.run(job)
self.futures.extend(futures)
self._state = FunctionExecutor.State.Running
return futures[0]
def lidar_map(self, map_function, map_iterdata, extra_params=None, extra_env=None, runtime_memory=None,
partition_type = None, rows=1, cols=1, timeout=EXECUTION_TIMEOUT, invoke_pool_threads=500,
include_modules=[], exclude_modules=[]):
"""
:param map_function: the function to map over the data
:param map_iterdata: An iterable of input data
:param extra_params: Additional parameters to pass to the function activation. Default None.
:param extra_env: Additional environment variables for action environment. Default None.
:param runtime_memory: Memory to use to run the function. Default None (loaded from config).
:param chunk_size: the size of the data chunks to split each object. 'None' for processing
the whole file in one function activation.
:param chunk_n: Number of chunks to split each object. 'None' for processing the whole
file in one function activation.
:param remote_invocation: Enable or disable remote_invocation mechanism. Default 'False'
:param timeout: Time that the functions have to complete their execution before raising a timeout.
:param invoke_pool_threads: Number of threads to use to invoke.
:param include_modules: Explicitly pickle these dependencies.
:param exclude_modules: Explicitly keep these modules from pickled dependencies.
:return: A list with size `len(iterdata)` of futures.
"""
job_id = self._create_job_id('M')
runtime_meta = self.invoker.select_runtime(job_id, runtime_memory)
job = create_map_job(self.config, self.internal_storage,
self.executor_id, job_id,
map_function=map_function,
iterdata=map_iterdata,
runtime_meta=runtime_meta,
runtime_memory=runtime_memory,
partition_type = partition_type,
extra_params=extra_params,
extra_env=extra_env,
obj_rows=rows,
obj_cols=cols,
invoke_pool_threads=invoke_pool_threads,
include_modules=include_modules,
exclude_modules=exclude_modules,
execution_timeout=timeout)
futures = self.invoker.run(job)
self.futures.extend(futures)
self._state = FunctionExecutor.State.Running
if len(futures) == 1:
return futures[0]
return futures
def lidar_map_reduce(self, map_function, map_iterdata, reduce_function, extra_params=None, extra_env=None,
map_runtime_memory=None, reduce_runtime_memory=None, chunk_size=None, chunk_n=None,
timeout=EXECUTION_TIMEOUT, invoke_pool_threads=500, reducer_one_per_object=False,
reducer_wait_local=False, include_modules=[], exclude_modules=[]):
"""
Map the map_function over the data and apply the reduce_function across all futures.
This method is executed all within CF.
:param map_function: the function to map over the data
:param map_iterdata: the function to reduce over the futures
:param reduce_function: the function to reduce over the futures
:param extra_env: Additional environment variables for action environment. Default None.
:param extra_params: Additional parameters to pass to function activation. Default None.
:param map_runtime_memory: Memory to use to run the map function. Default None (loaded from config).
:param reduce_runtime_memory: Memory to use to run the reduce function. Default None (loaded from config).
:param chunk_size: the size of the data chunks to split each object. 'None' for processing
the whole file in one function activation.
:param chunk_n: Number of chunks to split each object. 'None' for processing the whole
file in one function activation.
:param remote_invocation: Enable or disable remote_invocation mechanism. Default 'False'
:param timeout: Time that the functions have to complete their execution before raising a timeout.
:param reducer_one_per_object: Set one reducer per object after running the partitioner
:param reducer_wait_local: Wait for results locally
:param invoke_pool_threads: Number of threads to use to invoke.
:param include_modules: Explicitly pickle these dependencies.
:param exclude_modules: Explicitly keep these modules from pickled dependencies.
:return: A list with size `len(map_iterdata)` of futures.
"""
map_job_id = self._create_job_id('M')
runtime_meta = self.invoker.select_runtime(map_job_id, map_runtime_memory)
map_job = create_map_job(self.config, self.internal_storage,
self.executor_id, map_job_id,
map_function=map_function,
iterdata=map_iterdata,
runtime_meta=runtime_meta,
runtime_memory=map_runtime_memory,
extra_params=extra_params,
extra_env=extra_env,
obj_chunk_size=chunk_size,
obj_chunk_number=chunk_n,
invoke_pool_threads=invoke_pool_threads,
include_modules=include_modules,
exclude_modules=exclude_modules,
execution_timeout=timeout)
map_futures = self.invoker.run(map_job)
self.futures.extend(map_futures)
if reducer_wait_local:
self.wait(fs=map_futures)
reduce_job_id = map_job_id.replace('M', 'R')
runtime_meta = self.invoker.select_runtime(reduce_job_id, reduce_runtime_memory)
reduce_job = create_reduce_job(self.config, self.internal_storage,
self.executor_id, reduce_job_id,
reduce_function, map_job, map_futures,
runtime_meta=runtime_meta,
reducer_one_per_object=reducer_one_per_object,
runtime_memory=reduce_runtime_memory,
extra_env=extra_env,
include_modules=include_modules,
exclude_modules=exclude_modules)
reduce_futures = self.invoker.run(reduce_job)
self.futures.extend(reduce_futures)
for f in map_futures:
f.produce_output = False
self._state = FunctionExecutor.State.Running
return map_futures + reduce_futures
def wait(self, fs=None, throw_except=True, return_when=ALL_COMPLETED, download_results=False,
timeout=None, THREADPOOL_SIZE=128, WAIT_DUR_SEC=1):
"""
Wait for the Future instances (possibly created by different Executor instances)
given by fs to complete. Returns a named 2-tuple of sets. The first set, named done,
contains the futures that completed (finished or cancelled futures) before the wait
completed. The second set, named not_done, contains the futures that did not complete
(pending or running futures). timeout can be used to control the maximum number of
seconds to wait before returning.
:param fs: Futures list. Default None
:param throw_except: Re-raise exception if call raised. Default True.
:param return_when: One of `ALL_COMPLETED`, `ANY_COMPLETED`, `ALWAYS`
:param download_results: Download results. Default false (Only get statuses)
:param timeout: Timeout of waiting for results.
:param THREADPOOL_SIZE: Number of threads to use. Default 64
:param WAIT_DUR_SEC: Time interval between each check.
:return: `(fs_done, fs_notdone)`
where `fs_done` is a list of futures that have completed
and `fs_notdone` is a list of futures that have not completed.
:rtype: 2-tuple of list
"""
futures = self.futures if not fs else fs
if type(futures) != list:
futures = [futures]
if not futures:
raise Exception('You must run the call_async(), map() or map_reduce(), or provide'
' a list of futures before calling the wait()/get_result() method')
if download_results:
msg = 'ExecutorID {} - Getting results...'.format(self.executor_id)
else:
msg = 'ExecutorID {} - Waiting for functions to complete...'.format(self.executor_id)
logger.info(msg)
if not self.log_level and self._state == FunctionExecutor.State.Running:
print(msg)
if is_unix_system() and timeout is not None:
signal.signal(signal.SIGALRM, timeout_handler)
signal.alarm(timeout)
pbar = None
if not self.is_pywren_function and self._state == FunctionExecutor.State.Running \
and not self.log_level:
from tqdm.auto import tqdm
if download_results:
total_to_check = len([f for f in futures if not f.done])
else:
total_to_check = len([f for f in futures if not f.ready and not (f.ready or f.done)])
if is_notebook():
pbar = tqdm(bar_format='{n}/|/ {n_fmt}/{total_fmt}', total=total_to_check) # ncols=800
else:
print()
pbar = tqdm(bar_format=' {l_bar}{bar}| {n_fmt}/{total_fmt} ', total=total_to_check, disable=False)
try:
if self.rabbitmq_monitor:
logger.info('Using RabbitMQ to monitor function activations')
wait_rabbitmq(futures, self.internal_storage, rabbit_amqp_url=self.rabbit_amqp_url,
download_results=download_results, throw_except=throw_except,
pbar=pbar, return_when=return_when, THREADPOOL_SIZE=THREADPOOL_SIZE)
else:
wait_storage(futures, self.internal_storage, download_results=download_results,
throw_except=throw_except, return_when=return_when, pbar=pbar,
THREADPOOL_SIZE=THREADPOOL_SIZE, WAIT_DUR_SEC=WAIT_DUR_SEC)
except FunctionException as e:
if is_unix_system():
signal.alarm(0)
if pbar:
pbar.close()
print()
msg = None
logger.info(e.msg)
if not self.log_level:
print(e.msg)
if e.exc_msg:
logger.info('Exception: ' + e.exc_msg)
if not self.log_level:
print('--> Exception: ' + e.exc_msg)
else:
print()
traceback.print_exception(*e.exception)
self._state = FunctionExecutor.State.Error
except TimeoutError:
if download_results:
not_dones_call_ids = [(f.job_id, f.call_id) for f in futures if not f.done]
else:
not_dones_call_ids = [(f.job_id, f.call_id) for f in futures if not f.ready and not f.done]
msg = ('ExecutorID {} - Raised timeout of {} seconds waiting for results - Total Activations not done: {}'
.format(self.executor_id, timeout, len(not_dones_call_ids)))
self._state = FunctionExecutor.State.Error
except KeyboardInterrupt:
if download_results:
not_dones_call_ids = [(f.job_id, f.call_id) for f in futures if not f.done]
else:
not_dones_call_ids = [(f.job_id, f.call_id) for f in futures if not f.ready and not f.done]
msg = ('ExecutorID {} - Cancelled - Total Activations not done: {}'
.format(self.executor_id, len(not_dones_call_ids)))
self._state = FunctionExecutor.State.Error
except Exception as e:
self.invoker.stop()
if pbar:
pbar.close()
print()
if not self.is_pywren_function:
self.clean()
raise e
finally:
self.invoker.stop()
if is_unix_system():
signal.alarm(0)
if pbar:
pbar.close()
if not is_notebook():
print()
if self._state == FunctionExecutor.State.Error and msg:
logger.debug(msg)
if not self.log_level:
print(msg)
if self.data_cleaner and not self.is_pywren_function:
self.clean()
if not fs and self._state == FunctionExecutor.State.Error and is_notebook():
del self.futures[len(self.futures)-len(futures):]
if download_results:
fs_done = [f for f in futures if f.done]
fs_notdone = [f for f in futures if not f.done]
self._state = FunctionExecutor.State.Done
else:
fs_done = [f for f in futures if f.ready or f.done]
fs_notdone = [f for f in futures if not f.ready and not f.done]
self._state = FunctionExecutor.State.Ready
return fs_done, fs_notdone
def get_result(self, fs=None, throw_except=True, timeout=None, THREADPOOL_SIZE=128, WAIT_DUR_SEC=1):
"""
For getting the results from all function activations
:param fs: Futures list. Default None
:param throw_except: Reraise exception if call raised. Default True.
:param verbose: Shows some information prints. Default False
:param timeout: Timeout for waiting for results.
:param THREADPOOL_SIZE: Number of threads to use. Default 128
:param WAIT_DUR_SEC: Time interval between each check.
:return: The result of the future/s
"""
fs_done, unused_fs_notdone = self.wait(fs=fs, throw_except=throw_except,
timeout=timeout, download_results=True,
THREADPOOL_SIZE=THREADPOOL_SIZE,
WAIT_DUR_SEC=WAIT_DUR_SEC)
result = []
for f in fs_done:
if fs and not f.futures and f.produce_output:
# Process futures provided by the user
result.append(f.result(throw_except=throw_except, internal_storage=self.internal_storage))
elif not fs and not f.futures and f.produce_output and not f.read:
# Process internally stored futures
result.append(f.result(throw_except=throw_except, internal_storage=self.internal_storage))
f.read = True
logger.debug("ExecutorID {} Finished getting results".format(self.executor_id))
if result and len(result) == 1:
return result[0]
return result
def clean(self, fs=None, local_execution=True):
"""
Deletes all the files from COS. These files include the function,
the data serialization and the function invocation results.
"""
futures = self.futures if not fs else fs
if type(futures) != list:
futures = [futures]
if not futures:
return
if not fs:
present_jobs = {(f.executor_id, f.job_id) for f in futures
if (f.done or not f.produce_output)
and f.executor_id.count('/') == 1}
else:
present_jobs = {(f.executor_id, f.job_id) for f in futures
if f.executor_id.count('/') == 1}
jobs_to_clean = present_jobs - self.cleaned_jobs
if jobs_to_clean:
msg = "ExecutorID {} - Cleaning temporary data".format(self.executor_id)
logger.info(msg)
if not self.log_level:
print(msg)
for executor_id, job_id in jobs_to_clean:
storage_bucket = self.config['pywren']['storage_bucket']
storage_prerix = '/'.join([JOBS_PREFIX, executor_id, job_id])
if local_execution:
# 1st case: Not background. The main code waits until the cleaner finishes its execution.
# It is not ideal for performance tests, since it can take long time to complete.
# clean_os_bucket(storage_bucket, storage_prerix, self.internal_storage)
# 2nd case: Execute in Background as a subprocess. The main program does not wait for its completion.
storage_config = json.dumps(self.internal_storage.get_storage_config())
storage_config = storage_config.replace('"', '\\"')
cmdstr = ('{} -c "from pywren_ibm_cloud.storage.utils import clean_bucket; \
clean_bucket(\'{}\', \'{}\', \'{}\')"'.format(sys.executable,
storage_bucket,
storage_prerix,
storage_config))
os.popen(cmdstr)
else:
extra_env = {'STORE_STATUS': False,
'STORE_RESULT': False}
old_stdout = sys.stdout
sys.stdout = open(os.devnull, 'w')
self.call_async(clean_os_bucket, [storage_bucket, storage_prerix], extra_env=extra_env)
sys.stdout = old_stdout
self.cleaned_jobs.update(jobs_to_clean)
class JobRunner:
def __init__(self, jr_config, jobrunner_conn):
start_time = time.time()
self.jr_config = jr_config
self.jobrunner_conn = jobrunner_conn
log_level = self.jr_config['log_level']
cloud_logging_config(log_level)
self.pywren_config = self.jr_config['pywren_config']
self.storage_config = extract_storage_config(self.pywren_config)
self.call_id = self.jr_config['call_id']
self.job_id = self.jr_config['job_id']
self.executor_id = self.jr_config['executor_id']
self.func_key = self.jr_config['func_key']
self.data_key = self.jr_config['data_key']
self.data_byte_range = self.jr_config['data_byte_range']
self.output_key = self.jr_config['output_key']
self.stats = stats(self.jr_config['stats_filename'])
self.stats.write('jobrunner_start', start_time)
self.show_memory = strtobool(
os.environ.get('SHOW_MEMORY_USAGE', 'False'))
def _get_function_and_modules(self):
"""
Gets and unpickles function and modules from storage
"""
logger.debug("Getting function and modules")
func_download_time_t1 = time.time()
func_obj = self.internal_storage.get_func(self.func_key)
loaded_func_all = pickle.loads(func_obj)
func_download_time_t2 = time.time()
self.stats.write(
'func_download_time',
round(func_download_time_t2 - func_download_time_t1, 8))
logger.debug("Finished getting Function and modules")
return loaded_func_all
def _save_modules(self, module_data):
"""
Save modules, before we unpickle actual function
"""
if module_data:
logger.debug("Writing Function dependencies to local disk")
module_path = os.path.join(PYTHON_MODULE_PATH, self.executor_id,
self.job_id, self.call_id)
# shutil.rmtree(PYTHON_MODULE_PATH, True) # delete old modules
os.makedirs(module_path, exist_ok=True)
sys.path.append(module_path)
for m_filename, m_data in module_data.items():
m_path = os.path.dirname(m_filename)
if len(m_path) > 0 and m_path[0] == "/":
m_path = m_path[1:]
to_make = os.path.join(module_path, m_path)
try:
os.makedirs(to_make)
except OSError as e:
if e.errno == 17:
pass
else:
raise e
full_filename = os.path.join(to_make,
os.path.basename(m_filename))
with open(full_filename, 'wb') as fid:
fid.write(b64str_to_bytes(m_data))
#logger.info("Finished writing {} module files".format(len(module_data)))
#logger.debug(subprocess.check_output("find {}".format(module_path), shell=True))
#logger.debug(subprocess.check_output("find {}".format(os.getcwd()), shell=True))
logger.debug("Finished writing Function dependencies")
def _unpickle_function(self, pickled_func):
"""
Unpickle function; it will expect modules to be there
"""
logger.debug("Unpickle Function")
loaded_func = pickle.loads(pickled_func)
logger.debug("Finished Function unpickle")
return loaded_func
def _load_data(self):
extra_get_args = {}
if self.data_byte_range is not None:
range_str = 'bytes={}-{}'.format(*self.data_byte_range)
extra_get_args['Range'] = range_str
logger.debug("Getting function data")
data_download_time_t1 = time.time()
data_obj = self.internal_storage.get_data(
self.data_key, extra_get_args=extra_get_args)
logger.debug("Finished getting Function data")
logger.debug("Unpickle Function data")
loaded_data = pickle.loads(data_obj)
logger.debug("Finished unpickle Function data")
data_download_time_t2 = time.time()
self.stats.write(
'data_download_time',
round(data_download_time_t2 - data_download_time_t1, 8))
return loaded_data
def _fill_optional_args(self, function, data):
"""
Fills in those reserved, optional parameters that might be write to the function signature
"""
func_sig = inspect.signature(function)
if 'ibm_cos' in func_sig.parameters:
if 'ibm_cos' in self.pywren_config:
try:
ibm_boto3_client = Storage(self.pywren_config,
'ibm_cos').get_client()
data['ibm_cos'] = ibm_boto3_client
except Exception as e:
logger.error('Cannot create the ibm_cos connection: {}',
str(e))
data['ibm_cos'] = None
else:
logger.error(
'Cannot create the ibm_cos connection: Configuration not provided'
)
data['ibm_cos'] = None
if 'internal_storage' in func_sig.parameters:
data['internal_storage'] = self.internal_storage
if 'rabbitmq' in func_sig.parameters:
if 'rabbitmq' in self.pywren_config:
try:
rabbit_amqp_url = self.pywren_config['rabbitmq'].get(
'amqp_url')
params = pika.URLParameters(rabbit_amqp_url)
connection = pika.BlockingConnection(params)
data['rabbitmq'] = connection
except Exception as e:
logger.error('Cannot create the rabbitmq connection: {}',
str(e))
data['rabbitmq'] = None
else:
logger.error(
'Cannot create the rabbitmq connection: Configuration not provided'
)
data['rabbitmq'] = None
if 'id' in func_sig.parameters:
data['id'] = int(self.call_id)
def _create_data_stream(self, data):
"""
Creates the data stream in case of object processing
"""
extra_get_args = {}
if 'url' in data:
url = data['url']
logger.info('Getting dataset from {}'.format(url.path))
if url.data_byte_range is not None:
range_str = 'bytes={}-{}'.format(*url.data_byte_range)
extra_get_args['Range'] = range_str
logger.info('Chunk: {} - Range: {}'.format(
url.part, extra_get_args['Range']))
resp = requests.get(url.path, headers=extra_get_args, stream=True)
url.data_stream = resp.raw
if 'obj' in data:
obj = data['obj']
obj.storage_backend
storage_handler = Storage(
self.pywren_config, obj.storage_backend).get_storage_handler()
logger.info('Getting dataset from {}://{}/{}'.format(
obj.storage_backend, obj.bucket, obj.key))
# logger.info("ob.limit_X_values {}".format(obj.limit_X_values))#####
if obj.data_byte_range is not None:
extra_get_args['Range'] = 'bytes={}-{}'.format(
*obj.data_byte_range)
logger.info('Chunk: {} - Range: {}'.format(
obj.part, extra_get_args['Range']))
sb = storage_handler.get_object(obj.bucket,
obj.key,
stream=True,
extra_get_args=extra_get_args)
obj.data_stream = WrappedStreamingBodyPartition(
sb, obj.chunk_size, obj.data_byte_range)
elif obj.data_byte_range is None and (obj.limit_X_values is None or
obj.limit_Y_values is None):
obj.data_stream = storage_handler.get_object(obj.bucket,
obj.key,
stream=True)
else:
obj.data_stream = storage_handler.get_object(obj.bucket,
obj.key,
stream=True)
obj.data_stream = file_part(obj.data_stream,
obj) #obj.bord_meta,
def run(self):
"""
Runs the function
"""
logger.info("Started")
result = None
exception = False
try:
self.internal_storage = InternalStorage(self.storage_config)
self.internal_storage.tmp_obj_prefix = self.output_key.rsplit(
'/', 1)[0]
loaded_func_all = self._get_function_and_modules()
self._save_modules(loaded_func_all['module_data'])
function = self._unpickle_function(loaded_func_all['func'])
data = self._load_data()
logger.info("data_obj {}".format(data))
if is_object_processing_function(function):
self._create_data_stream(data)
self._fill_optional_args(function, data)
if self.show_memory:
logger.debug(
"Memory usage before call the function: {}".format(
get_current_memory_usage()))
logger.info("Going to execute '{}()'".format(str(
function.__name__)))
print('---------------------- FUNCTION LOG ----------------------',
flush=True)
func_exec_time_t1 = time.time()
result = function(**data)
func_exec_time_t2 = time.time()
print('----------------------------------------------------------',
flush=True)
logger.info("Success function execution")
if self.show_memory:
logger.debug("Memory usage after call the function: {}".format(
get_current_memory_usage()))
self.stats.write('function_exec_time',
round(func_exec_time_t2 - func_exec_time_t1, 8))
# Check for new futures
if result is not None:
self.stats.write("result", True)
if isinstance(result, ResponseFuture) or \
(type(result) == list and len(result) > 0 and isinstance(result[0], ResponseFuture)):
self.stats.write('new_futures', True)
logger.debug("Pickling result")
output_dict = {'result': result}
pickled_output = pickle.dumps(output_dict)
if self.show_memory:
logger.debug(
"Memory usage after output serialization: {}".format(
get_current_memory_usage()))
else:
logger.debug("No result to store")
self.stats.write("result", False)
except Exception:
exception = True
self.stats.write("exception", True)
exc_type, exc_value, exc_traceback = sys.exc_info()
print('----------------------- EXCEPTION !-----------------------',
flush=True)
traceback.print_exc(file=sys.stdout)
print('----------------------------------------------------------',
flush=True)
if self.show_memory:
logger.debug("Memory usage after call the function: {}".format(
get_current_memory_usage()))
try:
logger.debug("Pickling exception")
pickled_exc = pickle.dumps(
(exc_type, exc_value, exc_traceback))
pickle.loads(
pickled_exc
) # this is just to make sure they can be unpickled
self.stats.write("exc_info", str(pickled_exc))
except Exception as pickle_exception:
# Shockingly often, modules like subprocess don't properly
# call the base Exception.__init__, which results in them
# being unpickleable. As a result, we actually wrap this in a try/catch block
# and more-carefully handle the exceptions if any part of this save / test-reload
# fails
self.stats.write("exc_pickle_fail", True)
pickled_exc = pickle.dumps({
'exc_type': str(exc_type),
'exc_value': str(exc_value),
'exc_traceback': exc_traceback,
'pickle_exception': pickle_exception
})
pickle.loads(
pickled_exc
) # this is just to make sure they can be unpickled
self.stats.write("exc_info", str(pickled_exc))
finally:
store_result = strtobool(os.environ.get('STORE_RESULT', 'True'))
if result is not None and store_result and not exception:
output_upload_timestamp_t1 = time.time()
logger.info(
"Storing function result - output.pickle - Size: {}".
format(sizeof_fmt(len(pickled_output))))
self.internal_storage.put_data(self.output_key, pickled_output)
output_upload_timestamp_t2 = time.time()
self.stats.write(
"output_upload_time",
round(
output_upload_timestamp_t2 -
output_upload_timestamp_t1, 8))
self.jobrunner_conn.send("Finished")
logger.info("Finished")
def run(self):
"""
Runs the function
"""
logger.info("Started")
result = None
exception = False
try:
self.internal_storage = InternalStorage(self.storage_config)
self.internal_storage.tmp_obj_prefix = self.output_key.rsplit(
'/', 1)[0]
loaded_func_all = self._get_function_and_modules()
self._save_modules(loaded_func_all['module_data'])
function = self._unpickle_function(loaded_func_all['func'])
data = self._load_data()
logger.info("data_obj {}".format(data))
if is_object_processing_function(function):
self._create_data_stream(data)
self._fill_optional_args(function, data)
if self.show_memory:
logger.debug(
"Memory usage before call the function: {}".format(
get_current_memory_usage()))
logger.info("Going to execute '{}()'".format(str(
function.__name__)))
print('---------------------- FUNCTION LOG ----------------------',
flush=True)
func_exec_time_t1 = time.time()
result = function(**data)
func_exec_time_t2 = time.time()
print('----------------------------------------------------------',
flush=True)
logger.info("Success function execution")
if self.show_memory:
logger.debug("Memory usage after call the function: {}".format(
get_current_memory_usage()))
self.stats.write('function_exec_time',
round(func_exec_time_t2 - func_exec_time_t1, 8))
# Check for new futures
if result is not None:
self.stats.write("result", True)
if isinstance(result, ResponseFuture) or \
(type(result) == list and len(result) > 0 and isinstance(result[0], ResponseFuture)):
self.stats.write('new_futures', True)
logger.debug("Pickling result")
output_dict = {'result': result}
pickled_output = pickle.dumps(output_dict)
if self.show_memory:
logger.debug(
"Memory usage after output serialization: {}".format(
get_current_memory_usage()))
else:
logger.debug("No result to store")
self.stats.write("result", False)
except Exception:
exception = True
self.stats.write("exception", True)
exc_type, exc_value, exc_traceback = sys.exc_info()
print('----------------------- EXCEPTION !-----------------------',
flush=True)
traceback.print_exc(file=sys.stdout)
print('----------------------------------------------------------',
flush=True)
if self.show_memory:
logger.debug("Memory usage after call the function: {}".format(
get_current_memory_usage()))
try:
logger.debug("Pickling exception")
pickled_exc = pickle.dumps(
(exc_type, exc_value, exc_traceback))
pickle.loads(
pickled_exc
) # this is just to make sure they can be unpickled
self.stats.write("exc_info", str(pickled_exc))
except Exception as pickle_exception:
# Shockingly often, modules like subprocess don't properly
# call the base Exception.__init__, which results in them
# being unpickleable. As a result, we actually wrap this in a try/catch block
# and more-carefully handle the exceptions if any part of this save / test-reload
# fails
self.stats.write("exc_pickle_fail", True)
pickled_exc = pickle.dumps({
'exc_type': str(exc_type),
'exc_value': str(exc_value),
'exc_traceback': exc_traceback,
'pickle_exception': pickle_exception
})
pickle.loads(
pickled_exc
) # this is just to make sure they can be unpickled
self.stats.write("exc_info", str(pickled_exc))
finally:
store_result = strtobool(os.environ.get('STORE_RESULT', 'True'))
if result is not None and store_result and not exception:
output_upload_timestamp_t1 = time.time()
logger.info(
"Storing function result - output.pickle - Size: {}".
format(sizeof_fmt(len(pickled_output))))
self.internal_storage.put_data(self.output_key, pickled_output)
output_upload_timestamp_t2 = time.time()
self.stats.write(
"output_upload_time",
round(
output_upload_timestamp_t2 -
output_upload_timestamp_t1, 8))
self.jobrunner_conn.send("Finished")
logger.info("Finished")
class CallStatus:
def __init__(self, pywren_config):
self.config = pywren_config
self.rabbitmq_monitor = self.config['pywren'].get(
'rabbitmq_monitor', False)
self.store_status = strtobool(os.environ.get('STORE_STATUS', 'True'))
storage_config = extract_storage_config(self.config)
self.internal_storage = InternalStorage(storage_config)
self.response = {'exception': False}
def send(self, event_type):
self.response['type'] = event_type
if self.store_status:
if self.rabbitmq_monitor:
self._send_status_rabbitmq()
if not self.rabbitmq_monitor or event_type == '__end__':
self._send_status_os()
def _send_status_os(self):
"""
Send the status event to the Object Storage
"""
executor_id = self.response['executor_id']
job_id = self.response['job_id']
call_id = self.response['call_id']
if self.response['type'] == '__init__':
init_key = create_init_key(JOBS_PREFIX, executor_id, job_id,
call_id)
self.internal_storage.put_data(init_key, '')
elif self.response['type'] == '__end__':
status_key = create_status_key(JOBS_PREFIX, executor_id, job_id,
call_id)
dmpd_response_status = json.dumps(self.response)
drs = sizeof_fmt(len(dmpd_response_status))
logger.info(
"Storing execution stats - status.json - Size: {}".format(drs))
self.internal_storage.put_data(status_key, dmpd_response_status)
def _send_status_rabbitmq(self):
"""
Send the status event to RabbitMQ
"""
dmpd_response_status = json.dumps(self.response)
drs = sizeof_fmt(len(dmpd_response_status))
executor_id = self.response['executor_id']
job_id = self.response['job_id']
rabbit_amqp_url = self.config['rabbitmq'].get('amqp_url')
status_sent = False
output_query_count = 0
params = pika.URLParameters(rabbit_amqp_url)
exchange = 'pywren-{}-{}'.format(executor_id, job_id)
while not status_sent and output_query_count < 5:
output_query_count = output_query_count + 1
try:
connection = pika.BlockingConnection(params)
channel = connection.channel()
channel.exchange_declare(exchange=exchange,
exchange_type='fanout',
auto_delete=True)
channel.basic_publish(exchange=exchange,
routing_key='',
body=dmpd_response_status)
connection.close()
logger.info(
"Execution status sent to rabbitmq - Size: {}".format(drs))
status_sent = True
except Exception as e:
logger.error("Unable to send status to rabbitmq")
logger.error(str(e))
logger.info('Retrying to send status to rabbitmq...')
time.sleep(0.2)
def function_handler(event):
start_time = time.time()
logger.debug("Action handler started")
response_status = {'exception': False}
response_status['host_submit_time'] = event['host_submit_time']
response_status['start_time'] = start_time
context_dict = {
'ibm_cf_request_id': os.environ.get("__OW_ACTIVATION_ID"),
'ibm_cf_python_version': os.environ.get("PYTHON_VERSION"),
}
config = event['config']
storage_config = wrenconfig.extract_storage_config(config)
log_level = event['log_level']
ibm_cf_logging_config(log_level)
call_id = event['call_id']
callgroup_id = event['callgroup_id']
executor_id = event['executor_id']
logger.info("Execution ID: {}/{}/{}".format(executor_id, callgroup_id, call_id))
job_max_runtime = event.get("job_max_runtime", 590) # default for CF
status_key = event['status_key']
func_key = event['func_key']
data_key = event['data_key']
data_byte_range = event['data_byte_range']
output_key = event['output_key']
extra_env = event.get('extra_env', {})
response_status['call_id'] = call_id
response_status['callgroup_id'] = callgroup_id
response_status['executor_id'] = executor_id
# response_status['func_key'] = func_key
# response_status['data_key'] = data_key
# response_status['output_key'] = output_key
# response_status['status_key'] = status_key
try:
if version.__version__ != event['pywren_version']:
raise Exception("WRONGVERSION", "PyWren version mismatch",
version.__version__, event['pywren_version'])
# response_status['free_disk_bytes'] = free_disk_space("/tmp")
custom_env = {'PYWREN_CONFIG': json.dumps(config),
'PYWREN_EXECUTOR_ID': executor_id,
'PYTHONPATH': "{}:{}".format(os.getcwd(), PYWREN_LIBS_PATH),
'PYTHONUNBUFFERED': 'True'}
os.environ.update(custom_env)
os.environ.update(extra_env)
# pass a full json blob
jobrunner_config = {'func_key': func_key,
'data_key': data_key,
'log_level': log_level,
'data_byte_range': data_byte_range,
'python_module_path': PYTHON_MODULE_PATH,
'output_key': output_key,
'stats_filename': JOBRUNNER_STATS_FILENAME}
if os.path.exists(JOBRUNNER_STATS_FILENAME):
os.remove(JOBRUNNER_STATS_FILENAME)
setup_time = time.time()
response_status['setup_time'] = round(setup_time - start_time, 8)
result_queue = multiprocessing.Queue()
jr = jobrunner(jobrunner_config, result_queue)
jr.daemon = True
logger.info("Starting jobrunner process")
jr.start()
jr.join(job_max_runtime)
response_status['exec_time'] = round(time.time() - setup_time, 8)
if jr.is_alive():
# If process is still alive after jr.join(job_max_runtime), kill it
logger.error("Process exceeded maximum runtime of {} seconds".format(job_max_runtime))
# Send the signal to all the process groups
jr.terminate()
raise Exception("OUTATIME", "Process executed for too long and was killed")
try:
# Only 1 message is returned by jobrunner
result_queue.get(block=False)
except Exception:
# If no message, this means that the process was killed due an exception pickling an exception
raise Exception("EXCPICKLEERROR", "PyWren was unable to pickle the exception, check function logs")
# print(subprocess.check_output("find {}".format(PYTHON_MODULE_PATH), shell=True))
# print(subprocess.check_output("find {}".format(os.getcwd()), shell=True))
if os.path.exists(JOBRUNNER_STATS_FILENAME):
with open(JOBRUNNER_STATS_FILENAME, 'r') as fid:
for l in fid.readlines():
key, value = l.strip().split(" ", 1)
try:
response_status[key] = float(value)
except Exception:
response_status[key] = value
if key == 'exception' or key == 'exc_pickle_fail' \
or key == 'result':
response_status[key] = eval(value)
# response_status['server_info'] = get_server_info()
response_status.update(context_dict)
response_status['end_time'] = time.time()
except Exception as e:
# internal runtime exceptions
logger.error("There was an exception: {}".format(str(e)))
response_status['end_time'] = time.time()
response_status['exception'] = True
pickled_exc = pickle.dumps(sys.exc_info())
pickle.loads(pickled_exc) # this is just to make sure they can be unpickled
response_status['exc_info'] = str(pickled_exc)
finally:
store_status = strtobool(os.environ.get('STORE_STATUS', 'True'))
rabbit_amqp_url = config['rabbitmq'].get('amqp_url')
dmpd_response_status = json.dumps(response_status)
drs = sizeof_fmt(len(dmpd_response_status))
if rabbit_amqp_url and store_status:
status_sent = False
output_query_count = 0
while not status_sent and output_query_count < 5:
output_query_count = output_query_count + 1
try:
params = pika.URLParameters(rabbit_amqp_url)
connection = pika.BlockingConnection(params)
channel = connection.channel()
channel.queue_declare(queue=executor_id, auto_delete=True)
channel.basic_publish(exchange='', routing_key=executor_id,
body=dmpd_response_status)
connection.close()
logger.info("Execution stats sent to rabbitmq - Size: {}".format(drs))
status_sent = True
except Exception as e:
logger.error("Unable to send status to rabbitmq")
logger.error(str(e))
logger.info('Retrying to send stats to rabbitmq...')
time.sleep(0.2)
if store_status:
internal_storage = InternalStorage(storage_config)
logger.info("Storing execution stats - status.json - Size: {}".format(drs))
internal_storage.put_data(status_key, dmpd_response_status)
class jobrunner(Process):
def __init__(self, jr_config, result_queue):
super().__init__()
start_time = time.time()
self.config = jr_config
log_level = self.config['log_level']
self.result_queue = result_queue
ibm_cf_logging_config(log_level)
self.stats = stats(self.config['stats_filename'])
self.stats.write('jobrunner_start', start_time)
pw_config = json.loads(os.environ.get('PYWREN_CONFIG'))
self.storage_config = extract_storage_config(pw_config)
if 'SHOW_MEMORY_USAGE' in os.environ:
self.show_memory = eval(os.environ['SHOW_MEMORY_USAGE'])
else:
self.show_memory = False
self.func_key = self.config['func_key']
self.data_key = self.config['data_key']
self.data_byte_range = self.config['data_byte_range']
self.output_key = self.config['output_key']
def _get_function_and_modules(self):
"""
Gets and unpickles function and modules from storage
"""
logger.debug("Getting function and modules")
func_download_time_t1 = time.time()
func_obj = self.internal_storage.get_func(self.func_key)
loaded_func_all = pickle.loads(func_obj)
func_download_time_t2 = time.time()
self.stats.write('func_download_time', round(func_download_time_t2-func_download_time_t1, 8))
logger.debug("Finished getting Function and modules")
return loaded_func_all
def _save_modules(self, module_data):
"""
Save modules, before we unpickle actual function
"""
logger.debug("Writing Function dependencies to local disk")
PYTHON_MODULE_PATH = self.config['python_module_path']
shutil.rmtree(PYTHON_MODULE_PATH, True) # delete old modules
os.mkdir(PYTHON_MODULE_PATH)
sys.path.append(PYTHON_MODULE_PATH)
for m_filename, m_data in module_data.items():
m_path = os.path.dirname(m_filename)
if len(m_path) > 0 and m_path[0] == "/":
m_path = m_path[1:]
to_make = os.path.join(PYTHON_MODULE_PATH, m_path)
try:
os.makedirs(to_make)
except OSError as e:
if e.errno == 17:
pass
else:
raise e
full_filename = os.path.join(to_make, os.path.basename(m_filename))
with open(full_filename, 'wb') as fid:
fid.write(b64str_to_bytes(m_data))
#logger.info("Finished writing {} module files".format(len(loaded_func_all['module_data'])))
#logger.debug(subprocess.check_output("find {}".format(PYTHON_MODULE_PATH), shell=True))
#logger.debug(subprocess.check_output("find {}".format(os.getcwd()), shell=True))
logger.debug("Finished writing Function dependencies")
def _unpickle_function(self, pickled_func):
"""
Unpickle function; it will expect modules to be there
"""
logger.debug("Unpickle Function")
loaded_func = pickle.loads(pickled_func)
logger.debug("Finished Function unpickle")
return loaded_func
def _load_data(self):
extra_get_args = {}
if self.data_byte_range is not None:
range_str = 'bytes={}-{}'.format(*self.data_byte_range)
extra_get_args['Range'] = range_str
logger.debug("Getting function data")
data_download_time_t1 = time.time()
data_obj = self.internal_storage.get_data(self.data_key, extra_get_args=extra_get_args)
logger.debug("Finished getting Function data")
logger.debug("Unpickle Function data")
loaded_data = pickle.loads(data_obj)
logger.debug("Finished unpickle Function data")
data_download_time_t2 = time.time()
self.stats.write('data_download_time', round(data_download_time_t2-data_download_time_t1, 8))
return loaded_data
def _create_storage_clients(self, function, data):
# Verify storage parameters - Create clients
func_sig = inspect.signature(function)
if 'ibm_cos' in func_sig.parameters:
ibm_boto3_client = IbmCosStorageBackend(self.storage_config['ibm_cos']).get_client()
data['ibm_cos'] = ibm_boto3_client
if 'swift' in func_sig.parameters:
swift_client = SwiftStorageBackend(self.storage_config['swift'])
data['swift'] = swift_client
if 'internal_storage' in func_sig.parameters:
data['internal_storage'] = self.internal_storage
return data
def run(self):
"""
Runs the function
"""
logger.info("Started")
# initial output file in case job fails
result = None
exception = False
try:
self.internal_storage = InternalStorage(self.storage_config)
loaded_func_all = self._get_function_and_modules()
self._save_modules(loaded_func_all['module_data'])
function = self._unpickle_function(loaded_func_all['func'])
data = self._load_data()
data = self._create_storage_clients(function, data)
if self.show_memory:
logger.debug("Memory usage before call the function: {}".format(get_current_memory_usage()))
logger.info("Function: Going to execute '{}()'".format(str(function.__name__)))
print('---------------------- FUNCTION LOG ----------------------', flush=True)
func_exec_time_t1 = time.time()
result = function(**data)
func_exec_time_t2 = time.time()
print('----------------------------------------------------------', flush=True)
logger.info("Function: Success execution")
if self.show_memory:
logger.debug("Memory usage after call the function: {}".format(get_current_memory_usage()))
self.stats.write('function_exec_time', round(func_exec_time_t2-func_exec_time_t1, 8))
# Check for new futures
if result is not None:
self.stats.write("result", True)
if isinstance(result, ResponseFuture):
callgroup_id = result.callgroup_id
self.stats.write('new_futures', '{}/{}'.format(callgroup_id, 1))
elif type(result) == list and len(result) > 0 and isinstance(result[0], ResponseFuture):
callgroup_id = result[0].callgroup_id
self.stats.write('new_futures', '{}/{}'.format(callgroup_id, len(result)))
else:
self.stats.write('new_futures', '{}/{}'.format(None, 0))
logger.debug("Pickling result")
output_dict = {'result': result}
pickled_output = pickle.dumps(output_dict)
if self.show_memory:
logger.debug("Memory usage after output serialization: {}".format(get_current_memory_usage()))
else:
logger.debug("No result to store")
self.stats.write("result", False)
except Exception as e:
exception = True
self.stats.write("exception", True)
print('----------------------- EXCEPTION !-----------------------')
logger.error("There was an exception: {}".format(str(e)))
print('----------------------------------------------------------', flush=True)
if self.show_memory:
logger.debug("Memory usage after call the function: {}".format(get_current_memory_usage()))
try:
logger.debug("Pickling exception")
pickled_exc = pickle.dumps(sys.exc_info())
pickle.loads(pickled_exc) # this is just to make sure they can be unpickled
self.stats.write("exc_info", str(pickled_exc))
except Exception as pickle_exception:
# Shockingly often, modules like subprocess don't properly
# call the base Exception.__init__, which results in them
# being unpickleable. As a result, we actually wrap this in a try/catch block
# and more-carefully handle the exceptions if any part of this save / test-reload
# fails
logger.debug("Failed pickling exception: {}".format(str(pickle_exception)))
self.stats.write("exc_pickle_fail", True)
exc_type, exc_value, exc_traceback = sys.exc_info()
pickled_exc = pickle.dumps({'exc_type': str(exc_type),
'exc_value': str(exc_value),
'exc_traceback': exc_traceback,
'pickle_exception': pickle_exception})
pickle.loads(pickled_exc) # this is just to make sure they can be unpickled
self.stats.write("exc_info", str(pickled_exc))
finally:
store_result = strtobool(os.environ.get('STORE_RESULT', 'True'))
if result is not None and store_result and not exception:
output_upload_timestamp_t1 = time.time()
logger.info("Storing function result - output.pickle - Size: {}".format(sizeof_fmt(len(pickled_output))))
self.internal_storage.put_data(self.output_key, pickled_output)
output_upload_timestamp_t2 = time.time()
self.stats.write("output_upload_time", round(output_upload_timestamp_t2 - output_upload_timestamp_t1, 8))
self.result_queue.put("Finished")
logger.info("Finished")
def result(self, throw_except=True, internal_storage=None):
"""
Return the value returned by the call.
If the call raised an exception, this method will raise the same exception
If the future is cancelled before completing then CancelledError will be raised.
:param throw_except: Reraise exception if call raised. Default true.
:param internal_storage: Storage handler to poll cloud storage. Default None.
:return: Result of the call.
:raises CancelledError: If the job is cancelled before completed.
:raises TimeoutError: If job is not complete after `timeout` seconds.
"""
if self._state == ResponseFuture.State.New:
raise ValueError("task not yet invoked")
if self._state == ResponseFuture.State.Success:
return self._return_val
if self._state == ResponseFuture.State.Futures:
return self._new_futures
if self._state == ResponseFuture.State.Error:
if throw_except:
reraise(*self._exception)
else:
raise FunctionException(self.executor_id, self.job_id,
self.activation_id, self._exception)
if internal_storage is None:
internal_storage = InternalStorage(
storage_config=self.storage_config)
self.status(throw_except=throw_except,
internal_storage=internal_storage)
if not self.produce_output:
self._set_state(ResponseFuture.State.Success)
if self._state == ResponseFuture.State.Success:
return self._return_val
if self._state == ResponseFuture.State.Futures:
return self._new_futures
call_output_time = time.time()
call_output = internal_storage.get_call_output(self.executor_id,
self.job_id,
self.call_id)
self.output_query_count += 1
while call_output is None and self.output_query_count < self.GET_RESULT_MAX_RETRIES:
time.sleep(self.GET_RESULT_SLEEP_SECS)
call_output = internal_storage.get_call_output(
self.executor_id, self.job_id, self.call_id)
self.output_query_count += 1
if call_output is None:
if throw_except:
raise Exception('Unable to get the output from call {} - '
'Activation ID: {}'.format(
self.call_id, self.activation_id))
else:
self._set_state(ResponseFuture.State.Error)
return None
call_output = pickle.loads(call_output)
call_output_time_done = time.time()
self._call_output = call_output
self._call_metadata[
'download_output_time'] = call_output_time_done - call_output_time
self._call_metadata['output_query_count'] = self.output_query_count
self._call_metadata[
'download_output_timestamp'] = call_output_time_done
log_msg = (
'ExecutorID {} | JobID {} - Got output from call {} - Activation '
'ID: {}'.format(self.executor_id, self.job_id, self.call_id,
self.activation_id))
logger.info(log_msg)
function_result = call_output['result']
if isinstance(function_result, ResponseFuture) or \
(type(function_result) == list and len(function_result) > 0 and isinstance(function_result[0], ResponseFuture)):
self._new_futures = [
function_result
] if type(function_result) == ResponseFuture else function_result
self._set_state(ResponseFuture.State.Futures)
self._call_metadata['status_done_timestamp'] = self._call_metadata[
'download_output_timestamp']
del self._call_metadata['download_output_timestamp']
return self._new_futures
else:
self._return_val = function_result
self._set_state(ResponseFuture.State.Success)
return self._return_val
def status(self, throw_except=True, internal_storage=None):
"""
Return the status returned by the call.
If the call raised an exception, this method will raise the same exception
If the future is cancelled before completing then CancelledError will be raised.
:param check_only: Return None immediately if job is not complete. Default False.
:param throw_except: Reraise exception if call raised. Default true.
:param storage_handler: Storage handler to poll cloud storage. Default None.
:return: Result of the call.
:raises CancelledError: If the job is cancelled before completed.
:raises TimeoutError: If job is not complete after `timeout` seconds.
"""
if self._state == ResponseFuture.State.New:
raise ValueError("task not yet invoked")
if self._state in [
ResponseFuture.State.Ready, ResponseFuture.State.Success
]:
return self._call_status
if internal_storage is None:
internal_storage = InternalStorage(self.storage_config)
if self._call_status is None:
check_storage_path(internal_storage.get_storage_config(),
self.storage_path)
self._call_status = internal_storage.get_call_status(
self.executor_id, self.job_id, self.call_id)
self.status_query_count += 1
while self._call_status is None:
time.sleep(self.GET_RESULT_SLEEP_SECS)
self._call_status = internal_storage.get_call_status(
self.executor_id, self.job_id, self.call_id)
self.status_query_count += 1
self.activation_id = self._call_status['activation_id']
if self._call_status['type'] == '__init__':
self._set_state(ResponseFuture.State.Running)
return self._call_status
self._call_metadata['host_submit_time'] = self._call_status[
'host_submit_time']
self._call_metadata['status_done_timestamp'] = time.time()
self._call_metadata['status_query_count'] = self.status_query_count
total_time = format(
round(
self._call_status['end_time'] -
self._call_status['start_time'], 2), '.2f')
if self._call_status['exception']:
# the action handler/jobrunner/function had an exception
self._set_state(ResponseFuture.State.Error)
self._exception = pickle.loads(eval(self._call_status['exc_info']))
msg = None
if not self._call_status.get('exc_pickle_fail', False):
exception_args = self._exception[1].args
if exception_args and exception_args[0] == "WRONGVERSION":
msg = "PyWren version mismatch: remote library is version {}, local " \
"library is version {}".format(exception_args[2], exception_args[3])
elif exception_args and exception_args[0] == "OUTATIME":
msg = "Process ran out of time and was killed"
elif exception_args and exception_args[0] == "OUTOFMEMORY":
msg = "Process exceeded maximum memory and was killed"
else:
fault = Exception(self._exception['exc_value'])
self._exception = (Exception, fault,
self._exception['exc_traceback'])
if throw_except:
reraise(*self._exception)
raise FunctionException(self.executor_id, self.job_id,
self.activation_id, self._exception, msg)
log_msg = (
'ExecutorID {} | JobID {} - Got status from call {} - Activation '
'ID: {} - Time: {} seconds'.format(self.executor_id, self.job_id,
self.call_id,
self.activation_id,
str(total_time)))
logger.info(log_msg)
self._set_state(ResponseFuture.State.Ready)
if not self._call_status['result']:
self._set_state(ResponseFuture.State.Success)
self.produce_output = False
if 'new_futures' in self._call_status:
self.result(throw_except=throw_except,
internal_storage=internal_storage)
return self._call_status
