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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/base_handler.py | JsonHandler.base_path | def base_path(self):
"""Base path for all mapreduce-related urls.
JSON handlers are mapped to /base_path/command/command_name thus they
require special treatment.
Raises:
BadRequestPathError: if the path does not end with "/command".
Returns:
The base path.
"""
path = self.request.path
base_path = path[:path.rfind("/")]
if not base_path.endswith("/command"):
raise BadRequestPathError(
"Json handlers should have /command path prefix")
return base_path[:base_path.rfind("/")] | python | def base_path(self):
"""Base path for all mapreduce-related urls.
JSON handlers are mapped to /base_path/command/command_name thus they
require special treatment.
Raises:
BadRequestPathError: if the path does not end with "/command".
Returns:
The base path.
"""
path = self.request.path
base_path = path[:path.rfind("/")]
if not base_path.endswith("/command"):
raise BadRequestPathError(
"Json handlers should have /command path prefix")
return base_path[:base_path.rfind("/")] | Base path for all mapreduce-related urls.
JSON handlers are mapped to /base_path/command/command_name thus they
require special treatment.
Raises:
BadRequestPathError: if the path does not end with "/command".
Returns:
The base path. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/base_handler.py#L189-L206 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/base_handler.py | JsonHandler._handle_wrapper | def _handle_wrapper(self):
"""The helper method for handling JSON Post and Get requests."""
if self.request.headers.get("X-Requested-With") != "XMLHttpRequest":
logging.error("Got JSON request with no X-Requested-With header")
self.response.set_status(
403, message="Got JSON request with no X-Requested-With header")
return
self.json_response.clear()
try:
self.handle()
except errors.MissingYamlError:
logging.debug("Could not find 'mapreduce.yaml' file.")
self.json_response.clear()
self.json_response["error_class"] = "Notice"
self.json_response["error_message"] = "Could not find 'mapreduce.yaml'"
except Exception, e:
logging.exception("Error in JsonHandler, returning exception.")
# TODO(user): Include full traceback here for the end-user.
self.json_response.clear()
self.json_response["error_class"] = e.__class__.__name__
self.json_response["error_message"] = str(e)
self.response.headers["Content-Type"] = "text/javascript"
try:
output = json.dumps(self.json_response, cls=json_util.JsonEncoder)
# pylint: disable=broad-except
except Exception, e:
logging.exception("Could not serialize to JSON")
self.response.set_status(500, message="Could not serialize to JSON")
return
else:
self.response.out.write(output) | python | def _handle_wrapper(self):
"""The helper method for handling JSON Post and Get requests."""
if self.request.headers.get("X-Requested-With") != "XMLHttpRequest":
logging.error("Got JSON request with no X-Requested-With header")
self.response.set_status(
403, message="Got JSON request with no X-Requested-With header")
return
self.json_response.clear()
try:
self.handle()
except errors.MissingYamlError:
logging.debug("Could not find 'mapreduce.yaml' file.")
self.json_response.clear()
self.json_response["error_class"] = "Notice"
self.json_response["error_message"] = "Could not find 'mapreduce.yaml'"
except Exception, e:
logging.exception("Error in JsonHandler, returning exception.")
# TODO(user): Include full traceback here for the end-user.
self.json_response.clear()
self.json_response["error_class"] = e.__class__.__name__
self.json_response["error_message"] = str(e)
self.response.headers["Content-Type"] = "text/javascript"
try:
output = json.dumps(self.json_response, cls=json_util.JsonEncoder)
# pylint: disable=broad-except
except Exception, e:
logging.exception("Could not serialize to JSON")
self.response.set_status(500, message="Could not serialize to JSON")
return
else:
self.response.out.write(output) | The helper method for handling JSON Post and Get requests. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/base_handler.py#L208-L240 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/tools/gcs_file_seg_reader.py | _GCSFileSegReader.read | def read(self, n):
"""Read data from file segs.
Args:
n: max bytes to read. Must be positive.
Returns:
some bytes. May be smaller than n bytes. "" when no more data is left.
"""
if self._EOF:
return ""
while self._seg_index <= self._last_seg_index:
result = self._read_from_seg(n)
if result != "":
return result
else:
self._next_seg()
self._EOF = True
return "" | python | def read(self, n):
"""Read data from file segs.
Args:
n: max bytes to read. Must be positive.
Returns:
some bytes. May be smaller than n bytes. "" when no more data is left.
"""
if self._EOF:
return ""
while self._seg_index <= self._last_seg_index:
result = self._read_from_seg(n)
if result != "":
return result
else:
self._next_seg()
self._EOF = True
return "" | Read data from file segs.
Args:
n: max bytes to read. Must be positive.
Returns:
some bytes. May be smaller than n bytes. "" when no more data is left. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/tools/gcs_file_seg_reader.py#L62-L82 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/tools/gcs_file_seg_reader.py | _GCSFileSegReader._next_seg | def _next_seg(self):
"""Get next seg."""
if self._seg:
self._seg.close()
self._seg_index += 1
if self._seg_index > self._last_seg_index:
self._seg = None
return
filename = self._seg_prefix + str(self._seg_index)
stat = cloudstorage.stat(filename)
writer = output_writers._GoogleCloudStorageOutputWriter
if writer._VALID_LENGTH not in stat.metadata:
raise ValueError(
"Expect %s in metadata for file %s." %
(writer._VALID_LENGTH, filename))
self._seg_valid_length = int(stat.metadata[writer._VALID_LENGTH])
if self._seg_valid_length > stat.st_size:
raise ValueError(
"Valid length %s is too big for file %s of length %s" %
(self._seg_valid_length, filename, stat.st_size))
self._seg = cloudstorage.open(filename) | python | def _next_seg(self):
"""Get next seg."""
if self._seg:
self._seg.close()
self._seg_index += 1
if self._seg_index > self._last_seg_index:
self._seg = None
return
filename = self._seg_prefix + str(self._seg_index)
stat = cloudstorage.stat(filename)
writer = output_writers._GoogleCloudStorageOutputWriter
if writer._VALID_LENGTH not in stat.metadata:
raise ValueError(
"Expect %s in metadata for file %s." %
(writer._VALID_LENGTH, filename))
self._seg_valid_length = int(stat.metadata[writer._VALID_LENGTH])
if self._seg_valid_length > stat.st_size:
raise ValueError(
"Valid length %s is too big for file %s of length %s" %
(self._seg_valid_length, filename, stat.st_size))
self._seg = cloudstorage.open(filename) | Get next seg. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/tools/gcs_file_seg_reader.py#L92-L113 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/tools/gcs_file_seg_reader.py | _GCSFileSegReader._read_from_seg | def _read_from_seg(self, n):
"""Read from current seg.
Args:
n: max number of bytes to read.
Returns:
valid bytes from the current seg. "" if no more is left.
"""
result = self._seg.read(size=n)
if result == "":
return result
offset = self._seg.tell()
if offset > self._seg_valid_length:
extra = offset - self._seg_valid_length
result = result[:-1*extra]
self._offset += len(result)
return result | python | def _read_from_seg(self, n):
"""Read from current seg.
Args:
n: max number of bytes to read.
Returns:
valid bytes from the current seg. "" if no more is left.
"""
result = self._seg.read(size=n)
if result == "":
return result
offset = self._seg.tell()
if offset > self._seg_valid_length:
extra = offset - self._seg_valid_length
result = result[:-1*extra]
self._offset += len(result)
return result | Read from current seg.
Args:
n: max number of bytes to read.
Returns:
valid bytes from the current seg. "" if no more is left. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/tools/gcs_file_seg_reader.py#L115-L132 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | _get_descending_key | def _get_descending_key(gettime=time.time):
"""Returns a key name lexically ordered by time descending.
This lets us have a key name for use with Datastore entities which returns
rows in time descending order when it is scanned in lexically ascending order,
allowing us to bypass index building for descending indexes.
Args:
gettime: Used for testing.
Returns:
A string with a time descending key.
"""
now_descending = int((_FUTURE_TIME - gettime()) * 100)
request_id_hash = os.environ.get("REQUEST_ID_HASH")
if not request_id_hash:
request_id_hash = str(random.getrandbits(32))
return "%d%s" % (now_descending, request_id_hash) | python | def _get_descending_key(gettime=time.time):
"""Returns a key name lexically ordered by time descending.
This lets us have a key name for use with Datastore entities which returns
rows in time descending order when it is scanned in lexically ascending order,
allowing us to bypass index building for descending indexes.
Args:
gettime: Used for testing.
Returns:
A string with a time descending key.
"""
now_descending = int((_FUTURE_TIME - gettime()) * 100)
request_id_hash = os.environ.get("REQUEST_ID_HASH")
if not request_id_hash:
request_id_hash = str(random.getrandbits(32))
return "%d%s" % (now_descending, request_id_hash) | Returns a key name lexically ordered by time descending.
This lets us have a key name for use with Datastore entities which returns
rows in time descending order when it is scanned in lexically ascending order,
allowing us to bypass index building for descending indexes.
Args:
gettime: Used for testing.
Returns:
A string with a time descending key. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L62-L79 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | _get_task_host | def _get_task_host():
"""Get the Host header value for all mr tasks.
Task Host header determines which instance this task would be routed to.
Current version id format is: v7.368834058928280579
Current module id is just the module's name. It could be "default"
Default version hostname is app_id.appspot.com
Returns:
A complete host name is of format version.module.app_id.appspot.com
If module is the default module, just version.app_id.appspot.com. The reason
is if an app doesn't have modules enabled and the url is
"version.default.app_id", "version" is ignored and "default" is used as
version. If "default" version doesn't exist, the url is routed to the
default version.
"""
version = os.environ["CURRENT_VERSION_ID"].split(".")[0]
default_host = os.environ["DEFAULT_VERSION_HOSTNAME"]
module = os.environ["CURRENT_MODULE_ID"]
if os.environ["CURRENT_MODULE_ID"] == "default":
return "%s.%s" % (version, default_host)
return "%s.%s.%s" % (version, module, default_host) | python | def _get_task_host():
"""Get the Host header value for all mr tasks.
Task Host header determines which instance this task would be routed to.
Current version id format is: v7.368834058928280579
Current module id is just the module's name. It could be "default"
Default version hostname is app_id.appspot.com
Returns:
A complete host name is of format version.module.app_id.appspot.com
If module is the default module, just version.app_id.appspot.com. The reason
is if an app doesn't have modules enabled and the url is
"version.default.app_id", "version" is ignored and "default" is used as
version. If "default" version doesn't exist, the url is routed to the
default version.
"""
version = os.environ["CURRENT_VERSION_ID"].split(".")[0]
default_host = os.environ["DEFAULT_VERSION_HOSTNAME"]
module = os.environ["CURRENT_MODULE_ID"]
if os.environ["CURRENT_MODULE_ID"] == "default":
return "%s.%s" % (version, default_host)
return "%s.%s.%s" % (version, module, default_host) | Get the Host header value for all mr tasks.
Task Host header determines which instance this task would be routed to.
Current version id format is: v7.368834058928280579
Current module id is just the module's name. It could be "default"
Default version hostname is app_id.appspot.com
Returns:
A complete host name is of format version.module.app_id.appspot.com
If module is the default module, just version.app_id.appspot.com. The reason
is if an app doesn't have modules enabled and the url is
"version.default.app_id", "version" is ignored and "default" is used as
version. If "default" version doesn't exist, the url is routed to the
default version. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L82-L104 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | get_queue_name | def get_queue_name(queue_name):
"""Determine which queue MR should run on.
How to choose the queue:
1. If user provided one, use that.
2. If we are starting a mr from taskqueue, inherit that queue.
If it's a special queue, fall back to the default queue.
3. Default queue.
If user is using any MR pipeline interface, pipeline.start takes a
"queue_name" argument. The pipeline will run on that queue and MR will
simply inherit the queue_name.
Args:
queue_name: queue_name from user. Maybe None.
Returns:
The queue name to run on.
"""
if queue_name:
return queue_name
queue_name = os.environ.get("HTTP_X_APPENGINE_QUEUENAME",
parameters.config.QUEUE_NAME)
if len(queue_name) > 1 and queue_name[0:2] == "__":
# We are currently in some special queue. E.g. __cron.
return parameters.config.QUEUE_NAME
else:
return queue_name | python | def get_queue_name(queue_name):
"""Determine which queue MR should run on.
How to choose the queue:
1. If user provided one, use that.
2. If we are starting a mr from taskqueue, inherit that queue.
If it's a special queue, fall back to the default queue.
3. Default queue.
If user is using any MR pipeline interface, pipeline.start takes a
"queue_name" argument. The pipeline will run on that queue and MR will
simply inherit the queue_name.
Args:
queue_name: queue_name from user. Maybe None.
Returns:
The queue name to run on.
"""
if queue_name:
return queue_name
queue_name = os.environ.get("HTTP_X_APPENGINE_QUEUENAME",
parameters.config.QUEUE_NAME)
if len(queue_name) > 1 and queue_name[0:2] == "__":
# We are currently in some special queue. E.g. __cron.
return parameters.config.QUEUE_NAME
else:
return queue_name | Determine which queue MR should run on.
How to choose the queue:
1. If user provided one, use that.
2. If we are starting a mr from taskqueue, inherit that queue.
If it's a special queue, fall back to the default queue.
3. Default queue.
If user is using any MR pipeline interface, pipeline.start takes a
"queue_name" argument. The pipeline will run on that queue and MR will
simply inherit the queue_name.
Args:
queue_name: queue_name from user. Maybe None.
Returns:
The queue name to run on. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L127-L154 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | total_seconds | def total_seconds(td):
"""convert a timedelta to seconds.
This is patterned after timedelta.total_seconds, which is only
available in python 27.
Args:
td: a timedelta object.
Returns:
total seconds within a timedelta. Rounded up to seconds.
"""
secs = td.seconds + td.days * 24 * 3600
if td.microseconds:
secs += 1
return secs | python | def total_seconds(td):
"""convert a timedelta to seconds.
This is patterned after timedelta.total_seconds, which is only
available in python 27.
Args:
td: a timedelta object.
Returns:
total seconds within a timedelta. Rounded up to seconds.
"""
secs = td.seconds + td.days * 24 * 3600
if td.microseconds:
secs += 1
return secs | convert a timedelta to seconds.
This is patterned after timedelta.total_seconds, which is only
available in python 27.
Args:
td: a timedelta object.
Returns:
total seconds within a timedelta. Rounded up to seconds. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L157-L172 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | handler_for_name | def handler_for_name(fq_name):
"""Resolves and instantiates handler by fully qualified name.
First resolves the name using for_name call. Then if it resolves to a class,
instantiates a class, if it resolves to a method - instantiates the class and
binds method to the instance.
Args:
fq_name: fully qualified name of something to find.
Returns:
handler instance which is ready to be called.
"""
resolved_name = for_name(fq_name)
if isinstance(resolved_name, (type, types.ClassType)):
# create new instance if this is type
return resolved_name()
elif isinstance(resolved_name, types.MethodType):
# bind the method
return getattr(resolved_name.im_class(), resolved_name.__name__)
else:
return resolved_name | python | def handler_for_name(fq_name):
"""Resolves and instantiates handler by fully qualified name.
First resolves the name using for_name call. Then if it resolves to a class,
instantiates a class, if it resolves to a method - instantiates the class and
binds method to the instance.
Args:
fq_name: fully qualified name of something to find.
Returns:
handler instance which is ready to be called.
"""
resolved_name = for_name(fq_name)
if isinstance(resolved_name, (type, types.ClassType)):
# create new instance if this is type
return resolved_name()
elif isinstance(resolved_name, types.MethodType):
# bind the method
return getattr(resolved_name.im_class(), resolved_name.__name__)
else:
return resolved_name | Resolves and instantiates handler by fully qualified name.
First resolves the name using for_name call. Then if it resolves to a class,
instantiates a class, if it resolves to a method - instantiates the class and
binds method to the instance.
Args:
fq_name: fully qualified name of something to find.
Returns:
handler instance which is ready to be called. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L249-L270 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | try_serialize_handler | def try_serialize_handler(handler):
"""Try to serialize map/reduce handler.
Args:
handler: handler function/instance. Handler can be a function or an
instance of a callable class. In the latter case, the handler will
be serialized across slices to allow users to save states.
Returns:
serialized handler string or None.
"""
if (isinstance(handler, types.InstanceType) or # old style class
(isinstance(handler, object) and # new style class
not inspect.isfunction(handler) and
not inspect.ismethod(handler)) and
hasattr(handler, "__call__")):
return pickle.dumps(handler)
return None | python | def try_serialize_handler(handler):
"""Try to serialize map/reduce handler.
Args:
handler: handler function/instance. Handler can be a function or an
instance of a callable class. In the latter case, the handler will
be serialized across slices to allow users to save states.
Returns:
serialized handler string or None.
"""
if (isinstance(handler, types.InstanceType) or # old style class
(isinstance(handler, object) and # new style class
not inspect.isfunction(handler) and
not inspect.ismethod(handler)) and
hasattr(handler, "__call__")):
return pickle.dumps(handler)
return None | Try to serialize map/reduce handler.
Args:
handler: handler function/instance. Handler can be a function or an
instance of a callable class. In the latter case, the handler will
be serialized across slices to allow users to save states.
Returns:
serialized handler string or None. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L273-L290 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | is_generator | def is_generator(obj):
"""Return true if the object is generator or generator function.
Generator function objects provides same attributes as functions.
See isfunction.__doc__ for attributes listing.
Adapted from Python 2.6.
Args:
obj: an object to test.
Returns:
true if the object is generator function.
"""
if isinstance(obj, types.GeneratorType):
return True
CO_GENERATOR = 0x20
return bool(((inspect.isfunction(obj) or inspect.ismethod(obj)) and
obj.func_code.co_flags & CO_GENERATOR)) | python | def is_generator(obj):
"""Return true if the object is generator or generator function.
Generator function objects provides same attributes as functions.
See isfunction.__doc__ for attributes listing.
Adapted from Python 2.6.
Args:
obj: an object to test.
Returns:
true if the object is generator function.
"""
if isinstance(obj, types.GeneratorType):
return True
CO_GENERATOR = 0x20
return bool(((inspect.isfunction(obj) or inspect.ismethod(obj)) and
obj.func_code.co_flags & CO_GENERATOR)) | Return true if the object is generator or generator function.
Generator function objects provides same attributes as functions.
See isfunction.__doc__ for attributes listing.
Adapted from Python 2.6.
Args:
obj: an object to test.
Returns:
true if the object is generator function. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L306-L325 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | parse_bool | def parse_bool(obj):
"""Return true if the object represents a truth value, false otherwise.
For bool and numeric objects, uses Python's built-in bool function. For
str objects, checks string against a list of possible truth values.
Args:
obj: object to determine boolean value of; expected
Returns:
Boolean value according to 5.1 of Python docs if object is not a str
object. For str objects, return True if str is in TRUTH_VALUE_SET
and False otherwise.
http://docs.python.org/library/stdtypes.html
"""
if type(obj) is str:
TRUTH_VALUE_SET = ["true", "1", "yes", "t", "on"]
return obj.lower() in TRUTH_VALUE_SET
else:
return bool(obj) | python | def parse_bool(obj):
"""Return true if the object represents a truth value, false otherwise.
For bool and numeric objects, uses Python's built-in bool function. For
str objects, checks string against a list of possible truth values.
Args:
obj: object to determine boolean value of; expected
Returns:
Boolean value according to 5.1 of Python docs if object is not a str
object. For str objects, return True if str is in TRUTH_VALUE_SET
and False otherwise.
http://docs.python.org/library/stdtypes.html
"""
if type(obj) is str:
TRUTH_VALUE_SET = ["true", "1", "yes", "t", "on"]
return obj.lower() in TRUTH_VALUE_SET
else:
return bool(obj) | Return true if the object represents a truth value, false otherwise.
For bool and numeric objects, uses Python's built-in bool function. For
str objects, checks string against a list of possible truth values.
Args:
obj: object to determine boolean value of; expected
Returns:
Boolean value according to 5.1 of Python docs if object is not a str
object. For str objects, return True if str is in TRUTH_VALUE_SET
and False otherwise.
http://docs.python.org/library/stdtypes.html | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L333-L352 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | create_datastore_write_config | def create_datastore_write_config(mapreduce_spec):
"""Creates datastore config to use in write operations.
Args:
mapreduce_spec: current mapreduce specification as MapreduceSpec.
Returns:
an instance of datastore_rpc.Configuration to use for all write
operations in the mapreduce.
"""
force_writes = parse_bool(mapreduce_spec.params.get("force_writes", "false"))
if force_writes:
return datastore_rpc.Configuration(force_writes=force_writes)
else:
# dev server doesn't support force_writes.
return datastore_rpc.Configuration() | python | def create_datastore_write_config(mapreduce_spec):
"""Creates datastore config to use in write operations.
Args:
mapreduce_spec: current mapreduce specification as MapreduceSpec.
Returns:
an instance of datastore_rpc.Configuration to use for all write
operations in the mapreduce.
"""
force_writes = parse_bool(mapreduce_spec.params.get("force_writes", "false"))
if force_writes:
return datastore_rpc.Configuration(force_writes=force_writes)
else:
# dev server doesn't support force_writes.
return datastore_rpc.Configuration() | Creates datastore config to use in write operations.
Args:
mapreduce_spec: current mapreduce specification as MapreduceSpec.
Returns:
an instance of datastore_rpc.Configuration to use for all write
operations in the mapreduce. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L355-L370 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | _set_ndb_cache_policy | def _set_ndb_cache_policy():
"""Tell NDB to never cache anything in memcache or in-process.
This ensures that entities fetched from Datastore input_readers via NDB
will not bloat up the request memory size and Datastore Puts will avoid
doing calls to memcache. Without this you get soft memory limit exits,
which hurts overall throughput.
"""
ndb_ctx = ndb.get_context()
ndb_ctx.set_cache_policy(lambda key: False)
ndb_ctx.set_memcache_policy(lambda key: False) | python | def _set_ndb_cache_policy():
"""Tell NDB to never cache anything in memcache or in-process.
This ensures that entities fetched from Datastore input_readers via NDB
will not bloat up the request memory size and Datastore Puts will avoid
doing calls to memcache. Without this you get soft memory limit exits,
which hurts overall throughput.
"""
ndb_ctx = ndb.get_context()
ndb_ctx.set_cache_policy(lambda key: False)
ndb_ctx.set_memcache_policy(lambda key: False) | Tell NDB to never cache anything in memcache or in-process.
This ensures that entities fetched from Datastore input_readers via NDB
will not bloat up the request memory size and Datastore Puts will avoid
doing calls to memcache. Without this you get soft memory limit exits,
which hurts overall throughput. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L373-L383 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | _obj_to_path | def _obj_to_path(obj):
"""Returns the fully qualified path to the object.
Args:
obj: obj must be a new style top level class, or a top level function.
No inner function or static method.
Returns:
Fully qualified path to the object.
Raises:
TypeError: when argument obj has unsupported type.
ValueError: when obj can't be discovered on the top level.
"""
if obj is None:
return obj
if inspect.isclass(obj) or inspect.isfunction(obj):
fetched = getattr(sys.modules[obj.__module__], obj.__name__, None)
if fetched is None:
raise ValueError(
"Object %r must be defined on the top level of a module." % obj)
return "%s.%s" % (obj.__module__, obj.__name__)
raise TypeError("Unexpected type %s." % type(obj)) | python | def _obj_to_path(obj):
"""Returns the fully qualified path to the object.
Args:
obj: obj must be a new style top level class, or a top level function.
No inner function or static method.
Returns:
Fully qualified path to the object.
Raises:
TypeError: when argument obj has unsupported type.
ValueError: when obj can't be discovered on the top level.
"""
if obj is None:
return obj
if inspect.isclass(obj) or inspect.isfunction(obj):
fetched = getattr(sys.modules[obj.__module__], obj.__name__, None)
if fetched is None:
raise ValueError(
"Object %r must be defined on the top level of a module." % obj)
return "%s.%s" % (obj.__module__, obj.__name__)
raise TypeError("Unexpected type %s." % type(obj)) | Returns the fully qualified path to the object.
Args:
obj: obj must be a new style top level class, or a top level function.
No inner function or static method.
Returns:
Fully qualified path to the object.
Raises:
TypeError: when argument obj has unsupported type.
ValueError: when obj can't be discovered on the top level. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L386-L409 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/util.py | strip_prefix_from_items | def strip_prefix_from_items(prefix, items):
"""Strips out the prefix from each of the items if it is present.
Args:
prefix: the string for that you wish to strip from the beginning of each
of the items.
items: a list of strings that may or may not contain the prefix you want
to strip out.
Returns:
items_no_prefix: a copy of the list of items (same order) without the
prefix (if present).
"""
items_no_prefix = []
for item in items:
if item.startswith(prefix):
items_no_prefix.append(item[len(prefix):])
else:
items_no_prefix.append(item)
return items_no_prefix | python | def strip_prefix_from_items(prefix, items):
"""Strips out the prefix from each of the items if it is present.
Args:
prefix: the string for that you wish to strip from the beginning of each
of the items.
items: a list of strings that may or may not contain the prefix you want
to strip out.
Returns:
items_no_prefix: a copy of the list of items (same order) without the
prefix (if present).
"""
items_no_prefix = []
for item in items:
if item.startswith(prefix):
items_no_prefix.append(item[len(prefix):])
else:
items_no_prefix.append(item)
return items_no_prefix | Strips out the prefix from each of the items if it is present.
Args:
prefix: the string for that you wish to strip from the beginning of each
of the items.
items: a list of strings that may or may not contain the prefix you want
to strip out.
Returns:
items_no_prefix: a copy of the list of items (same order) without the
prefix (if present). | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/util.py#L412-L431 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | _run_task_hook | def _run_task_hook(hooks, method, task, queue_name):
"""Invokes hooks.method(task, queue_name).
Args:
hooks: A hooks.Hooks instance or None.
method: The name of the method to invoke on the hooks class e.g.
"enqueue_kickoff_task".
task: The taskqueue.Task to pass to the hook method.
queue_name: The name of the queue to pass to the hook method.
Returns:
True if the hooks.Hooks instance handled the method, False otherwise.
"""
if hooks is not None:
try:
getattr(hooks, method)(task, queue_name)
except NotImplementedError:
# Use the default task addition implementation.
return False
return True
return False | python | def _run_task_hook(hooks, method, task, queue_name):
"""Invokes hooks.method(task, queue_name).
Args:
hooks: A hooks.Hooks instance or None.
method: The name of the method to invoke on the hooks class e.g.
"enqueue_kickoff_task".
task: The taskqueue.Task to pass to the hook method.
queue_name: The name of the queue to pass to the hook method.
Returns:
True if the hooks.Hooks instance handled the method, False otherwise.
"""
if hooks is not None:
try:
getattr(hooks, method)(task, queue_name)
except NotImplementedError:
# Use the default task addition implementation.
return False
return True
return False | Invokes hooks.method(task, queue_name).
Args:
hooks: A hooks.Hooks instance or None.
method: The name of the method to invoke on the hooks class e.g.
"enqueue_kickoff_task".
task: The taskqueue.Task to pass to the hook method.
queue_name: The name of the queue to pass to the hook method.
Returns:
True if the hooks.Hooks instance handled the method, False otherwise. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L85-L106 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._drop_gracefully | def _drop_gracefully(self):
"""Drop worker task gracefully.
Set current shard_state to failed. Controller logic will take care of
other shards and the entire MR.
"""
shard_id = self.request.headers[util._MR_SHARD_ID_TASK_HEADER]
mr_id = self.request.headers[util._MR_ID_TASK_HEADER]
shard_state, mr_state = db.get([
model.ShardState.get_key_by_shard_id(shard_id),
model.MapreduceState.get_key_by_job_id(mr_id)])
if shard_state and shard_state.active:
shard_state.set_for_failure()
config = util.create_datastore_write_config(mr_state.mapreduce_spec)
shard_state.put(config=config) | python | def _drop_gracefully(self):
"""Drop worker task gracefully.
Set current shard_state to failed. Controller logic will take care of
other shards and the entire MR.
"""
shard_id = self.request.headers[util._MR_SHARD_ID_TASK_HEADER]
mr_id = self.request.headers[util._MR_ID_TASK_HEADER]
shard_state, mr_state = db.get([
model.ShardState.get_key_by_shard_id(shard_id),
model.MapreduceState.get_key_by_job_id(mr_id)])
if shard_state and shard_state.active:
shard_state.set_for_failure()
config = util.create_datastore_write_config(mr_state.mapreduce_spec)
shard_state.put(config=config) | Drop worker task gracefully.
Set current shard_state to failed. Controller logic will take care of
other shards and the entire MR. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L140-L155 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._try_acquire_lease | def _try_acquire_lease(self, shard_state, tstate):
"""Validate datastore and the task payload are consistent.
If so, attempt to get a lease on this slice's execution.
See model.ShardState doc on slice_start_time.
Args:
shard_state: model.ShardState from datastore.
tstate: model.TransientShardState from taskqueue paylod.
Returns:
A _TASK_DIRECTIVE enum. PROCEED_TASK if lock is acquired.
RETRY_TASK if task should be retried, DROP_TASK if task should
be dropped. Only old tasks (comparing to datastore state)
will be dropped. Future tasks are retried until they naturally
become old so that we don't ever stuck MR.
"""
# Controller will tally shard_states and properly handle the situation.
if not shard_state:
logging.warning("State not found for shard %s; Possible spurious task "
"execution. Dropping this task.",
tstate.shard_id)
return self._TASK_DIRECTIVE.DROP_TASK
if not shard_state.active:
logging.warning("Shard %s is not active. Possible spurious task "
"execution. Dropping this task.", tstate.shard_id)
logging.warning(str(shard_state))
return self._TASK_DIRECTIVE.DROP_TASK
# Validate shard retry count.
if shard_state.retries > tstate.retries:
logging.warning(
"Got shard %s from previous shard retry %s. Possible spurious "
"task execution. Dropping this task.",
tstate.shard_id,
tstate.retries)
logging.warning(str(shard_state))
return self._TASK_DIRECTIVE.DROP_TASK
elif shard_state.retries < tstate.retries:
# By the end of last slice, task enqueue succeeded but datastore commit
# failed. That transaction will be retried and adding the same task
# will pass.
logging.warning(
"ShardState for %s is behind slice. Waiting for it to catch up",
shard_state.shard_id)
return self._TASK_DIRECTIVE.RETRY_TASK
# Validate slice id.
# Taskqueue executes old successful tasks.
if shard_state.slice_id > tstate.slice_id:
logging.warning(
"Task %s-%s is behind ShardState %s. Dropping task.""",
tstate.shard_id, tstate.slice_id, shard_state.slice_id)
return self._TASK_DIRECTIVE.DROP_TASK
# By the end of last slice, task enqueue succeeded but datastore commit
# failed. That transaction will be retried and adding the same task
# will pass. User data is duplicated in this case.
elif shard_state.slice_id < tstate.slice_id:
logging.warning(
"Task %s-%s is ahead of ShardState %s. Waiting for it to catch up.",
tstate.shard_id, tstate.slice_id, shard_state.slice_id)
return self._TASK_DIRECTIVE.RETRY_TASK
# Check potential duplicated tasks for the same slice.
# See model.ShardState doc.
if shard_state.slice_start_time:
countdown = self._wait_time(shard_state,
parameters._LEASE_DURATION_SEC)
if countdown > 0:
logging.warning(
"Last retry of slice %s-%s may be still running."
"Will try again in %s seconds", tstate.shard_id, tstate.slice_id,
countdown)
# TODO(user): There might be a better way. Taskqueue's countdown
# only applies to add new tasks, not retry of tasks.
# Reduce contention.
time.sleep(countdown)
return self._TASK_DIRECTIVE.RETRY_TASK
# lease could have expired. Verify with logs API.
else:
if self._wait_time(shard_state,
parameters._MAX_LEASE_DURATION_SEC):
if not self._has_old_request_ended(shard_state):
logging.warning(
"Last retry of slice %s-%s is still in flight with request_id "
"%s. Will try again later.", tstate.shard_id, tstate.slice_id,
shard_state.slice_request_id)
return self._TASK_DIRECTIVE.RETRY_TASK
else:
logging.warning(
"Last retry of slice %s-%s has no log entry and has"
"timed out after %s seconds",
tstate.shard_id, tstate.slice_id,
parameters._MAX_LEASE_DURATION_SEC)
# Lease expired or slice_start_time not set.
config = util.create_datastore_write_config(tstate.mapreduce_spec)
@db.transactional(retries=5)
def _tx():
"""Use datastore to set slice_start_time to now.
If failed for any reason, raise error to retry the task (hence all
the previous validation code). The task would die naturally eventually.
Raises:
Rollback: If the shard state is missing.
Returns:
A _TASK_DIRECTIVE enum.
"""
fresh_state = model.ShardState.get_by_shard_id(tstate.shard_id)
if not fresh_state:
logging.warning("ShardState missing.")
raise db.Rollback()
if (fresh_state.active and
fresh_state.slice_id == shard_state.slice_id and
fresh_state.slice_start_time == shard_state.slice_start_time):
shard_state.slice_start_time = datetime.datetime.now()
shard_state.slice_request_id = os.environ.get("REQUEST_LOG_ID")
shard_state.acquired_once = True
shard_state.put(config=config)
return self._TASK_DIRECTIVE.PROCEED_TASK
else:
logging.warning(
"Contention on slice %s-%s execution. Will retry again.",
tstate.shard_id, tstate.slice_id)
# One proposer should win. In case all lost, back off arbitrarily.
time.sleep(random.randrange(1, 5))
return self._TASK_DIRECTIVE.RETRY_TASK
return _tx() | python | def _try_acquire_lease(self, shard_state, tstate):
"""Validate datastore and the task payload are consistent.
If so, attempt to get a lease on this slice's execution.
See model.ShardState doc on slice_start_time.
Args:
shard_state: model.ShardState from datastore.
tstate: model.TransientShardState from taskqueue paylod.
Returns:
A _TASK_DIRECTIVE enum. PROCEED_TASK if lock is acquired.
RETRY_TASK if task should be retried, DROP_TASK if task should
be dropped. Only old tasks (comparing to datastore state)
will be dropped. Future tasks are retried until they naturally
become old so that we don't ever stuck MR.
"""
# Controller will tally shard_states and properly handle the situation.
if not shard_state:
logging.warning("State not found for shard %s; Possible spurious task "
"execution. Dropping this task.",
tstate.shard_id)
return self._TASK_DIRECTIVE.DROP_TASK
if not shard_state.active:
logging.warning("Shard %s is not active. Possible spurious task "
"execution. Dropping this task.", tstate.shard_id)
logging.warning(str(shard_state))
return self._TASK_DIRECTIVE.DROP_TASK
# Validate shard retry count.
if shard_state.retries > tstate.retries:
logging.warning(
"Got shard %s from previous shard retry %s. Possible spurious "
"task execution. Dropping this task.",
tstate.shard_id,
tstate.retries)
logging.warning(str(shard_state))
return self._TASK_DIRECTIVE.DROP_TASK
elif shard_state.retries < tstate.retries:
# By the end of last slice, task enqueue succeeded but datastore commit
# failed. That transaction will be retried and adding the same task
# will pass.
logging.warning(
"ShardState for %s is behind slice. Waiting for it to catch up",
shard_state.shard_id)
return self._TASK_DIRECTIVE.RETRY_TASK
# Validate slice id.
# Taskqueue executes old successful tasks.
if shard_state.slice_id > tstate.slice_id:
logging.warning(
"Task %s-%s is behind ShardState %s. Dropping task.""",
tstate.shard_id, tstate.slice_id, shard_state.slice_id)
return self._TASK_DIRECTIVE.DROP_TASK
# By the end of last slice, task enqueue succeeded but datastore commit
# failed. That transaction will be retried and adding the same task
# will pass. User data is duplicated in this case.
elif shard_state.slice_id < tstate.slice_id:
logging.warning(
"Task %s-%s is ahead of ShardState %s. Waiting for it to catch up.",
tstate.shard_id, tstate.slice_id, shard_state.slice_id)
return self._TASK_DIRECTIVE.RETRY_TASK
# Check potential duplicated tasks for the same slice.
# See model.ShardState doc.
if shard_state.slice_start_time:
countdown = self._wait_time(shard_state,
parameters._LEASE_DURATION_SEC)
if countdown > 0:
logging.warning(
"Last retry of slice %s-%s may be still running."
"Will try again in %s seconds", tstate.shard_id, tstate.slice_id,
countdown)
# TODO(user): There might be a better way. Taskqueue's countdown
# only applies to add new tasks, not retry of tasks.
# Reduce contention.
time.sleep(countdown)
return self._TASK_DIRECTIVE.RETRY_TASK
# lease could have expired. Verify with logs API.
else:
if self._wait_time(shard_state,
parameters._MAX_LEASE_DURATION_SEC):
if not self._has_old_request_ended(shard_state):
logging.warning(
"Last retry of slice %s-%s is still in flight with request_id "
"%s. Will try again later.", tstate.shard_id, tstate.slice_id,
shard_state.slice_request_id)
return self._TASK_DIRECTIVE.RETRY_TASK
else:
logging.warning(
"Last retry of slice %s-%s has no log entry and has"
"timed out after %s seconds",
tstate.shard_id, tstate.slice_id,
parameters._MAX_LEASE_DURATION_SEC)
# Lease expired or slice_start_time not set.
config = util.create_datastore_write_config(tstate.mapreduce_spec)
@db.transactional(retries=5)
def _tx():
"""Use datastore to set slice_start_time to now.
If failed for any reason, raise error to retry the task (hence all
the previous validation code). The task would die naturally eventually.
Raises:
Rollback: If the shard state is missing.
Returns:
A _TASK_DIRECTIVE enum.
"""
fresh_state = model.ShardState.get_by_shard_id(tstate.shard_id)
if not fresh_state:
logging.warning("ShardState missing.")
raise db.Rollback()
if (fresh_state.active and
fresh_state.slice_id == shard_state.slice_id and
fresh_state.slice_start_time == shard_state.slice_start_time):
shard_state.slice_start_time = datetime.datetime.now()
shard_state.slice_request_id = os.environ.get("REQUEST_LOG_ID")
shard_state.acquired_once = True
shard_state.put(config=config)
return self._TASK_DIRECTIVE.PROCEED_TASK
else:
logging.warning(
"Contention on slice %s-%s execution. Will retry again.",
tstate.shard_id, tstate.slice_id)
# One proposer should win. In case all lost, back off arbitrarily.
time.sleep(random.randrange(1, 5))
return self._TASK_DIRECTIVE.RETRY_TASK
return _tx() | Validate datastore and the task payload are consistent.
If so, attempt to get a lease on this slice's execution.
See model.ShardState doc on slice_start_time.
Args:
shard_state: model.ShardState from datastore.
tstate: model.TransientShardState from taskqueue paylod.
Returns:
A _TASK_DIRECTIVE enum. PROCEED_TASK if lock is acquired.
RETRY_TASK if task should be retried, DROP_TASK if task should
be dropped. Only old tasks (comparing to datastore state)
will be dropped. Future tasks are retried until they naturally
become old so that we don't ever stuck MR. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L157-L288 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._has_old_request_ended | def _has_old_request_ended(self, shard_state):
"""Whether previous slice retry has ended according to Logs API.
Args:
shard_state: shard state.
Returns:
True if the request of previous slice retry has ended. False if it has
not or unknown.
"""
assert shard_state.slice_start_time is not None
assert shard_state.slice_request_id is not None
request_ids = [shard_state.slice_request_id]
logs = None
try:
logs = list(logservice.fetch(request_ids=request_ids))
except (apiproxy_errors.FeatureNotEnabledError,
apiproxy_errors.CapabilityDisabledError) as e:
# Managed VMs do not have access to the logservice API
# See https://groups.google.com/forum/#!topic/app-engine-managed-vms/r8i65uiFW0w
logging.warning("Ignoring exception: %s", e)
if not logs or not logs[0].finished:
return False
return True | python | def _has_old_request_ended(self, shard_state):
"""Whether previous slice retry has ended according to Logs API.
Args:
shard_state: shard state.
Returns:
True if the request of previous slice retry has ended. False if it has
not or unknown.
"""
assert shard_state.slice_start_time is not None
assert shard_state.slice_request_id is not None
request_ids = [shard_state.slice_request_id]
logs = None
try:
logs = list(logservice.fetch(request_ids=request_ids))
except (apiproxy_errors.FeatureNotEnabledError,
apiproxy_errors.CapabilityDisabledError) as e:
# Managed VMs do not have access to the logservice API
# See https://groups.google.com/forum/#!topic/app-engine-managed-vms/r8i65uiFW0w
logging.warning("Ignoring exception: %s", e)
if not logs or not logs[0].finished:
return False
return True | Whether previous slice retry has ended according to Logs API.
Args:
shard_state: shard state.
Returns:
True if the request of previous slice retry has ended. False if it has
not or unknown. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L290-L314 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._wait_time | def _wait_time(self, shard_state, secs, now=datetime.datetime.now):
"""Time to wait until slice_start_time is secs ago from now.
Args:
shard_state: shard state.
secs: duration in seconds.
now: a func that gets now.
Returns:
0 if no wait. A positive int in seconds otherwise. Always around up.
"""
assert shard_state.slice_start_time is not None
delta = now() - shard_state.slice_start_time
duration = datetime.timedelta(seconds=secs)
if delta < duration:
return util.total_seconds(duration - delta)
else:
return 0 | python | def _wait_time(self, shard_state, secs, now=datetime.datetime.now):
"""Time to wait until slice_start_time is secs ago from now.
Args:
shard_state: shard state.
secs: duration in seconds.
now: a func that gets now.
Returns:
0 if no wait. A positive int in seconds otherwise. Always around up.
"""
assert shard_state.slice_start_time is not None
delta = now() - shard_state.slice_start_time
duration = datetime.timedelta(seconds=secs)
if delta < duration:
return util.total_seconds(duration - delta)
else:
return 0 | Time to wait until slice_start_time is secs ago from now.
Args:
shard_state: shard state.
secs: duration in seconds.
now: a func that gets now.
Returns:
0 if no wait. A positive int in seconds otherwise. Always around up. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L316-L333 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._try_free_lease | def _try_free_lease(self, shard_state, slice_retry=False):
"""Try to free lease.
A lightweight transaction to update shard_state and unset
slice_start_time to allow the next retry to happen without blocking.
We don't care if this fails or not because the lease will expire
anyway.
Under normal execution, _save_state_and_schedule_next is the exit point.
It updates/saves shard state and schedules the next slice or returns.
Other exit points are:
1. _are_states_consistent: at the beginning of handle, checks
if datastore states and the task are in sync.
If not, raise or return.
2. _attempt_slice_retry: may raise exception to taskqueue.
3. _save_state_and_schedule_next: may raise exception when taskqueue/db
unreachable.
This handler should try to free the lease on every exceptional exit point.
Args:
shard_state: model.ShardState.
slice_retry: whether to count this as a failed slice execution.
"""
@db.transactional
def _tx():
fresh_state = model.ShardState.get_by_shard_id(shard_state.shard_id)
if fresh_state and fresh_state.active:
# Free lease.
fresh_state.slice_start_time = None
fresh_state.slice_request_id = None
if slice_retry:
fresh_state.slice_retries += 1
fresh_state.put()
try:
_tx()
# pylint: disable=broad-except
except Exception, e:
logging.warning(e)
logging.warning(
"Release lock for shard %s failed. Wait for lease to expire.",
shard_state.shard_id) | python | def _try_free_lease(self, shard_state, slice_retry=False):
"""Try to free lease.
A lightweight transaction to update shard_state and unset
slice_start_time to allow the next retry to happen without blocking.
We don't care if this fails or not because the lease will expire
anyway.
Under normal execution, _save_state_and_schedule_next is the exit point.
It updates/saves shard state and schedules the next slice or returns.
Other exit points are:
1. _are_states_consistent: at the beginning of handle, checks
if datastore states and the task are in sync.
If not, raise or return.
2. _attempt_slice_retry: may raise exception to taskqueue.
3. _save_state_and_schedule_next: may raise exception when taskqueue/db
unreachable.
This handler should try to free the lease on every exceptional exit point.
Args:
shard_state: model.ShardState.
slice_retry: whether to count this as a failed slice execution.
"""
@db.transactional
def _tx():
fresh_state = model.ShardState.get_by_shard_id(shard_state.shard_id)
if fresh_state and fresh_state.active:
# Free lease.
fresh_state.slice_start_time = None
fresh_state.slice_request_id = None
if slice_retry:
fresh_state.slice_retries += 1
fresh_state.put()
try:
_tx()
# pylint: disable=broad-except
except Exception, e:
logging.warning(e)
logging.warning(
"Release lock for shard %s failed. Wait for lease to expire.",
shard_state.shard_id) | Try to free lease.
A lightweight transaction to update shard_state and unset
slice_start_time to allow the next retry to happen without blocking.
We don't care if this fails or not because the lease will expire
anyway.
Under normal execution, _save_state_and_schedule_next is the exit point.
It updates/saves shard state and schedules the next slice or returns.
Other exit points are:
1. _are_states_consistent: at the beginning of handle, checks
if datastore states and the task are in sync.
If not, raise or return.
2. _attempt_slice_retry: may raise exception to taskqueue.
3. _save_state_and_schedule_next: may raise exception when taskqueue/db
unreachable.
This handler should try to free the lease on every exceptional exit point.
Args:
shard_state: model.ShardState.
slice_retry: whether to count this as a failed slice execution. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L335-L376 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._maintain_LC | def _maintain_LC(self, obj, slice_id, last_slice=False, begin_slice=True,
shard_ctx=None, slice_ctx=None):
"""Makes sure shard life cycle interface are respected.
Args:
obj: the obj that may have implemented _ShardLifeCycle.
slice_id: current slice_id
last_slice: whether this is the last slice.
begin_slice: whether this is the beginning or the end of a slice.
shard_ctx: shard ctx for dependency injection. If None, it will be read
from self.
slice_ctx: slice ctx for dependency injection. If None, it will be read
from self.
"""
if obj is None or not isinstance(obj, shard_life_cycle._ShardLifeCycle):
return
shard_context = shard_ctx or self.shard_context
slice_context = slice_ctx or self.slice_context
if begin_slice:
if slice_id == 0:
obj.begin_shard(shard_context)
obj.begin_slice(slice_context)
else:
obj.end_slice(slice_context)
if last_slice:
obj.end_shard(shard_context) | python | def _maintain_LC(self, obj, slice_id, last_slice=False, begin_slice=True,
shard_ctx=None, slice_ctx=None):
"""Makes sure shard life cycle interface are respected.
Args:
obj: the obj that may have implemented _ShardLifeCycle.
slice_id: current slice_id
last_slice: whether this is the last slice.
begin_slice: whether this is the beginning or the end of a slice.
shard_ctx: shard ctx for dependency injection. If None, it will be read
from self.
slice_ctx: slice ctx for dependency injection. If None, it will be read
from self.
"""
if obj is None or not isinstance(obj, shard_life_cycle._ShardLifeCycle):
return
shard_context = shard_ctx or self.shard_context
slice_context = slice_ctx or self.slice_context
if begin_slice:
if slice_id == 0:
obj.begin_shard(shard_context)
obj.begin_slice(slice_context)
else:
obj.end_slice(slice_context)
if last_slice:
obj.end_shard(shard_context) | Makes sure shard life cycle interface are respected.
Args:
obj: the obj that may have implemented _ShardLifeCycle.
slice_id: current slice_id
last_slice: whether this is the last slice.
begin_slice: whether this is the beginning or the end of a slice.
shard_ctx: shard ctx for dependency injection. If None, it will be read
from self.
slice_ctx: slice ctx for dependency injection. If None, it will be read
from self. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L378-L404 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler.handle | def handle(self):
"""Handle request.
This method has to be careful to pass the same ShardState instance to
its subroutines calls if the calls mutate or read from ShardState.
Note especially that Context instance caches and updates the ShardState
instance.
Returns:
Set HTTP status code and always returns None.
"""
# Reconstruct basic states.
self._start_time = self._time()
shard_id = self.request.headers[util._MR_SHARD_ID_TASK_HEADER]
mr_id = self.request.headers[util._MR_ID_TASK_HEADER]
spec = model.MapreduceSpec._get_mapreduce_spec(mr_id)
shard_state, control = db.get([
model.ShardState.get_key_by_shard_id(shard_id),
model.MapreduceControl.get_key_by_job_id(mr_id),
])
# Set context before any IO code is called.
ctx = context.Context(spec, shard_state,
task_retry_count=self.task_retry_count())
context.Context._set(ctx)
# Unmarshall input reader, output writer, and other transient states.
tstate = model.TransientShardState.from_request(self.request)
# Try acquire a lease on the shard.
if shard_state:
is_this_a_retry = shard_state.acquired_once
task_directive = self._try_acquire_lease(shard_state, tstate)
if task_directive in (self._TASK_DIRECTIVE.RETRY_TASK,
self._TASK_DIRECTIVE.DROP_TASK):
return self.__return(shard_state, tstate, task_directive)
assert task_directive == self._TASK_DIRECTIVE.PROCEED_TASK
# Abort shard if received signal.
if control and control.command == model.MapreduceControl.ABORT:
task_directive = self._TASK_DIRECTIVE.ABORT_SHARD
return self.__return(shard_state, tstate, task_directive)
# Retry shard if user disabled slice retry.
if (is_this_a_retry and
parameters.config.TASK_MAX_DATA_PROCESSING_ATTEMPTS <= 1):
task_directive = self._TASK_DIRECTIVE.RETRY_SHARD
return self.__return(shard_state, tstate, task_directive)
# TODO(user): Find a better way to set these per thread configs.
# E.g. what if user change it?
util._set_ndb_cache_policy()
job_config = map_job.JobConfig._to_map_job_config(
spec,
os.environ.get("HTTP_X_APPENGINE_QUEUENAME"))
job_context = map_job_context.JobContext(job_config)
self.shard_context = map_job_context.ShardContext(job_context, shard_state)
self.slice_context = map_job_context.SliceContext(self.shard_context,
shard_state,
tstate)
try:
slice_id = tstate.slice_id
self._lc_start_slice(tstate, slice_id)
if shard_state.is_input_finished():
self._lc_end_slice(tstate, slice_id, last_slice=True)
# Finalize the stream and set status if there's no more input.
if (tstate.output_writer and
isinstance(tstate.output_writer, output_writers.OutputWriter)):
# It's possible that finalization is successful but
# saving state failed. In this case this shard will retry upon
# finalization error.
# TODO(user): make finalize method idempotent!
tstate.output_writer.finalize(ctx, shard_state)
shard_state.set_for_success()
return self.__return(shard_state, tstate, task_directive)
if is_this_a_retry:
task_directive = self._attempt_slice_recovery(shard_state, tstate)
if task_directive != self._TASK_DIRECTIVE.PROCEED_TASK:
return self.__return(shard_state, tstate, task_directive)
last_slice = self._process_inputs(
tstate.input_reader, shard_state, tstate, ctx)
self._lc_end_slice(tstate, slice_id)
ctx.flush()
if last_slice:
# We're done processing data but we still need to finalize the output
# stream. We save this condition in datastore and force a new slice.
# That way if finalize fails no input data will be retried.
shard_state.set_input_finished()
# pylint: disable=broad-except
except Exception, e:
logging.warning("Shard %s got error.", shard_state.shard_id)
logging.error(traceback.format_exc())
# Fail fast.
if type(e) is errors.FailJobError:
logging.error("Got FailJobError.")
task_directive = self._TASK_DIRECTIVE.FAIL_TASK
else:
task_directive = self._TASK_DIRECTIVE.RETRY_SLICE
self.__return(shard_state, tstate, task_directive) | python | def handle(self):
"""Handle request.
This method has to be careful to pass the same ShardState instance to
its subroutines calls if the calls mutate or read from ShardState.
Note especially that Context instance caches and updates the ShardState
instance.
Returns:
Set HTTP status code and always returns None.
"""
# Reconstruct basic states.
self._start_time = self._time()
shard_id = self.request.headers[util._MR_SHARD_ID_TASK_HEADER]
mr_id = self.request.headers[util._MR_ID_TASK_HEADER]
spec = model.MapreduceSpec._get_mapreduce_spec(mr_id)
shard_state, control = db.get([
model.ShardState.get_key_by_shard_id(shard_id),
model.MapreduceControl.get_key_by_job_id(mr_id),
])
# Set context before any IO code is called.
ctx = context.Context(spec, shard_state,
task_retry_count=self.task_retry_count())
context.Context._set(ctx)
# Unmarshall input reader, output writer, and other transient states.
tstate = model.TransientShardState.from_request(self.request)
# Try acquire a lease on the shard.
if shard_state:
is_this_a_retry = shard_state.acquired_once
task_directive = self._try_acquire_lease(shard_state, tstate)
if task_directive in (self._TASK_DIRECTIVE.RETRY_TASK,
self._TASK_DIRECTIVE.DROP_TASK):
return self.__return(shard_state, tstate, task_directive)
assert task_directive == self._TASK_DIRECTIVE.PROCEED_TASK
# Abort shard if received signal.
if control and control.command == model.MapreduceControl.ABORT:
task_directive = self._TASK_DIRECTIVE.ABORT_SHARD
return self.__return(shard_state, tstate, task_directive)
# Retry shard if user disabled slice retry.
if (is_this_a_retry and
parameters.config.TASK_MAX_DATA_PROCESSING_ATTEMPTS <= 1):
task_directive = self._TASK_DIRECTIVE.RETRY_SHARD
return self.__return(shard_state, tstate, task_directive)
# TODO(user): Find a better way to set these per thread configs.
# E.g. what if user change it?
util._set_ndb_cache_policy()
job_config = map_job.JobConfig._to_map_job_config(
spec,
os.environ.get("HTTP_X_APPENGINE_QUEUENAME"))
job_context = map_job_context.JobContext(job_config)
self.shard_context = map_job_context.ShardContext(job_context, shard_state)
self.slice_context = map_job_context.SliceContext(self.shard_context,
shard_state,
tstate)
try:
slice_id = tstate.slice_id
self._lc_start_slice(tstate, slice_id)
if shard_state.is_input_finished():
self._lc_end_slice(tstate, slice_id, last_slice=True)
# Finalize the stream and set status if there's no more input.
if (tstate.output_writer and
isinstance(tstate.output_writer, output_writers.OutputWriter)):
# It's possible that finalization is successful but
# saving state failed. In this case this shard will retry upon
# finalization error.
# TODO(user): make finalize method idempotent!
tstate.output_writer.finalize(ctx, shard_state)
shard_state.set_for_success()
return self.__return(shard_state, tstate, task_directive)
if is_this_a_retry:
task_directive = self._attempt_slice_recovery(shard_state, tstate)
if task_directive != self._TASK_DIRECTIVE.PROCEED_TASK:
return self.__return(shard_state, tstate, task_directive)
last_slice = self._process_inputs(
tstate.input_reader, shard_state, tstate, ctx)
self._lc_end_slice(tstate, slice_id)
ctx.flush()
if last_slice:
# We're done processing data but we still need to finalize the output
# stream. We save this condition in datastore and force a new slice.
# That way if finalize fails no input data will be retried.
shard_state.set_input_finished()
# pylint: disable=broad-except
except Exception, e:
logging.warning("Shard %s got error.", shard_state.shard_id)
logging.error(traceback.format_exc())
# Fail fast.
if type(e) is errors.FailJobError:
logging.error("Got FailJobError.")
task_directive = self._TASK_DIRECTIVE.FAIL_TASK
else:
task_directive = self._TASK_DIRECTIVE.RETRY_SLICE
self.__return(shard_state, tstate, task_directive) | Handle request.
This method has to be careful to pass the same ShardState instance to
its subroutines calls if the calls mutate or read from ShardState.
Note especially that Context instance caches and updates the ShardState
instance.
Returns:
Set HTTP status code and always returns None. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L419-L526 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler.__return | def __return(self, shard_state, tstate, task_directive):
"""Handler should always call this as the last statement."""
task_directive = self._set_state(shard_state, tstate, task_directive)
self._save_state_and_schedule_next(shard_state, tstate, task_directive)
context.Context._set(None) | python | def __return(self, shard_state, tstate, task_directive):
"""Handler should always call this as the last statement."""
task_directive = self._set_state(shard_state, tstate, task_directive)
self._save_state_and_schedule_next(shard_state, tstate, task_directive)
context.Context._set(None) | Handler should always call this as the last statement. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L528-L532 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._process_inputs | def _process_inputs(self,
input_reader,
shard_state,
tstate,
ctx):
"""Read inputs, process them, and write out outputs.
This is the core logic of MapReduce. It reads inputs from input reader,
invokes user specified mapper function, and writes output with
output writer. It also updates shard_state accordingly.
e.g. if shard processing is done, set shard_state.active to False.
If errors.FailJobError is caught, it will fail this MR job.
All other exceptions will be logged and raised to taskqueue for retry
until the number of retries exceeds a limit.
Args:
input_reader: input reader.
shard_state: shard state.
tstate: transient shard state.
ctx: mapreduce context.
Returns:
Whether this shard has finished processing all its input split.
"""
processing_limit = self._processing_limit(tstate.mapreduce_spec)
if processing_limit == 0:
return
finished_shard = True
# Input reader may not be an iterator. It is only a container.
iterator = iter(input_reader)
while True:
try:
entity = iterator.next()
except StopIteration:
break
# Reading input got exception. If we assume
# 1. The input reader have done enough retries.
# 2. The input reader can still serialize correctly after this exception.
# 3. The input reader, upon resume, will try to re-read this failed
# record.
# 4. This exception doesn't imply the input reader is permanently stuck.
# we can serialize current slice immediately to avoid duplicated
# outputs.
# TODO(user): Validate these assumptions on all readers. MR should
# also have a way to detect fake forward progress.
if isinstance(entity, db.Model):
shard_state.last_work_item = repr(entity.key())
elif isinstance(entity, ndb.Model):
shard_state.last_work_item = repr(entity.key)
else:
shard_state.last_work_item = repr(entity)[:100]
processing_limit -= 1
if not self._process_datum(
entity, input_reader, ctx, tstate):
finished_shard = False
break
elif processing_limit == 0:
finished_shard = False
break
# Flush context and its pools.
self.slice_context.incr(
context.COUNTER_MAPPER_WALLTIME_MS,
int((self._time() - self._start_time)*1000))
return finished_shard | python | def _process_inputs(self,
input_reader,
shard_state,
tstate,
ctx):
"""Read inputs, process them, and write out outputs.
This is the core logic of MapReduce. It reads inputs from input reader,
invokes user specified mapper function, and writes output with
output writer. It also updates shard_state accordingly.
e.g. if shard processing is done, set shard_state.active to False.
If errors.FailJobError is caught, it will fail this MR job.
All other exceptions will be logged and raised to taskqueue for retry
until the number of retries exceeds a limit.
Args:
input_reader: input reader.
shard_state: shard state.
tstate: transient shard state.
ctx: mapreduce context.
Returns:
Whether this shard has finished processing all its input split.
"""
processing_limit = self._processing_limit(tstate.mapreduce_spec)
if processing_limit == 0:
return
finished_shard = True
# Input reader may not be an iterator. It is only a container.
iterator = iter(input_reader)
while True:
try:
entity = iterator.next()
except StopIteration:
break
# Reading input got exception. If we assume
# 1. The input reader have done enough retries.
# 2. The input reader can still serialize correctly after this exception.
# 3. The input reader, upon resume, will try to re-read this failed
# record.
# 4. This exception doesn't imply the input reader is permanently stuck.
# we can serialize current slice immediately to avoid duplicated
# outputs.
# TODO(user): Validate these assumptions on all readers. MR should
# also have a way to detect fake forward progress.
if isinstance(entity, db.Model):
shard_state.last_work_item = repr(entity.key())
elif isinstance(entity, ndb.Model):
shard_state.last_work_item = repr(entity.key)
else:
shard_state.last_work_item = repr(entity)[:100]
processing_limit -= 1
if not self._process_datum(
entity, input_reader, ctx, tstate):
finished_shard = False
break
elif processing_limit == 0:
finished_shard = False
break
# Flush context and its pools.
self.slice_context.incr(
context.COUNTER_MAPPER_WALLTIME_MS,
int((self._time() - self._start_time)*1000))
return finished_shard | Read inputs, process them, and write out outputs.
This is the core logic of MapReduce. It reads inputs from input reader,
invokes user specified mapper function, and writes output with
output writer. It also updates shard_state accordingly.
e.g. if shard processing is done, set shard_state.active to False.
If errors.FailJobError is caught, it will fail this MR job.
All other exceptions will be logged and raised to taskqueue for retry
until the number of retries exceeds a limit.
Args:
input_reader: input reader.
shard_state: shard state.
tstate: transient shard state.
ctx: mapreduce context.
Returns:
Whether this shard has finished processing all its input split. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L534-L605 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._process_datum | def _process_datum(self, data, input_reader, ctx, transient_shard_state):
"""Process a single data piece.
Call mapper handler on the data.
Args:
data: a datum to process.
input_reader: input reader.
ctx: mapreduce context
transient_shard_state: transient shard state.
Returns:
True if scan should be continued, False if scan should be stopped.
"""
if data is not input_readers.ALLOW_CHECKPOINT:
self.slice_context.incr(context.COUNTER_MAPPER_CALLS)
handler = transient_shard_state.handler
if isinstance(handler, map_job.Mapper):
handler(self.slice_context, data)
else:
if input_reader.expand_parameters:
result = handler(*data)
else:
result = handler(data)
if util.is_generator(result):
for output in result:
if isinstance(output, operation.Operation):
output(ctx)
else:
output_writer = transient_shard_state.output_writer
if not output_writer:
logging.warning(
"Handler yielded %s, but no output writer is set.", output)
else:
output_writer.write(output)
if self._time() - self._start_time >= parameters.config._SLICE_DURATION_SEC:
return False
return True | python | def _process_datum(self, data, input_reader, ctx, transient_shard_state):
"""Process a single data piece.
Call mapper handler on the data.
Args:
data: a datum to process.
input_reader: input reader.
ctx: mapreduce context
transient_shard_state: transient shard state.
Returns:
True if scan should be continued, False if scan should be stopped.
"""
if data is not input_readers.ALLOW_CHECKPOINT:
self.slice_context.incr(context.COUNTER_MAPPER_CALLS)
handler = transient_shard_state.handler
if isinstance(handler, map_job.Mapper):
handler(self.slice_context, data)
else:
if input_reader.expand_parameters:
result = handler(*data)
else:
result = handler(data)
if util.is_generator(result):
for output in result:
if isinstance(output, operation.Operation):
output(ctx)
else:
output_writer = transient_shard_state.output_writer
if not output_writer:
logging.warning(
"Handler yielded %s, but no output writer is set.", output)
else:
output_writer.write(output)
if self._time() - self._start_time >= parameters.config._SLICE_DURATION_SEC:
return False
return True | Process a single data piece.
Call mapper handler on the data.
Args:
data: a datum to process.
input_reader: input reader.
ctx: mapreduce context
transient_shard_state: transient shard state.
Returns:
True if scan should be continued, False if scan should be stopped. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L607-L648 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._set_state | def _set_state(self, shard_state, tstate, task_directive):
"""Set shard_state and tstate based on task_directive.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
task_directive: self._TASK_DIRECTIVE for current shard.
Returns:
A _TASK_DIRECTIVE enum.
PROCEED_TASK if task should proceed normally.
RETRY_SHARD if shard should be retried.
RETRY_SLICE if slice should be retried.
FAIL_TASK if sahrd should fail.
RECOVER_SLICE if slice should be recovered.
ABORT_SHARD if shard should be aborted.
RETRY_TASK if task should be retried.
DROP_TASK if task should be dropped.
"""
if task_directive in (self._TASK_DIRECTIVE.RETRY_TASK,
self._TASK_DIRECTIVE.DROP_TASK):
return task_directive
if task_directive == self._TASK_DIRECTIVE.ABORT_SHARD:
shard_state.set_for_abort()
return task_directive
if task_directive == self._TASK_DIRECTIVE.PROCEED_TASK:
shard_state.advance_for_next_slice()
tstate.advance_for_next_slice()
return task_directive
if task_directive == self._TASK_DIRECTIVE.RECOVER_SLICE:
tstate.advance_for_next_slice(recovery_slice=True)
shard_state.advance_for_next_slice(recovery_slice=True)
return task_directive
if task_directive == self._TASK_DIRECTIVE.RETRY_SLICE:
task_directive = self._attempt_slice_retry(shard_state, tstate)
if task_directive == self._TASK_DIRECTIVE.RETRY_SHARD:
task_directive = self._attempt_shard_retry(shard_state, tstate)
if task_directive == self._TASK_DIRECTIVE.FAIL_TASK:
shard_state.set_for_failure()
return task_directive | python | def _set_state(self, shard_state, tstate, task_directive):
"""Set shard_state and tstate based on task_directive.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
task_directive: self._TASK_DIRECTIVE for current shard.
Returns:
A _TASK_DIRECTIVE enum.
PROCEED_TASK if task should proceed normally.
RETRY_SHARD if shard should be retried.
RETRY_SLICE if slice should be retried.
FAIL_TASK if sahrd should fail.
RECOVER_SLICE if slice should be recovered.
ABORT_SHARD if shard should be aborted.
RETRY_TASK if task should be retried.
DROP_TASK if task should be dropped.
"""
if task_directive in (self._TASK_DIRECTIVE.RETRY_TASK,
self._TASK_DIRECTIVE.DROP_TASK):
return task_directive
if task_directive == self._TASK_DIRECTIVE.ABORT_SHARD:
shard_state.set_for_abort()
return task_directive
if task_directive == self._TASK_DIRECTIVE.PROCEED_TASK:
shard_state.advance_for_next_slice()
tstate.advance_for_next_slice()
return task_directive
if task_directive == self._TASK_DIRECTIVE.RECOVER_SLICE:
tstate.advance_for_next_slice(recovery_slice=True)
shard_state.advance_for_next_slice(recovery_slice=True)
return task_directive
if task_directive == self._TASK_DIRECTIVE.RETRY_SLICE:
task_directive = self._attempt_slice_retry(shard_state, tstate)
if task_directive == self._TASK_DIRECTIVE.RETRY_SHARD:
task_directive = self._attempt_shard_retry(shard_state, tstate)
if task_directive == self._TASK_DIRECTIVE.FAIL_TASK:
shard_state.set_for_failure()
return task_directive | Set shard_state and tstate based on task_directive.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
task_directive: self._TASK_DIRECTIVE for current shard.
Returns:
A _TASK_DIRECTIVE enum.
PROCEED_TASK if task should proceed normally.
RETRY_SHARD if shard should be retried.
RETRY_SLICE if slice should be retried.
FAIL_TASK if sahrd should fail.
RECOVER_SLICE if slice should be recovered.
ABORT_SHARD if shard should be aborted.
RETRY_TASK if task should be retried.
DROP_TASK if task should be dropped. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L650-L694 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._save_state_and_schedule_next | def _save_state_and_schedule_next(self, shard_state, tstate, task_directive):
"""Save state and schedule task.
Save shard state to datastore.
Schedule next slice if needed.
Set HTTP response code.
No modification to any shard_state or tstate.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
task_directive: enum _TASK_DIRECTIVE.
Returns:
The task to retry if applicable.
"""
spec = tstate.mapreduce_spec
if task_directive == self._TASK_DIRECTIVE.DROP_TASK:
return
if task_directive in (self._TASK_DIRECTIVE.RETRY_SLICE,
self._TASK_DIRECTIVE.RETRY_TASK):
# Set HTTP code to 500.
return self.retry_task()
elif task_directive == self._TASK_DIRECTIVE.ABORT_SHARD:
logging.info("Aborting shard %d of job '%s'",
shard_state.shard_number, shard_state.mapreduce_id)
task = None
elif task_directive == self._TASK_DIRECTIVE.FAIL_TASK:
logging.critical("Shard %s failed permanently.", shard_state.shard_id)
task = None
elif task_directive == self._TASK_DIRECTIVE.RETRY_SHARD:
logging.warning("Shard %s is going to be attempted for the %s time.",
shard_state.shard_id,
shard_state.retries + 1)
task = self._state_to_task(tstate, shard_state)
elif task_directive == self._TASK_DIRECTIVE.RECOVER_SLICE:
logging.warning("Shard %s slice %s is being recovered.",
shard_state.shard_id,
shard_state.slice_id)
task = self._state_to_task(tstate, shard_state)
else:
assert task_directive == self._TASK_DIRECTIVE.PROCEED_TASK
countdown = self._get_countdown_for_next_slice(spec)
task = self._state_to_task(tstate, shard_state, countdown=countdown)
# Prepare parameters for db transaction and taskqueue.
queue_name = os.environ.get("HTTP_X_APPENGINE_QUEUENAME",
# For test only.
# TODO(user): Remove this.
"default")
config = util.create_datastore_write_config(spec)
@db.transactional(retries=5)
def _tx():
"""The Transaction helper."""
fresh_shard_state = model.ShardState.get_by_shard_id(tstate.shard_id)
if not fresh_shard_state:
raise db.Rollback()
if (not fresh_shard_state.active or
"worker_active_state_collision" in _TEST_INJECTED_FAULTS):
logging.warning("Shard %s is not active. Possible spurious task "
"execution. Dropping this task.", tstate.shard_id)
logging.warning("Datastore's %s", str(fresh_shard_state))
logging.warning("Slice's %s", str(shard_state))
return
fresh_shard_state.copy_from(shard_state)
fresh_shard_state.put(config=config)
# Add task in the same datastore transaction.
# This way we guarantee taskqueue is never behind datastore states.
# Old tasks will be dropped.
# Future task won't run until datastore states catches up.
if fresh_shard_state.active:
# Not adding task transactionally.
# transactional enqueue requires tasks with no name.
self._add_task(task, spec, queue_name)
try:
_tx()
except (datastore_errors.Error,
taskqueue.Error,
runtime.DeadlineExceededError,
apiproxy_errors.Error), e:
logging.warning(
"Can't transactionally continue shard. "
"Will retry slice %s %s for the %s time.",
tstate.shard_id,
tstate.slice_id,
self.task_retry_count() + 1)
self._try_free_lease(shard_state)
raise e | python | def _save_state_and_schedule_next(self, shard_state, tstate, task_directive):
"""Save state and schedule task.
Save shard state to datastore.
Schedule next slice if needed.
Set HTTP response code.
No modification to any shard_state or tstate.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
task_directive: enum _TASK_DIRECTIVE.
Returns:
The task to retry if applicable.
"""
spec = tstate.mapreduce_spec
if task_directive == self._TASK_DIRECTIVE.DROP_TASK:
return
if task_directive in (self._TASK_DIRECTIVE.RETRY_SLICE,
self._TASK_DIRECTIVE.RETRY_TASK):
# Set HTTP code to 500.
return self.retry_task()
elif task_directive == self._TASK_DIRECTIVE.ABORT_SHARD:
logging.info("Aborting shard %d of job '%s'",
shard_state.shard_number, shard_state.mapreduce_id)
task = None
elif task_directive == self._TASK_DIRECTIVE.FAIL_TASK:
logging.critical("Shard %s failed permanently.", shard_state.shard_id)
task = None
elif task_directive == self._TASK_DIRECTIVE.RETRY_SHARD:
logging.warning("Shard %s is going to be attempted for the %s time.",
shard_state.shard_id,
shard_state.retries + 1)
task = self._state_to_task(tstate, shard_state)
elif task_directive == self._TASK_DIRECTIVE.RECOVER_SLICE:
logging.warning("Shard %s slice %s is being recovered.",
shard_state.shard_id,
shard_state.slice_id)
task = self._state_to_task(tstate, shard_state)
else:
assert task_directive == self._TASK_DIRECTIVE.PROCEED_TASK
countdown = self._get_countdown_for_next_slice(spec)
task = self._state_to_task(tstate, shard_state, countdown=countdown)
# Prepare parameters for db transaction and taskqueue.
queue_name = os.environ.get("HTTP_X_APPENGINE_QUEUENAME",
# For test only.
# TODO(user): Remove this.
"default")
config = util.create_datastore_write_config(spec)
@db.transactional(retries=5)
def _tx():
"""The Transaction helper."""
fresh_shard_state = model.ShardState.get_by_shard_id(tstate.shard_id)
if not fresh_shard_state:
raise db.Rollback()
if (not fresh_shard_state.active or
"worker_active_state_collision" in _TEST_INJECTED_FAULTS):
logging.warning("Shard %s is not active. Possible spurious task "
"execution. Dropping this task.", tstate.shard_id)
logging.warning("Datastore's %s", str(fresh_shard_state))
logging.warning("Slice's %s", str(shard_state))
return
fresh_shard_state.copy_from(shard_state)
fresh_shard_state.put(config=config)
# Add task in the same datastore transaction.
# This way we guarantee taskqueue is never behind datastore states.
# Old tasks will be dropped.
# Future task won't run until datastore states catches up.
if fresh_shard_state.active:
# Not adding task transactionally.
# transactional enqueue requires tasks with no name.
self._add_task(task, spec, queue_name)
try:
_tx()
except (datastore_errors.Error,
taskqueue.Error,
runtime.DeadlineExceededError,
apiproxy_errors.Error), e:
logging.warning(
"Can't transactionally continue shard. "
"Will retry slice %s %s for the %s time.",
tstate.shard_id,
tstate.slice_id,
self.task_retry_count() + 1)
self._try_free_lease(shard_state)
raise e | Save state and schedule task.
Save shard state to datastore.
Schedule next slice if needed.
Set HTTP response code.
No modification to any shard_state or tstate.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
task_directive: enum _TASK_DIRECTIVE.
Returns:
The task to retry if applicable. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L696-L786 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._attempt_slice_recovery | def _attempt_slice_recovery(self, shard_state, tstate):
"""Recover a slice.
This is run when a slice had been previously attempted and output
may have been written. If an output writer requires slice recovery,
we run those logic to remove output duplicates. Otherwise we just retry
the slice.
If recovery is needed, then the entire slice will be dedicated
to recovery logic. No data processing will take place. Thus we call
the slice "recovery slice". This is needed for correctness:
An output writer instance can be out of sync from its physical
medium only when the slice dies after acquring the shard lock but before
committing shard state to db. The worst failure case is when
shard state failed to commit after the NAMED task for the next slice was
added. Thus, recovery slice has a special logic to increment current
slice_id n to n+2. If the task for n+1 had been added, it will be dropped
because it is behind shard state.
Args:
shard_state: an instance of Model.ShardState.
tstate: an instance of Model.TransientShardState.
Returns:
_TASK_DIRECTIVE.PROCEED_TASK to continue with this retry.
_TASK_DIRECTIVE.RECOVER_SLICE to recover this slice.
The next slice will start at the same input as
this slice but output to a new instance of output writer.
Combining outputs from all writer instances is up to implementation.
"""
mapper_spec = tstate.mapreduce_spec.mapper
if not (tstate.output_writer and
tstate.output_writer._supports_slice_recovery(mapper_spec)):
return self._TASK_DIRECTIVE.PROCEED_TASK
tstate.output_writer = tstate.output_writer._recover(
tstate.mapreduce_spec, shard_state.shard_number,
shard_state.retries + 1)
return self._TASK_DIRECTIVE.RECOVER_SLICE | python | def _attempt_slice_recovery(self, shard_state, tstate):
"""Recover a slice.
This is run when a slice had been previously attempted and output
may have been written. If an output writer requires slice recovery,
we run those logic to remove output duplicates. Otherwise we just retry
the slice.
If recovery is needed, then the entire slice will be dedicated
to recovery logic. No data processing will take place. Thus we call
the slice "recovery slice". This is needed for correctness:
An output writer instance can be out of sync from its physical
medium only when the slice dies after acquring the shard lock but before
committing shard state to db. The worst failure case is when
shard state failed to commit after the NAMED task for the next slice was
added. Thus, recovery slice has a special logic to increment current
slice_id n to n+2. If the task for n+1 had been added, it will be dropped
because it is behind shard state.
Args:
shard_state: an instance of Model.ShardState.
tstate: an instance of Model.TransientShardState.
Returns:
_TASK_DIRECTIVE.PROCEED_TASK to continue with this retry.
_TASK_DIRECTIVE.RECOVER_SLICE to recover this slice.
The next slice will start at the same input as
this slice but output to a new instance of output writer.
Combining outputs from all writer instances is up to implementation.
"""
mapper_spec = tstate.mapreduce_spec.mapper
if not (tstate.output_writer and
tstate.output_writer._supports_slice_recovery(mapper_spec)):
return self._TASK_DIRECTIVE.PROCEED_TASK
tstate.output_writer = tstate.output_writer._recover(
tstate.mapreduce_spec, shard_state.shard_number,
shard_state.retries + 1)
return self._TASK_DIRECTIVE.RECOVER_SLICE | Recover a slice.
This is run when a slice had been previously attempted and output
may have been written. If an output writer requires slice recovery,
we run those logic to remove output duplicates. Otherwise we just retry
the slice.
If recovery is needed, then the entire slice will be dedicated
to recovery logic. No data processing will take place. Thus we call
the slice "recovery slice". This is needed for correctness:
An output writer instance can be out of sync from its physical
medium only when the slice dies after acquring the shard lock but before
committing shard state to db. The worst failure case is when
shard state failed to commit after the NAMED task for the next slice was
added. Thus, recovery slice has a special logic to increment current
slice_id n to n+2. If the task for n+1 had been added, it will be dropped
because it is behind shard state.
Args:
shard_state: an instance of Model.ShardState.
tstate: an instance of Model.TransientShardState.
Returns:
_TASK_DIRECTIVE.PROCEED_TASK to continue with this retry.
_TASK_DIRECTIVE.RECOVER_SLICE to recover this slice.
The next slice will start at the same input as
this slice but output to a new instance of output writer.
Combining outputs from all writer instances is up to implementation. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L788-L826 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._attempt_shard_retry | def _attempt_shard_retry(self, shard_state, tstate):
"""Whether to retry shard.
This method may modify shard_state and tstate to prepare for retry or fail.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
Returns:
A _TASK_DIRECTIVE enum. RETRY_SHARD if shard should be retried.
FAIL_TASK otherwise.
"""
shard_attempts = shard_state.retries + 1
if shard_attempts >= parameters.config.SHARD_MAX_ATTEMPTS:
logging.warning(
"Shard attempt %s exceeded %s max attempts.",
shard_attempts, parameters.config.SHARD_MAX_ATTEMPTS)
return self._TASK_DIRECTIVE.FAIL_TASK
if tstate.output_writer and (
not tstate.output_writer._supports_shard_retry(tstate)):
logging.warning("Output writer %s does not support shard retry.",
tstate.output_writer.__class__.__name__)
return self._TASK_DIRECTIVE.FAIL_TASK
shard_state.reset_for_retry()
logging.warning("Shard %s attempt %s failed with up to %s attempts.",
shard_state.shard_id,
shard_state.retries,
parameters.config.SHARD_MAX_ATTEMPTS)
output_writer = None
if tstate.output_writer:
output_writer = tstate.output_writer.create(
tstate.mapreduce_spec, shard_state.shard_number, shard_attempts + 1)
tstate.reset_for_retry(output_writer)
return self._TASK_DIRECTIVE.RETRY_SHARD | python | def _attempt_shard_retry(self, shard_state, tstate):
"""Whether to retry shard.
This method may modify shard_state and tstate to prepare for retry or fail.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
Returns:
A _TASK_DIRECTIVE enum. RETRY_SHARD if shard should be retried.
FAIL_TASK otherwise.
"""
shard_attempts = shard_state.retries + 1
if shard_attempts >= parameters.config.SHARD_MAX_ATTEMPTS:
logging.warning(
"Shard attempt %s exceeded %s max attempts.",
shard_attempts, parameters.config.SHARD_MAX_ATTEMPTS)
return self._TASK_DIRECTIVE.FAIL_TASK
if tstate.output_writer and (
not tstate.output_writer._supports_shard_retry(tstate)):
logging.warning("Output writer %s does not support shard retry.",
tstate.output_writer.__class__.__name__)
return self._TASK_DIRECTIVE.FAIL_TASK
shard_state.reset_for_retry()
logging.warning("Shard %s attempt %s failed with up to %s attempts.",
shard_state.shard_id,
shard_state.retries,
parameters.config.SHARD_MAX_ATTEMPTS)
output_writer = None
if tstate.output_writer:
output_writer = tstate.output_writer.create(
tstate.mapreduce_spec, shard_state.shard_number, shard_attempts + 1)
tstate.reset_for_retry(output_writer)
return self._TASK_DIRECTIVE.RETRY_SHARD | Whether to retry shard.
This method may modify shard_state and tstate to prepare for retry or fail.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
Returns:
A _TASK_DIRECTIVE enum. RETRY_SHARD if shard should be retried.
FAIL_TASK otherwise. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L828-L864 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._attempt_slice_retry | def _attempt_slice_retry(self, shard_state, tstate):
"""Attempt to retry this slice.
This method may modify shard_state and tstate to prepare for retry or fail.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
Returns:
A _TASK_DIRECTIVE enum. RETRY_SLICE if slice should be retried.
RETRY_SHARD if shard retry should be attempted.
"""
if (shard_state.slice_retries + 1 <
parameters.config.TASK_MAX_DATA_PROCESSING_ATTEMPTS):
logging.warning(
"Slice %s %s failed for the %s of up to %s attempts "
"(%s of %s taskqueue execution attempts). "
"Will retry now.",
tstate.shard_id,
tstate.slice_id,
shard_state.slice_retries + 1,
parameters.config.TASK_MAX_DATA_PROCESSING_ATTEMPTS,
self.task_retry_count() + 1,
parameters.config.TASK_MAX_ATTEMPTS)
# Clear info related to current exception. Otherwise, the real
# callstack that includes a frame for this method will show up
# in log.
sys.exc_clear()
self._try_free_lease(shard_state, slice_retry=True)
return self._TASK_DIRECTIVE.RETRY_SLICE
if parameters.config.TASK_MAX_DATA_PROCESSING_ATTEMPTS > 0:
logging.warning("Slice attempt %s exceeded %s max attempts.",
self.task_retry_count() + 1,
parameters.config.TASK_MAX_DATA_PROCESSING_ATTEMPTS)
return self._TASK_DIRECTIVE.RETRY_SHARD | python | def _attempt_slice_retry(self, shard_state, tstate):
"""Attempt to retry this slice.
This method may modify shard_state and tstate to prepare for retry or fail.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
Returns:
A _TASK_DIRECTIVE enum. RETRY_SLICE if slice should be retried.
RETRY_SHARD if shard retry should be attempted.
"""
if (shard_state.slice_retries + 1 <
parameters.config.TASK_MAX_DATA_PROCESSING_ATTEMPTS):
logging.warning(
"Slice %s %s failed for the %s of up to %s attempts "
"(%s of %s taskqueue execution attempts). "
"Will retry now.",
tstate.shard_id,
tstate.slice_id,
shard_state.slice_retries + 1,
parameters.config.TASK_MAX_DATA_PROCESSING_ATTEMPTS,
self.task_retry_count() + 1,
parameters.config.TASK_MAX_ATTEMPTS)
# Clear info related to current exception. Otherwise, the real
# callstack that includes a frame for this method will show up
# in log.
sys.exc_clear()
self._try_free_lease(shard_state, slice_retry=True)
return self._TASK_DIRECTIVE.RETRY_SLICE
if parameters.config.TASK_MAX_DATA_PROCESSING_ATTEMPTS > 0:
logging.warning("Slice attempt %s exceeded %s max attempts.",
self.task_retry_count() + 1,
parameters.config.TASK_MAX_DATA_PROCESSING_ATTEMPTS)
return self._TASK_DIRECTIVE.RETRY_SHARD | Attempt to retry this slice.
This method may modify shard_state and tstate to prepare for retry or fail.
Args:
shard_state: model.ShardState for current shard.
tstate: model.TransientShardState for current shard.
Returns:
A _TASK_DIRECTIVE enum. RETRY_SLICE if slice should be retried.
RETRY_SHARD if shard retry should be attempted. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L866-L902 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._get_countdown_for_next_slice | def _get_countdown_for_next_slice(self, spec):
"""Get countdown for next slice's task.
When user sets processing rate, we set countdown to delay task execution.
Args:
spec: model.MapreduceSpec
Returns:
countdown in int.
"""
countdown = 0
if self._processing_limit(spec) != -1:
countdown = max(
int(parameters.config._SLICE_DURATION_SEC -
(self._time() - self._start_time)), 0)
return countdown | python | def _get_countdown_for_next_slice(self, spec):
"""Get countdown for next slice's task.
When user sets processing rate, we set countdown to delay task execution.
Args:
spec: model.MapreduceSpec
Returns:
countdown in int.
"""
countdown = 0
if self._processing_limit(spec) != -1:
countdown = max(
int(parameters.config._SLICE_DURATION_SEC -
(self._time() - self._start_time)), 0)
return countdown | Get countdown for next slice's task.
When user sets processing rate, we set countdown to delay task execution.
Args:
spec: model.MapreduceSpec
Returns:
countdown in int. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L921-L937 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._state_to_task | def _state_to_task(cls,
tstate,
shard_state,
eta=None,
countdown=None):
"""Generate task for slice according to current states.
Args:
tstate: An instance of TransientShardState.
shard_state: An instance of ShardState.
eta: Absolute time when the MR should execute. May not be specified
if 'countdown' is also supplied. This may be timezone-aware or
timezone-naive.
countdown: Time in seconds into the future that this MR should execute.
Defaults to zero.
Returns:
A model.HugeTask instance for the slice specified by current states.
"""
base_path = tstate.base_path
task_name = MapperWorkerCallbackHandler.get_task_name(
tstate.shard_id,
tstate.slice_id,
tstate.retries)
headers = util._get_task_headers(tstate.mapreduce_spec.mapreduce_id)
headers[util._MR_SHARD_ID_TASK_HEADER] = tstate.shard_id
worker_task = model.HugeTask(
url=base_path + "/worker_callback/" + tstate.shard_id,
params=tstate.to_dict(),
name=task_name,
eta=eta,
countdown=countdown,
parent=shard_state,
headers=headers)
return worker_task | python | def _state_to_task(cls,
tstate,
shard_state,
eta=None,
countdown=None):
"""Generate task for slice according to current states.
Args:
tstate: An instance of TransientShardState.
shard_state: An instance of ShardState.
eta: Absolute time when the MR should execute. May not be specified
if 'countdown' is also supplied. This may be timezone-aware or
timezone-naive.
countdown: Time in seconds into the future that this MR should execute.
Defaults to zero.
Returns:
A model.HugeTask instance for the slice specified by current states.
"""
base_path = tstate.base_path
task_name = MapperWorkerCallbackHandler.get_task_name(
tstate.shard_id,
tstate.slice_id,
tstate.retries)
headers = util._get_task_headers(tstate.mapreduce_spec.mapreduce_id)
headers[util._MR_SHARD_ID_TASK_HEADER] = tstate.shard_id
worker_task = model.HugeTask(
url=base_path + "/worker_callback/" + tstate.shard_id,
params=tstate.to_dict(),
name=task_name,
eta=eta,
countdown=countdown,
parent=shard_state,
headers=headers)
return worker_task | Generate task for slice according to current states.
Args:
tstate: An instance of TransientShardState.
shard_state: An instance of ShardState.
eta: Absolute time when the MR should execute. May not be specified
if 'countdown' is also supplied. This may be timezone-aware or
timezone-naive.
countdown: Time in seconds into the future that this MR should execute.
Defaults to zero.
Returns:
A model.HugeTask instance for the slice specified by current states. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L940-L977 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._add_task | def _add_task(cls,
worker_task,
mapreduce_spec,
queue_name):
"""Schedule slice scanning by adding it to the task queue.
Args:
worker_task: a model.HugeTask task for slice. This is NOT a taskqueue
task.
mapreduce_spec: an instance of model.MapreduceSpec.
queue_name: Optional queue to run on; uses the current queue of
execution or the default queue if unspecified.
"""
if not _run_task_hook(mapreduce_spec.get_hooks(),
"enqueue_worker_task",
worker_task,
queue_name):
try:
# Not adding transactionally because worker_task has name.
# Named task is not allowed for transactional add.
worker_task.add(queue_name)
except (taskqueue.TombstonedTaskError,
taskqueue.TaskAlreadyExistsError), e:
logging.warning("Task %r already exists. %s: %s",
worker_task.name,
e.__class__,
e) | python | def _add_task(cls,
worker_task,
mapreduce_spec,
queue_name):
"""Schedule slice scanning by adding it to the task queue.
Args:
worker_task: a model.HugeTask task for slice. This is NOT a taskqueue
task.
mapreduce_spec: an instance of model.MapreduceSpec.
queue_name: Optional queue to run on; uses the current queue of
execution or the default queue if unspecified.
"""
if not _run_task_hook(mapreduce_spec.get_hooks(),
"enqueue_worker_task",
worker_task,
queue_name):
try:
# Not adding transactionally because worker_task has name.
# Named task is not allowed for transactional add.
worker_task.add(queue_name)
except (taskqueue.TombstonedTaskError,
taskqueue.TaskAlreadyExistsError), e:
logging.warning("Task %r already exists. %s: %s",
worker_task.name,
e.__class__,
e) | Schedule slice scanning by adding it to the task queue.
Args:
worker_task: a model.HugeTask task for slice. This is NOT a taskqueue
task.
mapreduce_spec: an instance of model.MapreduceSpec.
queue_name: Optional queue to run on; uses the current queue of
execution or the default queue if unspecified. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L980-L1006 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._processing_limit | def _processing_limit(self, spec):
"""Get the limit on the number of map calls allowed by this slice.
Args:
spec: a Mapreduce spec.
Returns:
The limit as a positive int if specified by user. -1 otherwise.
"""
processing_rate = float(spec.mapper.params.get("processing_rate", 0))
slice_processing_limit = -1
if processing_rate > 0:
slice_processing_limit = int(math.ceil(
parameters.config._SLICE_DURATION_SEC*processing_rate/
int(spec.mapper.shard_count)))
return slice_processing_limit | python | def _processing_limit(self, spec):
"""Get the limit on the number of map calls allowed by this slice.
Args:
spec: a Mapreduce spec.
Returns:
The limit as a positive int if specified by user. -1 otherwise.
"""
processing_rate = float(spec.mapper.params.get("processing_rate", 0))
slice_processing_limit = -1
if processing_rate > 0:
slice_processing_limit = int(math.ceil(
parameters.config._SLICE_DURATION_SEC*processing_rate/
int(spec.mapper.shard_count)))
return slice_processing_limit | Get the limit on the number of map calls allowed by this slice.
Args:
spec: a Mapreduce spec.
Returns:
The limit as a positive int if specified by user. -1 otherwise. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1008-L1023 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | MapperWorkerCallbackHandler._schedule_slice | def _schedule_slice(cls,
shard_state,
tstate,
queue_name=None,
eta=None,
countdown=None):
"""Schedule slice scanning by adding it to the task queue.
Args:
shard_state: An instance of ShardState.
tstate: An instance of TransientShardState.
queue_name: Optional queue to run on; uses the current queue of
execution or the default queue if unspecified.
eta: Absolute time when the MR should execute. May not be specified
if 'countdown' is also supplied. This may be timezone-aware or
timezone-naive.
countdown: Time in seconds into the future that this MR should execute.
Defaults to zero.
"""
queue_name = queue_name or os.environ.get("HTTP_X_APPENGINE_QUEUENAME",
"default")
task = cls._state_to_task(tstate, shard_state, eta, countdown)
cls._add_task(task, tstate.mapreduce_spec, queue_name) | python | def _schedule_slice(cls,
shard_state,
tstate,
queue_name=None,
eta=None,
countdown=None):
"""Schedule slice scanning by adding it to the task queue.
Args:
shard_state: An instance of ShardState.
tstate: An instance of TransientShardState.
queue_name: Optional queue to run on; uses the current queue of
execution or the default queue if unspecified.
eta: Absolute time when the MR should execute. May not be specified
if 'countdown' is also supplied. This may be timezone-aware or
timezone-naive.
countdown: Time in seconds into the future that this MR should execute.
Defaults to zero.
"""
queue_name = queue_name or os.environ.get("HTTP_X_APPENGINE_QUEUENAME",
"default")
task = cls._state_to_task(tstate, shard_state, eta, countdown)
cls._add_task(task, tstate.mapreduce_spec, queue_name) | Schedule slice scanning by adding it to the task queue.
Args:
shard_state: An instance of ShardState.
tstate: An instance of TransientShardState.
queue_name: Optional queue to run on; uses the current queue of
execution or the default queue if unspecified.
eta: Absolute time when the MR should execute. May not be specified
if 'countdown' is also supplied. This may be timezone-aware or
timezone-naive.
countdown: Time in seconds into the future that this MR should execute.
Defaults to zero. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1028-L1050 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | ControllerCallbackHandler._drop_gracefully | def _drop_gracefully(self):
"""Gracefully drop controller task.
This method is called when decoding controller task payload failed.
Upon this we mark ShardState and MapreduceState as failed so all
tasks can stop.
Writing to datastore is forced (ignore read-only mode) because we
want the tasks to stop badly, and if force_writes was False,
the job would have never been started.
"""
mr_id = self.request.headers[util._MR_ID_TASK_HEADER]
state = model.MapreduceState.get_by_job_id(mr_id)
if not state or not state.active:
return
state.active = False
state.result_status = model.MapreduceState.RESULT_FAILED
config = util.create_datastore_write_config(state.mapreduce_spec)
puts = []
for ss in model.ShardState.find_all_by_mapreduce_state(state):
if ss.active:
ss.set_for_failure()
puts.append(ss)
# Avoid having too many shard states in memory.
if len(puts) > model.ShardState._MAX_STATES_IN_MEMORY:
db.put(puts, config=config)
puts = []
db.put(puts, config=config)
# Put mr_state only after all shard_states are put.
db.put(state, config=config) | python | def _drop_gracefully(self):
"""Gracefully drop controller task.
This method is called when decoding controller task payload failed.
Upon this we mark ShardState and MapreduceState as failed so all
tasks can stop.
Writing to datastore is forced (ignore read-only mode) because we
want the tasks to stop badly, and if force_writes was False,
the job would have never been started.
"""
mr_id = self.request.headers[util._MR_ID_TASK_HEADER]
state = model.MapreduceState.get_by_job_id(mr_id)
if not state or not state.active:
return
state.active = False
state.result_status = model.MapreduceState.RESULT_FAILED
config = util.create_datastore_write_config(state.mapreduce_spec)
puts = []
for ss in model.ShardState.find_all_by_mapreduce_state(state):
if ss.active:
ss.set_for_failure()
puts.append(ss)
# Avoid having too many shard states in memory.
if len(puts) > model.ShardState._MAX_STATES_IN_MEMORY:
db.put(puts, config=config)
puts = []
db.put(puts, config=config)
# Put mr_state only after all shard_states are put.
db.put(state, config=config) | Gracefully drop controller task.
This method is called when decoding controller task payload failed.
Upon this we mark ShardState and MapreduceState as failed so all
tasks can stop.
Writing to datastore is forced (ignore read-only mode) because we
want the tasks to stop badly, and if force_writes was False,
the job would have never been started. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1068-L1098 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | ControllerCallbackHandler.handle | def handle(self):
"""Handle request."""
spec = model.MapreduceSpec.from_json_str(
self.request.get("mapreduce_spec"))
state, control = db.get([
model.MapreduceState.get_key_by_job_id(spec.mapreduce_id),
model.MapreduceControl.get_key_by_job_id(spec.mapreduce_id),
])
if not state:
logging.warning("State not found for MR '%s'; dropping controller task.",
spec.mapreduce_id)
return
if not state.active:
logging.warning(
"MR %r is not active. Looks like spurious controller task execution.",
spec.mapreduce_id)
self._clean_up_mr(spec)
return
shard_states = model.ShardState.find_all_by_mapreduce_state(state)
self._update_state_from_shard_states(state, shard_states, control)
if state.active:
ControllerCallbackHandler.reschedule(
state, spec, self.serial_id() + 1) | python | def handle(self):
"""Handle request."""
spec = model.MapreduceSpec.from_json_str(
self.request.get("mapreduce_spec"))
state, control = db.get([
model.MapreduceState.get_key_by_job_id(spec.mapreduce_id),
model.MapreduceControl.get_key_by_job_id(spec.mapreduce_id),
])
if not state:
logging.warning("State not found for MR '%s'; dropping controller task.",
spec.mapreduce_id)
return
if not state.active:
logging.warning(
"MR %r is not active. Looks like spurious controller task execution.",
spec.mapreduce_id)
self._clean_up_mr(spec)
return
shard_states = model.ShardState.find_all_by_mapreduce_state(state)
self._update_state_from_shard_states(state, shard_states, control)
if state.active:
ControllerCallbackHandler.reschedule(
state, spec, self.serial_id() + 1) | Handle request. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1100-L1125 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | ControllerCallbackHandler._update_state_from_shard_states | def _update_state_from_shard_states(self, state, shard_states, control):
"""Update mr state by examing shard states.
Args:
state: current mapreduce state as MapreduceState.
shard_states: an iterator over shard states.
control: model.MapreduceControl entity.
"""
# Initialize vars.
state.active_shards, state.aborted_shards, state.failed_shards = 0, 0, 0
total_shards = 0
processed_counts = []
processed_status = []
state.counters_map.clear()
# Tally across shard states once.
for s in shard_states:
total_shards += 1
status = 'unknown'
if s.active:
state.active_shards += 1
status = 'running'
if s.result_status == model.ShardState.RESULT_SUCCESS:
status = 'success'
elif s.result_status == model.ShardState.RESULT_ABORTED:
state.aborted_shards += 1
status = 'aborted'
elif s.result_status == model.ShardState.RESULT_FAILED:
state.failed_shards += 1
status = 'failed'
# Update stats in mapreduce state by aggregating stats from shard states.
state.counters_map.add_map(s.counters_map)
processed_counts.append(s.counters_map.get(context.COUNTER_MAPPER_CALLS))
processed_status.append(status)
state.set_processed_counts(processed_counts, processed_status)
state.last_poll_time = datetime.datetime.utcfromtimestamp(self._time())
spec = state.mapreduce_spec
if total_shards != spec.mapper.shard_count:
logging.error("Found %d shard states. Expect %d. "
"Issuing abort command to job '%s'",
total_shards, spec.mapper.shard_count,
spec.mapreduce_id)
# We issue abort command to allow shards to stop themselves.
model.MapreduceControl.abort(spec.mapreduce_id)
# If any shard is active then the mr is active.
# This way, controller won't prematurely stop before all the shards have.
state.active = bool(state.active_shards)
if not control and (state.failed_shards or state.aborted_shards):
# Issue abort command if there are failed shards.
model.MapreduceControl.abort(spec.mapreduce_id)
if not state.active:
# Set final result status derived from shard states.
if state.failed_shards or not total_shards:
state.result_status = model.MapreduceState.RESULT_FAILED
# It's important failed shards is checked before aborted shards
# because failed shards will trigger other shards to abort.
elif state.aborted_shards:
state.result_status = model.MapreduceState.RESULT_ABORTED
else:
state.result_status = model.MapreduceState.RESULT_SUCCESS
self._finalize_outputs(spec, state)
self._finalize_job(spec, state)
else:
@db.transactional(retries=5)
def _put_state():
"""The helper for storing the state."""
fresh_state = model.MapreduceState.get_by_job_id(spec.mapreduce_id)
# We don't check anything other than active because we are only
# updating stats. It's OK if they are briefly inconsistent.
if not fresh_state.active:
logging.warning(
"Job %s is not active. Looks like spurious task execution. "
"Dropping controller task.", spec.mapreduce_id)
return
config = util.create_datastore_write_config(spec)
state.put(config=config)
_put_state() | python | def _update_state_from_shard_states(self, state, shard_states, control):
"""Update mr state by examing shard states.
Args:
state: current mapreduce state as MapreduceState.
shard_states: an iterator over shard states.
control: model.MapreduceControl entity.
"""
# Initialize vars.
state.active_shards, state.aborted_shards, state.failed_shards = 0, 0, 0
total_shards = 0
processed_counts = []
processed_status = []
state.counters_map.clear()
# Tally across shard states once.
for s in shard_states:
total_shards += 1
status = 'unknown'
if s.active:
state.active_shards += 1
status = 'running'
if s.result_status == model.ShardState.RESULT_SUCCESS:
status = 'success'
elif s.result_status == model.ShardState.RESULT_ABORTED:
state.aborted_shards += 1
status = 'aborted'
elif s.result_status == model.ShardState.RESULT_FAILED:
state.failed_shards += 1
status = 'failed'
# Update stats in mapreduce state by aggregating stats from shard states.
state.counters_map.add_map(s.counters_map)
processed_counts.append(s.counters_map.get(context.COUNTER_MAPPER_CALLS))
processed_status.append(status)
state.set_processed_counts(processed_counts, processed_status)
state.last_poll_time = datetime.datetime.utcfromtimestamp(self._time())
spec = state.mapreduce_spec
if total_shards != spec.mapper.shard_count:
logging.error("Found %d shard states. Expect %d. "
"Issuing abort command to job '%s'",
total_shards, spec.mapper.shard_count,
spec.mapreduce_id)
# We issue abort command to allow shards to stop themselves.
model.MapreduceControl.abort(spec.mapreduce_id)
# If any shard is active then the mr is active.
# This way, controller won't prematurely stop before all the shards have.
state.active = bool(state.active_shards)
if not control and (state.failed_shards or state.aborted_shards):
# Issue abort command if there are failed shards.
model.MapreduceControl.abort(spec.mapreduce_id)
if not state.active:
# Set final result status derived from shard states.
if state.failed_shards or not total_shards:
state.result_status = model.MapreduceState.RESULT_FAILED
# It's important failed shards is checked before aborted shards
# because failed shards will trigger other shards to abort.
elif state.aborted_shards:
state.result_status = model.MapreduceState.RESULT_ABORTED
else:
state.result_status = model.MapreduceState.RESULT_SUCCESS
self._finalize_outputs(spec, state)
self._finalize_job(spec, state)
else:
@db.transactional(retries=5)
def _put_state():
"""The helper for storing the state."""
fresh_state = model.MapreduceState.get_by_job_id(spec.mapreduce_id)
# We don't check anything other than active because we are only
# updating stats. It's OK if they are briefly inconsistent.
if not fresh_state.active:
logging.warning(
"Job %s is not active. Looks like spurious task execution. "
"Dropping controller task.", spec.mapreduce_id)
return
config = util.create_datastore_write_config(spec)
state.put(config=config)
_put_state() | Update mr state by examing shard states.
Args:
state: current mapreduce state as MapreduceState.
shard_states: an iterator over shard states.
control: model.MapreduceControl entity. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1127-L1210 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | ControllerCallbackHandler._finalize_outputs | def _finalize_outputs(cls, mapreduce_spec, mapreduce_state):
"""Finalize outputs.
Args:
mapreduce_spec: an instance of MapreduceSpec.
mapreduce_state: an instance of MapreduceState.
"""
# Only finalize the output writers if the job is successful.
if (mapreduce_spec.mapper.output_writer_class() and
mapreduce_state.result_status == model.MapreduceState.RESULT_SUCCESS):
mapreduce_spec.mapper.output_writer_class().finalize_job(mapreduce_state) | python | def _finalize_outputs(cls, mapreduce_spec, mapreduce_state):
"""Finalize outputs.
Args:
mapreduce_spec: an instance of MapreduceSpec.
mapreduce_state: an instance of MapreduceState.
"""
# Only finalize the output writers if the job is successful.
if (mapreduce_spec.mapper.output_writer_class() and
mapreduce_state.result_status == model.MapreduceState.RESULT_SUCCESS):
mapreduce_spec.mapper.output_writer_class().finalize_job(mapreduce_state) | Finalize outputs.
Args:
mapreduce_spec: an instance of MapreduceSpec.
mapreduce_state: an instance of MapreduceState. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1221-L1231 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | ControllerCallbackHandler._finalize_job | def _finalize_job(cls, mapreduce_spec, mapreduce_state):
"""Finalize job execution.
Invokes done callback and save mapreduce state in a transaction,
and schedule necessary clean ups. This method is idempotent.
Args:
mapreduce_spec: an instance of MapreduceSpec
mapreduce_state: an instance of MapreduceState
"""
config = util.create_datastore_write_config(mapreduce_spec)
queue_name = util.get_queue_name(mapreduce_spec.params.get(
model.MapreduceSpec.PARAM_DONE_CALLBACK_QUEUE))
done_callback = mapreduce_spec.params.get(
model.MapreduceSpec.PARAM_DONE_CALLBACK)
done_task = None
if done_callback:
done_task = taskqueue.Task(
url=done_callback,
headers=util._get_task_headers(mapreduce_spec.mapreduce_id,
util.CALLBACK_MR_ID_TASK_HEADER),
method=mapreduce_spec.params.get("done_callback_method", "POST"))
@db.transactional(retries=5)
def _put_state():
"""Helper to store state."""
fresh_state = model.MapreduceState.get_by_job_id(
mapreduce_spec.mapreduce_id)
if not fresh_state.active:
logging.warning(
"Job %s is not active. Looks like spurious task execution. "
"Dropping task.", mapreduce_spec.mapreduce_id)
return
mapreduce_state.put(config=config)
# Enqueue done_callback if needed.
if done_task and not _run_task_hook(
mapreduce_spec.get_hooks(),
"enqueue_done_task",
done_task,
queue_name):
done_task.add(queue_name, transactional=True)
_put_state()
logging.info("Final result for job '%s' is '%s'",
mapreduce_spec.mapreduce_id, mapreduce_state.result_status)
cls._clean_up_mr(mapreduce_spec) | python | def _finalize_job(cls, mapreduce_spec, mapreduce_state):
"""Finalize job execution.
Invokes done callback and save mapreduce state in a transaction,
and schedule necessary clean ups. This method is idempotent.
Args:
mapreduce_spec: an instance of MapreduceSpec
mapreduce_state: an instance of MapreduceState
"""
config = util.create_datastore_write_config(mapreduce_spec)
queue_name = util.get_queue_name(mapreduce_spec.params.get(
model.MapreduceSpec.PARAM_DONE_CALLBACK_QUEUE))
done_callback = mapreduce_spec.params.get(
model.MapreduceSpec.PARAM_DONE_CALLBACK)
done_task = None
if done_callback:
done_task = taskqueue.Task(
url=done_callback,
headers=util._get_task_headers(mapreduce_spec.mapreduce_id,
util.CALLBACK_MR_ID_TASK_HEADER),
method=mapreduce_spec.params.get("done_callback_method", "POST"))
@db.transactional(retries=5)
def _put_state():
"""Helper to store state."""
fresh_state = model.MapreduceState.get_by_job_id(
mapreduce_spec.mapreduce_id)
if not fresh_state.active:
logging.warning(
"Job %s is not active. Looks like spurious task execution. "
"Dropping task.", mapreduce_spec.mapreduce_id)
return
mapreduce_state.put(config=config)
# Enqueue done_callback if needed.
if done_task and not _run_task_hook(
mapreduce_spec.get_hooks(),
"enqueue_done_task",
done_task,
queue_name):
done_task.add(queue_name, transactional=True)
_put_state()
logging.info("Final result for job '%s' is '%s'",
mapreduce_spec.mapreduce_id, mapreduce_state.result_status)
cls._clean_up_mr(mapreduce_spec) | Finalize job execution.
Invokes done callback and save mapreduce state in a transaction,
and schedule necessary clean ups. This method is idempotent.
Args:
mapreduce_spec: an instance of MapreduceSpec
mapreduce_state: an instance of MapreduceState | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1234-L1279 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | ControllerCallbackHandler.reschedule | def reschedule(cls,
mapreduce_state,
mapreduce_spec,
serial_id,
queue_name=None):
"""Schedule new update status callback task.
Args:
mapreduce_state: mapreduce state as model.MapreduceState
mapreduce_spec: mapreduce specification as MapreduceSpec.
serial_id: id of the invocation as int.
queue_name: The queue to schedule this task on. Will use the current
queue of execution if not supplied.
"""
task_name = ControllerCallbackHandler.get_task_name(
mapreduce_spec, serial_id)
task_params = ControllerCallbackHandler.controller_parameters(
mapreduce_spec, serial_id)
if not queue_name:
queue_name = os.environ.get("HTTP_X_APPENGINE_QUEUENAME", "default")
controller_callback_task = model.HugeTask(
url=(mapreduce_spec.params["base_path"] + "/controller_callback/" +
mapreduce_spec.mapreduce_id),
name=task_name, params=task_params,
countdown=parameters.config._CONTROLLER_PERIOD_SEC,
parent=mapreduce_state,
headers=util._get_task_headers(mapreduce_spec.mapreduce_id))
if not _run_task_hook(mapreduce_spec.get_hooks(),
"enqueue_controller_task",
controller_callback_task,
queue_name):
try:
controller_callback_task.add(queue_name)
except (taskqueue.TombstonedTaskError,
taskqueue.TaskAlreadyExistsError), e:
logging.warning("Task %r with params %r already exists. %s: %s",
task_name, task_params, e.__class__, e) | python | def reschedule(cls,
mapreduce_state,
mapreduce_spec,
serial_id,
queue_name=None):
"""Schedule new update status callback task.
Args:
mapreduce_state: mapreduce state as model.MapreduceState
mapreduce_spec: mapreduce specification as MapreduceSpec.
serial_id: id of the invocation as int.
queue_name: The queue to schedule this task on. Will use the current
queue of execution if not supplied.
"""
task_name = ControllerCallbackHandler.get_task_name(
mapreduce_spec, serial_id)
task_params = ControllerCallbackHandler.controller_parameters(
mapreduce_spec, serial_id)
if not queue_name:
queue_name = os.environ.get("HTTP_X_APPENGINE_QUEUENAME", "default")
controller_callback_task = model.HugeTask(
url=(mapreduce_spec.params["base_path"] + "/controller_callback/" +
mapreduce_spec.mapreduce_id),
name=task_name, params=task_params,
countdown=parameters.config._CONTROLLER_PERIOD_SEC,
parent=mapreduce_state,
headers=util._get_task_headers(mapreduce_spec.mapreduce_id))
if not _run_task_hook(mapreduce_spec.get_hooks(),
"enqueue_controller_task",
controller_callback_task,
queue_name):
try:
controller_callback_task.add(queue_name)
except (taskqueue.TombstonedTaskError,
taskqueue.TaskAlreadyExistsError), e:
logging.warning("Task %r with params %r already exists. %s: %s",
task_name, task_params, e.__class__, e) | Schedule new update status callback task.
Args:
mapreduce_state: mapreduce state as model.MapreduceState
mapreduce_spec: mapreduce specification as MapreduceSpec.
serial_id: id of the invocation as int.
queue_name: The queue to schedule this task on. Will use the current
queue of execution if not supplied. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1319-L1357 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | KickOffJobHandler.handle | def handle(self):
"""Handles kick off request."""
# Get and verify mr state.
mr_id = self.request.get("mapreduce_id")
# Log the mr_id since this is started in an unnamed task
logging.info("Processing kickoff for job %s", mr_id)
state = model.MapreduceState.get_by_job_id(mr_id)
if not self._check_mr_state(state, mr_id):
return
# Create input readers.
readers, serialized_readers_entity = self._get_input_readers(state)
if readers is None:
# We don't have any data. Finish map.
logging.warning("Found no mapper input data to process.")
state.active = False
state.result_status = model.MapreduceState.RESULT_SUCCESS
ControllerCallbackHandler._finalize_job(
state.mapreduce_spec, state)
return False
# Create output writers.
self._setup_output_writer(state)
# Save states and make sure we use the saved input readers for
# subsequent operations.
result = self._save_states(state, serialized_readers_entity)
if result is None:
readers, _ = self._get_input_readers(state)
elif not result:
return
queue_name = self.request.headers.get("X-AppEngine-QueueName")
KickOffJobHandler._schedule_shards(state.mapreduce_spec, readers,
queue_name,
state.mapreduce_spec.params["base_path"],
state)
ControllerCallbackHandler.reschedule(
state, state.mapreduce_spec, serial_id=0, queue_name=queue_name) | python | def handle(self):
"""Handles kick off request."""
# Get and verify mr state.
mr_id = self.request.get("mapreduce_id")
# Log the mr_id since this is started in an unnamed task
logging.info("Processing kickoff for job %s", mr_id)
state = model.MapreduceState.get_by_job_id(mr_id)
if not self._check_mr_state(state, mr_id):
return
# Create input readers.
readers, serialized_readers_entity = self._get_input_readers(state)
if readers is None:
# We don't have any data. Finish map.
logging.warning("Found no mapper input data to process.")
state.active = False
state.result_status = model.MapreduceState.RESULT_SUCCESS
ControllerCallbackHandler._finalize_job(
state.mapreduce_spec, state)
return False
# Create output writers.
self._setup_output_writer(state)
# Save states and make sure we use the saved input readers for
# subsequent operations.
result = self._save_states(state, serialized_readers_entity)
if result is None:
readers, _ = self._get_input_readers(state)
elif not result:
return
queue_name = self.request.headers.get("X-AppEngine-QueueName")
KickOffJobHandler._schedule_shards(state.mapreduce_spec, readers,
queue_name,
state.mapreduce_spec.params["base_path"],
state)
ControllerCallbackHandler.reschedule(
state, state.mapreduce_spec, serial_id=0, queue_name=queue_name) | Handles kick off request. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1376-L1415 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | KickOffJobHandler._drop_gracefully | def _drop_gracefully(self):
"""See parent."""
mr_id = self.request.get("mapreduce_id")
logging.error("Failed to kick off job %s", mr_id)
state = model.MapreduceState.get_by_job_id(mr_id)
if not self._check_mr_state(state, mr_id):
return
# Issue abort command just in case there are running tasks.
config = util.create_datastore_write_config(state.mapreduce_spec)
model.MapreduceControl.abort(mr_id, config=config)
# Finalize job and invoke callback.
state.active = False
state.result_status = model.MapreduceState.RESULT_FAILED
ControllerCallbackHandler._finalize_job(state.mapreduce_spec, state) | python | def _drop_gracefully(self):
"""See parent."""
mr_id = self.request.get("mapreduce_id")
logging.error("Failed to kick off job %s", mr_id)
state = model.MapreduceState.get_by_job_id(mr_id)
if not self._check_mr_state(state, mr_id):
return
# Issue abort command just in case there are running tasks.
config = util.create_datastore_write_config(state.mapreduce_spec)
model.MapreduceControl.abort(mr_id, config=config)
# Finalize job and invoke callback.
state.active = False
state.result_status = model.MapreduceState.RESULT_FAILED
ControllerCallbackHandler._finalize_job(state.mapreduce_spec, state) | See parent. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1417-L1433 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | KickOffJobHandler._get_input_readers | def _get_input_readers(self, state):
"""Get input readers.
Args:
state: a MapreduceState model.
Returns:
A tuple: (a list of input readers, a model._HugeTaskPayload entity).
The payload entity contains the json serialized input readers.
(None, None) when input reader inplitting returned no data to process.
"""
serialized_input_readers_key = (self._SERIALIZED_INPUT_READERS_KEY %
state.key().id_or_name())
serialized_input_readers = model._HugeTaskPayload.get_by_key_name(
serialized_input_readers_key, parent=state)
# Initialize input readers.
input_reader_class = state.mapreduce_spec.mapper.input_reader_class()
split_param = state.mapreduce_spec.mapper
if issubclass(input_reader_class, map_job.InputReader):
split_param = map_job.JobConfig._to_map_job_config(
state.mapreduce_spec,
os.environ.get("HTTP_X_APPENGINE_QUEUENAME"))
if serialized_input_readers is None:
readers = input_reader_class.split_input(split_param)
else:
readers = [input_reader_class.from_json_str(_json) for _json in
json.loads(zlib.decompress(
serialized_input_readers.payload))]
if not readers:
return None, None
# Update state and spec with actual shard count.
state.mapreduce_spec.mapper.shard_count = len(readers)
state.active_shards = len(readers)
# Prepare to save serialized input readers.
if serialized_input_readers is None:
# Use mr_state as parent so it can be easily cleaned up later.
serialized_input_readers = model._HugeTaskPayload(
key_name=serialized_input_readers_key, parent=state)
readers_json_str = [i.to_json_str() for i in readers]
serialized_input_readers.payload = zlib.compress(json.dumps(
readers_json_str))
return readers, serialized_input_readers | python | def _get_input_readers(self, state):
"""Get input readers.
Args:
state: a MapreduceState model.
Returns:
A tuple: (a list of input readers, a model._HugeTaskPayload entity).
The payload entity contains the json serialized input readers.
(None, None) when input reader inplitting returned no data to process.
"""
serialized_input_readers_key = (self._SERIALIZED_INPUT_READERS_KEY %
state.key().id_or_name())
serialized_input_readers = model._HugeTaskPayload.get_by_key_name(
serialized_input_readers_key, parent=state)
# Initialize input readers.
input_reader_class = state.mapreduce_spec.mapper.input_reader_class()
split_param = state.mapreduce_spec.mapper
if issubclass(input_reader_class, map_job.InputReader):
split_param = map_job.JobConfig._to_map_job_config(
state.mapreduce_spec,
os.environ.get("HTTP_X_APPENGINE_QUEUENAME"))
if serialized_input_readers is None:
readers = input_reader_class.split_input(split_param)
else:
readers = [input_reader_class.from_json_str(_json) for _json in
json.loads(zlib.decompress(
serialized_input_readers.payload))]
if not readers:
return None, None
# Update state and spec with actual shard count.
state.mapreduce_spec.mapper.shard_count = len(readers)
state.active_shards = len(readers)
# Prepare to save serialized input readers.
if serialized_input_readers is None:
# Use mr_state as parent so it can be easily cleaned up later.
serialized_input_readers = model._HugeTaskPayload(
key_name=serialized_input_readers_key, parent=state)
readers_json_str = [i.to_json_str() for i in readers]
serialized_input_readers.payload = zlib.compress(json.dumps(
readers_json_str))
return readers, serialized_input_readers | Get input readers.
Args:
state: a MapreduceState model.
Returns:
A tuple: (a list of input readers, a model._HugeTaskPayload entity).
The payload entity contains the json serialized input readers.
(None, None) when input reader inplitting returned no data to process. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1435-L1480 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | KickOffJobHandler._save_states | def _save_states(self, state, serialized_readers_entity):
"""Run transaction to save state.
Args:
state: a model.MapreduceState entity.
serialized_readers_entity: a model._HugeTaskPayload entity containing
json serialized input readers.
Returns:
False if a fatal error is encountered and this task should be dropped
immediately. True if transaction is successful. None if a previous
attempt of this same transaction has already succeeded.
"""
mr_id = state.key().id_or_name()
fresh_state = model.MapreduceState.get_by_job_id(mr_id)
if not self._check_mr_state(fresh_state, mr_id):
return False
if fresh_state.active_shards != 0:
logging.warning(
"Mapreduce %s already has active shards. Looks like spurious task "
"execution.", mr_id)
return None
config = util.create_datastore_write_config(state.mapreduce_spec)
db.put([state, serialized_readers_entity], config=config)
return True | python | def _save_states(self, state, serialized_readers_entity):
"""Run transaction to save state.
Args:
state: a model.MapreduceState entity.
serialized_readers_entity: a model._HugeTaskPayload entity containing
json serialized input readers.
Returns:
False if a fatal error is encountered and this task should be dropped
immediately. True if transaction is successful. None if a previous
attempt of this same transaction has already succeeded.
"""
mr_id = state.key().id_or_name()
fresh_state = model.MapreduceState.get_by_job_id(mr_id)
if not self._check_mr_state(fresh_state, mr_id):
return False
if fresh_state.active_shards != 0:
logging.warning(
"Mapreduce %s already has active shards. Looks like spurious task "
"execution.", mr_id)
return None
config = util.create_datastore_write_config(state.mapreduce_spec)
db.put([state, serialized_readers_entity], config=config)
return True | Run transaction to save state.
Args:
state: a model.MapreduceState entity.
serialized_readers_entity: a model._HugeTaskPayload entity containing
json serialized input readers.
Returns:
False if a fatal error is encountered and this task should be dropped
immediately. True if transaction is successful. None if a previous
attempt of this same transaction has already succeeded. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1489-L1513 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | KickOffJobHandler._schedule_shards | def _schedule_shards(cls,
spec,
readers,
queue_name,
base_path,
mr_state):
"""Prepares shard states and schedules their execution.
Even though this method does not schedule shard task and save shard state
transactionally, it's safe for taskqueue to retry this logic because
the initial shard_state for each shard is the same from any retry.
This is an important yet reasonable assumption on model.ShardState.
Args:
spec: mapreduce specification as MapreduceSpec.
readers: list of InputReaders describing shard splits.
queue_name: The queue to run this job on.
base_path: The base url path of mapreduce callbacks.
mr_state: The MapReduceState of current job.
"""
# Create shard states.
shard_states = []
for shard_number, input_reader in enumerate(readers):
shard_state = model.ShardState.create_new(spec.mapreduce_id, shard_number)
shard_state.shard_description = str(input_reader)
shard_states.append(shard_state)
# Retrieves already existing shard states.
existing_shard_states = db.get(shard.key() for shard in shard_states)
existing_shard_keys = set(shard.key() for shard in existing_shard_states
if shard is not None)
# Save non existent shard states.
# Note: we could do this transactionally if necessary.
db.put((shard for shard in shard_states
if shard.key() not in existing_shard_keys),
config=util.create_datastore_write_config(spec))
# Create output writers.
writer_class = spec.mapper.output_writer_class()
writers = [None] * len(readers)
if writer_class:
for shard_number, shard_state in enumerate(shard_states):
writers[shard_number] = writer_class.create(
mr_state.mapreduce_spec,
shard_state.shard_number, shard_state.retries + 1,
mr_state.writer_state)
# Schedule ALL shard tasks.
# Since each task is named, _add_task will fall back gracefully if a
# task already exists.
for shard_number, (input_reader, output_writer) in enumerate(
zip(readers, writers)):
shard_id = model.ShardState.shard_id_from_number(
spec.mapreduce_id, shard_number)
task = MapperWorkerCallbackHandler._state_to_task(
model.TransientShardState(
base_path, spec, shard_id, 0, input_reader, input_reader,
output_writer=output_writer,
handler=spec.mapper.handler),
shard_states[shard_number])
MapperWorkerCallbackHandler._add_task(task,
spec,
queue_name) | python | def _schedule_shards(cls,
spec,
readers,
queue_name,
base_path,
mr_state):
"""Prepares shard states and schedules their execution.
Even though this method does not schedule shard task and save shard state
transactionally, it's safe for taskqueue to retry this logic because
the initial shard_state for each shard is the same from any retry.
This is an important yet reasonable assumption on model.ShardState.
Args:
spec: mapreduce specification as MapreduceSpec.
readers: list of InputReaders describing shard splits.
queue_name: The queue to run this job on.
base_path: The base url path of mapreduce callbacks.
mr_state: The MapReduceState of current job.
"""
# Create shard states.
shard_states = []
for shard_number, input_reader in enumerate(readers):
shard_state = model.ShardState.create_new(spec.mapreduce_id, shard_number)
shard_state.shard_description = str(input_reader)
shard_states.append(shard_state)
# Retrieves already existing shard states.
existing_shard_states = db.get(shard.key() for shard in shard_states)
existing_shard_keys = set(shard.key() for shard in existing_shard_states
if shard is not None)
# Save non existent shard states.
# Note: we could do this transactionally if necessary.
db.put((shard for shard in shard_states
if shard.key() not in existing_shard_keys),
config=util.create_datastore_write_config(spec))
# Create output writers.
writer_class = spec.mapper.output_writer_class()
writers = [None] * len(readers)
if writer_class:
for shard_number, shard_state in enumerate(shard_states):
writers[shard_number] = writer_class.create(
mr_state.mapreduce_spec,
shard_state.shard_number, shard_state.retries + 1,
mr_state.writer_state)
# Schedule ALL shard tasks.
# Since each task is named, _add_task will fall back gracefully if a
# task already exists.
for shard_number, (input_reader, output_writer) in enumerate(
zip(readers, writers)):
shard_id = model.ShardState.shard_id_from_number(
spec.mapreduce_id, shard_number)
task = MapperWorkerCallbackHandler._state_to_task(
model.TransientShardState(
base_path, spec, shard_id, 0, input_reader, input_reader,
output_writer=output_writer,
handler=spec.mapper.handler),
shard_states[shard_number])
MapperWorkerCallbackHandler._add_task(task,
spec,
queue_name) | Prepares shard states and schedules their execution.
Even though this method does not schedule shard task and save shard state
transactionally, it's safe for taskqueue to retry this logic because
the initial shard_state for each shard is the same from any retry.
This is an important yet reasonable assumption on model.ShardState.
Args:
spec: mapreduce specification as MapreduceSpec.
readers: list of InputReaders describing shard splits.
queue_name: The queue to run this job on.
base_path: The base url path of mapreduce callbacks.
mr_state: The MapReduceState of current job. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1516-L1579 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | KickOffJobHandler._check_mr_state | def _check_mr_state(cls, state, mr_id):
"""Check MapreduceState.
Args:
state: an MapreduceState instance.
mr_id: mapreduce id.
Returns:
True if state is valid. False if not and this task should be dropped.
"""
if state is None:
logging.warning(
"Mapreduce State for job %s is missing. Dropping Task.",
mr_id)
return False
if not state.active:
logging.warning(
"Mapreduce %s is not active. Looks like spurious task "
"execution. Dropping Task.", mr_id)
return False
return True | python | def _check_mr_state(cls, state, mr_id):
"""Check MapreduceState.
Args:
state: an MapreduceState instance.
mr_id: mapreduce id.
Returns:
True if state is valid. False if not and this task should be dropped.
"""
if state is None:
logging.warning(
"Mapreduce State for job %s is missing. Dropping Task.",
mr_id)
return False
if not state.active:
logging.warning(
"Mapreduce %s is not active. Looks like spurious task "
"execution. Dropping Task.", mr_id)
return False
return True | Check MapreduceState.
Args:
state: an MapreduceState instance.
mr_id: mapreduce id.
Returns:
True if state is valid. False if not and this task should be dropped. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1582-L1602 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | StartJobHandler.handle | def handle(self):
"""Handles start request."""
# Mapper spec as form arguments.
mapreduce_name = self._get_required_param("name")
mapper_input_reader_spec = self._get_required_param("mapper_input_reader")
mapper_handler_spec = self._get_required_param("mapper_handler")
mapper_output_writer_spec = self.request.get("mapper_output_writer")
mapper_params = self._get_params(
"mapper_params_validator", "mapper_params.")
params = self._get_params(
"params_validator", "params.")
# Default values.
mr_params = map_job.JobConfig._get_default_mr_params()
mr_params.update(params)
if "queue_name" in mapper_params:
mr_params["queue_name"] = mapper_params["queue_name"]
# Set some mapper param defaults if not present.
mapper_params["processing_rate"] = int(mapper_params.get(
"processing_rate") or parameters.config.PROCESSING_RATE_PER_SEC)
# Validate the Mapper spec, handler, and input reader.
mapper_spec = model.MapperSpec(
mapper_handler_spec,
mapper_input_reader_spec,
mapper_params,
int(mapper_params.get("shard_count", parameters.config.SHARD_COUNT)),
output_writer_spec=mapper_output_writer_spec)
mapreduce_id = self._start_map(
mapreduce_name,
mapper_spec,
mr_params,
queue_name=mr_params["queue_name"],
_app=mapper_params.get("_app"))
self.json_response["mapreduce_id"] = mapreduce_id | python | def handle(self):
"""Handles start request."""
# Mapper spec as form arguments.
mapreduce_name = self._get_required_param("name")
mapper_input_reader_spec = self._get_required_param("mapper_input_reader")
mapper_handler_spec = self._get_required_param("mapper_handler")
mapper_output_writer_spec = self.request.get("mapper_output_writer")
mapper_params = self._get_params(
"mapper_params_validator", "mapper_params.")
params = self._get_params(
"params_validator", "params.")
# Default values.
mr_params = map_job.JobConfig._get_default_mr_params()
mr_params.update(params)
if "queue_name" in mapper_params:
mr_params["queue_name"] = mapper_params["queue_name"]
# Set some mapper param defaults if not present.
mapper_params["processing_rate"] = int(mapper_params.get(
"processing_rate") or parameters.config.PROCESSING_RATE_PER_SEC)
# Validate the Mapper spec, handler, and input reader.
mapper_spec = model.MapperSpec(
mapper_handler_spec,
mapper_input_reader_spec,
mapper_params,
int(mapper_params.get("shard_count", parameters.config.SHARD_COUNT)),
output_writer_spec=mapper_output_writer_spec)
mapreduce_id = self._start_map(
mapreduce_name,
mapper_spec,
mr_params,
queue_name=mr_params["queue_name"],
_app=mapper_params.get("_app"))
self.json_response["mapreduce_id"] = mapreduce_id | Handles start request. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1612-L1648 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | StartJobHandler._get_params | def _get_params(self, validator_parameter, name_prefix):
"""Retrieves additional user-supplied params for the job and validates them.
Args:
validator_parameter: name of the request parameter which supplies
validator for this parameter set.
name_prefix: common prefix for all parameter names in the request.
Raises:
Any exception raised by the 'params_validator' request parameter if
the params fail to validate.
Returns:
The user parameters.
"""
params_validator = self.request.get(validator_parameter)
user_params = {}
for key in self.request.arguments():
if key.startswith(name_prefix):
values = self.request.get_all(key)
adjusted_key = key[len(name_prefix):]
if len(values) == 1:
user_params[adjusted_key] = values[0]
else:
user_params[adjusted_key] = values
if params_validator:
resolved_validator = util.for_name(params_validator)
resolved_validator(user_params)
return user_params | python | def _get_params(self, validator_parameter, name_prefix):
"""Retrieves additional user-supplied params for the job and validates them.
Args:
validator_parameter: name of the request parameter which supplies
validator for this parameter set.
name_prefix: common prefix for all parameter names in the request.
Raises:
Any exception raised by the 'params_validator' request parameter if
the params fail to validate.
Returns:
The user parameters.
"""
params_validator = self.request.get(validator_parameter)
user_params = {}
for key in self.request.arguments():
if key.startswith(name_prefix):
values = self.request.get_all(key)
adjusted_key = key[len(name_prefix):]
if len(values) == 1:
user_params[adjusted_key] = values[0]
else:
user_params[adjusted_key] = values
if params_validator:
resolved_validator = util.for_name(params_validator)
resolved_validator(user_params)
return user_params | Retrieves additional user-supplied params for the job and validates them.
Args:
validator_parameter: name of the request parameter which supplies
validator for this parameter set.
name_prefix: common prefix for all parameter names in the request.
Raises:
Any exception raised by the 'params_validator' request parameter if
the params fail to validate.
Returns:
The user parameters. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1650-L1681 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | StartJobHandler._get_required_param | def _get_required_param(self, param_name):
"""Get a required request parameter.
Args:
param_name: name of request parameter to fetch.
Returns:
parameter value
Raises:
errors.NotEnoughArgumentsError: if parameter is not specified.
"""
value = self.request.get(param_name)
if not value:
raise errors.NotEnoughArgumentsError(param_name + " not specified")
return value | python | def _get_required_param(self, param_name):
"""Get a required request parameter.
Args:
param_name: name of request parameter to fetch.
Returns:
parameter value
Raises:
errors.NotEnoughArgumentsError: if parameter is not specified.
"""
value = self.request.get(param_name)
if not value:
raise errors.NotEnoughArgumentsError(param_name + " not specified")
return value | Get a required request parameter.
Args:
param_name: name of request parameter to fetch.
Returns:
parameter value
Raises:
errors.NotEnoughArgumentsError: if parameter is not specified. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1683-L1698 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | StartJobHandler._start_map | def _start_map(cls,
name,
mapper_spec,
mapreduce_params,
queue_name,
eta=None,
countdown=None,
hooks_class_name=None,
_app=None,
in_xg_transaction=False):
# pylint: disable=g-doc-args
# pylint: disable=g-doc-return-or-yield
"""See control.start_map.
Requirements for this method:
1. The request that invokes this method can either be regular or
from taskqueue. So taskqueue specific headers can not be used.
2. Each invocation transactionally starts an isolated mapreduce job with
a unique id. MapreduceState should be immediately available after
returning. See control.start_map's doc on transactional.
3. Method should be lightweight.
"""
# Validate input reader.
mapper_input_reader_class = mapper_spec.input_reader_class()
mapper_input_reader_class.validate(mapper_spec)
# Validate output writer.
mapper_output_writer_class = mapper_spec.output_writer_class()
if mapper_output_writer_class:
mapper_output_writer_class.validate(mapper_spec)
# Create a new id and mr spec.
mapreduce_id = model.MapreduceState.new_mapreduce_id()
mapreduce_spec = model.MapreduceSpec(
name,
mapreduce_id,
mapper_spec.to_json(),
mapreduce_params,
hooks_class_name)
# Validate mapper handler.
ctx = context.Context(mapreduce_spec, None)
context.Context._set(ctx)
try:
# pylint: disable=pointless-statement
mapper_spec.handler
finally:
context.Context._set(None)
# Save states and enqueue task.
if in_xg_transaction:
propagation = db.MANDATORY
else:
propagation = db.INDEPENDENT
@db.transactional(propagation=propagation)
def _txn():
cls._create_and_save_state(mapreduce_spec, _app)
cls._add_kickoff_task(mapreduce_params["base_path"], mapreduce_spec, eta,
countdown, queue_name)
_txn()
return mapreduce_id | python | def _start_map(cls,
name,
mapper_spec,
mapreduce_params,
queue_name,
eta=None,
countdown=None,
hooks_class_name=None,
_app=None,
in_xg_transaction=False):
# pylint: disable=g-doc-args
# pylint: disable=g-doc-return-or-yield
"""See control.start_map.
Requirements for this method:
1. The request that invokes this method can either be regular or
from taskqueue. So taskqueue specific headers can not be used.
2. Each invocation transactionally starts an isolated mapreduce job with
a unique id. MapreduceState should be immediately available after
returning. See control.start_map's doc on transactional.
3. Method should be lightweight.
"""
# Validate input reader.
mapper_input_reader_class = mapper_spec.input_reader_class()
mapper_input_reader_class.validate(mapper_spec)
# Validate output writer.
mapper_output_writer_class = mapper_spec.output_writer_class()
if mapper_output_writer_class:
mapper_output_writer_class.validate(mapper_spec)
# Create a new id and mr spec.
mapreduce_id = model.MapreduceState.new_mapreduce_id()
mapreduce_spec = model.MapreduceSpec(
name,
mapreduce_id,
mapper_spec.to_json(),
mapreduce_params,
hooks_class_name)
# Validate mapper handler.
ctx = context.Context(mapreduce_spec, None)
context.Context._set(ctx)
try:
# pylint: disable=pointless-statement
mapper_spec.handler
finally:
context.Context._set(None)
# Save states and enqueue task.
if in_xg_transaction:
propagation = db.MANDATORY
else:
propagation = db.INDEPENDENT
@db.transactional(propagation=propagation)
def _txn():
cls._create_and_save_state(mapreduce_spec, _app)
cls._add_kickoff_task(mapreduce_params["base_path"], mapreduce_spec, eta,
countdown, queue_name)
_txn()
return mapreduce_id | See control.start_map.
Requirements for this method:
1. The request that invokes this method can either be regular or
from taskqueue. So taskqueue specific headers can not be used.
2. Each invocation transactionally starts an isolated mapreduce job with
a unique id. MapreduceState should be immediately available after
returning. See control.start_map's doc on transactional.
3. Method should be lightweight. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1701-L1763 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | StartJobHandler._create_and_save_state | def _create_and_save_state(cls, mapreduce_spec, _app):
"""Save mapreduce state to datastore.
Save state to datastore so that UI can see it immediately.
Args:
mapreduce_spec: model.MapreduceSpec,
_app: app id if specified. None otherwise.
Returns:
The saved Mapreduce state.
"""
state = model.MapreduceState.create_new(mapreduce_spec.mapreduce_id)
state.mapreduce_spec = mapreduce_spec
state.active = True
state.active_shards = 0
if _app:
state.app_id = _app
config = util.create_datastore_write_config(mapreduce_spec)
state.put(config=config)
return state | python | def _create_and_save_state(cls, mapreduce_spec, _app):
"""Save mapreduce state to datastore.
Save state to datastore so that UI can see it immediately.
Args:
mapreduce_spec: model.MapreduceSpec,
_app: app id if specified. None otherwise.
Returns:
The saved Mapreduce state.
"""
state = model.MapreduceState.create_new(mapreduce_spec.mapreduce_id)
state.mapreduce_spec = mapreduce_spec
state.active = True
state.active_shards = 0
if _app:
state.app_id = _app
config = util.create_datastore_write_config(mapreduce_spec)
state.put(config=config)
return state | Save mapreduce state to datastore.
Save state to datastore so that UI can see it immediately.
Args:
mapreduce_spec: model.MapreduceSpec,
_app: app id if specified. None otherwise.
Returns:
The saved Mapreduce state. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1766-L1786 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | StartJobHandler._add_kickoff_task | def _add_kickoff_task(cls,
base_path,
mapreduce_spec,
eta,
countdown,
queue_name):
"""Enqueues a new kickoff task."""
params = {"mapreduce_id": mapreduce_spec.mapreduce_id}
# Task is not named so that it can be added within a transaction.
kickoff_task = taskqueue.Task(
url=base_path + "/kickoffjob_callback/" + mapreduce_spec.mapreduce_id,
headers=util._get_task_headers(mapreduce_spec.mapreduce_id),
params=params,
eta=eta,
countdown=countdown)
hooks = mapreduce_spec.get_hooks()
if hooks is not None:
try:
hooks.enqueue_kickoff_task(kickoff_task, queue_name)
return
except NotImplementedError:
pass
kickoff_task.add(queue_name, transactional=True) | python | def _add_kickoff_task(cls,
base_path,
mapreduce_spec,
eta,
countdown,
queue_name):
"""Enqueues a new kickoff task."""
params = {"mapreduce_id": mapreduce_spec.mapreduce_id}
# Task is not named so that it can be added within a transaction.
kickoff_task = taskqueue.Task(
url=base_path + "/kickoffjob_callback/" + mapreduce_spec.mapreduce_id,
headers=util._get_task_headers(mapreduce_spec.mapreduce_id),
params=params,
eta=eta,
countdown=countdown)
hooks = mapreduce_spec.get_hooks()
if hooks is not None:
try:
hooks.enqueue_kickoff_task(kickoff_task, queue_name)
return
except NotImplementedError:
pass
kickoff_task.add(queue_name, transactional=True) | Enqueues a new kickoff task. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1789-L1811 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/handlers.py | FinalizeJobHandler.schedule | def schedule(cls, mapreduce_spec):
"""Schedule finalize task.
Args:
mapreduce_spec: mapreduce specification as MapreduceSpec.
"""
task_name = mapreduce_spec.mapreduce_id + "-finalize"
finalize_task = taskqueue.Task(
name=task_name,
url=(mapreduce_spec.params["base_path"] + "/finalizejob_callback/" +
mapreduce_spec.mapreduce_id),
params={"mapreduce_id": mapreduce_spec.mapreduce_id},
headers=util._get_task_headers(mapreduce_spec.mapreduce_id))
queue_name = util.get_queue_name(None)
if not _run_task_hook(mapreduce_spec.get_hooks(),
"enqueue_controller_task",
finalize_task,
queue_name):
try:
finalize_task.add(queue_name)
except (taskqueue.TombstonedTaskError,
taskqueue.TaskAlreadyExistsError), e:
logging.warning("Task %r already exists. %s: %s",
task_name, e.__class__, e) | python | def schedule(cls, mapreduce_spec):
"""Schedule finalize task.
Args:
mapreduce_spec: mapreduce specification as MapreduceSpec.
"""
task_name = mapreduce_spec.mapreduce_id + "-finalize"
finalize_task = taskqueue.Task(
name=task_name,
url=(mapreduce_spec.params["base_path"] + "/finalizejob_callback/" +
mapreduce_spec.mapreduce_id),
params={"mapreduce_id": mapreduce_spec.mapreduce_id},
headers=util._get_task_headers(mapreduce_spec.mapreduce_id))
queue_name = util.get_queue_name(None)
if not _run_task_hook(mapreduce_spec.get_hooks(),
"enqueue_controller_task",
finalize_task,
queue_name):
try:
finalize_task.add(queue_name)
except (taskqueue.TombstonedTaskError,
taskqueue.TaskAlreadyExistsError), e:
logging.warning("Task %r already exists. %s: %s",
task_name, e.__class__, e) | Schedule finalize task.
Args:
mapreduce_spec: mapreduce specification as MapreduceSpec. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/handlers.py#L1832-L1855 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _get_params | def _get_params(mapper_spec, allowed_keys=None, allow_old=True):
"""Obtain input reader parameters.
Utility function for input readers implementation. Fetches parameters
from mapreduce specification giving appropriate usage warnings.
Args:
mapper_spec: The MapperSpec for the job
allowed_keys: set of all allowed keys in parameters as strings. If it is not
None, then parameters are expected to be in a separate "input_reader"
subdictionary of mapper_spec parameters.
allow_old: Allow parameters to exist outside of the input_reader
subdictionary for compatability.
Returns:
mapper parameters as dict
Raises:
BadReaderParamsError: if parameters are invalid/missing or not allowed.
"""
if "input_reader" not in mapper_spec.params:
message = ("Input reader's parameters should be specified in "
"input_reader subdictionary.")
if not allow_old or allowed_keys:
raise errors.BadReaderParamsError(message)
params = mapper_spec.params
params = dict((str(n), v) for n, v in params.iteritems())
else:
if not isinstance(mapper_spec.params.get("input_reader"), dict):
raise errors.BadReaderParamsError(
"Input reader parameters should be a dictionary")
params = mapper_spec.params.get("input_reader")
params = dict((str(n), v) for n, v in params.iteritems())
if allowed_keys:
params_diff = set(params.keys()) - allowed_keys
if params_diff:
raise errors.BadReaderParamsError(
"Invalid input_reader parameters: %s" % ",".join(params_diff))
return params | python | def _get_params(mapper_spec, allowed_keys=None, allow_old=True):
"""Obtain input reader parameters.
Utility function for input readers implementation. Fetches parameters
from mapreduce specification giving appropriate usage warnings.
Args:
mapper_spec: The MapperSpec for the job
allowed_keys: set of all allowed keys in parameters as strings. If it is not
None, then parameters are expected to be in a separate "input_reader"
subdictionary of mapper_spec parameters.
allow_old: Allow parameters to exist outside of the input_reader
subdictionary for compatability.
Returns:
mapper parameters as dict
Raises:
BadReaderParamsError: if parameters are invalid/missing or not allowed.
"""
if "input_reader" not in mapper_spec.params:
message = ("Input reader's parameters should be specified in "
"input_reader subdictionary.")
if not allow_old or allowed_keys:
raise errors.BadReaderParamsError(message)
params = mapper_spec.params
params = dict((str(n), v) for n, v in params.iteritems())
else:
if not isinstance(mapper_spec.params.get("input_reader"), dict):
raise errors.BadReaderParamsError(
"Input reader parameters should be a dictionary")
params = mapper_spec.params.get("input_reader")
params = dict((str(n), v) for n, v in params.iteritems())
if allowed_keys:
params_diff = set(params.keys()) - allowed_keys
if params_diff:
raise errors.BadReaderParamsError(
"Invalid input_reader parameters: %s" % ",".join(params_diff))
return params | Obtain input reader parameters.
Utility function for input readers implementation. Fetches parameters
from mapreduce specification giving appropriate usage warnings.
Args:
mapper_spec: The MapperSpec for the job
allowed_keys: set of all allowed keys in parameters as strings. If it is not
None, then parameters are expected to be in a separate "input_reader"
subdictionary of mapper_spec parameters.
allow_old: Allow parameters to exist outside of the input_reader
subdictionary for compatability.
Returns:
mapper parameters as dict
Raises:
BadReaderParamsError: if parameters are invalid/missing or not allowed. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L206-L244 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | AbstractDatastoreInputReader._split_ns_by_scatter | def _split_ns_by_scatter(cls,
shard_count,
namespace,
raw_entity_kind,
filters,
app):
"""Split a namespace by scatter index into key_range.KeyRange.
TODO(user): Power this with key_range.KeyRange.compute_split_points.
Args:
shard_count: number of shards.
namespace: namespace name to split. str.
raw_entity_kind: low level datastore API entity kind.
app: app id in str.
Returns:
A list of key_range.KeyRange objects. If there are not enough entities to
splits into requested shards, the returned list will contain KeyRanges
ordered lexicographically with any Nones appearing at the end.
"""
if shard_count == 1:
# With one shard we don't need to calculate any split points at all.
return [key_range.KeyRange(namespace=namespace, _app=app)]
ds_query = datastore.Query(kind=raw_entity_kind,
namespace=namespace,
_app=app,
keys_only=True)
ds_query.Order("__scatter__")
oversampling_factor = 32
random_keys = None
if filters:
ds_query_with_filters = copy.copy(ds_query)
for (key, op, value) in filters:
ds_query_with_filters.update({'%s %s' % (key, op): value})
try:
random_keys = ds_query_with_filters.Get(shard_count *
oversampling_factor)
except db.NeedIndexError, why:
logging.warning('Need to add an index for optimal mapreduce-input'
' splitting:\n%s' % why)
# We'll try again without the filter. We hope the filter
# will filter keys uniformly across the key-name space!
if not random_keys:
random_keys = ds_query.Get(shard_count * oversampling_factor)
if not random_keys:
# There are no entities with scatter property. We have no idea
# how to split.
return ([key_range.KeyRange(namespace=namespace, _app=app)] +
[None] * (shard_count - 1))
random_keys.sort()
if len(random_keys) >= shard_count:
# We've got a lot of scatter values. Sample them down.
random_keys = cls._choose_split_points(random_keys, shard_count)
k_ranges = []
k_ranges.append(key_range.KeyRange(
key_start=None,
key_end=random_keys[0],
direction=key_range.KeyRange.ASC,
include_start=False,
include_end=False,
namespace=namespace,
_app=app))
for i in range(0, len(random_keys) - 1):
k_ranges.append(key_range.KeyRange(
key_start=random_keys[i],
key_end=random_keys[i+1],
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
k_ranges.append(key_range.KeyRange(
key_start=random_keys[-1],
key_end=None,
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
if len(k_ranges) < shard_count:
# We need to have as many shards as it was requested. Add some Nones.
k_ranges += [None] * (shard_count - len(k_ranges))
return k_ranges | python | def _split_ns_by_scatter(cls,
shard_count,
namespace,
raw_entity_kind,
filters,
app):
"""Split a namespace by scatter index into key_range.KeyRange.
TODO(user): Power this with key_range.KeyRange.compute_split_points.
Args:
shard_count: number of shards.
namespace: namespace name to split. str.
raw_entity_kind: low level datastore API entity kind.
app: app id in str.
Returns:
A list of key_range.KeyRange objects. If there are not enough entities to
splits into requested shards, the returned list will contain KeyRanges
ordered lexicographically with any Nones appearing at the end.
"""
if shard_count == 1:
# With one shard we don't need to calculate any split points at all.
return [key_range.KeyRange(namespace=namespace, _app=app)]
ds_query = datastore.Query(kind=raw_entity_kind,
namespace=namespace,
_app=app,
keys_only=True)
ds_query.Order("__scatter__")
oversampling_factor = 32
random_keys = None
if filters:
ds_query_with_filters = copy.copy(ds_query)
for (key, op, value) in filters:
ds_query_with_filters.update({'%s %s' % (key, op): value})
try:
random_keys = ds_query_with_filters.Get(shard_count *
oversampling_factor)
except db.NeedIndexError, why:
logging.warning('Need to add an index for optimal mapreduce-input'
' splitting:\n%s' % why)
# We'll try again without the filter. We hope the filter
# will filter keys uniformly across the key-name space!
if not random_keys:
random_keys = ds_query.Get(shard_count * oversampling_factor)
if not random_keys:
# There are no entities with scatter property. We have no idea
# how to split.
return ([key_range.KeyRange(namespace=namespace, _app=app)] +
[None] * (shard_count - 1))
random_keys.sort()
if len(random_keys) >= shard_count:
# We've got a lot of scatter values. Sample them down.
random_keys = cls._choose_split_points(random_keys, shard_count)
k_ranges = []
k_ranges.append(key_range.KeyRange(
key_start=None,
key_end=random_keys[0],
direction=key_range.KeyRange.ASC,
include_start=False,
include_end=False,
namespace=namespace,
_app=app))
for i in range(0, len(random_keys) - 1):
k_ranges.append(key_range.KeyRange(
key_start=random_keys[i],
key_end=random_keys[i+1],
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
k_ranges.append(key_range.KeyRange(
key_start=random_keys[-1],
key_end=None,
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
if len(k_ranges) < shard_count:
# We need to have as many shards as it was requested. Add some Nones.
k_ranges += [None] * (shard_count - len(k_ranges))
return k_ranges | Split a namespace by scatter index into key_range.KeyRange.
TODO(user): Power this with key_range.KeyRange.compute_split_points.
Args:
shard_count: number of shards.
namespace: namespace name to split. str.
raw_entity_kind: low level datastore API entity kind.
app: app id in str.
Returns:
A list of key_range.KeyRange objects. If there are not enough entities to
splits into requested shards, the returned list will contain KeyRanges
ordered lexicographically with any Nones appearing at the end. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L418-L511 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | AbstractDatastoreInputReader._choose_split_points | def _choose_split_points(cls, sorted_keys, shard_count):
"""Returns the best split points given a random set of datastore.Keys."""
assert len(sorted_keys) >= shard_count
index_stride = len(sorted_keys) / float(shard_count)
return [sorted_keys[int(round(index_stride * i))]
for i in range(1, shard_count)] | python | def _choose_split_points(cls, sorted_keys, shard_count):
"""Returns the best split points given a random set of datastore.Keys."""
assert len(sorted_keys) >= shard_count
index_stride = len(sorted_keys) / float(shard_count)
return [sorted_keys[int(round(index_stride * i))]
for i in range(1, shard_count)] | Returns the best split points given a random set of datastore.Keys. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L514-L519 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | AbstractDatastoreInputReader.validate | def validate(cls, mapper_spec):
"""Inherit docs."""
params = _get_params(mapper_spec)
if cls.ENTITY_KIND_PARAM not in params:
raise BadReaderParamsError("Missing input reader parameter 'entity_kind'")
if cls.BATCH_SIZE_PARAM in params:
try:
batch_size = int(params[cls.BATCH_SIZE_PARAM])
if batch_size < 1:
raise BadReaderParamsError("Bad batch size: %s" % batch_size)
except ValueError, e:
raise BadReaderParamsError("Bad batch size: %s" % e)
if cls.OVERSPLIT_FACTOR_PARAM in params:
try:
oversplit_factor = int(params[cls.OVERSPLIT_FACTOR_PARAM])
if oversplit_factor < 1:
raise BadReaderParamsError("Bad oversplit factor:"
" %s" % oversplit_factor)
except ValueError, e:
raise BadReaderParamsError("Bad oversplit factor: %s" % e)
try:
bool(params.get(cls.KEYS_ONLY_PARAM, False))
except:
raise BadReaderParamsError("keys_only expects a boolean value but got %s",
params[cls.KEYS_ONLY_PARAM])
if cls.NAMESPACE_PARAM in params:
if not isinstance(params[cls.NAMESPACE_PARAM],
(str, unicode, type(None))):
raise BadReaderParamsError(
"Expected a single namespace string")
if cls.NAMESPACES_PARAM in params:
raise BadReaderParamsError("Multiple namespaces are no longer supported")
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
if not isinstance(filters, list):
raise BadReaderParamsError("Expected list for filters parameter")
for f in filters:
if not isinstance(f, (tuple, list)):
raise BadReaderParamsError("Filter should be a tuple or list: %s", f)
if len(f) != 3:
raise BadReaderParamsError("Filter should be a 3-tuple: %s", f)
prop, op, _ = f
if not isinstance(prop, basestring):
raise BadReaderParamsError("Property should be string: %s", prop)
if not isinstance(op, basestring):
raise BadReaderParamsError("Operator should be string: %s", op) | python | def validate(cls, mapper_spec):
"""Inherit docs."""
params = _get_params(mapper_spec)
if cls.ENTITY_KIND_PARAM not in params:
raise BadReaderParamsError("Missing input reader parameter 'entity_kind'")
if cls.BATCH_SIZE_PARAM in params:
try:
batch_size = int(params[cls.BATCH_SIZE_PARAM])
if batch_size < 1:
raise BadReaderParamsError("Bad batch size: %s" % batch_size)
except ValueError, e:
raise BadReaderParamsError("Bad batch size: %s" % e)
if cls.OVERSPLIT_FACTOR_PARAM in params:
try:
oversplit_factor = int(params[cls.OVERSPLIT_FACTOR_PARAM])
if oversplit_factor < 1:
raise BadReaderParamsError("Bad oversplit factor:"
" %s" % oversplit_factor)
except ValueError, e:
raise BadReaderParamsError("Bad oversplit factor: %s" % e)
try:
bool(params.get(cls.KEYS_ONLY_PARAM, False))
except:
raise BadReaderParamsError("keys_only expects a boolean value but got %s",
params[cls.KEYS_ONLY_PARAM])
if cls.NAMESPACE_PARAM in params:
if not isinstance(params[cls.NAMESPACE_PARAM],
(str, unicode, type(None))):
raise BadReaderParamsError(
"Expected a single namespace string")
if cls.NAMESPACES_PARAM in params:
raise BadReaderParamsError("Multiple namespaces are no longer supported")
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
if not isinstance(filters, list):
raise BadReaderParamsError("Expected list for filters parameter")
for f in filters:
if not isinstance(f, (tuple, list)):
raise BadReaderParamsError("Filter should be a tuple or list: %s", f)
if len(f) != 3:
raise BadReaderParamsError("Filter should be a 3-tuple: %s", f)
prop, op, _ = f
if not isinstance(prop, basestring):
raise BadReaderParamsError("Property should be string: %s", prop)
if not isinstance(op, basestring):
raise BadReaderParamsError("Operator should be string: %s", op) | Inherit docs. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L522-L567 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | RawDatastoreInputReader.validate | def validate(cls, mapper_spec):
"""Inherit docs."""
super(RawDatastoreInputReader, cls).validate(mapper_spec)
params = _get_params(mapper_spec)
entity_kind = params[cls.ENTITY_KIND_PARAM]
if "." in entity_kind:
logging.warning(
". detected in entity kind %s specified for reader %s."
"Assuming entity kind contains the dot.",
entity_kind, cls.__name__)
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
for f in filters:
if f[1] != "=":
raise BadReaderParamsError(
"Only equality filters are supported: %s", f) | python | def validate(cls, mapper_spec):
"""Inherit docs."""
super(RawDatastoreInputReader, cls).validate(mapper_spec)
params = _get_params(mapper_spec)
entity_kind = params[cls.ENTITY_KIND_PARAM]
if "." in entity_kind:
logging.warning(
". detected in entity kind %s specified for reader %s."
"Assuming entity kind contains the dot.",
entity_kind, cls.__name__)
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
for f in filters:
if f[1] != "=":
raise BadReaderParamsError(
"Only equality filters are supported: %s", f) | Inherit docs. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L589-L604 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | DatastoreInputReader.validate | def validate(cls, mapper_spec):
"""Inherit docs."""
super(DatastoreInputReader, cls).validate(mapper_spec)
params = _get_params(mapper_spec)
entity_kind = params[cls.ENTITY_KIND_PARAM]
# Fail fast if Model cannot be located.
try:
model_class = util.for_name(entity_kind)
except ImportError, e:
raise BadReaderParamsError("Bad entity kind: %s" % e)
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
if issubclass(model_class, db.Model):
cls._validate_filters(filters, model_class)
else:
cls._validate_filters_ndb(filters, model_class)
property_range.PropertyRange(filters, entity_kind) | python | def validate(cls, mapper_spec):
"""Inherit docs."""
super(DatastoreInputReader, cls).validate(mapper_spec)
params = _get_params(mapper_spec)
entity_kind = params[cls.ENTITY_KIND_PARAM]
# Fail fast if Model cannot be located.
try:
model_class = util.for_name(entity_kind)
except ImportError, e:
raise BadReaderParamsError("Bad entity kind: %s" % e)
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
if issubclass(model_class, db.Model):
cls._validate_filters(filters, model_class)
else:
cls._validate_filters_ndb(filters, model_class)
property_range.PropertyRange(filters, entity_kind) | Inherit docs. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L627-L643 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | DatastoreInputReader._validate_filters_ndb | def _validate_filters_ndb(cls, filters, model_class):
"""Validate ndb.Model filters."""
if not filters:
return
properties = model_class._properties
for idx, f in enumerate(filters):
prop, ineq, val = f
if prop not in properties:
raise errors.BadReaderParamsError(
"Property %s is not defined for entity type %s",
prop, model_class._get_kind())
# Attempt to cast the value to a KeyProperty if appropriate.
# This enables filtering against keys.
try:
if (isinstance(val, basestring) and
isinstance(properties[prop],
(ndb.KeyProperty, ndb.ComputedProperty))):
val = ndb.Key(urlsafe=val)
filters[idx] = [prop, ineq, val]
except:
pass
# Validate the value of each filter. We need to know filters have
# valid value to carry out splits.
try:
properties[prop]._do_validate(val)
except db.BadValueError, e:
raise errors.BadReaderParamsError(e) | python | def _validate_filters_ndb(cls, filters, model_class):
"""Validate ndb.Model filters."""
if not filters:
return
properties = model_class._properties
for idx, f in enumerate(filters):
prop, ineq, val = f
if prop not in properties:
raise errors.BadReaderParamsError(
"Property %s is not defined for entity type %s",
prop, model_class._get_kind())
# Attempt to cast the value to a KeyProperty if appropriate.
# This enables filtering against keys.
try:
if (isinstance(val, basestring) and
isinstance(properties[prop],
(ndb.KeyProperty, ndb.ComputedProperty))):
val = ndb.Key(urlsafe=val)
filters[idx] = [prop, ineq, val]
except:
pass
# Validate the value of each filter. We need to know filters have
# valid value to carry out splits.
try:
properties[prop]._do_validate(val)
except db.BadValueError, e:
raise errors.BadReaderParamsError(e) | Validate ndb.Model filters. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L682-L713 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | DatastoreInputReader.split_input | def split_input(cls, mapper_spec):
"""Inherit docs."""
shard_count = mapper_spec.shard_count
query_spec = cls._get_query_spec(mapper_spec)
if not property_range.should_shard_by_property_range(query_spec.filters):
return super(DatastoreInputReader, cls).split_input(mapper_spec)
# Artificially increase the number of shards to get a more even split.
# For example, if we are creating 7 shards for one week of data based on a
# Day property and the data points tend to be clumped on certain days (say,
# Monday and Wednesday), instead of assigning each shard a single day of
# the week, we will split each day into "oversplit_factor" pieces, and
# assign each shard "oversplit_factor" pieces with "1 / oversplit_factor"
# the work, so that the data from Monday and Wednesday is more evenly
# spread across all shards.
oversplit_factor = query_spec.oversplit_factor
oversplit_shard_count = oversplit_factor * shard_count
p_range = property_range.PropertyRange(query_spec.filters,
query_spec.model_class_path)
p_ranges = p_range.split(oversplit_shard_count)
# User specified a namespace.
if query_spec.ns is not None:
ns_range = namespace_range.NamespaceRange(
namespace_start=query_spec.ns,
namespace_end=query_spec.ns,
_app=query_spec.app)
ns_ranges = [copy.copy(ns_range) for _ in p_ranges]
else:
ns_keys = namespace_range.get_namespace_keys(
query_spec.app, cls.MAX_NAMESPACES_FOR_KEY_SHARD+1)
if not ns_keys:
return
# User doesn't specify ns but the number of ns is small.
# We still split by property range.
if len(ns_keys) <= cls.MAX_NAMESPACES_FOR_KEY_SHARD:
ns_ranges = [namespace_range.NamespaceRange(_app=query_spec.app)
for _ in p_ranges]
# Lots of namespaces. Split by ns.
else:
ns_ranges = namespace_range.NamespaceRange.split(n=oversplit_shard_count,
contiguous=False,
can_query=lambda: True,
_app=query_spec.app)
p_ranges = [copy.copy(p_range) for _ in ns_ranges]
assert len(p_ranges) == len(ns_ranges)
iters = [
db_iters.RangeIteratorFactory.create_property_range_iterator(
p, ns, query_spec) for p, ns in zip(p_ranges, ns_ranges)]
# Reduce the number of ranges back down to the shard count.
# It's possible that we didn't split into enough shards even
# after oversplitting, in which case we don't need to do anything.
if len(iters) > shard_count:
# We cycle through the iterators and chain them together, e.g.
# if we look at the indices chained together, we get:
# Shard #0 gets 0, num_shards, 2 * num_shards, ...
# Shard #1 gets 1, num_shards + 1, 2 * num_shards + 1, ...
# Shard #2 gets 2, num_shards + 2, 2 * num_shards + 2, ...
# and so on. This should split fairly evenly.
iters = [
db_iters.RangeIteratorFactory.create_multi_property_range_iterator(
[iters[i] for i in xrange(start_index, len(iters), shard_count)]
) for start_index in xrange(shard_count)
]
return [cls(i) for i in iters] | python | def split_input(cls, mapper_spec):
"""Inherit docs."""
shard_count = mapper_spec.shard_count
query_spec = cls._get_query_spec(mapper_spec)
if not property_range.should_shard_by_property_range(query_spec.filters):
return super(DatastoreInputReader, cls).split_input(mapper_spec)
# Artificially increase the number of shards to get a more even split.
# For example, if we are creating 7 shards for one week of data based on a
# Day property and the data points tend to be clumped on certain days (say,
# Monday and Wednesday), instead of assigning each shard a single day of
# the week, we will split each day into "oversplit_factor" pieces, and
# assign each shard "oversplit_factor" pieces with "1 / oversplit_factor"
# the work, so that the data from Monday and Wednesday is more evenly
# spread across all shards.
oversplit_factor = query_spec.oversplit_factor
oversplit_shard_count = oversplit_factor * shard_count
p_range = property_range.PropertyRange(query_spec.filters,
query_spec.model_class_path)
p_ranges = p_range.split(oversplit_shard_count)
# User specified a namespace.
if query_spec.ns is not None:
ns_range = namespace_range.NamespaceRange(
namespace_start=query_spec.ns,
namespace_end=query_spec.ns,
_app=query_spec.app)
ns_ranges = [copy.copy(ns_range) for _ in p_ranges]
else:
ns_keys = namespace_range.get_namespace_keys(
query_spec.app, cls.MAX_NAMESPACES_FOR_KEY_SHARD+1)
if not ns_keys:
return
# User doesn't specify ns but the number of ns is small.
# We still split by property range.
if len(ns_keys) <= cls.MAX_NAMESPACES_FOR_KEY_SHARD:
ns_ranges = [namespace_range.NamespaceRange(_app=query_spec.app)
for _ in p_ranges]
# Lots of namespaces. Split by ns.
else:
ns_ranges = namespace_range.NamespaceRange.split(n=oversplit_shard_count,
contiguous=False,
can_query=lambda: True,
_app=query_spec.app)
p_ranges = [copy.copy(p_range) for _ in ns_ranges]
assert len(p_ranges) == len(ns_ranges)
iters = [
db_iters.RangeIteratorFactory.create_property_range_iterator(
p, ns, query_spec) for p, ns in zip(p_ranges, ns_ranges)]
# Reduce the number of ranges back down to the shard count.
# It's possible that we didn't split into enough shards even
# after oversplitting, in which case we don't need to do anything.
if len(iters) > shard_count:
# We cycle through the iterators and chain them together, e.g.
# if we look at the indices chained together, we get:
# Shard #0 gets 0, num_shards, 2 * num_shards, ...
# Shard #1 gets 1, num_shards + 1, 2 * num_shards + 1, ...
# Shard #2 gets 2, num_shards + 2, 2 * num_shards + 2, ...
# and so on. This should split fairly evenly.
iters = [
db_iters.RangeIteratorFactory.create_multi_property_range_iterator(
[iters[i] for i in xrange(start_index, len(iters), shard_count)]
) for start_index in xrange(shard_count)
]
return [cls(i) for i in iters] | Inherit docs. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L716-L785 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _OldAbstractDatastoreInputReader._iter_key_ranges | def _iter_key_ranges(self):
"""Iterates over self._key_ranges, delegating to self._iter_key_range()."""
while True:
if self._current_key_range is None:
if self._key_ranges:
self._current_key_range = self._key_ranges.pop()
# The most recently popped key_range may be None, so continue here
# to find the next keyrange that's valid.
continue
else:
break
for key, o in self._iter_key_range(
copy.deepcopy(self._current_key_range)):
# The caller must consume yielded values so advancing the KeyRange
# before yielding is safe.
self._current_key_range.advance(key)
yield o
self._current_key_range = None | python | def _iter_key_ranges(self):
"""Iterates over self._key_ranges, delegating to self._iter_key_range()."""
while True:
if self._current_key_range is None:
if self._key_ranges:
self._current_key_range = self._key_ranges.pop()
# The most recently popped key_range may be None, so continue here
# to find the next keyrange that's valid.
continue
else:
break
for key, o in self._iter_key_range(
copy.deepcopy(self._current_key_range)):
# The caller must consume yielded values so advancing the KeyRange
# before yielding is safe.
self._current_key_range.advance(key)
yield o
self._current_key_range = None | Iterates over self._key_ranges, delegating to self._iter_key_range(). | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L902-L920 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _OldAbstractDatastoreInputReader._iter_ns_range | def _iter_ns_range(self):
"""Iterates over self._ns_range, delegating to self._iter_key_range()."""
while True:
if self._current_key_range is None:
query = self._ns_range.make_datastore_query()
namespace_result = query.Get(1)
if not namespace_result:
break
namespace = namespace_result[0].name() or ""
self._current_key_range = key_range.KeyRange(
namespace=namespace, _app=self._ns_range.app)
yield ALLOW_CHECKPOINT
for key, o in self._iter_key_range(
copy.deepcopy(self._current_key_range)):
# The caller must consume yielded values so advancing the KeyRange
# before yielding is safe.
self._current_key_range.advance(key)
yield o
if (self._ns_range.is_single_namespace or
self._current_key_range.namespace == self._ns_range.namespace_end):
break
self._ns_range = self._ns_range.with_start_after(
self._current_key_range.namespace)
self._current_key_range = None | python | def _iter_ns_range(self):
"""Iterates over self._ns_range, delegating to self._iter_key_range()."""
while True:
if self._current_key_range is None:
query = self._ns_range.make_datastore_query()
namespace_result = query.Get(1)
if not namespace_result:
break
namespace = namespace_result[0].name() or ""
self._current_key_range = key_range.KeyRange(
namespace=namespace, _app=self._ns_range.app)
yield ALLOW_CHECKPOINT
for key, o in self._iter_key_range(
copy.deepcopy(self._current_key_range)):
# The caller must consume yielded values so advancing the KeyRange
# before yielding is safe.
self._current_key_range.advance(key)
yield o
if (self._ns_range.is_single_namespace or
self._current_key_range.namespace == self._ns_range.namespace_end):
break
self._ns_range = self._ns_range.with_start_after(
self._current_key_range.namespace)
self._current_key_range = None | Iterates over self._ns_range, delegating to self._iter_key_range(). | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L922-L948 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _OldAbstractDatastoreInputReader._split_input_from_namespace | def _split_input_from_namespace(cls, app, namespace, entity_kind,
shard_count):
"""Helper for _split_input_from_params.
If there are not enough Entities to make all of the given shards, the
returned list of KeyRanges will include Nones. The returned list will
contain KeyRanges ordered lexographically with any Nones appearing at the
end.
Args:
app: the app.
namespace: the namespace.
entity_kind: entity kind as string.
shard_count: the number of shards.
Returns:
KeyRange objects.
"""
raw_entity_kind = cls._get_raw_entity_kind(entity_kind)
if shard_count == 1:
# With one shard we don't need to calculate any splitpoints at all.
return [key_range.KeyRange(namespace=namespace, _app=app)]
ds_query = datastore.Query(kind=raw_entity_kind,
namespace=namespace,
_app=app,
keys_only=True)
ds_query.Order("__scatter__")
random_keys = ds_query.Get(shard_count * cls._OVERSAMPLING_FACTOR)
if not random_keys:
# There are no entities with scatter property. We have no idea
# how to split.
return ([key_range.KeyRange(namespace=namespace, _app=app)] +
[None] * (shard_count - 1))
random_keys.sort()
if len(random_keys) >= shard_count:
# We've got a lot of scatter values. Sample them down.
random_keys = cls._choose_split_points(random_keys, shard_count)
# pylint: disable=redefined-outer-name
key_ranges = []
key_ranges.append(key_range.KeyRange(
key_start=None,
key_end=random_keys[0],
direction=key_range.KeyRange.ASC,
include_start=False,
include_end=False,
namespace=namespace,
_app=app))
for i in range(0, len(random_keys) - 1):
key_ranges.append(key_range.KeyRange(
key_start=random_keys[i],
key_end=random_keys[i+1],
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
key_ranges.append(key_range.KeyRange(
key_start=random_keys[-1],
key_end=None,
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
if len(key_ranges) < shard_count:
# We need to have as many shards as it was requested. Add some Nones.
key_ranges += [None] * (shard_count - len(key_ranges))
return key_ranges | python | def _split_input_from_namespace(cls, app, namespace, entity_kind,
shard_count):
"""Helper for _split_input_from_params.
If there are not enough Entities to make all of the given shards, the
returned list of KeyRanges will include Nones. The returned list will
contain KeyRanges ordered lexographically with any Nones appearing at the
end.
Args:
app: the app.
namespace: the namespace.
entity_kind: entity kind as string.
shard_count: the number of shards.
Returns:
KeyRange objects.
"""
raw_entity_kind = cls._get_raw_entity_kind(entity_kind)
if shard_count == 1:
# With one shard we don't need to calculate any splitpoints at all.
return [key_range.KeyRange(namespace=namespace, _app=app)]
ds_query = datastore.Query(kind=raw_entity_kind,
namespace=namespace,
_app=app,
keys_only=True)
ds_query.Order("__scatter__")
random_keys = ds_query.Get(shard_count * cls._OVERSAMPLING_FACTOR)
if not random_keys:
# There are no entities with scatter property. We have no idea
# how to split.
return ([key_range.KeyRange(namespace=namespace, _app=app)] +
[None] * (shard_count - 1))
random_keys.sort()
if len(random_keys) >= shard_count:
# We've got a lot of scatter values. Sample them down.
random_keys = cls._choose_split_points(random_keys, shard_count)
# pylint: disable=redefined-outer-name
key_ranges = []
key_ranges.append(key_range.KeyRange(
key_start=None,
key_end=random_keys[0],
direction=key_range.KeyRange.ASC,
include_start=False,
include_end=False,
namespace=namespace,
_app=app))
for i in range(0, len(random_keys) - 1):
key_ranges.append(key_range.KeyRange(
key_start=random_keys[i],
key_end=random_keys[i+1],
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
key_ranges.append(key_range.KeyRange(
key_start=random_keys[-1],
key_end=None,
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
if len(key_ranges) < shard_count:
# We need to have as many shards as it was requested. Add some Nones.
key_ranges += [None] * (shard_count - len(key_ranges))
return key_ranges | Helper for _split_input_from_params.
If there are not enough Entities to make all of the given shards, the
returned list of KeyRanges will include Nones. The returned list will
contain KeyRanges ordered lexographically with any Nones appearing at the
end.
Args:
app: the app.
namespace: the namespace.
entity_kind: entity kind as string.
shard_count: the number of shards.
Returns:
KeyRange objects. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L982-L1060 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _OldAbstractDatastoreInputReader._split_input_from_params | def _split_input_from_params(cls, app, namespaces, entity_kind_name,
params, shard_count):
"""Return input reader objects. Helper for split_input."""
# pylint: disable=redefined-outer-name
key_ranges = [] # KeyRanges for all namespaces
for namespace in namespaces:
key_ranges.extend(
cls._split_input_from_namespace(app,
namespace,
entity_kind_name,
shard_count))
# Divide the KeyRanges into shard_count shards. The KeyRanges for different
# namespaces might be very different in size so the assignment of KeyRanges
# to shards is done round-robin.
shared_ranges = [[] for _ in range(shard_count)]
for i, k_range in enumerate(key_ranges):
shared_ranges[i % shard_count].append(k_range)
batch_size = int(params.get(cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE))
return [cls(entity_kind_name,
key_ranges=key_ranges,
ns_range=None,
batch_size=batch_size)
for key_ranges in shared_ranges if key_ranges] | python | def _split_input_from_params(cls, app, namespaces, entity_kind_name,
params, shard_count):
"""Return input reader objects. Helper for split_input."""
# pylint: disable=redefined-outer-name
key_ranges = [] # KeyRanges for all namespaces
for namespace in namespaces:
key_ranges.extend(
cls._split_input_from_namespace(app,
namespace,
entity_kind_name,
shard_count))
# Divide the KeyRanges into shard_count shards. The KeyRanges for different
# namespaces might be very different in size so the assignment of KeyRanges
# to shards is done round-robin.
shared_ranges = [[] for _ in range(shard_count)]
for i, k_range in enumerate(key_ranges):
shared_ranges[i % shard_count].append(k_range)
batch_size = int(params.get(cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE))
return [cls(entity_kind_name,
key_ranges=key_ranges,
ns_range=None,
batch_size=batch_size)
for key_ranges in shared_ranges if key_ranges] | Return input reader objects. Helper for split_input. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1063-L1087 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _OldAbstractDatastoreInputReader.validate | def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Input reader class mismatch")
params = _get_params(mapper_spec)
if cls.ENTITY_KIND_PARAM not in params:
raise BadReaderParamsError("Missing mapper parameter 'entity_kind'")
if cls.BATCH_SIZE_PARAM in params:
try:
batch_size = int(params[cls.BATCH_SIZE_PARAM])
if batch_size < 1:
raise BadReaderParamsError("Bad batch size: %s" % batch_size)
except ValueError, e:
raise BadReaderParamsError("Bad batch size: %s" % e)
if cls.NAMESPACE_PARAM in params:
if not isinstance(params[cls.NAMESPACE_PARAM],
(str, unicode, type(None))):
raise BadReaderParamsError(
"Expected a single namespace string")
if cls.NAMESPACES_PARAM in params:
raise BadReaderParamsError("Multiple namespaces are no longer supported")
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
if not isinstance(filters, list):
raise BadReaderParamsError("Expected list for filters parameter")
for f in filters:
if not isinstance(f, (tuple, list)):
raise BadReaderParamsError("Filter should be a tuple or list: %s", f)
if len(f) != 3:
raise BadReaderParamsError("Filter should be a 3-tuple: %s", f)
if not isinstance(f[0], basestring):
raise BadReaderParamsError("First element should be string: %s", f)
if f[1] != "=":
raise BadReaderParamsError(
"Only equality filters are supported: %s", f) | python | def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Input reader class mismatch")
params = _get_params(mapper_spec)
if cls.ENTITY_KIND_PARAM not in params:
raise BadReaderParamsError("Missing mapper parameter 'entity_kind'")
if cls.BATCH_SIZE_PARAM in params:
try:
batch_size = int(params[cls.BATCH_SIZE_PARAM])
if batch_size < 1:
raise BadReaderParamsError("Bad batch size: %s" % batch_size)
except ValueError, e:
raise BadReaderParamsError("Bad batch size: %s" % e)
if cls.NAMESPACE_PARAM in params:
if not isinstance(params[cls.NAMESPACE_PARAM],
(str, unicode, type(None))):
raise BadReaderParamsError(
"Expected a single namespace string")
if cls.NAMESPACES_PARAM in params:
raise BadReaderParamsError("Multiple namespaces are no longer supported")
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
if not isinstance(filters, list):
raise BadReaderParamsError("Expected list for filters parameter")
for f in filters:
if not isinstance(f, (tuple, list)):
raise BadReaderParamsError("Filter should be a tuple or list: %s", f)
if len(f) != 3:
raise BadReaderParamsError("Filter should be a 3-tuple: %s", f)
if not isinstance(f[0], basestring):
raise BadReaderParamsError("First element should be string: %s", f)
if f[1] != "=":
raise BadReaderParamsError(
"Only equality filters are supported: %s", f) | Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1090-L1131 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _OldAbstractDatastoreInputReader.split_input | def split_input(cls, mapper_spec):
"""Splits query into shards without fetching query results.
Tries as best as it can to split the whole query result set into equal
shards. Due to difficulty of making the perfect split, resulting shards'
sizes might differ significantly from each other.
Args:
mapper_spec: MapperSpec with params containing 'entity_kind'.
May have 'namespace' in the params as a string containing a single
namespace. If specified then the input reader will only yield values
in the given namespace. If 'namespace' is not given then values from
all namespaces will be yielded. May also have 'batch_size' in the params
to specify the number of entities to process in each batch.
Returns:
A list of InputReader objects. If the query results are empty then the
empty list will be returned. Otherwise, the list will always have a length
equal to number_of_shards but may be padded with Nones if there are too
few results for effective sharding.
"""
params = _get_params(mapper_spec)
entity_kind_name = params[cls.ENTITY_KIND_PARAM]
batch_size = int(params.get(cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE))
shard_count = mapper_spec.shard_count
namespace = params.get(cls.NAMESPACE_PARAM)
app = params.get(cls._APP_PARAM)
filters = params.get(cls.FILTERS_PARAM)
if namespace is None:
# It is difficult to efficiently shard large numbers of namespaces because
# there can be an arbitrary number of them. So the strategy is:
# 1. if there are a small number of namespaces in the datastore then
# generate one KeyRange per namespace per shard and assign each shard a
# KeyRange for every namespace. This should lead to nearly perfect
# sharding.
# 2. if there are a large number of namespaces in the datastore then
# generate one NamespaceRange per worker. This can lead to very bad
# sharding because namespaces can contain very different numbers of
# entities and each NamespaceRange may contain very different numbers
# of namespaces.
namespace_query = datastore.Query("__namespace__",
keys_only=True,
_app=app)
namespace_keys = namespace_query.Get(
limit=cls.MAX_NAMESPACES_FOR_KEY_SHARD+1)
if len(namespace_keys) > cls.MAX_NAMESPACES_FOR_KEY_SHARD:
ns_ranges = namespace_range.NamespaceRange.split(n=shard_count,
contiguous=True,
_app=app)
return [cls(entity_kind_name,
key_ranges=None,
ns_range=ns_range,
batch_size=batch_size,
filters=filters)
for ns_range in ns_ranges]
elif not namespace_keys:
return [cls(entity_kind_name,
key_ranges=None,
ns_range=namespace_range.NamespaceRange(_app=app),
batch_size=shard_count,
filters=filters)]
else:
namespaces = [namespace_key.name() or ""
for namespace_key in namespace_keys]
else:
namespaces = [namespace]
readers = cls._split_input_from_params(
app, namespaces, entity_kind_name, params, shard_count)
if filters:
for reader in readers:
reader._filters = filters
return readers | python | def split_input(cls, mapper_spec):
"""Splits query into shards without fetching query results.
Tries as best as it can to split the whole query result set into equal
shards. Due to difficulty of making the perfect split, resulting shards'
sizes might differ significantly from each other.
Args:
mapper_spec: MapperSpec with params containing 'entity_kind'.
May have 'namespace' in the params as a string containing a single
namespace. If specified then the input reader will only yield values
in the given namespace. If 'namespace' is not given then values from
all namespaces will be yielded. May also have 'batch_size' in the params
to specify the number of entities to process in each batch.
Returns:
A list of InputReader objects. If the query results are empty then the
empty list will be returned. Otherwise, the list will always have a length
equal to number_of_shards but may be padded with Nones if there are too
few results for effective sharding.
"""
params = _get_params(mapper_spec)
entity_kind_name = params[cls.ENTITY_KIND_PARAM]
batch_size = int(params.get(cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE))
shard_count = mapper_spec.shard_count
namespace = params.get(cls.NAMESPACE_PARAM)
app = params.get(cls._APP_PARAM)
filters = params.get(cls.FILTERS_PARAM)
if namespace is None:
# It is difficult to efficiently shard large numbers of namespaces because
# there can be an arbitrary number of them. So the strategy is:
# 1. if there are a small number of namespaces in the datastore then
# generate one KeyRange per namespace per shard and assign each shard a
# KeyRange for every namespace. This should lead to nearly perfect
# sharding.
# 2. if there are a large number of namespaces in the datastore then
# generate one NamespaceRange per worker. This can lead to very bad
# sharding because namespaces can contain very different numbers of
# entities and each NamespaceRange may contain very different numbers
# of namespaces.
namespace_query = datastore.Query("__namespace__",
keys_only=True,
_app=app)
namespace_keys = namespace_query.Get(
limit=cls.MAX_NAMESPACES_FOR_KEY_SHARD+1)
if len(namespace_keys) > cls.MAX_NAMESPACES_FOR_KEY_SHARD:
ns_ranges = namespace_range.NamespaceRange.split(n=shard_count,
contiguous=True,
_app=app)
return [cls(entity_kind_name,
key_ranges=None,
ns_range=ns_range,
batch_size=batch_size,
filters=filters)
for ns_range in ns_ranges]
elif not namespace_keys:
return [cls(entity_kind_name,
key_ranges=None,
ns_range=namespace_range.NamespaceRange(_app=app),
batch_size=shard_count,
filters=filters)]
else:
namespaces = [namespace_key.name() or ""
for namespace_key in namespace_keys]
else:
namespaces = [namespace]
readers = cls._split_input_from_params(
app, namespaces, entity_kind_name, params, shard_count)
if filters:
for reader in readers:
reader._filters = filters
return readers | Splits query into shards without fetching query results.
Tries as best as it can to split the whole query result set into equal
shards. Due to difficulty of making the perfect split, resulting shards'
sizes might differ significantly from each other.
Args:
mapper_spec: MapperSpec with params containing 'entity_kind'.
May have 'namespace' in the params as a string containing a single
namespace. If specified then the input reader will only yield values
in the given namespace. If 'namespace' is not given then values from
all namespaces will be yielded. May also have 'batch_size' in the params
to specify the number of entities to process in each batch.
Returns:
A list of InputReader objects. If the query results are empty then the
empty list will be returned. Otherwise, the list will always have a length
equal to number_of_shards but may be padded with Nones if there are too
few results for effective sharding. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1134-L1208 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _OldAbstractDatastoreInputReader.to_json | def to_json(self):
"""Serializes all the data in this query range into json form.
Returns:
all the data in json-compatible map.
"""
if self._key_ranges is None:
key_ranges_json = None
else:
key_ranges_json = []
for k in self._key_ranges:
if k:
key_ranges_json.append(k.to_json())
else:
key_ranges_json.append(None)
if self._ns_range is None:
namespace_range_json = None
else:
namespace_range_json = self._ns_range.to_json_object()
if self._current_key_range is None:
current_key_range_json = None
else:
current_key_range_json = self._current_key_range.to_json()
json_dict = {self.KEY_RANGE_PARAM: key_ranges_json,
self.NAMESPACE_RANGE_PARAM: namespace_range_json,
self.CURRENT_KEY_RANGE_PARAM: current_key_range_json,
self.ENTITY_KIND_PARAM: self._entity_kind,
self.BATCH_SIZE_PARAM: self._batch_size,
self.FILTERS_PARAM: self._filters}
return json_dict | python | def to_json(self):
"""Serializes all the data in this query range into json form.
Returns:
all the data in json-compatible map.
"""
if self._key_ranges is None:
key_ranges_json = None
else:
key_ranges_json = []
for k in self._key_ranges:
if k:
key_ranges_json.append(k.to_json())
else:
key_ranges_json.append(None)
if self._ns_range is None:
namespace_range_json = None
else:
namespace_range_json = self._ns_range.to_json_object()
if self._current_key_range is None:
current_key_range_json = None
else:
current_key_range_json = self._current_key_range.to_json()
json_dict = {self.KEY_RANGE_PARAM: key_ranges_json,
self.NAMESPACE_RANGE_PARAM: namespace_range_json,
self.CURRENT_KEY_RANGE_PARAM: current_key_range_json,
self.ENTITY_KIND_PARAM: self._entity_kind,
self.BATCH_SIZE_PARAM: self._batch_size,
self.FILTERS_PARAM: self._filters}
return json_dict | Serializes all the data in this query range into json form.
Returns:
all the data in json-compatible map. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1210-L1242 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _OldAbstractDatastoreInputReader.from_json | def from_json(cls, json):
"""Create new DatastoreInputReader from the json, encoded by to_json.
Args:
json: json map representation of DatastoreInputReader.
Returns:
an instance of DatastoreInputReader with all data deserialized from json.
"""
if json[cls.KEY_RANGE_PARAM] is None:
# pylint: disable=redefined-outer-name
key_ranges = None
else:
key_ranges = []
for k in json[cls.KEY_RANGE_PARAM]:
if k:
key_ranges.append(key_range.KeyRange.from_json(k))
else:
key_ranges.append(None)
if json[cls.NAMESPACE_RANGE_PARAM] is None:
ns_range = None
else:
ns_range = namespace_range.NamespaceRange.from_json_object(
json[cls.NAMESPACE_RANGE_PARAM])
if json[cls.CURRENT_KEY_RANGE_PARAM] is None:
current_key_range = None
else:
current_key_range = key_range.KeyRange.from_json(
json[cls.CURRENT_KEY_RANGE_PARAM])
return cls(
json[cls.ENTITY_KIND_PARAM],
key_ranges,
ns_range,
json[cls.BATCH_SIZE_PARAM],
current_key_range,
filters=json.get(cls.FILTERS_PARAM)) | python | def from_json(cls, json):
"""Create new DatastoreInputReader from the json, encoded by to_json.
Args:
json: json map representation of DatastoreInputReader.
Returns:
an instance of DatastoreInputReader with all data deserialized from json.
"""
if json[cls.KEY_RANGE_PARAM] is None:
# pylint: disable=redefined-outer-name
key_ranges = None
else:
key_ranges = []
for k in json[cls.KEY_RANGE_PARAM]:
if k:
key_ranges.append(key_range.KeyRange.from_json(k))
else:
key_ranges.append(None)
if json[cls.NAMESPACE_RANGE_PARAM] is None:
ns_range = None
else:
ns_range = namespace_range.NamespaceRange.from_json_object(
json[cls.NAMESPACE_RANGE_PARAM])
if json[cls.CURRENT_KEY_RANGE_PARAM] is None:
current_key_range = None
else:
current_key_range = key_range.KeyRange.from_json(
json[cls.CURRENT_KEY_RANGE_PARAM])
return cls(
json[cls.ENTITY_KIND_PARAM],
key_ranges,
ns_range,
json[cls.BATCH_SIZE_PARAM],
current_key_range,
filters=json.get(cls.FILTERS_PARAM)) | Create new DatastoreInputReader from the json, encoded by to_json.
Args:
json: json map representation of DatastoreInputReader.
Returns:
an instance of DatastoreInputReader with all data deserialized from json. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1245-L1283 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreLineInputReader.next | def next(self):
"""Returns the next input from as an (offset, line) tuple."""
self._has_iterated = True
if self._read_before_start:
self._blob_reader.readline()
self._read_before_start = False
start_position = self._blob_reader.tell()
if start_position > self._end_position:
raise StopIteration()
line = self._blob_reader.readline()
if not line:
raise StopIteration()
return start_position, line.rstrip("\n") | python | def next(self):
"""Returns the next input from as an (offset, line) tuple."""
self._has_iterated = True
if self._read_before_start:
self._blob_reader.readline()
self._read_before_start = False
start_position = self._blob_reader.tell()
if start_position > self._end_position:
raise StopIteration()
line = self._blob_reader.readline()
if not line:
raise StopIteration()
return start_position, line.rstrip("\n") | Returns the next input from as an (offset, line) tuple. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1327-L1344 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreLineInputReader.to_json | def to_json(self):
"""Returns an json-compatible input shard spec for remaining inputs."""
new_pos = self._blob_reader.tell()
if self._has_iterated:
new_pos -= 1
return {self.BLOB_KEY_PARAM: self._blob_key,
self.INITIAL_POSITION_PARAM: new_pos,
self.END_POSITION_PARAM: self._end_position} | python | def to_json(self):
"""Returns an json-compatible input shard spec for remaining inputs."""
new_pos = self._blob_reader.tell()
if self._has_iterated:
new_pos -= 1
return {self.BLOB_KEY_PARAM: self._blob_key,
self.INITIAL_POSITION_PARAM: new_pos,
self.END_POSITION_PARAM: self._end_position} | Returns an json-compatible input shard spec for remaining inputs. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1346-L1353 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreLineInputReader.from_json | def from_json(cls, json):
"""Instantiates an instance of this InputReader for the given shard spec."""
return cls(json[cls.BLOB_KEY_PARAM],
json[cls.INITIAL_POSITION_PARAM],
json[cls.END_POSITION_PARAM]) | python | def from_json(cls, json):
"""Instantiates an instance of this InputReader for the given shard spec."""
return cls(json[cls.BLOB_KEY_PARAM],
json[cls.INITIAL_POSITION_PARAM],
json[cls.END_POSITION_PARAM]) | Instantiates an instance of this InputReader for the given shard spec. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1361-L1365 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreLineInputReader.validate | def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Mapper input reader class mismatch")
params = _get_params(mapper_spec)
if cls.BLOB_KEYS_PARAM not in params:
raise BadReaderParamsError("Must specify 'blob_keys' for mapper input")
blob_keys = params[cls.BLOB_KEYS_PARAM]
if isinstance(blob_keys, basestring):
# This is a mechanism to allow multiple blob keys (which do not contain
# commas) in a single string. It may go away.
blob_keys = blob_keys.split(",")
if len(blob_keys) > cls._MAX_BLOB_KEYS_COUNT:
raise BadReaderParamsError("Too many 'blob_keys' for mapper input")
if not blob_keys:
raise BadReaderParamsError("No 'blob_keys' specified for mapper input")
for blob_key in blob_keys:
blob_info = blobstore.BlobInfo.get(blobstore.BlobKey(blob_key))
if not blob_info:
raise BadReaderParamsError("Could not find blobinfo for key %s" %
blob_key) | python | def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Mapper input reader class mismatch")
params = _get_params(mapper_spec)
if cls.BLOB_KEYS_PARAM not in params:
raise BadReaderParamsError("Must specify 'blob_keys' for mapper input")
blob_keys = params[cls.BLOB_KEYS_PARAM]
if isinstance(blob_keys, basestring):
# This is a mechanism to allow multiple blob keys (which do not contain
# commas) in a single string. It may go away.
blob_keys = blob_keys.split(",")
if len(blob_keys) > cls._MAX_BLOB_KEYS_COUNT:
raise BadReaderParamsError("Too many 'blob_keys' for mapper input")
if not blob_keys:
raise BadReaderParamsError("No 'blob_keys' specified for mapper input")
for blob_key in blob_keys:
blob_info = blobstore.BlobInfo.get(blobstore.BlobKey(blob_key))
if not blob_info:
raise BadReaderParamsError("Could not find blobinfo for key %s" %
blob_key) | Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1368-L1395 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreLineInputReader.split_input | def split_input(cls, mapper_spec):
"""Returns a list of shard_count input_spec_shards for input_spec.
Args:
mapper_spec: The mapper specification to split from. Must contain
'blob_keys' parameter with one or more blob keys.
Returns:
A list of BlobstoreInputReaders corresponding to the specified shards.
"""
params = _get_params(mapper_spec)
blob_keys = params[cls.BLOB_KEYS_PARAM]
if isinstance(blob_keys, basestring):
# This is a mechanism to allow multiple blob keys (which do not contain
# commas) in a single string. It may go away.
blob_keys = blob_keys.split(",")
blob_sizes = {}
for blob_key in blob_keys:
blob_info = blobstore.BlobInfo.get(blobstore.BlobKey(blob_key))
blob_sizes[blob_key] = blob_info.size
shard_count = min(cls._MAX_SHARD_COUNT, mapper_spec.shard_count)
shards_per_blob = shard_count // len(blob_keys)
if shards_per_blob == 0:
shards_per_blob = 1
chunks = []
for blob_key, blob_size in blob_sizes.items():
blob_chunk_size = blob_size // shards_per_blob
for i in xrange(shards_per_blob - 1):
chunks.append(BlobstoreLineInputReader.from_json(
{cls.BLOB_KEY_PARAM: blob_key,
cls.INITIAL_POSITION_PARAM: blob_chunk_size * i,
cls.END_POSITION_PARAM: blob_chunk_size * (i + 1)}))
chunks.append(BlobstoreLineInputReader.from_json(
{cls.BLOB_KEY_PARAM: blob_key,
cls.INITIAL_POSITION_PARAM: blob_chunk_size * (shards_per_blob - 1),
cls.END_POSITION_PARAM: blob_size}))
return chunks | python | def split_input(cls, mapper_spec):
"""Returns a list of shard_count input_spec_shards for input_spec.
Args:
mapper_spec: The mapper specification to split from. Must contain
'blob_keys' parameter with one or more blob keys.
Returns:
A list of BlobstoreInputReaders corresponding to the specified shards.
"""
params = _get_params(mapper_spec)
blob_keys = params[cls.BLOB_KEYS_PARAM]
if isinstance(blob_keys, basestring):
# This is a mechanism to allow multiple blob keys (which do not contain
# commas) in a single string. It may go away.
blob_keys = blob_keys.split(",")
blob_sizes = {}
for blob_key in blob_keys:
blob_info = blobstore.BlobInfo.get(blobstore.BlobKey(blob_key))
blob_sizes[blob_key] = blob_info.size
shard_count = min(cls._MAX_SHARD_COUNT, mapper_spec.shard_count)
shards_per_blob = shard_count // len(blob_keys)
if shards_per_blob == 0:
shards_per_blob = 1
chunks = []
for blob_key, blob_size in blob_sizes.items():
blob_chunk_size = blob_size // shards_per_blob
for i in xrange(shards_per_blob - 1):
chunks.append(BlobstoreLineInputReader.from_json(
{cls.BLOB_KEY_PARAM: blob_key,
cls.INITIAL_POSITION_PARAM: blob_chunk_size * i,
cls.END_POSITION_PARAM: blob_chunk_size * (i + 1)}))
chunks.append(BlobstoreLineInputReader.from_json(
{cls.BLOB_KEY_PARAM: blob_key,
cls.INITIAL_POSITION_PARAM: blob_chunk_size * (shards_per_blob - 1),
cls.END_POSITION_PARAM: blob_size}))
return chunks | Returns a list of shard_count input_spec_shards for input_spec.
Args:
mapper_spec: The mapper specification to split from. Must contain
'blob_keys' parameter with one or more blob keys.
Returns:
A list of BlobstoreInputReaders corresponding to the specified shards. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1398-L1437 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreZipInputReader.next | def next(self):
"""Returns the next input from this input reader as (ZipInfo, opener) tuple.
Returns:
The next input from this input reader, in the form of a 2-tuple.
The first element of the tuple is a zipfile.ZipInfo object.
The second element of the tuple is a zero-argument function that, when
called, returns the complete body of the file.
"""
if not self._zip:
self._zip = zipfile.ZipFile(self._reader(self._blob_key))
# Get a list of entries, reversed so we can pop entries off in order
self._entries = self._zip.infolist()[self._start_index:self._end_index]
self._entries.reverse()
if not self._entries:
raise StopIteration()
entry = self._entries.pop()
self._start_index += 1
return (entry, lambda: self._read(entry)) | python | def next(self):
"""Returns the next input from this input reader as (ZipInfo, opener) tuple.
Returns:
The next input from this input reader, in the form of a 2-tuple.
The first element of the tuple is a zipfile.ZipInfo object.
The second element of the tuple is a zero-argument function that, when
called, returns the complete body of the file.
"""
if not self._zip:
self._zip = zipfile.ZipFile(self._reader(self._blob_key))
# Get a list of entries, reversed so we can pop entries off in order
self._entries = self._zip.infolist()[self._start_index:self._end_index]
self._entries.reverse()
if not self._entries:
raise StopIteration()
entry = self._entries.pop()
self._start_index += 1
return (entry, lambda: self._read(entry)) | Returns the next input from this input reader as (ZipInfo, opener) tuple.
Returns:
The next input from this input reader, in the form of a 2-tuple.
The first element of the tuple is a zipfile.ZipInfo object.
The second element of the tuple is a zero-argument function that, when
called, returns the complete body of the file. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1476-L1494 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreZipInputReader._read | def _read(self, entry):
"""Read entry content.
Args:
entry: zip file entry as zipfile.ZipInfo.
Returns:
Entry content as string.
"""
start_time = time.time()
content = self._zip.read(entry.filename)
ctx = context.get()
if ctx:
operation.counters.Increment(COUNTER_IO_READ_BYTES, len(content))(ctx)
operation.counters.Increment(
COUNTER_IO_READ_MSEC, int((time.time() - start_time) * 1000))(ctx)
return content | python | def _read(self, entry):
"""Read entry content.
Args:
entry: zip file entry as zipfile.ZipInfo.
Returns:
Entry content as string.
"""
start_time = time.time()
content = self._zip.read(entry.filename)
ctx = context.get()
if ctx:
operation.counters.Increment(COUNTER_IO_READ_BYTES, len(content))(ctx)
operation.counters.Increment(
COUNTER_IO_READ_MSEC, int((time.time() - start_time) * 1000))(ctx)
return content | Read entry content.
Args:
entry: zip file entry as zipfile.ZipInfo.
Returns:
Entry content as string. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1496-L1513 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreZipInputReader.from_json | def from_json(cls, json):
"""Creates an instance of the InputReader for the given input shard state.
Args:
json: The InputReader state as a dict-like object.
Returns:
An instance of the InputReader configured using the values of json.
"""
return cls(json[cls.BLOB_KEY_PARAM],
json[cls.START_INDEX_PARAM],
json[cls.END_INDEX_PARAM]) | python | def from_json(cls, json):
"""Creates an instance of the InputReader for the given input shard state.
Args:
json: The InputReader state as a dict-like object.
Returns:
An instance of the InputReader configured using the values of json.
"""
return cls(json[cls.BLOB_KEY_PARAM],
json[cls.START_INDEX_PARAM],
json[cls.END_INDEX_PARAM]) | Creates an instance of the InputReader for the given input shard state.
Args:
json: The InputReader state as a dict-like object.
Returns:
An instance of the InputReader configured using the values of json. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1516-L1527 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreZipInputReader.to_json | def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader.
"""
return {self.BLOB_KEY_PARAM: self._blob_key,
self.START_INDEX_PARAM: self._start_index,
self.END_INDEX_PARAM: self._end_index} | python | def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader.
"""
return {self.BLOB_KEY_PARAM: self._blob_key,
self.START_INDEX_PARAM: self._start_index,
self.END_INDEX_PARAM: self._end_index} | Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1529-L1537 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreZipInputReader.validate | def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Mapper input reader class mismatch")
params = _get_params(mapper_spec)
if cls.BLOB_KEY_PARAM not in params:
raise BadReaderParamsError("Must specify 'blob_key' for mapper input")
blob_key = params[cls.BLOB_KEY_PARAM]
blob_info = blobstore.BlobInfo.get(blobstore.BlobKey(blob_key))
if not blob_info:
raise BadReaderParamsError("Could not find blobinfo for key %s" %
blob_key) | python | def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Mapper input reader class mismatch")
params = _get_params(mapper_spec)
if cls.BLOB_KEY_PARAM not in params:
raise BadReaderParamsError("Must specify 'blob_key' for mapper input")
blob_key = params[cls.BLOB_KEY_PARAM]
blob_info = blobstore.BlobInfo.get(blobstore.BlobKey(blob_key))
if not blob_info:
raise BadReaderParamsError("Could not find blobinfo for key %s" %
blob_key) | Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1545-L1563 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreZipInputReader.split_input | def split_input(cls, mapper_spec, _reader=blobstore.BlobReader):
"""Returns a list of input shard states for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader. Must contain
'blob_key' parameter with one blob key.
_reader: a callable that returns a file-like object for reading blobs.
Used for dependency injection.
Returns:
A list of InputReaders spanning files within the zip.
"""
params = _get_params(mapper_spec)
blob_key = params[cls.BLOB_KEY_PARAM]
zip_input = zipfile.ZipFile(_reader(blob_key))
zfiles = zip_input.infolist()
total_size = sum(x.file_size for x in zfiles)
num_shards = min(mapper_spec.shard_count, cls._MAX_SHARD_COUNT)
size_per_shard = total_size // num_shards
# Break the list of files into sublists, each of approximately
# size_per_shard bytes.
shard_start_indexes = [0]
current_shard_size = 0
for i, fileinfo in enumerate(zfiles):
current_shard_size += fileinfo.file_size
if current_shard_size >= size_per_shard:
shard_start_indexes.append(i + 1)
current_shard_size = 0
if shard_start_indexes[-1] != len(zfiles):
shard_start_indexes.append(len(zfiles))
return [cls(blob_key, start_index, end_index, _reader)
for start_index, end_index
in zip(shard_start_indexes, shard_start_indexes[1:])] | python | def split_input(cls, mapper_spec, _reader=blobstore.BlobReader):
"""Returns a list of input shard states for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader. Must contain
'blob_key' parameter with one blob key.
_reader: a callable that returns a file-like object for reading blobs.
Used for dependency injection.
Returns:
A list of InputReaders spanning files within the zip.
"""
params = _get_params(mapper_spec)
blob_key = params[cls.BLOB_KEY_PARAM]
zip_input = zipfile.ZipFile(_reader(blob_key))
zfiles = zip_input.infolist()
total_size = sum(x.file_size for x in zfiles)
num_shards = min(mapper_spec.shard_count, cls._MAX_SHARD_COUNT)
size_per_shard = total_size // num_shards
# Break the list of files into sublists, each of approximately
# size_per_shard bytes.
shard_start_indexes = [0]
current_shard_size = 0
for i, fileinfo in enumerate(zfiles):
current_shard_size += fileinfo.file_size
if current_shard_size >= size_per_shard:
shard_start_indexes.append(i + 1)
current_shard_size = 0
if shard_start_indexes[-1] != len(zfiles):
shard_start_indexes.append(len(zfiles))
return [cls(blob_key, start_index, end_index, _reader)
for start_index, end_index
in zip(shard_start_indexes, shard_start_indexes[1:])] | Returns a list of input shard states for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader. Must contain
'blob_key' parameter with one blob key.
_reader: a callable that returns a file-like object for reading blobs.
Used for dependency injection.
Returns:
A list of InputReaders spanning files within the zip. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1566-L1601 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreZipLineInputReader.split_input | def split_input(cls, mapper_spec, _reader=blobstore.BlobReader):
"""Returns a list of input readers for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader. Must contain
'blob_keys' parameter with one or more blob keys.
_reader: a callable that returns a file-like object for reading blobs.
Used for dependency injection.
Returns:
A list of InputReaders spanning the subfiles within the blobs.
There will be at least one reader per blob, but it will otherwise
attempt to keep the expanded size even.
"""
params = _get_params(mapper_spec)
blob_keys = params[cls.BLOB_KEYS_PARAM]
if isinstance(blob_keys, basestring):
# This is a mechanism to allow multiple blob keys (which do not contain
# commas) in a single string. It may go away.
blob_keys = blob_keys.split(",")
blob_files = {}
total_size = 0
for blob_key in blob_keys:
zip_input = zipfile.ZipFile(_reader(blob_key))
blob_files[blob_key] = zip_input.infolist()
total_size += sum(x.file_size for x in blob_files[blob_key])
shard_count = min(cls._MAX_SHARD_COUNT, mapper_spec.shard_count)
# We can break on both blob key and file-within-zip boundaries.
# A shard will span at minimum a single blob key, but may only
# handle a few files within a blob.
size_per_shard = total_size // shard_count
readers = []
for blob_key in blob_keys:
bfiles = blob_files[blob_key]
current_shard_size = 0
start_file_index = 0
next_file_index = 0
for fileinfo in bfiles:
next_file_index += 1
current_shard_size += fileinfo.file_size
if current_shard_size >= size_per_shard:
readers.append(cls(blob_key, start_file_index, next_file_index, 0,
_reader))
current_shard_size = 0
start_file_index = next_file_index
if current_shard_size != 0:
readers.append(cls(blob_key, start_file_index, next_file_index, 0,
_reader))
return readers | python | def split_input(cls, mapper_spec, _reader=blobstore.BlobReader):
"""Returns a list of input readers for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader. Must contain
'blob_keys' parameter with one or more blob keys.
_reader: a callable that returns a file-like object for reading blobs.
Used for dependency injection.
Returns:
A list of InputReaders spanning the subfiles within the blobs.
There will be at least one reader per blob, but it will otherwise
attempt to keep the expanded size even.
"""
params = _get_params(mapper_spec)
blob_keys = params[cls.BLOB_KEYS_PARAM]
if isinstance(blob_keys, basestring):
# This is a mechanism to allow multiple blob keys (which do not contain
# commas) in a single string. It may go away.
blob_keys = blob_keys.split(",")
blob_files = {}
total_size = 0
for blob_key in blob_keys:
zip_input = zipfile.ZipFile(_reader(blob_key))
blob_files[blob_key] = zip_input.infolist()
total_size += sum(x.file_size for x in blob_files[blob_key])
shard_count = min(cls._MAX_SHARD_COUNT, mapper_spec.shard_count)
# We can break on both blob key and file-within-zip boundaries.
# A shard will span at minimum a single blob key, but may only
# handle a few files within a blob.
size_per_shard = total_size // shard_count
readers = []
for blob_key in blob_keys:
bfiles = blob_files[blob_key]
current_shard_size = 0
start_file_index = 0
next_file_index = 0
for fileinfo in bfiles:
next_file_index += 1
current_shard_size += fileinfo.file_size
if current_shard_size >= size_per_shard:
readers.append(cls(blob_key, start_file_index, next_file_index, 0,
_reader))
current_shard_size = 0
start_file_index = next_file_index
if current_shard_size != 0:
readers.append(cls(blob_key, start_file_index, next_file_index, 0,
_reader))
return readers | Returns a list of input readers for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader. Must contain
'blob_keys' parameter with one or more blob keys.
_reader: a callable that returns a file-like object for reading blobs.
Used for dependency injection.
Returns:
A list of InputReaders spanning the subfiles within the blobs.
There will be at least one reader per blob, but it will otherwise
attempt to keep the expanded size even. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1688-L1742 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreZipLineInputReader.next | def next(self):
"""Returns the next line from this input reader as (lineinfo, line) tuple.
Returns:
The next input from this input reader, in the form of a 2-tuple.
The first element of the tuple describes the source, it is itself
a tuple (blobkey, filenumber, byteoffset).
The second element of the tuple is the line found at that offset.
"""
if not self._filestream:
if not self._zip:
self._zip = zipfile.ZipFile(self._reader(self._blob_key))
# Get a list of entries, reversed so we can pop entries off in order
self._entries = self._zip.infolist()[self._start_file_index:
self._end_file_index]
self._entries.reverse()
if not self._entries:
raise StopIteration()
entry = self._entries.pop()
value = self._zip.read(entry.filename)
self._filestream = StringIO.StringIO(value)
if self._initial_offset:
self._filestream.seek(self._initial_offset)
self._filestream.readline()
start_position = self._filestream.tell()
line = self._filestream.readline()
if not line:
# Done with this file in the zip. Move on to the next file.
self._filestream.close()
self._filestream = None
self._start_file_index += 1
self._initial_offset = 0
return self.next()
return ((self._blob_key, self._start_file_index, start_position),
line.rstrip("\n")) | python | def next(self):
"""Returns the next line from this input reader as (lineinfo, line) tuple.
Returns:
The next input from this input reader, in the form of a 2-tuple.
The first element of the tuple describes the source, it is itself
a tuple (blobkey, filenumber, byteoffset).
The second element of the tuple is the line found at that offset.
"""
if not self._filestream:
if not self._zip:
self._zip = zipfile.ZipFile(self._reader(self._blob_key))
# Get a list of entries, reversed so we can pop entries off in order
self._entries = self._zip.infolist()[self._start_file_index:
self._end_file_index]
self._entries.reverse()
if not self._entries:
raise StopIteration()
entry = self._entries.pop()
value = self._zip.read(entry.filename)
self._filestream = StringIO.StringIO(value)
if self._initial_offset:
self._filestream.seek(self._initial_offset)
self._filestream.readline()
start_position = self._filestream.tell()
line = self._filestream.readline()
if not line:
# Done with this file in the zip. Move on to the next file.
self._filestream.close()
self._filestream = None
self._start_file_index += 1
self._initial_offset = 0
return self.next()
return ((self._blob_key, self._start_file_index, start_position),
line.rstrip("\n")) | Returns the next line from this input reader as (lineinfo, line) tuple.
Returns:
The next input from this input reader, in the form of a 2-tuple.
The first element of the tuple describes the source, it is itself
a tuple (blobkey, filenumber, byteoffset).
The second element of the tuple is the line found at that offset. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1744-L1781 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreZipLineInputReader._next_offset | def _next_offset(self):
"""Return the offset of the next line to read."""
if self._filestream:
offset = self._filestream.tell()
if offset:
offset -= 1
else:
offset = self._initial_offset
return offset | python | def _next_offset(self):
"""Return the offset of the next line to read."""
if self._filestream:
offset = self._filestream.tell()
if offset:
offset -= 1
else:
offset = self._initial_offset
return offset | Return the offset of the next line to read. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1783-L1792 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreZipLineInputReader.to_json | def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader.
"""
return {self.BLOB_KEY_PARAM: self._blob_key,
self.START_FILE_INDEX_PARAM: self._start_file_index,
self.END_FILE_INDEX_PARAM: self._end_file_index,
self.OFFSET_PARAM: self._next_offset()} | python | def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader.
"""
return {self.BLOB_KEY_PARAM: self._blob_key,
self.START_FILE_INDEX_PARAM: self._start_file_index,
self.END_FILE_INDEX_PARAM: self._end_file_index,
self.OFFSET_PARAM: self._next_offset()} | Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1794-L1804 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | BlobstoreZipLineInputReader.from_json | def from_json(cls, json, _reader=blobstore.BlobReader):
"""Creates an instance of the InputReader for the given input shard state.
Args:
json: The InputReader state as a dict-like object.
_reader: For dependency injection.
Returns:
An instance of the InputReader configured using the values of json.
"""
return cls(json[cls.BLOB_KEY_PARAM],
json[cls.START_FILE_INDEX_PARAM],
json[cls.END_FILE_INDEX_PARAM],
json[cls.OFFSET_PARAM],
_reader) | python | def from_json(cls, json, _reader=blobstore.BlobReader):
"""Creates an instance of the InputReader for the given input shard state.
Args:
json: The InputReader state as a dict-like object.
_reader: For dependency injection.
Returns:
An instance of the InputReader configured using the values of json.
"""
return cls(json[cls.BLOB_KEY_PARAM],
json[cls.START_FILE_INDEX_PARAM],
json[cls.END_FILE_INDEX_PARAM],
json[cls.OFFSET_PARAM],
_reader) | Creates an instance of the InputReader for the given input shard state.
Args:
json: The InputReader state as a dict-like object.
_reader: For dependency injection.
Returns:
An instance of the InputReader configured using the values of json. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1807-L1821 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | NamespaceInputReader.to_json | def to_json(self):
"""Serializes all the data in this query range into json form.
Returns:
all the data in json-compatible map.
"""
return {self.NAMESPACE_RANGE_PARAM: self.ns_range.to_json_object(),
self.BATCH_SIZE_PARAM: self._batch_size} | python | def to_json(self):
"""Serializes all the data in this query range into json form.
Returns:
all the data in json-compatible map.
"""
return {self.NAMESPACE_RANGE_PARAM: self.ns_range.to_json_object(),
self.BATCH_SIZE_PARAM: self._batch_size} | Serializes all the data in this query range into json form.
Returns:
all the data in json-compatible map. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1944-L1951 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | NamespaceInputReader.from_json | def from_json(cls, json):
"""Create new DatastoreInputReader from the json, encoded by to_json.
Args:
json: json map representation of DatastoreInputReader.
Returns:
an instance of DatastoreInputReader with all data deserialized from json.
"""
return cls(
namespace_range.NamespaceRange.from_json_object(
json[cls.NAMESPACE_RANGE_PARAM]),
json[cls.BATCH_SIZE_PARAM]) | python | def from_json(cls, json):
"""Create new DatastoreInputReader from the json, encoded by to_json.
Args:
json: json map representation of DatastoreInputReader.
Returns:
an instance of DatastoreInputReader with all data deserialized from json.
"""
return cls(
namespace_range.NamespaceRange.from_json_object(
json[cls.NAMESPACE_RANGE_PARAM]),
json[cls.BATCH_SIZE_PARAM]) | Create new DatastoreInputReader from the json, encoded by to_json.
Args:
json: json map representation of DatastoreInputReader.
Returns:
an instance of DatastoreInputReader with all data deserialized from json. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1954-L1966 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | NamespaceInputReader.validate | def validate(cls, mapper_spec):
"""Validates mapper spec.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Input reader class mismatch")
params = _get_params(mapper_spec)
if cls.BATCH_SIZE_PARAM in params:
try:
batch_size = int(params[cls.BATCH_SIZE_PARAM])
if batch_size < 1:
raise BadReaderParamsError("Bad batch size: %s" % batch_size)
except ValueError, e:
raise BadReaderParamsError("Bad batch size: %s" % e) | python | def validate(cls, mapper_spec):
"""Validates mapper spec.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Input reader class mismatch")
params = _get_params(mapper_spec)
if cls.BATCH_SIZE_PARAM in params:
try:
batch_size = int(params[cls.BATCH_SIZE_PARAM])
if batch_size < 1:
raise BadReaderParamsError("Bad batch size: %s" % batch_size)
except ValueError, e:
raise BadReaderParamsError("Bad batch size: %s" % e) | Validates mapper spec.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1969-L1987 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | NamespaceInputReader.split_input | def split_input(cls, mapper_spec):
"""Returns a list of input readers for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader.
Returns:
A list of InputReaders.
"""
batch_size = int(_get_params(mapper_spec).get(
cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE))
shard_count = mapper_spec.shard_count
namespace_ranges = namespace_range.NamespaceRange.split(shard_count,
contiguous=True)
return [NamespaceInputReader(ns_range, batch_size)
for ns_range in namespace_ranges] | python | def split_input(cls, mapper_spec):
"""Returns a list of input readers for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader.
Returns:
A list of InputReaders.
"""
batch_size = int(_get_params(mapper_spec).get(
cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE))
shard_count = mapper_spec.shard_count
namespace_ranges = namespace_range.NamespaceRange.split(shard_count,
contiguous=True)
return [NamespaceInputReader(ns_range, batch_size)
for ns_range in namespace_ranges] | Returns a list of input readers for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader.
Returns:
A list of InputReaders. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L1990-L2005 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | LogInputReader.from_json | def from_json(cls, json):
"""Creates an instance of the InputReader for the given input shard's state.
Args:
json: The InputReader state as a dict-like object.
Returns:
An instance of the InputReader configured using the given JSON parameters.
"""
# Strip out unrecognized parameters, as introduced by b/5960884.
params = dict((str(k), v) for k, v in json.iteritems()
if k in cls._PARAMS)
# This is not symmetric with to_json() wrt. PROTOTYPE_REQUEST_PARAM because
# the constructor parameters need to be JSON-encodable, so the decoding
# needs to happen there anyways.
if cls._OFFSET_PARAM in params:
params[cls._OFFSET_PARAM] = base64.b64decode(params[cls._OFFSET_PARAM])
return cls(**params) | python | def from_json(cls, json):
"""Creates an instance of the InputReader for the given input shard's state.
Args:
json: The InputReader state as a dict-like object.
Returns:
An instance of the InputReader configured using the given JSON parameters.
"""
# Strip out unrecognized parameters, as introduced by b/5960884.
params = dict((str(k), v) for k, v in json.iteritems()
if k in cls._PARAMS)
# This is not symmetric with to_json() wrt. PROTOTYPE_REQUEST_PARAM because
# the constructor parameters need to be JSON-encodable, so the decoding
# needs to happen there anyways.
if cls._OFFSET_PARAM in params:
params[cls._OFFSET_PARAM] = base64.b64decode(params[cls._OFFSET_PARAM])
return cls(**params) | Creates an instance of the InputReader for the given input shard's state.
Args:
json: The InputReader state as a dict-like object.
Returns:
An instance of the InputReader configured using the given JSON parameters. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L2115-L2133 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | LogInputReader.to_json | def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A JSON serializable version of the remaining input to read.
"""
params = dict(self.__params) # Shallow copy.
if self._PROTOTYPE_REQUEST_PARAM in params:
prototype_request = params[self._PROTOTYPE_REQUEST_PARAM]
params[self._PROTOTYPE_REQUEST_PARAM] = prototype_request.Encode()
if self._OFFSET_PARAM in params:
params[self._OFFSET_PARAM] = base64.b64encode(params[self._OFFSET_PARAM])
return params | python | def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A JSON serializable version of the remaining input to read.
"""
params = dict(self.__params) # Shallow copy.
if self._PROTOTYPE_REQUEST_PARAM in params:
prototype_request = params[self._PROTOTYPE_REQUEST_PARAM]
params[self._PROTOTYPE_REQUEST_PARAM] = prototype_request.Encode()
if self._OFFSET_PARAM in params:
params[self._OFFSET_PARAM] = base64.b64encode(params[self._OFFSET_PARAM])
return params | Returns an input shard state for the remaining inputs.
Returns:
A JSON serializable version of the remaining input to read. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L2135-L2148 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | LogInputReader.split_input | def split_input(cls, mapper_spec):
"""Returns a list of input readers for the given input specification.
Args:
mapper_spec: The MapperSpec for this InputReader.
Returns:
A list of InputReaders.
"""
params = _get_params(mapper_spec)
shard_count = mapper_spec.shard_count
# Pick out the overall start and end times and time step per shard.
start_time = params[cls.START_TIME_PARAM]
end_time = params[cls.END_TIME_PARAM]
seconds_per_shard = (end_time - start_time) / shard_count
# Create a LogInputReader for each shard, modulating the params as we go.
shards = []
for _ in xrange(shard_count - 1):
params[cls.END_TIME_PARAM] = (params[cls.START_TIME_PARAM] +
seconds_per_shard)
shards.append(LogInputReader(**params))
params[cls.START_TIME_PARAM] = params[cls.END_TIME_PARAM]
# Create a final shard to complete the time range.
params[cls.END_TIME_PARAM] = end_time
return shards + [LogInputReader(**params)] | python | def split_input(cls, mapper_spec):
"""Returns a list of input readers for the given input specification.
Args:
mapper_spec: The MapperSpec for this InputReader.
Returns:
A list of InputReaders.
"""
params = _get_params(mapper_spec)
shard_count = mapper_spec.shard_count
# Pick out the overall start and end times and time step per shard.
start_time = params[cls.START_TIME_PARAM]
end_time = params[cls.END_TIME_PARAM]
seconds_per_shard = (end_time - start_time) / shard_count
# Create a LogInputReader for each shard, modulating the params as we go.
shards = []
for _ in xrange(shard_count - 1):
params[cls.END_TIME_PARAM] = (params[cls.START_TIME_PARAM] +
seconds_per_shard)
shards.append(LogInputReader(**params))
params[cls.START_TIME_PARAM] = params[cls.END_TIME_PARAM]
# Create a final shard to complete the time range.
params[cls.END_TIME_PARAM] = end_time
return shards + [LogInputReader(**params)] | Returns a list of input readers for the given input specification.
Args:
mapper_spec: The MapperSpec for this InputReader.
Returns:
A list of InputReaders. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L2151-L2178 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | LogInputReader.validate | def validate(cls, mapper_spec):
"""Validates the mapper's specification and all necessary parameters.
Args:
mapper_spec: The MapperSpec to be used with this InputReader.
Raises:
BadReaderParamsError: If the user fails to specify both a starting time
and an ending time, or if the starting time is later than the ending
time.
"""
if mapper_spec.input_reader_class() != cls:
raise errors.BadReaderParamsError("Input reader class mismatch")
params = _get_params(mapper_spec, allowed_keys=cls._PARAMS)
if (cls.VERSION_IDS_PARAM not in params and
cls.MODULE_VERSIONS_PARAM not in params):
raise errors.BadReaderParamsError("Must specify a list of version ids or "
"module/version ids for mapper input")
if (cls.VERSION_IDS_PARAM in params and
cls.MODULE_VERSIONS_PARAM in params):
raise errors.BadReaderParamsError("Can not supply both version ids or "
"module/version ids. Use only one.")
if (cls.START_TIME_PARAM not in params or
params[cls.START_TIME_PARAM] is None):
raise errors.BadReaderParamsError("Must specify a starting time for "
"mapper input")
if cls.END_TIME_PARAM not in params or params[cls.END_TIME_PARAM] is None:
params[cls.END_TIME_PARAM] = time.time()
if params[cls.START_TIME_PARAM] >= params[cls.END_TIME_PARAM]:
raise errors.BadReaderParamsError("The starting time cannot be later "
"than or the same as the ending time.")
if cls._PROTOTYPE_REQUEST_PARAM in params:
try:
params[cls._PROTOTYPE_REQUEST_PARAM] = log_service_pb.LogReadRequest(
params[cls._PROTOTYPE_REQUEST_PARAM])
except (TypeError, ProtocolBuffer.ProtocolBufferDecodeError):
raise errors.BadReaderParamsError("The prototype request must be "
"parseable as a LogReadRequest.")
# Pass the parameters to logservice.fetch() to verify any underlying
# constraints on types or values. This only constructs an iterator, it
# doesn't trigger any requests for actual log records.
try:
logservice.fetch(**params)
except logservice.InvalidArgumentError, e:
raise errors.BadReaderParamsError("One or more parameters are not valid "
"inputs to logservice.fetch(): %s" % e) | python | def validate(cls, mapper_spec):
"""Validates the mapper's specification and all necessary parameters.
Args:
mapper_spec: The MapperSpec to be used with this InputReader.
Raises:
BadReaderParamsError: If the user fails to specify both a starting time
and an ending time, or if the starting time is later than the ending
time.
"""
if mapper_spec.input_reader_class() != cls:
raise errors.BadReaderParamsError("Input reader class mismatch")
params = _get_params(mapper_spec, allowed_keys=cls._PARAMS)
if (cls.VERSION_IDS_PARAM not in params and
cls.MODULE_VERSIONS_PARAM not in params):
raise errors.BadReaderParamsError("Must specify a list of version ids or "
"module/version ids for mapper input")
if (cls.VERSION_IDS_PARAM in params and
cls.MODULE_VERSIONS_PARAM in params):
raise errors.BadReaderParamsError("Can not supply both version ids or "
"module/version ids. Use only one.")
if (cls.START_TIME_PARAM not in params or
params[cls.START_TIME_PARAM] is None):
raise errors.BadReaderParamsError("Must specify a starting time for "
"mapper input")
if cls.END_TIME_PARAM not in params or params[cls.END_TIME_PARAM] is None:
params[cls.END_TIME_PARAM] = time.time()
if params[cls.START_TIME_PARAM] >= params[cls.END_TIME_PARAM]:
raise errors.BadReaderParamsError("The starting time cannot be later "
"than or the same as the ending time.")
if cls._PROTOTYPE_REQUEST_PARAM in params:
try:
params[cls._PROTOTYPE_REQUEST_PARAM] = log_service_pb.LogReadRequest(
params[cls._PROTOTYPE_REQUEST_PARAM])
except (TypeError, ProtocolBuffer.ProtocolBufferDecodeError):
raise errors.BadReaderParamsError("The prototype request must be "
"parseable as a LogReadRequest.")
# Pass the parameters to logservice.fetch() to verify any underlying
# constraints on types or values. This only constructs an iterator, it
# doesn't trigger any requests for actual log records.
try:
logservice.fetch(**params)
except logservice.InvalidArgumentError, e:
raise errors.BadReaderParamsError("One or more parameters are not valid "
"inputs to logservice.fetch(): %s" % e) | Validates the mapper's specification and all necessary parameters.
Args:
mapper_spec: The MapperSpec to be used with this InputReader.
Raises:
BadReaderParamsError: If the user fails to specify both a starting time
and an ending time, or if the starting time is later than the ending
time. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L2181-L2230 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _GoogleCloudStorageInputReader._next_file | def _next_file(self):
"""Find next filename.
self._filenames may need to be expanded via listbucket.
Returns:
None if no more file is left. Filename otherwise.
"""
while True:
if self._bucket_iter:
try:
return self._bucket_iter.next().filename
except StopIteration:
self._bucket_iter = None
self._bucket = None
if self._index >= len(self._filenames):
return
filename = self._filenames[self._index]
self._index += 1
if self._delimiter is None or not filename.endswith(self._delimiter):
return filename
self._bucket = cloudstorage.listbucket(filename,
delimiter=self._delimiter)
self._bucket_iter = iter(self._bucket) | python | def _next_file(self):
"""Find next filename.
self._filenames may need to be expanded via listbucket.
Returns:
None if no more file is left. Filename otherwise.
"""
while True:
if self._bucket_iter:
try:
return self._bucket_iter.next().filename
except StopIteration:
self._bucket_iter = None
self._bucket = None
if self._index >= len(self._filenames):
return
filename = self._filenames[self._index]
self._index += 1
if self._delimiter is None or not filename.endswith(self._delimiter):
return filename
self._bucket = cloudstorage.listbucket(filename,
delimiter=self._delimiter)
self._bucket_iter = iter(self._bucket) | Find next filename.
self._filenames may need to be expanded via listbucket.
Returns:
None if no more file is left. Filename otherwise. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L2326-L2349 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _GoogleCloudStorageInputReader.validate | def validate(cls, mapper_spec):
"""Validate mapper specification.
Args:
mapper_spec: an instance of model.MapperSpec
Raises:
BadReaderParamsError: if the specification is invalid for any reason such
as missing the bucket name or providing an invalid bucket name.
"""
reader_spec = cls.get_params(mapper_spec, allow_old=False)
# Bucket Name is required
if cls.BUCKET_NAME_PARAM not in reader_spec:
raise errors.BadReaderParamsError(
"%s is required for Google Cloud Storage" %
cls.BUCKET_NAME_PARAM)
try:
cloudstorage.validate_bucket_name(
reader_spec[cls.BUCKET_NAME_PARAM])
except ValueError, error:
raise errors.BadReaderParamsError("Bad bucket name, %s" % (error))
# Object Name(s) are required
if cls.OBJECT_NAMES_PARAM not in reader_spec:
raise errors.BadReaderParamsError(
"%s is required for Google Cloud Storage" %
cls.OBJECT_NAMES_PARAM)
filenames = reader_spec[cls.OBJECT_NAMES_PARAM]
if not isinstance(filenames, list):
raise errors.BadReaderParamsError(
"Object name list is not a list but a %s" %
filenames.__class__.__name__)
for filename in filenames:
if not isinstance(filename, basestring):
raise errors.BadReaderParamsError(
"Object name is not a string but a %s" %
filename.__class__.__name__)
if cls.DELIMITER_PARAM in reader_spec:
delimiter = reader_spec[cls.DELIMITER_PARAM]
if not isinstance(delimiter, basestring):
raise errors.BadReaderParamsError(
"%s is not a string but a %s" %
(cls.DELIMITER_PARAM, type(delimiter))) | python | def validate(cls, mapper_spec):
"""Validate mapper specification.
Args:
mapper_spec: an instance of model.MapperSpec
Raises:
BadReaderParamsError: if the specification is invalid for any reason such
as missing the bucket name or providing an invalid bucket name.
"""
reader_spec = cls.get_params(mapper_spec, allow_old=False)
# Bucket Name is required
if cls.BUCKET_NAME_PARAM not in reader_spec:
raise errors.BadReaderParamsError(
"%s is required for Google Cloud Storage" %
cls.BUCKET_NAME_PARAM)
try:
cloudstorage.validate_bucket_name(
reader_spec[cls.BUCKET_NAME_PARAM])
except ValueError, error:
raise errors.BadReaderParamsError("Bad bucket name, %s" % (error))
# Object Name(s) are required
if cls.OBJECT_NAMES_PARAM not in reader_spec:
raise errors.BadReaderParamsError(
"%s is required for Google Cloud Storage" %
cls.OBJECT_NAMES_PARAM)
filenames = reader_spec[cls.OBJECT_NAMES_PARAM]
if not isinstance(filenames, list):
raise errors.BadReaderParamsError(
"Object name list is not a list but a %s" %
filenames.__class__.__name__)
for filename in filenames:
if not isinstance(filename, basestring):
raise errors.BadReaderParamsError(
"Object name is not a string but a %s" %
filename.__class__.__name__)
if cls.DELIMITER_PARAM in reader_spec:
delimiter = reader_spec[cls.DELIMITER_PARAM]
if not isinstance(delimiter, basestring):
raise errors.BadReaderParamsError(
"%s is not a string but a %s" %
(cls.DELIMITER_PARAM, type(delimiter))) | Validate mapper specification.
Args:
mapper_spec: an instance of model.MapperSpec
Raises:
BadReaderParamsError: if the specification is invalid for any reason such
as missing the bucket name or providing an invalid bucket name. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L2362-L2405 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _GoogleCloudStorageInputReader.split_input | def split_input(cls, mapper_spec):
"""Returns a list of input readers.
An equal number of input files are assigned to each shard (+/- 1). If there
are fewer files than shards, fewer than the requested number of shards will
be used. Input files are currently never split (although for some formats
could be and may be split in a future implementation).
Args:
mapper_spec: an instance of model.MapperSpec.
Returns:
A list of InputReaders. None when no input data can be found.
"""
reader_spec = cls.get_params(mapper_spec, allow_old=False)
bucket = reader_spec[cls.BUCKET_NAME_PARAM]
filenames = reader_spec[cls.OBJECT_NAMES_PARAM]
delimiter = reader_spec.get(cls.DELIMITER_PARAM)
account_id = reader_spec.get(cls._ACCOUNT_ID_PARAM)
buffer_size = reader_spec.get(cls.BUFFER_SIZE_PARAM)
fail_on_missing_input = reader_spec.get(cls.FAIL_ON_MISSING_INPUT)
# Gather the complete list of files (expanding wildcards)
all_filenames = []
for filename in filenames:
if filename.endswith("*"):
all_filenames.extend(
[file_stat.filename for file_stat in cloudstorage.listbucket(
"/" + bucket + "/" + filename[:-1], delimiter=delimiter,
_account_id=account_id)])
else:
all_filenames.append("/%s/%s" % (bucket, filename))
# Split into shards
readers = []
for shard in range(0, mapper_spec.shard_count):
shard_filenames = all_filenames[shard::mapper_spec.shard_count]
if shard_filenames:
reader = cls(
shard_filenames, buffer_size=buffer_size, _account_id=account_id,
delimiter=delimiter)
reader._fail_on_missing_input = fail_on_missing_input
readers.append(reader)
return readers | python | def split_input(cls, mapper_spec):
"""Returns a list of input readers.
An equal number of input files are assigned to each shard (+/- 1). If there
are fewer files than shards, fewer than the requested number of shards will
be used. Input files are currently never split (although for some formats
could be and may be split in a future implementation).
Args:
mapper_spec: an instance of model.MapperSpec.
Returns:
A list of InputReaders. None when no input data can be found.
"""
reader_spec = cls.get_params(mapper_spec, allow_old=False)
bucket = reader_spec[cls.BUCKET_NAME_PARAM]
filenames = reader_spec[cls.OBJECT_NAMES_PARAM]
delimiter = reader_spec.get(cls.DELIMITER_PARAM)
account_id = reader_spec.get(cls._ACCOUNT_ID_PARAM)
buffer_size = reader_spec.get(cls.BUFFER_SIZE_PARAM)
fail_on_missing_input = reader_spec.get(cls.FAIL_ON_MISSING_INPUT)
# Gather the complete list of files (expanding wildcards)
all_filenames = []
for filename in filenames:
if filename.endswith("*"):
all_filenames.extend(
[file_stat.filename for file_stat in cloudstorage.listbucket(
"/" + bucket + "/" + filename[:-1], delimiter=delimiter,
_account_id=account_id)])
else:
all_filenames.append("/%s/%s" % (bucket, filename))
# Split into shards
readers = []
for shard in range(0, mapper_spec.shard_count):
shard_filenames = all_filenames[shard::mapper_spec.shard_count]
if shard_filenames:
reader = cls(
shard_filenames, buffer_size=buffer_size, _account_id=account_id,
delimiter=delimiter)
reader._fail_on_missing_input = fail_on_missing_input
readers.append(reader)
return readers | Returns a list of input readers.
An equal number of input files are assigned to each shard (+/- 1). If there
are fewer files than shards, fewer than the requested number of shards will
be used. Input files are currently never split (although for some formats
could be and may be split in a future implementation).
Args:
mapper_spec: an instance of model.MapperSpec.
Returns:
A list of InputReaders. None when no input data can be found. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/input_readers.py#L2408-L2451 |
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