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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/model_datastore_input_reader.py | ModelDatastoreInputReader.split_input | def split_input(cls, job_config):
"""Inherit docs."""
params = job_config.input_reader_params
shard_count = job_config.shard_count
query_spec = cls._get_query_spec(params)
if not property_range.should_shard_by_property_range(query_spec.filters):
return super(ModelDatastoreInputReader, cls).split_input(job_config)
p_range = property_range.PropertyRange(query_spec.filters,
query_spec.model_class_path)
p_ranges = p_range.split(shard_count)
# User specified a namespace.
if query_spec.ns:
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=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)]
return [cls(i) for i in iters] | python | def split_input(cls, job_config):
"""Inherit docs."""
params = job_config.input_reader_params
shard_count = job_config.shard_count
query_spec = cls._get_query_spec(params)
if not property_range.should_shard_by_property_range(query_spec.filters):
return super(ModelDatastoreInputReader, cls).split_input(job_config)
p_range = property_range.PropertyRange(query_spec.filters,
query_spec.model_class_path)
p_ranges = p_range.split(shard_count)
# User specified a namespace.
if query_spec.ns:
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=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)]
return [cls(i) for i in iters] | Inherit docs. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/model_datastore_input_reader.py#L53-L96 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/model_datastore_input_reader.py | ModelDatastoreInputReader.validate | def validate(cls, job_config):
"""Inherit docs."""
super(ModelDatastoreInputReader, cls).validate(job_config)
params = job_config.input_reader_params
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 errors.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, job_config):
"""Inherit docs."""
super(ModelDatastoreInputReader, cls).validate(job_config)
params = job_config.input_reader_params
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 errors.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/api/map_job/model_datastore_input_reader.py#L99-L115 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/model_datastore_input_reader.py | ModelDatastoreInputReader._validate_filters | def _validate_filters(cls, filters, model_class):
"""Validate user supplied filters.
Validate filters are on existing properties and filter values
have valid semantics.
Args:
filters: user supplied filters. Each filter should be a list or tuple of
format (<property_name_as_str>, <query_operator_as_str>,
<value_of_certain_type>). Value type is up to the property's type.
model_class: the db.Model class for the entity type to apply filters on.
Raises:
BadReaderParamsError: if any filter is invalid in any way.
"""
if not filters:
return
properties = model_class.properties()
for f in filters:
prop, _, val = f
if prop not in properties:
raise errors.BadReaderParamsError(
"Property %s is not defined for entity type %s",
prop, model_class.kind())
# Validate the value of each filter. We need to know filters have
# valid value to carry out splits.
try:
properties[prop].validate(val)
except db.BadValueError, e:
raise errors.BadReaderParamsError(e) | python | def _validate_filters(cls, filters, model_class):
"""Validate user supplied filters.
Validate filters are on existing properties and filter values
have valid semantics.
Args:
filters: user supplied filters. Each filter should be a list or tuple of
format (<property_name_as_str>, <query_operator_as_str>,
<value_of_certain_type>). Value type is up to the property's type.
model_class: the db.Model class for the entity type to apply filters on.
Raises:
BadReaderParamsError: if any filter is invalid in any way.
"""
if not filters:
return
properties = model_class.properties()
for f in filters:
prop, _, val = f
if prop not in properties:
raise errors.BadReaderParamsError(
"Property %s is not defined for entity type %s",
prop, model_class.kind())
# Validate the value of each filter. We need to know filters have
# valid value to carry out splits.
try:
properties[prop].validate(val)
except db.BadValueError, e:
raise errors.BadReaderParamsError(e) | Validate user supplied filters.
Validate filters are on existing properties and filter values
have valid semantics.
Args:
filters: user supplied filters. Each filter should be a list or tuple of
format (<property_name_as_str>, <query_operator_as_str>,
<value_of_certain_type>). Value type is up to the property's type.
model_class: the db.Model class for the entity type to apply filters on.
Raises:
BadReaderParamsError: if any filter is invalid in any way. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/model_datastore_input_reader.py#L118-L150 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/model_datastore_input_reader.py | ModelDatastoreInputReader._validate_filters_ndb | def _validate_filters_ndb(cls, filters, model_class):
"""Validate ndb.Model filters."""
if not filters:
return
properties = model_class._properties
for f in filters:
prop, _, val = f
if prop not in properties:
raise errors.BadReaderParamsError(
"Property %s is not defined for entity type %s",
prop, model_class._get_kind())
# 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 f in filters:
prop, _, val = f
if prop not in properties:
raise errors.BadReaderParamsError(
"Property %s is not defined for entity type %s",
prop, model_class._get_kind())
# 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/api/map_job/model_datastore_input_reader.py#L154-L173 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _normalize_entity | def _normalize_entity(value):
"""Return an entity from an entity or model instance."""
if ndb is not None and isinstance(value, ndb.Model):
return None
if getattr(value, "_populate_internal_entity", None):
return value._populate_internal_entity()
return value | python | def _normalize_entity(value):
"""Return an entity from an entity or model instance."""
if ndb is not None and isinstance(value, ndb.Model):
return None
if getattr(value, "_populate_internal_entity", None):
return value._populate_internal_entity()
return value | Return an entity from an entity or model instance. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L71-L77 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _normalize_key | def _normalize_key(value):
"""Return a key from an entity, model instance, key, or key string."""
if ndb is not None and isinstance(value, (ndb.Model, ndb.Key)):
return None
if getattr(value, "key", None):
return value.key()
elif isinstance(value, basestring):
return datastore.Key(value)
else:
return value | python | def _normalize_key(value):
"""Return a key from an entity, model instance, key, or key string."""
if ndb is not None and isinstance(value, (ndb.Model, ndb.Key)):
return None
if getattr(value, "key", None):
return value.key()
elif isinstance(value, basestring):
return datastore.Key(value)
else:
return value | Return a key from an entity, model instance, key, or key string. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L80-L89 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _ItemList.append | def append(self, item):
"""Add new item to the list.
If needed, append will first flush existing items and clear existing items.
Args:
item: an item to add to the list.
"""
if self.should_flush():
self.flush()
self.items.append(item) | python | def append(self, item):
"""Add new item to the list.
If needed, append will first flush existing items and clear existing items.
Args:
item: an item to add to the list.
"""
if self.should_flush():
self.flush()
self.items.append(item) | Add new item to the list.
If needed, append will first flush existing items and clear existing items.
Args:
item: an item to add to the list. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L133-L143 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _ItemList.flush | def flush(self):
"""Force a flush."""
if not self.items:
return
retry = 0
options = {"deadline": DATASTORE_DEADLINE}
while retry <= self.__timeout_retries:
try:
self.__flush_function(self.items, options)
self.clear()
break
except db.Timeout, e:
logging.warning(e)
logging.warning("Flushing '%s' timed out. Will retry for the %s time.",
self, retry)
retry += 1
options["deadline"] *= 2
except apiproxy_errors.RequestTooLargeError:
self._log_largest_items()
raise
else:
raise | python | def flush(self):
"""Force a flush."""
if not self.items:
return
retry = 0
options = {"deadline": DATASTORE_DEADLINE}
while retry <= self.__timeout_retries:
try:
self.__flush_function(self.items, options)
self.clear()
break
except db.Timeout, e:
logging.warning(e)
logging.warning("Flushing '%s' timed out. Will retry for the %s time.",
self, retry)
retry += 1
options["deadline"] *= 2
except apiproxy_errors.RequestTooLargeError:
self._log_largest_items()
raise
else:
raise | Force a flush. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L145-L167 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool.put | def put(self, entity):
"""Registers entity to put to datastore.
Args:
entity: an entity or model instance to put.
"""
actual_entity = _normalize_entity(entity)
if actual_entity is None:
return self.ndb_put(entity)
self.puts.append(actual_entity) | python | def put(self, entity):
"""Registers entity to put to datastore.
Args:
entity: an entity or model instance to put.
"""
actual_entity = _normalize_entity(entity)
if actual_entity is None:
return self.ndb_put(entity)
self.puts.append(actual_entity) | Registers entity to put to datastore.
Args:
entity: an entity or model instance to put. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L249-L258 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool.ndb_put | def ndb_put(self, entity):
"""Like put(), but for NDB entities."""
assert ndb is not None and isinstance(entity, ndb.Model)
self.ndb_puts.append(entity) | python | def ndb_put(self, entity):
"""Like put(), but for NDB entities."""
assert ndb is not None and isinstance(entity, ndb.Model)
self.ndb_puts.append(entity) | Like put(), but for NDB entities. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L260-L263 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool.delete | def delete(self, entity):
"""Registers entity to delete from datastore.
Args:
entity: an entity, model instance, or key to delete.
"""
key = _normalize_key(entity)
if key is None:
return self.ndb_delete(entity)
self.deletes.append(key) | python | def delete(self, entity):
"""Registers entity to delete from datastore.
Args:
entity: an entity, model instance, or key to delete.
"""
key = _normalize_key(entity)
if key is None:
return self.ndb_delete(entity)
self.deletes.append(key) | Registers entity to delete from datastore.
Args:
entity: an entity, model instance, or key to delete. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L265-L274 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool.ndb_delete | def ndb_delete(self, entity_or_key):
"""Like delete(), but for NDB entities/keys."""
if ndb is not None and isinstance(entity_or_key, ndb.Model):
key = entity_or_key.key
else:
key = entity_or_key
self.ndb_deletes.append(key) | python | def ndb_delete(self, entity_or_key):
"""Like delete(), but for NDB entities/keys."""
if ndb is not None and isinstance(entity_or_key, ndb.Model):
key = entity_or_key.key
else:
key = entity_or_key
self.ndb_deletes.append(key) | Like delete(), but for NDB entities/keys. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L276-L282 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool.flush | def flush(self):
"""Flush(apply) all changed to datastore."""
self.puts.flush()
self.deletes.flush()
self.ndb_puts.flush()
self.ndb_deletes.flush() | python | def flush(self):
"""Flush(apply) all changed to datastore."""
self.puts.flush()
self.deletes.flush()
self.ndb_puts.flush()
self.ndb_deletes.flush() | Flush(apply) all changed to datastore. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L284-L289 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool._flush_puts | def _flush_puts(self, items, options):
"""Flush all puts to datastore."""
datastore.Put(items, config=self._create_config(options)) | python | def _flush_puts(self, items, options):
"""Flush all puts to datastore."""
datastore.Put(items, config=self._create_config(options)) | Flush all puts to datastore. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L315-L317 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool._flush_deletes | def _flush_deletes(self, items, options):
"""Flush all deletes to datastore."""
datastore.Delete(items, config=self._create_config(options)) | python | def _flush_deletes(self, items, options):
"""Flush all deletes to datastore."""
datastore.Delete(items, config=self._create_config(options)) | Flush all deletes to datastore. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L319-L321 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool._flush_ndb_puts | def _flush_ndb_puts(self, items, options):
"""Flush all NDB puts to datastore."""
assert ndb is not None
ndb.put_multi(items, config=self._create_config(options)) | python | def _flush_ndb_puts(self, items, options):
"""Flush all NDB puts to datastore."""
assert ndb is not None
ndb.put_multi(items, config=self._create_config(options)) | Flush all NDB puts to datastore. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L323-L326 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool._flush_ndb_deletes | def _flush_ndb_deletes(self, items, options):
"""Flush all deletes to datastore."""
assert ndb is not None
ndb.delete_multi(items, config=self._create_config(options)) | python | def _flush_ndb_deletes(self, items, options):
"""Flush all deletes to datastore."""
assert ndb is not None
ndb.delete_multi(items, config=self._create_config(options)) | Flush all deletes to datastore. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L328-L331 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool._create_config | def _create_config(self, options):
"""Creates datastore Config.
Returns:
A datastore_rpc.Configuration instance.
"""
return datastore.CreateConfig(deadline=options["deadline"],
force_writes=self.force_writes) | python | def _create_config(self, options):
"""Creates datastore Config.
Returns:
A datastore_rpc.Configuration instance.
"""
return datastore.CreateConfig(deadline=options["deadline"],
force_writes=self.force_writes) | Creates datastore Config.
Returns:
A datastore_rpc.Configuration instance. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L333-L340 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _Counters.increment | def increment(self, counter_name, delta=1):
"""Increment counter value.
Args:
counter_name: name of the counter as string.
delta: increment delta as int.
"""
self._shard_state.counters_map.increment(counter_name, delta) | python | def increment(self, counter_name, delta=1):
"""Increment counter value.
Args:
counter_name: name of the counter as string.
delta: increment delta as int.
"""
self._shard_state.counters_map.increment(counter_name, delta) | Increment counter value.
Args:
counter_name: name of the counter as string.
delta: increment delta as int. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/context.py#L358-L365 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/datastore_range_iterators.py | _PropertyRangeModelIterator.to_json | def to_json(self):
"""Inherit doc."""
cursor = self._cursor
if self._query is not None:
if isinstance(self._query, db.Query):
cursor = self._query.cursor()
else:
cursor = self._query.cursor_after()
if cursor is None or isinstance(cursor, basestring):
cursor_object = False
else:
cursor_object = True
cursor = cursor.to_websafe_string()
return {"property_range": self._property_range.to_json(),
"query_spec": self._query_spec.to_json(),
"cursor": cursor,
"ns_range": self._ns_range.to_json_object(),
"name": self.__class__.__name__,
"cursor_object": cursor_object} | python | def to_json(self):
"""Inherit doc."""
cursor = self._cursor
if self._query is not None:
if isinstance(self._query, db.Query):
cursor = self._query.cursor()
else:
cursor = self._query.cursor_after()
if cursor is None or isinstance(cursor, basestring):
cursor_object = False
else:
cursor_object = True
cursor = cursor.to_websafe_string()
return {"property_range": self._property_range.to_json(),
"query_spec": self._query_spec.to_json(),
"cursor": cursor,
"ns_range": self._ns_range.to_json_object(),
"name": self.__class__.__name__,
"cursor_object": cursor_object} | Inherit doc. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/datastore_range_iterators.py#L195-L215 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/datastore_range_iterators.py | _PropertyRangeModelIterator.from_json | def from_json(cls, json):
"""Inherit doc."""
obj = cls(property_range.PropertyRange.from_json(json["property_range"]),
namespace_range.NamespaceRange.from_json_object(json["ns_range"]),
model.QuerySpec.from_json(json["query_spec"]))
cursor = json["cursor"]
# lint bug. Class method can access protected fields.
# pylint: disable=protected-access
if cursor and json["cursor_object"]:
obj._cursor = datastore_query.Cursor.from_websafe_string(cursor)
else:
obj._cursor = cursor
return obj | python | def from_json(cls, json):
"""Inherit doc."""
obj = cls(property_range.PropertyRange.from_json(json["property_range"]),
namespace_range.NamespaceRange.from_json_object(json["ns_range"]),
model.QuerySpec.from_json(json["query_spec"]))
cursor = json["cursor"]
# lint bug. Class method can access protected fields.
# pylint: disable=protected-access
if cursor and json["cursor_object"]:
obj._cursor = datastore_query.Cursor.from_websafe_string(cursor)
else:
obj._cursor = cursor
return obj | Inherit doc. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/datastore_range_iterators.py#L222-L234 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/datastore_range_iterators.py | _MultiPropertyRangeModelIterator.to_json | def to_json(self):
"""Inherit doc."""
json = {"name": self.__class__.__name__,
"num_ranges": len(self._iters)}
for i in xrange(len(self._iters)):
json_item = self._iters[i].to_json()
query_spec = json_item["query_spec"]
item_name = json_item["name"]
# Delete and move one level up
del json_item["query_spec"]
del json_item["name"]
json[str(i)] = json_item
# Store once to save space
json["query_spec"] = query_spec
json["item_name"] = item_name
return json | python | def to_json(self):
"""Inherit doc."""
json = {"name": self.__class__.__name__,
"num_ranges": len(self._iters)}
for i in xrange(len(self._iters)):
json_item = self._iters[i].to_json()
query_spec = json_item["query_spec"]
item_name = json_item["name"]
# Delete and move one level up
del json_item["query_spec"]
del json_item["name"]
json[str(i)] = json_item
# Store once to save space
json["query_spec"] = query_spec
json["item_name"] = item_name
return json | Inherit doc. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/datastore_range_iterators.py#L262-L279 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/datastore_range_iterators.py | _MultiPropertyRangeModelIterator.from_json | def from_json(cls, json):
"""Inherit doc."""
num_ranges = int(json["num_ranges"])
query_spec = json["query_spec"]
item_name = json["item_name"]
p_range_iters = []
for i in xrange(num_ranges):
json_item = json[str(i)]
# Place query_spec, name back into each iterator
json_item["query_spec"] = query_spec
json_item["name"] = item_name
p_range_iters.append(_PropertyRangeModelIterator.from_json(json_item))
obj = cls(p_range_iters)
return obj | python | def from_json(cls, json):
"""Inherit doc."""
num_ranges = int(json["num_ranges"])
query_spec = json["query_spec"]
item_name = json["item_name"]
p_range_iters = []
for i in xrange(num_ranges):
json_item = json[str(i)]
# Place query_spec, name back into each iterator
json_item["query_spec"] = query_spec
json_item["name"] = item_name
p_range_iters.append(_PropertyRangeModelIterator.from_json(json_item))
obj = cls(p_range_iters)
return obj | Inherit doc. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/datastore_range_iterators.py#L282-L297 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/datastore_range_iterators.py | _KeyRangesIterator.to_json | def to_json(self):
"""Inherit doc."""
current_iter = None
if self._current_iter:
current_iter = self._current_iter.to_json()
return {"key_ranges": self._key_ranges.to_json(),
"query_spec": self._query_spec.to_json(),
"current_iter": current_iter,
"key_range_iter_cls": self._key_range_iter_cls.__name__,
"name": self.__class__.__name__} | python | def to_json(self):
"""Inherit doc."""
current_iter = None
if self._current_iter:
current_iter = self._current_iter.to_json()
return {"key_ranges": self._key_ranges.to_json(),
"query_spec": self._query_spec.to_json(),
"current_iter": current_iter,
"key_range_iter_cls": self._key_range_iter_cls.__name__,
"name": self.__class__.__name__} | Inherit doc. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/datastore_range_iterators.py#L339-L349 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/datastore_range_iterators.py | _KeyRangesIterator.from_json | def from_json(cls, json):
"""Inherit doc."""
key_range_iter_cls = _KEY_RANGE_ITERATORS[json["key_range_iter_cls"]]
obj = cls(key_ranges.KeyRangesFactory.from_json(json["key_ranges"]),
model.QuerySpec.from_json(json["query_spec"]),
key_range_iter_cls)
current_iter = None
if json["current_iter"]:
current_iter = key_range_iter_cls.from_json(json["current_iter"])
# pylint: disable=protected-access
obj._current_iter = current_iter
return obj | python | def from_json(cls, json):
"""Inherit doc."""
key_range_iter_cls = _KEY_RANGE_ITERATORS[json["key_range_iter_cls"]]
obj = cls(key_ranges.KeyRangesFactory.from_json(json["key_ranges"]),
model.QuerySpec.from_json(json["query_spec"]),
key_range_iter_cls)
current_iter = None
if json["current_iter"]:
current_iter = key_range_iter_cls.from_json(json["current_iter"])
# pylint: disable=protected-access
obj._current_iter = current_iter
return obj | Inherit doc. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/datastore_range_iterators.py#L352-L364 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/datastore_range_iterators.py | AbstractKeyRangeIterator.to_json | def to_json(self):
"""Serializes all states into json form.
Returns:
all states in json-compatible map.
"""
cursor = self._get_cursor()
cursor_object = False
if cursor and isinstance(cursor, datastore_query.Cursor):
cursor = cursor.to_websafe_string()
cursor_object = True
return {"key_range": self._key_range.to_json(),
"query_spec": self._query_spec.to_json(),
"cursor": cursor,
"cursor_object": cursor_object} | python | def to_json(self):
"""Serializes all states into json form.
Returns:
all states in json-compatible map.
"""
cursor = self._get_cursor()
cursor_object = False
if cursor and isinstance(cursor, datastore_query.Cursor):
cursor = cursor.to_websafe_string()
cursor_object = True
return {"key_range": self._key_range.to_json(),
"query_spec": self._query_spec.to_json(),
"cursor": cursor,
"cursor_object": cursor_object} | Serializes all states into json form.
Returns:
all states in json-compatible map. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/datastore_range_iterators.py#L405-L419 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/datastore_range_iterators.py | AbstractKeyRangeIterator.from_json | def from_json(cls, json):
"""Reverse of to_json."""
obj = cls(key_range.KeyRange.from_json(json["key_range"]),
model.QuerySpec.from_json(json["query_spec"]))
cursor = json["cursor"]
# lint bug. Class method can access protected fields.
# pylint: disable=protected-access
if cursor and json["cursor_object"]:
obj._cursor = datastore_query.Cursor.from_websafe_string(cursor)
else:
obj._cursor = cursor
return obj | python | def from_json(cls, json):
"""Reverse of to_json."""
obj = cls(key_range.KeyRange.from_json(json["key_range"]),
model.QuerySpec.from_json(json["query_spec"]))
cursor = json["cursor"]
# lint bug. Class method can access protected fields.
# pylint: disable=protected-access
if cursor and json["cursor_object"]:
obj._cursor = datastore_query.Cursor.from_websafe_string(cursor)
else:
obj._cursor = cursor
return obj | Reverse of to_json. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/datastore_range_iterators.py#L422-L433 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/status.py | find_mapreduce_yaml | def find_mapreduce_yaml(status_file=__file__):
"""Traverse directory trees to find mapreduce.yaml file.
Begins with the location of status.py and then moves on to check the working
directory.
Args:
status_file: location of status.py, overridable for testing purposes.
Returns:
the path of mapreduce.yaml file or None if not found.
"""
checked = set()
yaml = _find_mapreduce_yaml(os.path.dirname(status_file), checked)
if not yaml:
yaml = _find_mapreduce_yaml(os.getcwd(), checked)
return yaml | python | def find_mapreduce_yaml(status_file=__file__):
"""Traverse directory trees to find mapreduce.yaml file.
Begins with the location of status.py and then moves on to check the working
directory.
Args:
status_file: location of status.py, overridable for testing purposes.
Returns:
the path of mapreduce.yaml file or None if not found.
"""
checked = set()
yaml = _find_mapreduce_yaml(os.path.dirname(status_file), checked)
if not yaml:
yaml = _find_mapreduce_yaml(os.getcwd(), checked)
return yaml | Traverse directory trees to find mapreduce.yaml file.
Begins with the location of status.py and then moves on to check the working
directory.
Args:
status_file: location of status.py, overridable for testing purposes.
Returns:
the path of mapreduce.yaml file or None if not found. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/status.py#L161-L177 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/status.py | _find_mapreduce_yaml | def _find_mapreduce_yaml(start, checked):
"""Traverse the directory tree identified by start until a directory already
in checked is encountered or the path of mapreduce.yaml is found.
Checked is present both to make loop termination easy to reason about and so
that the same directories do not get rechecked.
Args:
start: the path to start in and work upward from
checked: the set of already examined directories
Returns:
the path of mapreduce.yaml file or None if not found.
"""
dir = start
while dir not in checked:
checked.add(dir)
for mr_yaml_name in MR_YAML_NAMES:
yaml_path = os.path.join(dir, mr_yaml_name)
if os.path.exists(yaml_path):
return yaml_path
dir = os.path.dirname(dir)
return None | python | def _find_mapreduce_yaml(start, checked):
"""Traverse the directory tree identified by start until a directory already
in checked is encountered or the path of mapreduce.yaml is found.
Checked is present both to make loop termination easy to reason about and so
that the same directories do not get rechecked.
Args:
start: the path to start in and work upward from
checked: the set of already examined directories
Returns:
the path of mapreduce.yaml file or None if not found.
"""
dir = start
while dir not in checked:
checked.add(dir)
for mr_yaml_name in MR_YAML_NAMES:
yaml_path = os.path.join(dir, mr_yaml_name)
if os.path.exists(yaml_path):
return yaml_path
dir = os.path.dirname(dir)
return None | Traverse the directory tree identified by start until a directory already
in checked is encountered or the path of mapreduce.yaml is found.
Checked is present both to make loop termination easy to reason about and so
that the same directories do not get rechecked.
Args:
start: the path to start in and work upward from
checked: the set of already examined directories
Returns:
the path of mapreduce.yaml file or None if not found. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/status.py#L180-L202 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/status.py | parse_mapreduce_yaml | def parse_mapreduce_yaml(contents):
"""Parses mapreduce.yaml file contents.
Args:
contents: mapreduce.yaml file contents.
Returns:
MapReduceYaml object with all the data from original file.
Raises:
errors.BadYamlError: when contents is not a valid mapreduce.yaml file.
"""
try:
builder = yaml_object.ObjectBuilder(MapReduceYaml)
handler = yaml_builder.BuilderHandler(builder)
listener = yaml_listener.EventListener(handler)
listener.Parse(contents)
mr_info = handler.GetResults()
except (ValueError, yaml_errors.EventError), e:
raise errors.BadYamlError(e)
if len(mr_info) < 1:
raise errors.BadYamlError("No configs found in mapreduce.yaml")
if len(mr_info) > 1:
raise errors.MultipleDocumentsInMrYaml("Found %d YAML documents" %
len(mr_info))
jobs = mr_info[0]
job_names = set(j.name for j in jobs.mapreduce)
if len(jobs.mapreduce) != len(job_names):
raise errors.BadYamlError(
"Overlapping mapreduce names; names must be unique")
return jobs | python | def parse_mapreduce_yaml(contents):
"""Parses mapreduce.yaml file contents.
Args:
contents: mapreduce.yaml file contents.
Returns:
MapReduceYaml object with all the data from original file.
Raises:
errors.BadYamlError: when contents is not a valid mapreduce.yaml file.
"""
try:
builder = yaml_object.ObjectBuilder(MapReduceYaml)
handler = yaml_builder.BuilderHandler(builder)
listener = yaml_listener.EventListener(handler)
listener.Parse(contents)
mr_info = handler.GetResults()
except (ValueError, yaml_errors.EventError), e:
raise errors.BadYamlError(e)
if len(mr_info) < 1:
raise errors.BadYamlError("No configs found in mapreduce.yaml")
if len(mr_info) > 1:
raise errors.MultipleDocumentsInMrYaml("Found %d YAML documents" %
len(mr_info))
jobs = mr_info[0]
job_names = set(j.name for j in jobs.mapreduce)
if len(jobs.mapreduce) != len(job_names):
raise errors.BadYamlError(
"Overlapping mapreduce names; names must be unique")
return jobs | Parses mapreduce.yaml file contents.
Args:
contents: mapreduce.yaml file contents.
Returns:
MapReduceYaml object with all the data from original file.
Raises:
errors.BadYamlError: when contents is not a valid mapreduce.yaml file. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/status.py#L205-L239 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/status.py | get_mapreduce_yaml | def get_mapreduce_yaml(parse=parse_mapreduce_yaml):
"""Locates mapreduce.yaml, loads and parses its info.
Args:
parse: Used for testing.
Returns:
MapReduceYaml object.
Raises:
errors.BadYamlError: when contents is not a valid mapreduce.yaml file or the
file is missing.
"""
mr_yaml_path = find_mapreduce_yaml()
if not mr_yaml_path:
raise errors.MissingYamlError()
mr_yaml_file = open(mr_yaml_path)
try:
return parse(mr_yaml_file.read())
finally:
mr_yaml_file.close() | python | def get_mapreduce_yaml(parse=parse_mapreduce_yaml):
"""Locates mapreduce.yaml, loads and parses its info.
Args:
parse: Used for testing.
Returns:
MapReduceYaml object.
Raises:
errors.BadYamlError: when contents is not a valid mapreduce.yaml file or the
file is missing.
"""
mr_yaml_path = find_mapreduce_yaml()
if not mr_yaml_path:
raise errors.MissingYamlError()
mr_yaml_file = open(mr_yaml_path)
try:
return parse(mr_yaml_file.read())
finally:
mr_yaml_file.close() | Locates mapreduce.yaml, loads and parses its info.
Args:
parse: Used for testing.
Returns:
MapReduceYaml object.
Raises:
errors.BadYamlError: when contents is not a valid mapreduce.yaml file or the
file is missing. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/status.py#L242-L262 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/status.py | MapReduceYaml.to_dict | def to_dict(mapreduce_yaml):
"""Converts a MapReduceYaml file into a JSON-encodable dictionary.
For use in user-visible UI and internal methods for interfacing with
user code (like param validation). as a list
Args:
mapreduce_yaml: The Pyton representation of the mapreduce.yaml document.
Returns:
A list of configuration dictionaries.
"""
all_configs = []
for config in mapreduce_yaml.mapreduce:
out = {
"name": config.name,
"mapper_input_reader": config.mapper.input_reader,
"mapper_handler": config.mapper.handler,
}
if config.mapper.params_validator:
out["mapper_params_validator"] = config.mapper.params_validator
if config.mapper.params:
param_defaults = {}
for param in config.mapper.params:
param_defaults[param.name] = param.default or param.value
out["mapper_params"] = param_defaults
if config.params:
param_defaults = {}
for param in config.params:
param_defaults[param.name] = param.default or param.value
out["params"] = param_defaults
if config.mapper.output_writer:
out["mapper_output_writer"] = config.mapper.output_writer
all_configs.append(out)
return all_configs | python | def to_dict(mapreduce_yaml):
"""Converts a MapReduceYaml file into a JSON-encodable dictionary.
For use in user-visible UI and internal methods for interfacing with
user code (like param validation). as a list
Args:
mapreduce_yaml: The Pyton representation of the mapreduce.yaml document.
Returns:
A list of configuration dictionaries.
"""
all_configs = []
for config in mapreduce_yaml.mapreduce:
out = {
"name": config.name,
"mapper_input_reader": config.mapper.input_reader,
"mapper_handler": config.mapper.handler,
}
if config.mapper.params_validator:
out["mapper_params_validator"] = config.mapper.params_validator
if config.mapper.params:
param_defaults = {}
for param in config.mapper.params:
param_defaults[param.name] = param.default or param.value
out["mapper_params"] = param_defaults
if config.params:
param_defaults = {}
for param in config.params:
param_defaults[param.name] = param.default or param.value
out["params"] = param_defaults
if config.mapper.output_writer:
out["mapper_output_writer"] = config.mapper.output_writer
all_configs.append(out)
return all_configs | Converts a MapReduceYaml file into a JSON-encodable dictionary.
For use in user-visible UI and internal methods for interfacing with
user code (like param validation). as a list
Args:
mapreduce_yaml: The Pyton representation of the mapreduce.yaml document.
Returns:
A list of configuration dictionaries. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/status.py#L120-L155 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/input_reader.py | InputReader.validate | def validate(cls, job_config):
"""Validates relevant parameters.
This method can validate fields which it deems relevant.
Args:
job_config: an instance of map_job.JobConfig.
Raises:
errors.BadReaderParamsError: required parameters are missing or invalid.
"""
if job_config.input_reader_cls != cls:
raise errors.BadReaderParamsError(
"Expect input reader class %r, got %r." %
(cls, job_config.input_reader_cls)) | python | def validate(cls, job_config):
"""Validates relevant parameters.
This method can validate fields which it deems relevant.
Args:
job_config: an instance of map_job.JobConfig.
Raises:
errors.BadReaderParamsError: required parameters are missing or invalid.
"""
if job_config.input_reader_cls != cls:
raise errors.BadReaderParamsError(
"Expect input reader class %r, got %r." %
(cls, job_config.input_reader_cls)) | Validates relevant parameters.
This method can validate fields which it deems relevant.
Args:
job_config: an instance of map_job.JobConfig.
Raises:
errors.BadReaderParamsError: required parameters are missing or invalid. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/input_reader.py#L104-L118 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _sort_records_map | def _sort_records_map(records):
"""Map function sorting records.
Converts records to KeyValue protos, sorts them by key and writes them
into new GCS file. Creates _OutputFile entity to record resulting
file name.
Args:
records: list of records which are serialized KeyValue protos.
"""
ctx = context.get()
l = len(records)
key_records = [None] * l
logging.debug("Parsing")
for i in range(l):
proto = kv_pb.KeyValue()
proto.ParseFromString(records[i])
key_records[i] = (proto.key(), records[i])
logging.debug("Sorting")
key_records.sort(cmp=_compare_keys)
logging.debug("Writing")
mapper_spec = ctx.mapreduce_spec.mapper
params = input_readers._get_params(mapper_spec)
bucket_name = params.get("bucket_name")
filename = (ctx.mapreduce_spec.name + "/" + ctx.mapreduce_id + "/output-" +
ctx.shard_id + "-" + str(int(time.time())))
full_filename = "/%s/%s" % (bucket_name, filename)
filehandle = cloudstorage.open(full_filename, mode="w")
with output_writers.GCSRecordsPool(filehandle, ctx=ctx) as pool:
for key_record in key_records:
pool.append(key_record[1])
logging.debug("Finalizing")
filehandle.close()
entity = _OutputFile(key_name=full_filename,
parent=_OutputFile.get_root_key(ctx.mapreduce_id))
entity.put() | python | def _sort_records_map(records):
"""Map function sorting records.
Converts records to KeyValue protos, sorts them by key and writes them
into new GCS file. Creates _OutputFile entity to record resulting
file name.
Args:
records: list of records which are serialized KeyValue protos.
"""
ctx = context.get()
l = len(records)
key_records = [None] * l
logging.debug("Parsing")
for i in range(l):
proto = kv_pb.KeyValue()
proto.ParseFromString(records[i])
key_records[i] = (proto.key(), records[i])
logging.debug("Sorting")
key_records.sort(cmp=_compare_keys)
logging.debug("Writing")
mapper_spec = ctx.mapreduce_spec.mapper
params = input_readers._get_params(mapper_spec)
bucket_name = params.get("bucket_name")
filename = (ctx.mapreduce_spec.name + "/" + ctx.mapreduce_id + "/output-" +
ctx.shard_id + "-" + str(int(time.time())))
full_filename = "/%s/%s" % (bucket_name, filename)
filehandle = cloudstorage.open(full_filename, mode="w")
with output_writers.GCSRecordsPool(filehandle, ctx=ctx) as pool:
for key_record in key_records:
pool.append(key_record[1])
logging.debug("Finalizing")
filehandle.close()
entity = _OutputFile(key_name=full_filename,
parent=_OutputFile.get_root_key(ctx.mapreduce_id))
entity.put() | Map function sorting records.
Converts records to KeyValue protos, sorts them by key and writes them
into new GCS file. Creates _OutputFile entity to record resulting
file name.
Args:
records: list of records which are serialized KeyValue protos. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/shuffler.py#L124-L164 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _merge_map | def _merge_map(key, values, partial):
"""A map function used in merge phase.
Stores (key, values) into KeyValues proto and yields its serialization.
Args:
key: values key.
values: values themselves.
partial: True if more values for this key will follow. False otherwise.
Yields:
The proto.
"""
proto = kv_pb.KeyValues()
proto.set_key(key)
proto.value_list().extend(values)
yield proto.Encode() | python | def _merge_map(key, values, partial):
"""A map function used in merge phase.
Stores (key, values) into KeyValues proto and yields its serialization.
Args:
key: values key.
values: values themselves.
partial: True if more values for this key will follow. False otherwise.
Yields:
The proto.
"""
proto = kv_pb.KeyValues()
proto.set_key(key)
proto.value_list().extend(values)
yield proto.Encode() | A map function used in merge phase.
Stores (key, values) into KeyValues proto and yields its serialization.
Args:
key: values key.
values: values themselves.
partial: True if more values for this key will follow. False otherwise.
Yields:
The proto. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/shuffler.py#L561-L577 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _hashing_map | def _hashing_map(binary_record):
"""A map function used in hash phase.
Reads KeyValue from binary record.
Args:
binary_record: The binary record.
Yields:
The (key, value).
"""
proto = kv_pb.KeyValue()
proto.ParseFromString(binary_record)
yield (proto.key(), proto.value()) | python | def _hashing_map(binary_record):
"""A map function used in hash phase.
Reads KeyValue from binary record.
Args:
binary_record: The binary record.
Yields:
The (key, value).
"""
proto = kv_pb.KeyValue()
proto.ParseFromString(binary_record)
yield (proto.key(), proto.value()) | A map function used in hash phase.
Reads KeyValue from binary record.
Args:
binary_record: The binary record.
Yields:
The (key, value). | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/shuffler.py#L618-L631 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _MergingReader.split_input | def split_input(cls, mapper_spec):
"""Split input into multiple shards."""
filelists = mapper_spec.params[cls.FILES_PARAM]
max_values_count = mapper_spec.params.get(cls.MAX_VALUES_COUNT_PARAM, -1)
max_values_size = mapper_spec.params.get(cls.MAX_VALUES_SIZE_PARAM, -1)
return [cls([0] * len(files), max_values_count, max_values_size)
for files in filelists] | python | def split_input(cls, mapper_spec):
"""Split input into multiple shards."""
filelists = mapper_spec.params[cls.FILES_PARAM]
max_values_count = mapper_spec.params.get(cls.MAX_VALUES_COUNT_PARAM, -1)
max_values_size = mapper_spec.params.get(cls.MAX_VALUES_SIZE_PARAM, -1)
return [cls([0] * len(files), max_values_count, max_values_size)
for files in filelists] | Split input into multiple shards. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/shuffler.py#L386-L392 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _MergingReader.validate | def validate(cls, mapper_spec):
"""Validate reader parameters in mapper_spec."""
if mapper_spec.input_reader_class() != cls:
raise errors.BadReaderParamsError("Input reader class mismatch")
params = mapper_spec.params
if cls.FILES_PARAM not in params:
raise errors.BadReaderParamsError("Missing files parameter.") | python | def validate(cls, mapper_spec):
"""Validate reader parameters in mapper_spec."""
if mapper_spec.input_reader_class() != cls:
raise errors.BadReaderParamsError("Input reader class mismatch")
params = mapper_spec.params
if cls.FILES_PARAM not in params:
raise errors.BadReaderParamsError("Missing files parameter.") | Validate reader parameters in mapper_spec. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/shuffler.py#L395-L401 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _HashingGCSOutputWriter.validate | def validate(cls, mapper_spec):
"""Validates mapper specification.
Args:
mapper_spec: an instance of model.MapperSpec to validate.
Raises:
BadWriterParamsError: when Output writer class mismatch.
"""
if mapper_spec.output_writer_class() != cls:
raise errors.BadWriterParamsError("Output writer class mismatch")
params = output_writers._get_params(mapper_spec)
# Bucket Name is required
if cls.BUCKET_NAME_PARAM not in params:
raise errors.BadWriterParamsError(
"%s is required for the _HashingGCSOutputWriter" %
cls.BUCKET_NAME_PARAM) | python | def validate(cls, mapper_spec):
"""Validates mapper specification.
Args:
mapper_spec: an instance of model.MapperSpec to validate.
Raises:
BadWriterParamsError: when Output writer class mismatch.
"""
if mapper_spec.output_writer_class() != cls:
raise errors.BadWriterParamsError("Output writer class mismatch")
params = output_writers._get_params(mapper_spec)
# Bucket Name is required
if cls.BUCKET_NAME_PARAM not in params:
raise errors.BadWriterParamsError(
"%s is required for the _HashingGCSOutputWriter" %
cls.BUCKET_NAME_PARAM) | Validates mapper specification.
Args:
mapper_spec: an instance of model.MapperSpec to validate.
Raises:
BadWriterParamsError: when Output writer class mismatch. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/shuffler.py#L429-L444 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _HashingGCSOutputWriter.to_json | def to_json(self):
"""Returns writer state to serialize in json.
Returns:
A json-izable version of the OutputWriter state.
"""
# Use the member variable (since we don't have access to the context) to
# flush each pool to minimize the size of each filehandle before we
# serialize it.
for pool in self._pools:
if pool is not None:
pool.flush(True)
return {"filehandles": pickle.dumps(self._filehandles)} | python | def to_json(self):
"""Returns writer state to serialize in json.
Returns:
A json-izable version of the OutputWriter state.
"""
# Use the member variable (since we don't have access to the context) to
# flush each pool to minimize the size of each filehandle before we
# serialize it.
for pool in self._pools:
if pool is not None:
pool.flush(True)
return {"filehandles": pickle.dumps(self._filehandles)} | Returns writer state to serialize in json.
Returns:
A json-izable version of the OutputWriter state. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/shuffler.py#L458-L470 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _HashingGCSOutputWriter.create | def create(cls, mr_spec, shard_number, shard_attempt, _writer_state=None):
"""Inherit docs."""
mapper_spec = mr_spec.mapper
params = output_writers._get_params(mapper_spec)
bucket_name = params.get(cls.BUCKET_NAME_PARAM)
shards = mapper_spec.shard_count
filehandles = []
filename = (mr_spec.name + "/" + mr_spec.mapreduce_id +
"/shard-" + str(shard_number) + "-bucket-")
for i in range(shards):
full_filename = "/%s/%s%d" % (bucket_name, filename, i)
filehandles.append(cloudstorage.open(full_filename, mode="w"))
return cls(filehandles) | python | def create(cls, mr_spec, shard_number, shard_attempt, _writer_state=None):
"""Inherit docs."""
mapper_spec = mr_spec.mapper
params = output_writers._get_params(mapper_spec)
bucket_name = params.get(cls.BUCKET_NAME_PARAM)
shards = mapper_spec.shard_count
filehandles = []
filename = (mr_spec.name + "/" + mr_spec.mapreduce_id +
"/shard-" + str(shard_number) + "-bucket-")
for i in range(shards):
full_filename = "/%s/%s%d" % (bucket_name, filename, i)
filehandles.append(cloudstorage.open(full_filename, mode="w"))
return cls(filehandles) | Inherit docs. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/shuffler.py#L473-L486 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _HashingGCSOutputWriter.get_filenames | def get_filenames(cls, mapreduce_state):
"""See parent class."""
shards = mapreduce_state.mapreduce_spec.mapper.shard_count
filenames = []
for _ in range(shards):
filenames.append([None] * shards)
shard_states = model.ShardState.find_all_by_mapreduce_state(mapreduce_state)
for x, shard_state in enumerate(shard_states):
shard_filenames = shard_state.writer_state["shard_filenames"]
for y in range(shards):
filenames[y][x] = shard_filenames[y]
return filenames | python | def get_filenames(cls, mapreduce_state):
"""See parent class."""
shards = mapreduce_state.mapreduce_spec.mapper.shard_count
filenames = []
for _ in range(shards):
filenames.append([None] * shards)
shard_states = model.ShardState.find_all_by_mapreduce_state(mapreduce_state)
for x, shard_state in enumerate(shard_states):
shard_filenames = shard_state.writer_state["shard_filenames"]
for y in range(shards):
filenames[y][x] = shard_filenames[y]
return filenames | See parent class. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/shuffler.py#L489-L500 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _HashingGCSOutputWriter.finalize | def finalize(self, ctx, shard_state):
"""See parent class."""
filenames = []
for filehandle in self._filehandles:
filenames.append(filehandle.name)
filehandle.close()
shard_state.writer_state = {"shard_filenames": filenames} | python | def finalize(self, ctx, shard_state):
"""See parent class."""
filenames = []
for filehandle in self._filehandles:
filenames.append(filehandle.name)
filehandle.close()
shard_state.writer_state = {"shard_filenames": filenames} | See parent class. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/shuffler.py#L502-L508 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _HashingGCSOutputWriter.write | def write(self, data):
"""Write data.
Args:
data: actual data yielded from handler. Type is writer-specific.
"""
ctx = context.get()
if len(data) != 2:
logging.error("Got bad tuple of length %d (2-tuple expected): %s",
len(data), data)
try:
key = str(data[0])
value = str(data[1])
except TypeError:
logging.error("Expecting a tuple, but got %s: %s",
data.__class__.__name__, data)
file_index = key.__hash__() % len(self._filehandles)
# Work-around: Since we don't have access to the context in the to_json()
# function, but we need to flush each pool before we serialize the
# filehandle, we rely on a member variable instead of using context for
# pool management.
pool = self._pools[file_index]
if pool is None:
filehandle = self._filehandles[file_index]
pool = output_writers.GCSRecordsPool(filehandle=filehandle, ctx=ctx)
self._pools[file_index] = pool
proto = kv_pb.KeyValue()
proto.set_key(key)
proto.set_value(value)
pool.append(proto.Encode()) | python | def write(self, data):
"""Write data.
Args:
data: actual data yielded from handler. Type is writer-specific.
"""
ctx = context.get()
if len(data) != 2:
logging.error("Got bad tuple of length %d (2-tuple expected): %s",
len(data), data)
try:
key = str(data[0])
value = str(data[1])
except TypeError:
logging.error("Expecting a tuple, but got %s: %s",
data.__class__.__name__, data)
file_index = key.__hash__() % len(self._filehandles)
# Work-around: Since we don't have access to the context in the to_json()
# function, but we need to flush each pool before we serialize the
# filehandle, we rely on a member variable instead of using context for
# pool management.
pool = self._pools[file_index]
if pool is None:
filehandle = self._filehandles[file_index]
pool = output_writers.GCSRecordsPool(filehandle=filehandle, ctx=ctx)
self._pools[file_index] = pool
proto = kv_pb.KeyValue()
proto.set_key(key)
proto.set_value(value)
pool.append(proto.Encode()) | Write data.
Args:
data: actual data yielded from handler. Type is writer-specific. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/shuffler.py#L510-L543 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_config.py | JobConfig._get_mapper_params | def _get_mapper_params(self):
"""Converts self to model.MapperSpec.params."""
reader_params = self.input_reader_cls.params_to_json(
self.input_reader_params)
# TODO(user): Do the same for writer params.
return {"input_reader": reader_params,
"output_writer": self.output_writer_params} | python | def _get_mapper_params(self):
"""Converts self to model.MapperSpec.params."""
reader_params = self.input_reader_cls.params_to_json(
self.input_reader_params)
# TODO(user): Do the same for writer params.
return {"input_reader": reader_params,
"output_writer": self.output_writer_params} | Converts self to model.MapperSpec.params. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_config.py#L114-L120 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_config.py | JobConfig._get_mapper_spec | def _get_mapper_spec(self):
"""Converts self to model.MapperSpec."""
# pylint: disable=g-import-not-at-top
from mapreduce import model
return model.MapperSpec(
handler_spec=util._obj_to_path(self.mapper),
input_reader_spec=util._obj_to_path(self.input_reader_cls),
params=self._get_mapper_params(),
shard_count=self.shard_count,
output_writer_spec=util._obj_to_path(self.output_writer_cls)) | python | def _get_mapper_spec(self):
"""Converts self to model.MapperSpec."""
# pylint: disable=g-import-not-at-top
from mapreduce import model
return model.MapperSpec(
handler_spec=util._obj_to_path(self.mapper),
input_reader_spec=util._obj_to_path(self.input_reader_cls),
params=self._get_mapper_params(),
shard_count=self.shard_count,
output_writer_spec=util._obj_to_path(self.output_writer_cls)) | Converts self to model.MapperSpec. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_config.py#L122-L132 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_config.py | JobConfig._get_mr_params | def _get_mr_params(self):
"""Converts self to model.MapreduceSpec.params."""
return {"force_writes": self._force_writes,
"done_callback": self.done_callback_url,
"user_params": self.user_params,
"shard_max_attempts": self.shard_max_attempts,
"task_max_attempts": self._task_max_attempts,
"task_max_data_processing_attempts":
self._task_max_data_processing_attempts,
"queue_name": self.queue_name,
"base_path": self._base_path,
"app_id": self._app,
"api_version": self._api_version} | python | def _get_mr_params(self):
"""Converts self to model.MapreduceSpec.params."""
return {"force_writes": self._force_writes,
"done_callback": self.done_callback_url,
"user_params": self.user_params,
"shard_max_attempts": self.shard_max_attempts,
"task_max_attempts": self._task_max_attempts,
"task_max_data_processing_attempts":
self._task_max_data_processing_attempts,
"queue_name": self.queue_name,
"base_path": self._base_path,
"app_id": self._app,
"api_version": self._api_version} | Converts self to model.MapreduceSpec.params. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_config.py#L134-L146 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_config.py | JobConfig._get_default_mr_params | def _get_default_mr_params(cls):
"""Gets default values for old API."""
cfg = cls(_lenient=True)
mr_params = cfg._get_mr_params()
mr_params["api_version"] = 0
return mr_params | python | def _get_default_mr_params(cls):
"""Gets default values for old API."""
cfg = cls(_lenient=True)
mr_params = cfg._get_mr_params()
mr_params["api_version"] = 0
return mr_params | Gets default values for old API. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_config.py#L154-L159 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_config.py | JobConfig._to_map_job_config | def _to_map_job_config(cls,
mr_spec,
# TODO(user): Remove this parameter after it can be
# read from mr_spec.
queue_name):
"""Converts model.MapreduceSpec back to JobConfig.
This method allows our internal methods to use JobConfig directly.
This method also allows us to expose JobConfig as an API during execution,
despite that it is not saved into datastore.
Args:
mr_spec: model.MapreduceSpec.
queue_name: queue name.
Returns:
The JobConfig object for this job.
"""
mapper_spec = mr_spec.mapper
# 0 means all the old APIs before api_version is introduced.
api_version = mr_spec.params.get("api_version", 0)
old_api = api_version == 0
# Deserialize params from json if input_reader/output_writer are new API.
input_reader_cls = mapper_spec.input_reader_class()
input_reader_params = input_readers._get_params(mapper_spec)
if issubclass(input_reader_cls, input_reader.InputReader):
input_reader_params = input_reader_cls.params_from_json(
input_reader_params)
output_writer_cls = mapper_spec.output_writer_class()
output_writer_params = output_writers._get_params(mapper_spec)
# TODO(user): Call json (de)serialization for writer.
# if (output_writer_cls and
# issubclass(output_writer_cls, output_writer.OutputWriter)):
# output_writer_params = output_writer_cls.params_from_json(
# output_writer_params)
# We can not always convert MapreduceSpec generated by older API
# to JobConfig. Thus, mr framework should use/expose the returned JobConfig
# object with caution when a job is started with an old API.
# In this case, this method only tries not to blow up and assemble a
# JobConfig object as accurate as possible.
return cls(_lenient=old_api,
job_name=mr_spec.name,
job_id=mr_spec.mapreduce_id,
# handler_spec from older API may not have map_job.Mapper type.
mapper=util.for_name(mapper_spec.handler_spec),
input_reader_cls=input_reader_cls,
input_reader_params=input_reader_params,
output_writer_cls=output_writer_cls,
output_writer_params=output_writer_params,
shard_count=mapper_spec.shard_count,
queue_name=queue_name,
user_params=mr_spec.params.get("user_params"),
shard_max_attempts=mr_spec.params.get("shard_max_attempts"),
done_callback_url=mr_spec.params.get("done_callback"),
_force_writes=mr_spec.params.get("force_writes"),
_base_path=mr_spec.params["base_path"],
_task_max_attempts=mr_spec.params.get("task_max_attempts"),
_task_max_data_processing_attempts=(
mr_spec.params.get("task_max_data_processing_attempts")),
_hooks_cls=util.for_name(mr_spec.hooks_class_name),
_app=mr_spec.params.get("app_id"),
_api_version=api_version) | python | def _to_map_job_config(cls,
mr_spec,
# TODO(user): Remove this parameter after it can be
# read from mr_spec.
queue_name):
"""Converts model.MapreduceSpec back to JobConfig.
This method allows our internal methods to use JobConfig directly.
This method also allows us to expose JobConfig as an API during execution,
despite that it is not saved into datastore.
Args:
mr_spec: model.MapreduceSpec.
queue_name: queue name.
Returns:
The JobConfig object for this job.
"""
mapper_spec = mr_spec.mapper
# 0 means all the old APIs before api_version is introduced.
api_version = mr_spec.params.get("api_version", 0)
old_api = api_version == 0
# Deserialize params from json if input_reader/output_writer are new API.
input_reader_cls = mapper_spec.input_reader_class()
input_reader_params = input_readers._get_params(mapper_spec)
if issubclass(input_reader_cls, input_reader.InputReader):
input_reader_params = input_reader_cls.params_from_json(
input_reader_params)
output_writer_cls = mapper_spec.output_writer_class()
output_writer_params = output_writers._get_params(mapper_spec)
# TODO(user): Call json (de)serialization for writer.
# if (output_writer_cls and
# issubclass(output_writer_cls, output_writer.OutputWriter)):
# output_writer_params = output_writer_cls.params_from_json(
# output_writer_params)
# We can not always convert MapreduceSpec generated by older API
# to JobConfig. Thus, mr framework should use/expose the returned JobConfig
# object with caution when a job is started with an old API.
# In this case, this method only tries not to blow up and assemble a
# JobConfig object as accurate as possible.
return cls(_lenient=old_api,
job_name=mr_spec.name,
job_id=mr_spec.mapreduce_id,
# handler_spec from older API may not have map_job.Mapper type.
mapper=util.for_name(mapper_spec.handler_spec),
input_reader_cls=input_reader_cls,
input_reader_params=input_reader_params,
output_writer_cls=output_writer_cls,
output_writer_params=output_writer_params,
shard_count=mapper_spec.shard_count,
queue_name=queue_name,
user_params=mr_spec.params.get("user_params"),
shard_max_attempts=mr_spec.params.get("shard_max_attempts"),
done_callback_url=mr_spec.params.get("done_callback"),
_force_writes=mr_spec.params.get("force_writes"),
_base_path=mr_spec.params["base_path"],
_task_max_attempts=mr_spec.params.get("task_max_attempts"),
_task_max_data_processing_attempts=(
mr_spec.params.get("task_max_data_processing_attempts")),
_hooks_cls=util.for_name(mr_spec.hooks_class_name),
_app=mr_spec.params.get("app_id"),
_api_version=api_version) | Converts model.MapreduceSpec back to JobConfig.
This method allows our internal methods to use JobConfig directly.
This method also allows us to expose JobConfig as an API during execution,
despite that it is not saved into datastore.
Args:
mr_spec: model.MapreduceSpec.
queue_name: queue name.
Returns:
The JobConfig object for this job. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_config.py#L162-L226 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsWriter.__write_record | def __write_record(self, record_type, data):
"""Write single physical record."""
length = len(data)
crc = crc32c.crc_update(crc32c.CRC_INIT, [record_type])
crc = crc32c.crc_update(crc, data)
crc = crc32c.crc_finalize(crc)
self.__writer.write(
struct.pack(_HEADER_FORMAT, _mask_crc(crc), length, record_type))
self.__writer.write(data)
self.__position += _HEADER_LENGTH + length | python | def __write_record(self, record_type, data):
"""Write single physical record."""
length = len(data)
crc = crc32c.crc_update(crc32c.CRC_INIT, [record_type])
crc = crc32c.crc_update(crc, data)
crc = crc32c.crc_finalize(crc)
self.__writer.write(
struct.pack(_HEADER_FORMAT, _mask_crc(crc), length, record_type))
self.__writer.write(data)
self.__position += _HEADER_LENGTH + length | Write single physical record. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/records.py#L160-L171 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsWriter.write | def write(self, data):
"""Write single record.
Args:
data: record data to write as string, byte array or byte sequence.
"""
block_remaining = _BLOCK_SIZE - self.__position % _BLOCK_SIZE
if block_remaining < _HEADER_LENGTH:
# Header won't fit into remainder
self.__writer.write('\x00' * block_remaining)
self.__position += block_remaining
block_remaining = _BLOCK_SIZE
if block_remaining < len(data) + _HEADER_LENGTH:
first_chunk = data[:block_remaining - _HEADER_LENGTH]
self.__write_record(_RECORD_TYPE_FIRST, first_chunk)
data = data[len(first_chunk):]
while True:
block_remaining = _BLOCK_SIZE - self.__position % _BLOCK_SIZE
if block_remaining >= len(data) + _HEADER_LENGTH:
self.__write_record(_RECORD_TYPE_LAST, data)
break
else:
chunk = data[:block_remaining - _HEADER_LENGTH]
self.__write_record(_RECORD_TYPE_MIDDLE, chunk)
data = data[len(chunk):]
else:
self.__write_record(_RECORD_TYPE_FULL, data) | python | def write(self, data):
"""Write single record.
Args:
data: record data to write as string, byte array or byte sequence.
"""
block_remaining = _BLOCK_SIZE - self.__position % _BLOCK_SIZE
if block_remaining < _HEADER_LENGTH:
# Header won't fit into remainder
self.__writer.write('\x00' * block_remaining)
self.__position += block_remaining
block_remaining = _BLOCK_SIZE
if block_remaining < len(data) + _HEADER_LENGTH:
first_chunk = data[:block_remaining - _HEADER_LENGTH]
self.__write_record(_RECORD_TYPE_FIRST, first_chunk)
data = data[len(first_chunk):]
while True:
block_remaining = _BLOCK_SIZE - self.__position % _BLOCK_SIZE
if block_remaining >= len(data) + _HEADER_LENGTH:
self.__write_record(_RECORD_TYPE_LAST, data)
break
else:
chunk = data[:block_remaining - _HEADER_LENGTH]
self.__write_record(_RECORD_TYPE_MIDDLE, chunk)
data = data[len(chunk):]
else:
self.__write_record(_RECORD_TYPE_FULL, data) | Write single record.
Args:
data: record data to write as string, byte array or byte sequence. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/records.py#L173-L202 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsWriter._pad_block | def _pad_block(self):
"""Pad block with 0.
Pad current block with 0. Reader will simply treat these as corrupted
record and skip the block.
This method is idempotent.
"""
pad_length = _BLOCK_SIZE - self.__position % _BLOCK_SIZE
if pad_length and pad_length != _BLOCK_SIZE:
self.__writer.write('\x00' * pad_length)
self.__position += pad_length | python | def _pad_block(self):
"""Pad block with 0.
Pad current block with 0. Reader will simply treat these as corrupted
record and skip the block.
This method is idempotent.
"""
pad_length = _BLOCK_SIZE - self.__position % _BLOCK_SIZE
if pad_length and pad_length != _BLOCK_SIZE:
self.__writer.write('\x00' * pad_length)
self.__position += pad_length | Pad block with 0.
Pad current block with 0. Reader will simply treat these as corrupted
record and skip the block.
This method is idempotent. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/records.py#L213-L224 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsReader.__try_read_record | def __try_read_record(self):
"""Try reading a record.
Returns:
(data, record_type) tuple.
Raises:
EOFError: when end of file was reached.
InvalidRecordError: when valid record could not be read.
"""
block_remaining = _BLOCK_SIZE - self.__reader.tell() % _BLOCK_SIZE
if block_remaining < _HEADER_LENGTH:
return ('', _RECORD_TYPE_NONE)
header = self.__reader.read(_HEADER_LENGTH)
if len(header) != _HEADER_LENGTH:
raise EOFError('Read %s bytes instead of %s' %
(len(header), _HEADER_LENGTH))
(masked_crc, length, record_type) = struct.unpack(_HEADER_FORMAT, header)
crc = _unmask_crc(masked_crc)
if length + _HEADER_LENGTH > block_remaining:
# A record can't be bigger than one block.
raise errors.InvalidRecordError('Length is too big')
data = self.__reader.read(length)
if len(data) != length:
raise EOFError('Not enough data read. Expected: %s but got %s' %
(length, len(data)))
if record_type == _RECORD_TYPE_NONE:
return ('', record_type)
actual_crc = crc32c.crc_update(crc32c.CRC_INIT, [record_type])
actual_crc = crc32c.crc_update(actual_crc, data)
actual_crc = crc32c.crc_finalize(actual_crc)
if actual_crc != crc:
raise errors.InvalidRecordError('Data crc does not match')
return (data, record_type) | python | def __try_read_record(self):
"""Try reading a record.
Returns:
(data, record_type) tuple.
Raises:
EOFError: when end of file was reached.
InvalidRecordError: when valid record could not be read.
"""
block_remaining = _BLOCK_SIZE - self.__reader.tell() % _BLOCK_SIZE
if block_remaining < _HEADER_LENGTH:
return ('', _RECORD_TYPE_NONE)
header = self.__reader.read(_HEADER_LENGTH)
if len(header) != _HEADER_LENGTH:
raise EOFError('Read %s bytes instead of %s' %
(len(header), _HEADER_LENGTH))
(masked_crc, length, record_type) = struct.unpack(_HEADER_FORMAT, header)
crc = _unmask_crc(masked_crc)
if length + _HEADER_LENGTH > block_remaining:
# A record can't be bigger than one block.
raise errors.InvalidRecordError('Length is too big')
data = self.__reader.read(length)
if len(data) != length:
raise EOFError('Not enough data read. Expected: %s but got %s' %
(length, len(data)))
if record_type == _RECORD_TYPE_NONE:
return ('', record_type)
actual_crc = crc32c.crc_update(crc32c.CRC_INIT, [record_type])
actual_crc = crc32c.crc_update(actual_crc, data)
actual_crc = crc32c.crc_finalize(actual_crc)
if actual_crc != crc:
raise errors.InvalidRecordError('Data crc does not match')
return (data, record_type) | Try reading a record.
Returns:
(data, record_type) tuple.
Raises:
EOFError: when end of file was reached.
InvalidRecordError: when valid record could not be read. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/records.py#L239-L278 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsReader.__sync | def __sync(self):
"""Skip reader to the block boundary."""
pad_length = _BLOCK_SIZE - self.__reader.tell() % _BLOCK_SIZE
if pad_length and pad_length != _BLOCK_SIZE:
data = self.__reader.read(pad_length)
if len(data) != pad_length:
raise EOFError('Read %d bytes instead of %d' %
(len(data), pad_length)) | python | def __sync(self):
"""Skip reader to the block boundary."""
pad_length = _BLOCK_SIZE - self.__reader.tell() % _BLOCK_SIZE
if pad_length and pad_length != _BLOCK_SIZE:
data = self.__reader.read(pad_length)
if len(data) != pad_length:
raise EOFError('Read %d bytes instead of %d' %
(len(data), pad_length)) | Skip reader to the block boundary. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/records.py#L280-L287 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsReader.read | def read(self):
"""Reads record from current position in reader.
Returns:
original bytes stored in a single record.
"""
data = None
while True:
last_offset = self.tell()
try:
(chunk, record_type) = self.__try_read_record()
if record_type == _RECORD_TYPE_NONE:
self.__sync()
elif record_type == _RECORD_TYPE_FULL:
if data is not None:
logging.warning(
"Ordering corruption: Got FULL record while already "
"in a chunk at offset %d", last_offset)
return chunk
elif record_type == _RECORD_TYPE_FIRST:
if data is not None:
logging.warning(
"Ordering corruption: Got FIRST record while already "
"in a chunk at offset %d", last_offset)
data = chunk
elif record_type == _RECORD_TYPE_MIDDLE:
if data is None:
logging.warning(
"Ordering corruption: Got MIDDLE record before FIRST "
"record at offset %d", last_offset)
else:
data += chunk
elif record_type == _RECORD_TYPE_LAST:
if data is None:
logging.warning(
"Ordering corruption: Got LAST record but no chunk is in "
"progress at offset %d", last_offset)
else:
result = data + chunk
data = None
return result
else:
raise errors.InvalidRecordError(
"Unsupported record type: %s" % record_type)
except errors.InvalidRecordError, e:
logging.warning("Invalid record encountered at %s (%s). Syncing to "
"the next block", last_offset, e)
data = None
self.__sync() | python | def read(self):
"""Reads record from current position in reader.
Returns:
original bytes stored in a single record.
"""
data = None
while True:
last_offset = self.tell()
try:
(chunk, record_type) = self.__try_read_record()
if record_type == _RECORD_TYPE_NONE:
self.__sync()
elif record_type == _RECORD_TYPE_FULL:
if data is not None:
logging.warning(
"Ordering corruption: Got FULL record while already "
"in a chunk at offset %d", last_offset)
return chunk
elif record_type == _RECORD_TYPE_FIRST:
if data is not None:
logging.warning(
"Ordering corruption: Got FIRST record while already "
"in a chunk at offset %d", last_offset)
data = chunk
elif record_type == _RECORD_TYPE_MIDDLE:
if data is None:
logging.warning(
"Ordering corruption: Got MIDDLE record before FIRST "
"record at offset %d", last_offset)
else:
data += chunk
elif record_type == _RECORD_TYPE_LAST:
if data is None:
logging.warning(
"Ordering corruption: Got LAST record but no chunk is in "
"progress at offset %d", last_offset)
else:
result = data + chunk
data = None
return result
else:
raise errors.InvalidRecordError(
"Unsupported record type: %s" % record_type)
except errors.InvalidRecordError, e:
logging.warning("Invalid record encountered at %s (%s). Syncing to "
"the next block", last_offset, e)
data = None
self.__sync() | Reads record from current position in reader.
Returns:
original bytes stored in a single record. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/records.py#L289-L338 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/mapper_pipeline.py | MapperPipeline.run | def run(self,
job_name,
handler_spec,
input_reader_spec,
output_writer_spec=None,
params=None,
shards=None,
base_path=None):
"""Start a mapreduce job.
Args:
job_name: mapreduce name. Only for display purpose.
handler_spec: fully qualified name to your map function/class.
input_reader_spec: fully qualified name to input reader class.
output_writer_spec: fully qualified name to output writer class.
params: a dictionary of parameters for input reader and output writer
initialization.
shards: number of shards. This provides a guide to mapreduce. The real
number of shards is determined by how input are splited.
"""
if shards is None:
shards = parameters.config.SHARD_COUNT
if base_path is None:
base_path = parameters.config.BASE_PATH
mapreduce_id = control.start_map(
job_name,
handler_spec,
input_reader_spec,
params or {},
mapreduce_parameters={
"done_callback": self.get_callback_url(),
"done_callback_method": "GET",
"pipeline_id": self.pipeline_id,
"base_path": base_path,
},
shard_count=shards,
output_writer_spec=output_writer_spec,
queue_name=self.queue_name,
)
self.fill(self.outputs.job_id, mapreduce_id)
self.set_status(console_url="%s/detail?mapreduce_id=%s" % (
(base_path, mapreduce_id))) | python | def run(self,
job_name,
handler_spec,
input_reader_spec,
output_writer_spec=None,
params=None,
shards=None,
base_path=None):
"""Start a mapreduce job.
Args:
job_name: mapreduce name. Only for display purpose.
handler_spec: fully qualified name to your map function/class.
input_reader_spec: fully qualified name to input reader class.
output_writer_spec: fully qualified name to output writer class.
params: a dictionary of parameters for input reader and output writer
initialization.
shards: number of shards. This provides a guide to mapreduce. The real
number of shards is determined by how input are splited.
"""
if shards is None:
shards = parameters.config.SHARD_COUNT
if base_path is None:
base_path = parameters.config.BASE_PATH
mapreduce_id = control.start_map(
job_name,
handler_spec,
input_reader_spec,
params or {},
mapreduce_parameters={
"done_callback": self.get_callback_url(),
"done_callback_method": "GET",
"pipeline_id": self.pipeline_id,
"base_path": base_path,
},
shard_count=shards,
output_writer_spec=output_writer_spec,
queue_name=self.queue_name,
)
self.fill(self.outputs.job_id, mapreduce_id)
self.set_status(console_url="%s/detail?mapreduce_id=%s" % (
(base_path, mapreduce_id))) | Start a mapreduce job.
Args:
job_name: mapreduce name. Only for display purpose.
handler_spec: fully qualified name to your map function/class.
input_reader_spec: fully qualified name to input reader class.
output_writer_spec: fully qualified name to output writer class.
params: a dictionary of parameters for input reader and output writer
initialization.
shards: number of shards. This provides a guide to mapreduce. The real
number of shards is determined by how input are splited. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/mapper_pipeline.py#L65-L106 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/mapper_pipeline.py | MapperPipeline.callback | def callback(self):
"""Callback after this async pipeline finishes."""
if self.was_aborted:
return
mapreduce_id = self.outputs.job_id.value
mapreduce_state = model.MapreduceState.get_by_job_id(mapreduce_id)
if mapreduce_state.result_status != model.MapreduceState.RESULT_SUCCESS:
self.retry("Job %s had status %s" % (
mapreduce_id, mapreduce_state.result_status))
return
mapper_spec = mapreduce_state.mapreduce_spec.mapper
outputs = []
output_writer_class = mapper_spec.output_writer_class()
if (output_writer_class and
mapreduce_state.result_status == model.MapreduceState.RESULT_SUCCESS):
outputs = output_writer_class.get_filenames(mapreduce_state)
self.fill(self.outputs.result_status, mapreduce_state.result_status)
self.fill(self.outputs.counters, mapreduce_state.counters_map.to_dict())
self.complete(outputs) | python | def callback(self):
"""Callback after this async pipeline finishes."""
if self.was_aborted:
return
mapreduce_id = self.outputs.job_id.value
mapreduce_state = model.MapreduceState.get_by_job_id(mapreduce_id)
if mapreduce_state.result_status != model.MapreduceState.RESULT_SUCCESS:
self.retry("Job %s had status %s" % (
mapreduce_id, mapreduce_state.result_status))
return
mapper_spec = mapreduce_state.mapreduce_spec.mapper
outputs = []
output_writer_class = mapper_spec.output_writer_class()
if (output_writer_class and
mapreduce_state.result_status == model.MapreduceState.RESULT_SUCCESS):
outputs = output_writer_class.get_filenames(mapreduce_state)
self.fill(self.outputs.result_status, mapreduce_state.result_status)
self.fill(self.outputs.counters, mapreduce_state.counters_map.to_dict())
self.complete(outputs) | Callback after this async pipeline finishes. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/mapper_pipeline.py#L112-L133 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/control.py | start_map | def start_map(name,
handler_spec,
reader_spec,
mapper_parameters,
shard_count=None,
output_writer_spec=None,
mapreduce_parameters=None,
base_path=None,
queue_name=None,
eta=None,
countdown=None,
hooks_class_name=None,
_app=None,
in_xg_transaction=False):
"""Start a new, mapper-only mapreduce.
Deprecated! Use map_job.start instead.
If a value can be specified both from an explicit argument and from
a dictionary, the value from the explicit argument wins.
Args:
name: mapreduce name. Used only for display purposes.
handler_spec: fully qualified name of mapper handler function/class to call.
reader_spec: fully qualified name of mapper reader to use
mapper_parameters: dictionary of parameters to pass to mapper. These are
mapper-specific and also used for reader/writer initialization.
Should have format {"input_reader": {}, "output_writer":{}}. Old
deprecated style does not have sub dictionaries.
shard_count: number of shards to create.
mapreduce_parameters: dictionary of mapreduce parameters relevant to the
whole job.
base_path: base path of mapreduce library handler specified in app.yaml.
"/mapreduce" by default.
queue_name: taskqueue queue name to be used for mapreduce tasks.
see util.get_queue_name.
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.
hooks_class_name: fully qualified name of a hooks.Hooks subclass.
in_xg_transaction: controls what transaction scope to use to start this MR
job. If True, there has to be an already opened cross-group transaction
scope. MR will use one entity group from it.
If False, MR will create an independent transaction to start the job
regardless of any existing transaction scopes.
Returns:
mapreduce id as string.
"""
if shard_count is None:
shard_count = parameters.config.SHARD_COUNT
if mapper_parameters:
mapper_parameters = dict(mapper_parameters)
# Make sure this old API fill all parameters with default values.
mr_params = map_job.JobConfig._get_default_mr_params()
if mapreduce_parameters:
mr_params.update(mapreduce_parameters)
# Override default values if user specified them as arguments.
if base_path:
mr_params["base_path"] = base_path
mr_params["queue_name"] = util.get_queue_name(queue_name)
mapper_spec = model.MapperSpec(handler_spec,
reader_spec,
mapper_parameters,
shard_count,
output_writer_spec=output_writer_spec)
if in_xg_transaction and not db.is_in_transaction():
logging.warning("Expects an opened xg transaction to start mapreduce "
"when transactional is True.")
return handlers.StartJobHandler._start_map(
name,
mapper_spec,
mr_params,
# TODO(user): Now that "queue_name" is part of mr_params.
# Remove all the other ways to get queue_name after one release.
queue_name=mr_params["queue_name"],
eta=eta,
countdown=countdown,
hooks_class_name=hooks_class_name,
_app=_app,
in_xg_transaction=in_xg_transaction) | python | def start_map(name,
handler_spec,
reader_spec,
mapper_parameters,
shard_count=None,
output_writer_spec=None,
mapreduce_parameters=None,
base_path=None,
queue_name=None,
eta=None,
countdown=None,
hooks_class_name=None,
_app=None,
in_xg_transaction=False):
"""Start a new, mapper-only mapreduce.
Deprecated! Use map_job.start instead.
If a value can be specified both from an explicit argument and from
a dictionary, the value from the explicit argument wins.
Args:
name: mapreduce name. Used only for display purposes.
handler_spec: fully qualified name of mapper handler function/class to call.
reader_spec: fully qualified name of mapper reader to use
mapper_parameters: dictionary of parameters to pass to mapper. These are
mapper-specific and also used for reader/writer initialization.
Should have format {"input_reader": {}, "output_writer":{}}. Old
deprecated style does not have sub dictionaries.
shard_count: number of shards to create.
mapreduce_parameters: dictionary of mapreduce parameters relevant to the
whole job.
base_path: base path of mapreduce library handler specified in app.yaml.
"/mapreduce" by default.
queue_name: taskqueue queue name to be used for mapreduce tasks.
see util.get_queue_name.
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.
hooks_class_name: fully qualified name of a hooks.Hooks subclass.
in_xg_transaction: controls what transaction scope to use to start this MR
job. If True, there has to be an already opened cross-group transaction
scope. MR will use one entity group from it.
If False, MR will create an independent transaction to start the job
regardless of any existing transaction scopes.
Returns:
mapreduce id as string.
"""
if shard_count is None:
shard_count = parameters.config.SHARD_COUNT
if mapper_parameters:
mapper_parameters = dict(mapper_parameters)
# Make sure this old API fill all parameters with default values.
mr_params = map_job.JobConfig._get_default_mr_params()
if mapreduce_parameters:
mr_params.update(mapreduce_parameters)
# Override default values if user specified them as arguments.
if base_path:
mr_params["base_path"] = base_path
mr_params["queue_name"] = util.get_queue_name(queue_name)
mapper_spec = model.MapperSpec(handler_spec,
reader_spec,
mapper_parameters,
shard_count,
output_writer_spec=output_writer_spec)
if in_xg_transaction and not db.is_in_transaction():
logging.warning("Expects an opened xg transaction to start mapreduce "
"when transactional is True.")
return handlers.StartJobHandler._start_map(
name,
mapper_spec,
mr_params,
# TODO(user): Now that "queue_name" is part of mr_params.
# Remove all the other ways to get queue_name after one release.
queue_name=mr_params["queue_name"],
eta=eta,
countdown=countdown,
hooks_class_name=hooks_class_name,
_app=_app,
in_xg_transaction=in_xg_transaction) | Start a new, mapper-only mapreduce.
Deprecated! Use map_job.start instead.
If a value can be specified both from an explicit argument and from
a dictionary, the value from the explicit argument wins.
Args:
name: mapreduce name. Used only for display purposes.
handler_spec: fully qualified name of mapper handler function/class to call.
reader_spec: fully qualified name of mapper reader to use
mapper_parameters: dictionary of parameters to pass to mapper. These are
mapper-specific and also used for reader/writer initialization.
Should have format {"input_reader": {}, "output_writer":{}}. Old
deprecated style does not have sub dictionaries.
shard_count: number of shards to create.
mapreduce_parameters: dictionary of mapreduce parameters relevant to the
whole job.
base_path: base path of mapreduce library handler specified in app.yaml.
"/mapreduce" by default.
queue_name: taskqueue queue name to be used for mapreduce tasks.
see util.get_queue_name.
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.
hooks_class_name: fully qualified name of a hooks.Hooks subclass.
in_xg_transaction: controls what transaction scope to use to start this MR
job. If True, there has to be an already opened cross-group transaction
scope. MR will use one entity group from it.
If False, MR will create an independent transaction to start the job
regardless of any existing transaction scopes.
Returns:
mapreduce id as string. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/control.py#L37-L125 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.get_status | def get_status(self):
"""Get status enum.
Returns:
One of the status enum.
"""
self.__update_state()
if self._state.active:
return self.RUNNING
else:
return self._state.result_status | python | def get_status(self):
"""Get status enum.
Returns:
One of the status enum.
"""
self.__update_state()
if self._state.active:
return self.RUNNING
else:
return self._state.result_status | Get status enum.
Returns:
One of the status enum. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_control.py#L70-L80 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.get_counter | def get_counter(self, counter_name, default=0):
"""Get the value of the named counter from this job.
When a job is running, counter values won't be very accurate.
Args:
counter_name: name of the counter in string.
default: default value if the counter doesn't exist.
Returns:
Value in int of the named counter.
"""
self.__update_state()
return self._state.counters_map.get(counter_name, default) | python | def get_counter(self, counter_name, default=0):
"""Get the value of the named counter from this job.
When a job is running, counter values won't be very accurate.
Args:
counter_name: name of the counter in string.
default: default value if the counter doesn't exist.
Returns:
Value in int of the named counter.
"""
self.__update_state()
return self._state.counters_map.get(counter_name, default) | Get the value of the named counter from this job.
When a job is running, counter values won't be very accurate.
Args:
counter_name: name of the counter in string.
default: default value if the counter doesn't exist.
Returns:
Value in int of the named counter. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_control.py#L98-L111 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.get_outputs | def get_outputs(self):
"""Get outputs of this job.
Should only call if status is SUCCESS.
Yields:
Iterators, one for each shard. Each iterator is
from the argument of map_job.output_writer.commit_output.
"""
assert self.SUCCESS == self.get_status()
ss = model.ShardState.find_all_by_mapreduce_state(self._state)
for s in ss:
yield iter(s.writer_state.get("outs", [])) | python | def get_outputs(self):
"""Get outputs of this job.
Should only call if status is SUCCESS.
Yields:
Iterators, one for each shard. Each iterator is
from the argument of map_job.output_writer.commit_output.
"""
assert self.SUCCESS == self.get_status()
ss = model.ShardState.find_all_by_mapreduce_state(self._state)
for s in ss:
yield iter(s.writer_state.get("outs", [])) | Get outputs of this job.
Should only call if status is SUCCESS.
Yields:
Iterators, one for each shard. Each iterator is
from the argument of map_job.output_writer.commit_output. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_control.py#L113-L125 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.submit | def submit(cls, job_config, in_xg_transaction=False):
"""Submit the job to run.
Args:
job_config: an instance of map_job.MapJobConfig.
in_xg_transaction: controls what transaction scope to use to start this MR
job. If True, there has to be an already opened cross-group transaction
scope. MR will use one entity group from it.
If False, MR will create an independent transaction to start the job
regardless of any existing transaction scopes.
Returns:
a Job instance representing the submitted job.
"""
cls.__validate_job_config(job_config)
mapper_spec = job_config._get_mapper_spec()
# Create mr spec.
mapreduce_params = job_config._get_mr_params()
mapreduce_spec = model.MapreduceSpec(
job_config.job_name,
job_config.job_id,
mapper_spec.to_json(),
mapreduce_params,
util._obj_to_path(job_config._hooks_cls))
# Save states and enqueue task.
if in_xg_transaction:
propagation = db.MANDATORY
else:
propagation = db.INDEPENDENT
state = None
@db.transactional(propagation=propagation)
def _txn():
state = cls.__create_and_save_state(job_config, mapreduce_spec)
cls.__add_kickoff_task(job_config, mapreduce_spec)
return state
state = _txn()
return cls(state) | python | def submit(cls, job_config, in_xg_transaction=False):
"""Submit the job to run.
Args:
job_config: an instance of map_job.MapJobConfig.
in_xg_transaction: controls what transaction scope to use to start this MR
job. If True, there has to be an already opened cross-group transaction
scope. MR will use one entity group from it.
If False, MR will create an independent transaction to start the job
regardless of any existing transaction scopes.
Returns:
a Job instance representing the submitted job.
"""
cls.__validate_job_config(job_config)
mapper_spec = job_config._get_mapper_spec()
# Create mr spec.
mapreduce_params = job_config._get_mr_params()
mapreduce_spec = model.MapreduceSpec(
job_config.job_name,
job_config.job_id,
mapper_spec.to_json(),
mapreduce_params,
util._obj_to_path(job_config._hooks_cls))
# Save states and enqueue task.
if in_xg_transaction:
propagation = db.MANDATORY
else:
propagation = db.INDEPENDENT
state = None
@db.transactional(propagation=propagation)
def _txn():
state = cls.__create_and_save_state(job_config, mapreduce_spec)
cls.__add_kickoff_task(job_config, mapreduce_spec)
return state
state = _txn()
return cls(state) | Submit the job to run.
Args:
job_config: an instance of map_job.MapJobConfig.
in_xg_transaction: controls what transaction scope to use to start this MR
job. If True, there has to be an already opened cross-group transaction
scope. MR will use one entity group from it.
If False, MR will create an independent transaction to start the job
regardless of any existing transaction scopes.
Returns:
a Job instance representing the submitted job. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_control.py#L128-L168 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.__update_state | def __update_state(self):
"""Fetches most up to date state from db."""
# Only if the job was not in a terminal state.
if self._state.active:
self._state = self.__get_state_by_id(self.job_config.job_id) | python | def __update_state(self):
"""Fetches most up to date state from db."""
# Only if the job was not in a terminal state.
if self._state.active:
self._state = self.__get_state_by_id(self.job_config.job_id) | Fetches most up to date state from db. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_control.py#L170-L174 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.__get_state_by_id | def __get_state_by_id(cls, job_id):
"""Get job state by id.
Args:
job_id: job id.
Returns:
model.MapreduceState for the job.
Raises:
ValueError: if the job state is missing.
"""
state = model.MapreduceState.get_by_job_id(job_id)
if state is None:
raise ValueError("Job state for job %s is missing." % job_id)
return state | python | def __get_state_by_id(cls, job_id):
"""Get job state by id.
Args:
job_id: job id.
Returns:
model.MapreduceState for the job.
Raises:
ValueError: if the job state is missing.
"""
state = model.MapreduceState.get_by_job_id(job_id)
if state is None:
raise ValueError("Job state for job %s is missing." % job_id)
return state | Get job state by id.
Args:
job_id: job id.
Returns:
model.MapreduceState for the job.
Raises:
ValueError: if the job state is missing. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_control.py#L177-L192 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.__create_and_save_state | def __create_and_save_state(cls, job_config, mapreduce_spec):
"""Save map job state to datastore.
Save state to datastore so that UI can see it immediately.
Args:
job_config: map_job.JobConfig.
mapreduce_spec: model.MapreduceSpec.
Returns:
model.MapreduceState for this job.
"""
state = model.MapreduceState.create_new(job_config.job_id)
state.mapreduce_spec = mapreduce_spec
state.active = True
state.active_shards = 0
state.app_id = job_config._app
config = datastore_rpc.Configuration(force_writes=job_config._force_writes)
state.put(config=config)
return state | python | def __create_and_save_state(cls, job_config, mapreduce_spec):
"""Save map job state to datastore.
Save state to datastore so that UI can see it immediately.
Args:
job_config: map_job.JobConfig.
mapreduce_spec: model.MapreduceSpec.
Returns:
model.MapreduceState for this job.
"""
state = model.MapreduceState.create_new(job_config.job_id)
state.mapreduce_spec = mapreduce_spec
state.active = True
state.active_shards = 0
state.app_id = job_config._app
config = datastore_rpc.Configuration(force_writes=job_config._force_writes)
state.put(config=config)
return state | Save map job state to datastore.
Save state to datastore so that UI can see it immediately.
Args:
job_config: map_job.JobConfig.
mapreduce_spec: model.MapreduceSpec.
Returns:
model.MapreduceState for this job. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_control.py#L202-L221 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.__add_kickoff_task | def __add_kickoff_task(cls, job_config, mapreduce_spec):
"""Add kickoff task to taskqueue.
Args:
job_config: map_job.JobConfig.
mapreduce_spec: model.MapreduceSpec,
"""
params = {"mapreduce_id": job_config.job_id}
# Task is not named so that it can be added within a transaction.
kickoff_task = taskqueue.Task(
# TODO(user): Perhaps make this url a computed field of job_config.
url=job_config._base_path + "/kickoffjob_callback/" + job_config.job_id,
headers=util._get_task_headers(job_config.job_id),
params=params)
if job_config._hooks_cls:
hooks = job_config._hooks_cls(mapreduce_spec)
try:
hooks.enqueue_kickoff_task(kickoff_task, job_config.queue_name)
return
except NotImplementedError:
pass
kickoff_task.add(job_config.queue_name, transactional=True) | python | def __add_kickoff_task(cls, job_config, mapreduce_spec):
"""Add kickoff task to taskqueue.
Args:
job_config: map_job.JobConfig.
mapreduce_spec: model.MapreduceSpec,
"""
params = {"mapreduce_id": job_config.job_id}
# Task is not named so that it can be added within a transaction.
kickoff_task = taskqueue.Task(
# TODO(user): Perhaps make this url a computed field of job_config.
url=job_config._base_path + "/kickoffjob_callback/" + job_config.job_id,
headers=util._get_task_headers(job_config.job_id),
params=params)
if job_config._hooks_cls:
hooks = job_config._hooks_cls(mapreduce_spec)
try:
hooks.enqueue_kickoff_task(kickoff_task, job_config.queue_name)
return
except NotImplementedError:
pass
kickoff_task.add(job_config.queue_name, transactional=True) | Add kickoff task to taskqueue.
Args:
job_config: map_job.JobConfig.
mapreduce_spec: model.MapreduceSpec, | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/map_job_control.py#L224-L245 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/json_util.py | JsonMixin.to_json_str | def to_json_str(self):
"""Convert data to json string representation.
Returns:
json representation as string.
"""
_json = self.to_json()
try:
return json.dumps(_json, sort_keys=True, cls=JsonEncoder)
except:
logging.exception("Could not serialize JSON: %r", _json)
raise | python | def to_json_str(self):
"""Convert data to json string representation.
Returns:
json representation as string.
"""
_json = self.to_json()
try:
return json.dumps(_json, sort_keys=True, cls=JsonEncoder)
except:
logging.exception("Could not serialize JSON: %r", _json)
raise | Convert data to json string representation.
Returns:
json representation as string. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/json_util.py#L135-L146 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/json_util.py | JsonMixin.from_json_str | def from_json_str(cls, json_str):
"""Convert json string representation into class instance.
Args:
json_str: json representation as string.
Returns:
New instance of the class with data loaded from json string.
"""
return cls.from_json(json.loads(json_str, cls=JsonDecoder)) | python | def from_json_str(cls, json_str):
"""Convert json string representation into class instance.
Args:
json_str: json representation as string.
Returns:
New instance of the class with data loaded from json string.
"""
return cls.from_json(json.loads(json_str, cls=JsonDecoder)) | Convert json string representation into class instance.
Args:
json_str: json representation as string.
Returns:
New instance of the class with data loaded from json string. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/json_util.py#L149-L158 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/json_util.py | JsonProperty.get_value_for_datastore | def get_value_for_datastore(self, model_instance):
"""Gets value for datastore.
Args:
model_instance: instance of the model class.
Returns:
datastore-compatible value.
"""
value = super(JsonProperty, self).get_value_for_datastore(model_instance)
if not value:
return None
json_value = value
if not isinstance(value, dict):
json_value = value.to_json()
if not json_value:
return None
return datastore_types.Text(json.dumps(
json_value, sort_keys=True, cls=JsonEncoder)) | python | def get_value_for_datastore(self, model_instance):
"""Gets value for datastore.
Args:
model_instance: instance of the model class.
Returns:
datastore-compatible value.
"""
value = super(JsonProperty, self).get_value_for_datastore(model_instance)
if not value:
return None
json_value = value
if not isinstance(value, dict):
json_value = value.to_json()
if not json_value:
return None
return datastore_types.Text(json.dumps(
json_value, sort_keys=True, cls=JsonEncoder)) | Gets value for datastore.
Args:
model_instance: instance of the model class.
Returns:
datastore-compatible value. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/json_util.py#L183-L201 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/json_util.py | JsonProperty.make_value_from_datastore | def make_value_from_datastore(self, value):
"""Convert value from datastore representation.
Args:
value: datastore value.
Returns:
value to store in the model.
"""
if value is None:
return None
_json = json.loads(value, cls=JsonDecoder)
if self.data_type == dict:
return _json
return self.data_type.from_json(_json) | python | def make_value_from_datastore(self, value):
"""Convert value from datastore representation.
Args:
value: datastore value.
Returns:
value to store in the model.
"""
if value is None:
return None
_json = json.loads(value, cls=JsonDecoder)
if self.data_type == dict:
return _json
return self.data_type.from_json(_json) | Convert value from datastore representation.
Args:
value: datastore value.
Returns:
value to store in the model. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/json_util.py#L203-L218 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/json_util.py | JsonProperty.validate | def validate(self, value):
"""Validate value.
Args:
value: model value.
Returns:
Whether the specified value is valid data type value.
Raises:
BadValueError: when value is not of self.data_type type.
"""
if value is not None and not isinstance(value, self.data_type):
raise datastore_errors.BadValueError(
"Property %s must be convertible to a %s instance (%s)" %
(self.name, self.data_type, value))
return super(JsonProperty, self).validate(value) | python | def validate(self, value):
"""Validate value.
Args:
value: model value.
Returns:
Whether the specified value is valid data type value.
Raises:
BadValueError: when value is not of self.data_type type.
"""
if value is not None and not isinstance(value, self.data_type):
raise datastore_errors.BadValueError(
"Property %s must be convertible to a %s instance (%s)" %
(self.name, self.data_type, value))
return super(JsonProperty, self).validate(value) | Validate value.
Args:
value: model value.
Returns:
Whether the specified value is valid data type value.
Raises:
BadValueError: when value is not of self.data_type type. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/json_util.py#L220-L236 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | HugeTask.add | def add(self, queue_name, transactional=False):
"""Add task to the queue."""
task = self.to_task()
task.add(queue_name, transactional) | python | def add(self, queue_name, transactional=False):
"""Add task to the queue."""
task = self.to_task()
task.add(queue_name, transactional) | Add task to the queue. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L161-L164 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | HugeTask.to_task | def to_task(self):
"""Convert to a taskqueue task."""
# Never pass params to taskqueue.Task. Use payload instead. Otherwise,
# it's up to a particular taskqueue implementation to generate
# payload from params. It could blow up payload size over limit.
return taskqueue.Task(
url=self.url,
payload=self._payload,
name=self.name,
eta=self.eta,
countdown=self.countdown,
headers=self._headers) | python | def to_task(self):
"""Convert to a taskqueue task."""
# Never pass params to taskqueue.Task. Use payload instead. Otherwise,
# it's up to a particular taskqueue implementation to generate
# payload from params. It could blow up payload size over limit.
return taskqueue.Task(
url=self.url,
payload=self._payload,
name=self.name,
eta=self.eta,
countdown=self.countdown,
headers=self._headers) | Convert to a taskqueue task. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L166-L177 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | HugeTask.decode_payload | def decode_payload(cls, request):
"""Decode task payload.
HugeTask controls its own payload entirely including urlencoding.
It doesn't depend on any particular web framework.
Args:
request: a webapp Request instance.
Returns:
A dict of str to str. The same as the params argument to __init__.
Raises:
DeprecationWarning: When task payload constructed from an older
incompatible version of mapreduce.
"""
# TODO(user): Pass mr_id into headers. Otherwise when payload decoding
# failed, we can't abort a mr.
if request.headers.get(cls.PAYLOAD_VERSION_HEADER) != cls.PAYLOAD_VERSION:
raise DeprecationWarning(
"Task is generated by an older incompatible version of mapreduce. "
"Please kill this job manually")
return cls._decode_payload(request.body) | python | def decode_payload(cls, request):
"""Decode task payload.
HugeTask controls its own payload entirely including urlencoding.
It doesn't depend on any particular web framework.
Args:
request: a webapp Request instance.
Returns:
A dict of str to str. The same as the params argument to __init__.
Raises:
DeprecationWarning: When task payload constructed from an older
incompatible version of mapreduce.
"""
# TODO(user): Pass mr_id into headers. Otherwise when payload decoding
# failed, we can't abort a mr.
if request.headers.get(cls.PAYLOAD_VERSION_HEADER) != cls.PAYLOAD_VERSION:
raise DeprecationWarning(
"Task is generated by an older incompatible version of mapreduce. "
"Please kill this job manually")
return cls._decode_payload(request.body) | Decode task payload.
HugeTask controls its own payload entirely including urlencoding.
It doesn't depend on any particular web framework.
Args:
request: a webapp Request instance.
Returns:
A dict of str to str. The same as the params argument to __init__.
Raises:
DeprecationWarning: When task payload constructed from an older
incompatible version of mapreduce. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L180-L202 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | CountersMap.increment | def increment(self, counter_name, delta):
"""Increment counter value.
Args:
counter_name: counter name as String.
delta: increment delta as Integer.
Returns:
new counter value.
"""
current_value = self.counters.get(counter_name, 0)
new_value = current_value + delta
self.counters[counter_name] = new_value
return new_value | python | def increment(self, counter_name, delta):
"""Increment counter value.
Args:
counter_name: counter name as String.
delta: increment delta as Integer.
Returns:
new counter value.
"""
current_value = self.counters.get(counter_name, 0)
new_value = current_value + delta
self.counters[counter_name] = new_value
return new_value | Increment counter value.
Args:
counter_name: counter name as String.
delta: increment delta as Integer.
Returns:
new counter value. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L263-L276 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | CountersMap.add_map | def add_map(self, counters_map):
"""Add all counters from the map.
For each counter in the passed map, adds its value to the counter in this
map.
Args:
counters_map: CounterMap instance to add.
"""
for counter_name in counters_map.counters:
self.increment(counter_name, counters_map.counters[counter_name]) | python | def add_map(self, counters_map):
"""Add all counters from the map.
For each counter in the passed map, adds its value to the counter in this
map.
Args:
counters_map: CounterMap instance to add.
"""
for counter_name in counters_map.counters:
self.increment(counter_name, counters_map.counters[counter_name]) | Add all counters from the map.
For each counter in the passed map, adds its value to the counter in this
map.
Args:
counters_map: CounterMap instance to add. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L278-L288 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | CountersMap.sub_map | def sub_map(self, counters_map):
"""Subtracts all counters from the map.
For each counter in the passed map, subtracts its value to the counter in
this map.
Args:
counters_map: CounterMap instance to subtract.
"""
for counter_name in counters_map.counters:
self.increment(counter_name, -counters_map.counters[counter_name]) | python | def sub_map(self, counters_map):
"""Subtracts all counters from the map.
For each counter in the passed map, subtracts its value to the counter in
this map.
Args:
counters_map: CounterMap instance to subtract.
"""
for counter_name in counters_map.counters:
self.increment(counter_name, -counters_map.counters[counter_name]) | Subtracts all counters from the map.
For each counter in the passed map, subtracts its value to the counter in
this map.
Args:
counters_map: CounterMap instance to subtract. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L290-L300 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | MapperSpec.to_json | def to_json(self):
"""Serializes this MapperSpec into a json-izable object."""
result = {
"mapper_handler_spec": self.handler_spec,
"mapper_input_reader": self.input_reader_spec,
"mapper_params": self.params,
"mapper_shard_count": self.shard_count
}
if self.output_writer_spec:
result["mapper_output_writer"] = self.output_writer_spec
return result | python | def to_json(self):
"""Serializes this MapperSpec into a json-izable object."""
result = {
"mapper_handler_spec": self.handler_spec,
"mapper_input_reader": self.input_reader_spec,
"mapper_params": self.params,
"mapper_shard_count": self.shard_count
}
if self.output_writer_spec:
result["mapper_output_writer"] = self.output_writer_spec
return result | Serializes this MapperSpec into a json-izable object. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L412-L422 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | MapreduceSpec.get_hooks | def get_hooks(self):
"""Returns a hooks.Hooks class or None if no hooks class has been set."""
if self.__hooks is None and self.hooks_class_name is not None:
hooks_class = util.for_name(self.hooks_class_name)
if not isinstance(hooks_class, type):
raise ValueError("hooks_class_name must refer to a class, got %s" %
type(hooks_class).__name__)
if not issubclass(hooks_class, hooks.Hooks):
raise ValueError(
"hooks_class_name must refer to a hooks.Hooks subclass")
self.__hooks = hooks_class(self)
return self.__hooks | python | def get_hooks(self):
"""Returns a hooks.Hooks class or None if no hooks class has been set."""
if self.__hooks is None and self.hooks_class_name is not None:
hooks_class = util.for_name(self.hooks_class_name)
if not isinstance(hooks_class, type):
raise ValueError("hooks_class_name must refer to a class, got %s" %
type(hooks_class).__name__)
if not issubclass(hooks_class, hooks.Hooks):
raise ValueError(
"hooks_class_name must refer to a hooks.Hooks subclass")
self.__hooks = hooks_class(self)
return self.__hooks | Returns a hooks.Hooks class or None if no hooks class has been set. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L488-L500 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | MapreduceSpec.to_json | def to_json(self):
"""Serializes all data in this mapreduce spec into json form.
Returns:
data in json format.
"""
mapper_spec = self.mapper.to_json()
return {
"name": self.name,
"mapreduce_id": self.mapreduce_id,
"mapper_spec": mapper_spec,
"params": self.params,
"hooks_class_name": self.hooks_class_name,
} | python | def to_json(self):
"""Serializes all data in this mapreduce spec into json form.
Returns:
data in json format.
"""
mapper_spec = self.mapper.to_json()
return {
"name": self.name,
"mapreduce_id": self.mapreduce_id,
"mapper_spec": mapper_spec,
"params": self.params,
"hooks_class_name": self.hooks_class_name,
} | Serializes all data in this mapreduce spec into json form.
Returns:
data in json format. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L502-L515 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | MapreduceSpec.from_json | def from_json(cls, json):
"""Create new MapreduceSpec from the json, encoded by to_json.
Args:
json: json representation of MapreduceSpec.
Returns:
an instance of MapreduceSpec with all data deserialized from json.
"""
mapreduce_spec = cls(json["name"],
json["mapreduce_id"],
json["mapper_spec"],
json.get("params"),
json.get("hooks_class_name"))
return mapreduce_spec | python | def from_json(cls, json):
"""Create new MapreduceSpec from the json, encoded by to_json.
Args:
json: json representation of MapreduceSpec.
Returns:
an instance of MapreduceSpec with all data deserialized from json.
"""
mapreduce_spec = cls(json["name"],
json["mapreduce_id"],
json["mapper_spec"],
json.get("params"),
json.get("hooks_class_name"))
return mapreduce_spec | Create new MapreduceSpec from the json, encoded by to_json.
Args:
json: json representation of MapreduceSpec.
Returns:
an instance of MapreduceSpec with all data deserialized from json. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L518-L532 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | MapreduceSpec._get_mapreduce_spec | def _get_mapreduce_spec(cls, mr_id):
"""Get Mapreduce spec from mr id."""
key = 'GAE-MR-spec: %s' % mr_id
spec_json = memcache.get(key)
if spec_json:
return cls.from_json(spec_json)
state = MapreduceState.get_by_job_id(mr_id)
spec = state.mapreduce_spec
spec_json = spec.to_json()
memcache.set(key, spec_json)
return spec | python | def _get_mapreduce_spec(cls, mr_id):
"""Get Mapreduce spec from mr id."""
key = 'GAE-MR-spec: %s' % mr_id
spec_json = memcache.get(key)
if spec_json:
return cls.from_json(spec_json)
state = MapreduceState.get_by_job_id(mr_id)
spec = state.mapreduce_spec
spec_json = spec.to_json()
memcache.set(key, spec_json)
return spec | Get Mapreduce spec from mr id. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L543-L553 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | MapreduceState.get_key_by_job_id | def get_key_by_job_id(cls, mapreduce_id):
"""Retrieves the Key for a Job.
Args:
mapreduce_id: The job to retrieve.
Returns:
Datastore Key that can be used to fetch the MapreduceState.
"""
return db.Key.from_path(cls.kind(), str(mapreduce_id)) | python | def get_key_by_job_id(cls, mapreduce_id):
"""Retrieves the Key for a Job.
Args:
mapreduce_id: The job to retrieve.
Returns:
Datastore Key that can be used to fetch the MapreduceState.
"""
return db.Key.from_path(cls.kind(), str(mapreduce_id)) | Retrieves the Key for a Job.
Args:
mapreduce_id: The job to retrieve.
Returns:
Datastore Key that can be used to fetch the MapreduceState. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L614-L623 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | MapreduceState.set_processed_counts | def set_processed_counts(self, shards_processed, shards_status):
"""Updates a chart url to display processed count for each shard.
Args:
shards_processed: list of integers with number of processed entities in
each shard
"""
chart = google_chart_api.BarChart()
def filter_status(status_to_filter):
return [count if status == status_to_filter else 0
for count, status in zip(shards_processed, shards_status)]
if shards_status:
# Each index will have only one non-zero count, so stack them to color-
# code the bars by status
# These status values are computed in _update_state_from_shard_states,
# in mapreduce/handlers.py.
chart.stacked = True
chart.AddBars(filter_status("unknown"), color="404040")
chart.AddBars(filter_status("success"), color="00ac42")
chart.AddBars(filter_status("running"), color="3636a9")
chart.AddBars(filter_status("aborted"), color="e29e24")
chart.AddBars(filter_status("failed"), color="f6350f")
else:
chart.AddBars(shards_processed)
shard_count = len(shards_processed)
if shard_count > 95:
# Auto-spacing does not work for large numbers of shards.
pixels_per_shard = 700.0 / shard_count
bar_thickness = int(pixels_per_shard * .9)
chart.style = bar_chart.BarChartStyle(bar_thickness=bar_thickness,
bar_gap=0.1, use_fractional_gap_spacing=True)
if shards_processed and shard_count <= 95:
# Adding labels puts us in danger of exceeding the URL length, only
# do it when we have a small amount of data to plot.
# Only 16 labels on the whole chart.
stride_length = max(1, shard_count / 16)
chart.bottom.labels = []
for x in xrange(shard_count):
if (x % stride_length == 0 or
x == shard_count - 1):
chart.bottom.labels.append(x)
else:
chart.bottom.labels.append("")
chart.left.labels = ["0", str(max(shards_processed))]
chart.left.min = 0
self.chart_width = min(700, max(300, shard_count * 20))
self.chart_url = chart.display.Url(self.chart_width, 200) | python | def set_processed_counts(self, shards_processed, shards_status):
"""Updates a chart url to display processed count for each shard.
Args:
shards_processed: list of integers with number of processed entities in
each shard
"""
chart = google_chart_api.BarChart()
def filter_status(status_to_filter):
return [count if status == status_to_filter else 0
for count, status in zip(shards_processed, shards_status)]
if shards_status:
# Each index will have only one non-zero count, so stack them to color-
# code the bars by status
# These status values are computed in _update_state_from_shard_states,
# in mapreduce/handlers.py.
chart.stacked = True
chart.AddBars(filter_status("unknown"), color="404040")
chart.AddBars(filter_status("success"), color="00ac42")
chart.AddBars(filter_status("running"), color="3636a9")
chart.AddBars(filter_status("aborted"), color="e29e24")
chart.AddBars(filter_status("failed"), color="f6350f")
else:
chart.AddBars(shards_processed)
shard_count = len(shards_processed)
if shard_count > 95:
# Auto-spacing does not work for large numbers of shards.
pixels_per_shard = 700.0 / shard_count
bar_thickness = int(pixels_per_shard * .9)
chart.style = bar_chart.BarChartStyle(bar_thickness=bar_thickness,
bar_gap=0.1, use_fractional_gap_spacing=True)
if shards_processed and shard_count <= 95:
# Adding labels puts us in danger of exceeding the URL length, only
# do it when we have a small amount of data to plot.
# Only 16 labels on the whole chart.
stride_length = max(1, shard_count / 16)
chart.bottom.labels = []
for x in xrange(shard_count):
if (x % stride_length == 0 or
x == shard_count - 1):
chart.bottom.labels.append(x)
else:
chart.bottom.labels.append("")
chart.left.labels = ["0", str(max(shards_processed))]
chart.left.min = 0
self.chart_width = min(700, max(300, shard_count * 20))
self.chart_url = chart.display.Url(self.chart_width, 200) | Updates a chart url to display processed count for each shard.
Args:
shards_processed: list of integers with number of processed entities in
each shard | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L637-L690 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | MapreduceState.create_new | def create_new(mapreduce_id=None,
gettime=datetime.datetime.now):
"""Create a new MapreduceState.
Args:
mapreduce_id: Mapreduce id as string.
gettime: Used for testing.
"""
if not mapreduce_id:
mapreduce_id = MapreduceState.new_mapreduce_id()
state = MapreduceState(key_name=mapreduce_id,
last_poll_time=gettime())
state.set_processed_counts([], [])
return state | python | def create_new(mapreduce_id=None,
gettime=datetime.datetime.now):
"""Create a new MapreduceState.
Args:
mapreduce_id: Mapreduce id as string.
gettime: Used for testing.
"""
if not mapreduce_id:
mapreduce_id = MapreduceState.new_mapreduce_id()
state = MapreduceState(key_name=mapreduce_id,
last_poll_time=gettime())
state.set_processed_counts([], [])
return state | Create a new MapreduceState.
Args:
mapreduce_id: Mapreduce id as string.
gettime: Used for testing. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L703-L716 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | TransientShardState.reset_for_retry | def reset_for_retry(self, output_writer):
"""Reset self for shard retry.
Args:
output_writer: new output writer that contains new output files.
"""
self.input_reader = self.initial_input_reader
self.slice_id = 0
self.retries += 1
self.output_writer = output_writer
self.handler = self.mapreduce_spec.mapper.handler | python | def reset_for_retry(self, output_writer):
"""Reset self for shard retry.
Args:
output_writer: new output writer that contains new output files.
"""
self.input_reader = self.initial_input_reader
self.slice_id = 0
self.retries += 1
self.output_writer = output_writer
self.handler = self.mapreduce_spec.mapper.handler | Reset self for shard retry.
Args:
output_writer: new output writer that contains new output files. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L776-L786 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | TransientShardState.advance_for_next_slice | def advance_for_next_slice(self, recovery_slice=False):
"""Advance relavent states for next slice.
Args:
recovery_slice: True if this slice is running recovery logic.
See handlers.MapperWorkerCallbackHandler._attempt_slice_recovery
for more info.
"""
if recovery_slice:
self.slice_id += 2
# Restore input reader to the beginning of the slice.
self.input_reader = self.input_reader.from_json(self._input_reader_json)
else:
self.slice_id += 1 | python | def advance_for_next_slice(self, recovery_slice=False):
"""Advance relavent states for next slice.
Args:
recovery_slice: True if this slice is running recovery logic.
See handlers.MapperWorkerCallbackHandler._attempt_slice_recovery
for more info.
"""
if recovery_slice:
self.slice_id += 2
# Restore input reader to the beginning of the slice.
self.input_reader = self.input_reader.from_json(self._input_reader_json)
else:
self.slice_id += 1 | Advance relavent states for next slice.
Args:
recovery_slice: True if this slice is running recovery logic.
See handlers.MapperWorkerCallbackHandler._attempt_slice_recovery
for more info. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L788-L801 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | TransientShardState.to_dict | def to_dict(self):
"""Convert state to dictionary to save in task payload."""
result = {"mapreduce_spec": self.mapreduce_spec.to_json_str(),
"shard_id": self.shard_id,
"slice_id": str(self.slice_id),
"input_reader_state": self.input_reader.to_json_str(),
"initial_input_reader_state":
self.initial_input_reader.to_json_str(),
"retries": str(self.retries)}
if self.output_writer:
result["output_writer_state"] = self.output_writer.to_json_str()
serialized_handler = util.try_serialize_handler(self.handler)
if serialized_handler:
result["serialized_handler"] = serialized_handler
return result | python | def to_dict(self):
"""Convert state to dictionary to save in task payload."""
result = {"mapreduce_spec": self.mapreduce_spec.to_json_str(),
"shard_id": self.shard_id,
"slice_id": str(self.slice_id),
"input_reader_state": self.input_reader.to_json_str(),
"initial_input_reader_state":
self.initial_input_reader.to_json_str(),
"retries": str(self.retries)}
if self.output_writer:
result["output_writer_state"] = self.output_writer.to_json_str()
serialized_handler = util.try_serialize_handler(self.handler)
if serialized_handler:
result["serialized_handler"] = serialized_handler
return result | Convert state to dictionary to save in task payload. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L803-L817 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | TransientShardState.from_request | def from_request(cls, request):
"""Create new TransientShardState from webapp request."""
mapreduce_spec = MapreduceSpec.from_json_str(request.get("mapreduce_spec"))
mapper_spec = mapreduce_spec.mapper
input_reader_spec_dict = json.loads(request.get("input_reader_state"),
cls=json_util.JsonDecoder)
input_reader = mapper_spec.input_reader_class().from_json(
input_reader_spec_dict)
initial_input_reader_spec_dict = json.loads(
request.get("initial_input_reader_state"), cls=json_util.JsonDecoder)
initial_input_reader = mapper_spec.input_reader_class().from_json(
initial_input_reader_spec_dict)
output_writer = None
if mapper_spec.output_writer_class():
output_writer = mapper_spec.output_writer_class().from_json(
json.loads(request.get("output_writer_state", "{}"),
cls=json_util.JsonDecoder))
assert isinstance(output_writer, mapper_spec.output_writer_class()), (
"%s.from_json returned an instance of wrong class: %s" % (
mapper_spec.output_writer_class(),
output_writer.__class__))
handler = util.try_deserialize_handler(request.get("serialized_handler"))
if not handler:
handler = mapreduce_spec.mapper.handler
return cls(mapreduce_spec.params["base_path"],
mapreduce_spec,
str(request.get("shard_id")),
int(request.get("slice_id")),
input_reader,
initial_input_reader,
output_writer=output_writer,
retries=int(request.get("retries")),
handler=handler) | python | def from_request(cls, request):
"""Create new TransientShardState from webapp request."""
mapreduce_spec = MapreduceSpec.from_json_str(request.get("mapreduce_spec"))
mapper_spec = mapreduce_spec.mapper
input_reader_spec_dict = json.loads(request.get("input_reader_state"),
cls=json_util.JsonDecoder)
input_reader = mapper_spec.input_reader_class().from_json(
input_reader_spec_dict)
initial_input_reader_spec_dict = json.loads(
request.get("initial_input_reader_state"), cls=json_util.JsonDecoder)
initial_input_reader = mapper_spec.input_reader_class().from_json(
initial_input_reader_spec_dict)
output_writer = None
if mapper_spec.output_writer_class():
output_writer = mapper_spec.output_writer_class().from_json(
json.loads(request.get("output_writer_state", "{}"),
cls=json_util.JsonDecoder))
assert isinstance(output_writer, mapper_spec.output_writer_class()), (
"%s.from_json returned an instance of wrong class: %s" % (
mapper_spec.output_writer_class(),
output_writer.__class__))
handler = util.try_deserialize_handler(request.get("serialized_handler"))
if not handler:
handler = mapreduce_spec.mapper.handler
return cls(mapreduce_spec.params["base_path"],
mapreduce_spec,
str(request.get("shard_id")),
int(request.get("slice_id")),
input_reader,
initial_input_reader,
output_writer=output_writer,
retries=int(request.get("retries")),
handler=handler) | Create new TransientShardState from webapp request. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L820-L855 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | ShardState.reset_for_retry | def reset_for_retry(self):
"""Reset self for shard retry."""
self.retries += 1
self.last_work_item = ""
self.active = True
self.result_status = None
self.input_finished = False
self.counters_map = CountersMap()
self.slice_id = 0
self.slice_start_time = None
self.slice_request_id = None
self.slice_retries = 0
self.acquired_once = False | python | def reset_for_retry(self):
"""Reset self for shard retry."""
self.retries += 1
self.last_work_item = ""
self.active = True
self.result_status = None
self.input_finished = False
self.counters_map = CountersMap()
self.slice_id = 0
self.slice_start_time = None
self.slice_request_id = None
self.slice_retries = 0
self.acquired_once = False | Reset self for shard retry. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L973-L985 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | ShardState.advance_for_next_slice | def advance_for_next_slice(self, recovery_slice=False):
"""Advance self for next slice.
Args:
recovery_slice: True if this slice is running recovery logic.
See handlers.MapperWorkerCallbackHandler._attempt_slice_recovery
for more info.
"""
self.slice_start_time = None
self.slice_request_id = None
self.slice_retries = 0
self.acquired_once = False
if recovery_slice:
self.slice_id += 2
else:
self.slice_id += 1 | python | def advance_for_next_slice(self, recovery_slice=False):
"""Advance self for next slice.
Args:
recovery_slice: True if this slice is running recovery logic.
See handlers.MapperWorkerCallbackHandler._attempt_slice_recovery
for more info.
"""
self.slice_start_time = None
self.slice_request_id = None
self.slice_retries = 0
self.acquired_once = False
if recovery_slice:
self.slice_id += 2
else:
self.slice_id += 1 | Advance self for next slice.
Args:
recovery_slice: True if this slice is running recovery logic.
See handlers.MapperWorkerCallbackHandler._attempt_slice_recovery
for more info. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L987-L1002 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | ShardState.copy_from | def copy_from(self, other_state):
"""Copy data from another shard state entity to self."""
for prop in self.properties().values():
setattr(self, prop.name, getattr(other_state, prop.name)) | python | def copy_from(self, other_state):
"""Copy data from another shard state entity to self."""
for prop in self.properties().values():
setattr(self, prop.name, getattr(other_state, prop.name)) | Copy data from another shard state entity to self. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L1026-L1029 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | ShardState.find_all_by_mapreduce_state | def find_all_by_mapreduce_state(cls, mapreduce_state):
"""Find all shard states for given mapreduce.
Args:
mapreduce_state: MapreduceState instance
Yields:
shard states sorted by shard id.
"""
keys = cls.calculate_keys_by_mapreduce_state(mapreduce_state)
i = 0
while i < len(keys):
@db.non_transactional
def no_tx_get(i):
return db.get(keys[i:i+cls._MAX_STATES_IN_MEMORY])
# We need a separate function to so that we can mix non-transactional and
# use be a generator
states = no_tx_get(i)
for s in states:
i += 1
if s is not None:
yield s | python | def find_all_by_mapreduce_state(cls, mapreduce_state):
"""Find all shard states for given mapreduce.
Args:
mapreduce_state: MapreduceState instance
Yields:
shard states sorted by shard id.
"""
keys = cls.calculate_keys_by_mapreduce_state(mapreduce_state)
i = 0
while i < len(keys):
@db.non_transactional
def no_tx_get(i):
return db.get(keys[i:i+cls._MAX_STATES_IN_MEMORY])
# We need a separate function to so that we can mix non-transactional and
# use be a generator
states = no_tx_get(i)
for s in states:
i += 1
if s is not None:
yield s | Find all shard states for given mapreduce.
Args:
mapreduce_state: MapreduceState instance
Yields:
shard states sorted by shard id. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L1106-L1127 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | ShardState.calculate_keys_by_mapreduce_state | def calculate_keys_by_mapreduce_state(cls, mapreduce_state):
"""Calculate all shard states keys for given mapreduce.
Args:
mapreduce_state: MapreduceState instance
Returns:
A list of keys for shard states, sorted by shard id.
The corresponding shard states may not exist.
"""
if mapreduce_state is None:
return []
keys = []
for i in range(mapreduce_state.mapreduce_spec.mapper.shard_count):
shard_id = cls.shard_id_from_number(mapreduce_state.key().name(), i)
keys.append(cls.get_key_by_shard_id(shard_id))
return keys | python | def calculate_keys_by_mapreduce_state(cls, mapreduce_state):
"""Calculate all shard states keys for given mapreduce.
Args:
mapreduce_state: MapreduceState instance
Returns:
A list of keys for shard states, sorted by shard id.
The corresponding shard states may not exist.
"""
if mapreduce_state is None:
return []
keys = []
for i in range(mapreduce_state.mapreduce_spec.mapper.shard_count):
shard_id = cls.shard_id_from_number(mapreduce_state.key().name(), i)
keys.append(cls.get_key_by_shard_id(shard_id))
return keys | Calculate all shard states keys for given mapreduce.
Args:
mapreduce_state: MapreduceState instance
Returns:
A list of keys for shard states, sorted by shard id.
The corresponding shard states may not exist. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L1130-L1147 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | ShardState.create_new | def create_new(cls, mapreduce_id, shard_number):
"""Create new shard state.
Args:
mapreduce_id: unique mapreduce id as string.
shard_number: shard number for which to create shard state.
Returns:
new instance of ShardState ready to put into datastore.
"""
shard_id = cls.shard_id_from_number(mapreduce_id, shard_number)
state = cls(key_name=shard_id,
mapreduce_id=mapreduce_id)
return state | python | def create_new(cls, mapreduce_id, shard_number):
"""Create new shard state.
Args:
mapreduce_id: unique mapreduce id as string.
shard_number: shard number for which to create shard state.
Returns:
new instance of ShardState ready to put into datastore.
"""
shard_id = cls.shard_id_from_number(mapreduce_id, shard_number)
state = cls(key_name=shard_id,
mapreduce_id=mapreduce_id)
return state | Create new shard state.
Args:
mapreduce_id: unique mapreduce id as string.
shard_number: shard number for which to create shard state.
Returns:
new instance of ShardState ready to put into datastore. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L1150-L1163 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | MapreduceControl.get_key_by_job_id | def get_key_by_job_id(cls, mapreduce_id):
"""Retrieves the Key for a mapreduce ID.
Args:
mapreduce_id: The job to fetch.
Returns:
Datastore Key for the command for the given job ID.
"""
return db.Key.from_path(cls.kind(), "%s:%s" % (mapreduce_id, cls._KEY_NAME)) | python | def get_key_by_job_id(cls, mapreduce_id):
"""Retrieves the Key for a mapreduce ID.
Args:
mapreduce_id: The job to fetch.
Returns:
Datastore Key for the command for the given job ID.
"""
return db.Key.from_path(cls.kind(), "%s:%s" % (mapreduce_id, cls._KEY_NAME)) | Retrieves the Key for a mapreduce ID.
Args:
mapreduce_id: The job to fetch.
Returns:
Datastore Key for the command for the given job ID. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L1188-L1197 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/model.py | MapreduceControl.abort | def abort(cls, mapreduce_id, **kwargs):
"""Causes a job to abort.
Args:
mapreduce_id: The job to abort. Not verified as a valid job.
"""
cls(key_name="%s:%s" % (mapreduce_id, cls._KEY_NAME),
command=cls.ABORT).put(**kwargs) | python | def abort(cls, mapreduce_id, **kwargs):
"""Causes a job to abort.
Args:
mapreduce_id: The job to abort. Not verified as a valid job.
"""
cls(key_name="%s:%s" % (mapreduce_id, cls._KEY_NAME),
command=cls.ABORT).put(**kwargs) | Causes a job to abort.
Args:
mapreduce_id: The job to abort. Not verified as a valid job. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/model.py#L1200-L1207 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/datastore_input_reader.py | DatastoreInputReader.validate | def validate(cls, job_config):
"""Inherit docs."""
super(DatastoreInputReader, cls).validate(job_config)
params = job_config.input_reader_params
entity_kind = params[cls.ENTITY_KIND_PARAM]
# Check for a "." in the entity kind.
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__)
# Validate the filters parameters.
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
for f in filters:
if f[1] != "=":
raise errors.BadReaderParamsError(
"Only equality filters are supported: %s", f) | python | def validate(cls, job_config):
"""Inherit docs."""
super(DatastoreInputReader, cls).validate(job_config)
params = job_config.input_reader_params
entity_kind = params[cls.ENTITY_KIND_PARAM]
# Check for a "." in the entity kind.
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__)
# Validate the filters parameters.
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
for f in filters:
if f[1] != "=":
raise errors.BadReaderParamsError(
"Only equality filters are supported: %s", f) | Inherit docs. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/datastore_input_reader.py#L32-L49 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/base_handler.py | TaskQueueHandler.initialize | def initialize(self, request, response):
"""Initialize.
1. call webapp init.
2. check request is indeed from taskqueue.
3. check the task has not been retried too many times.
4. run handler specific processing logic.
5. run error handling logic if precessing failed.
Args:
request: a webapp.Request instance.
response: a webapp.Response instance.
"""
super(TaskQueueHandler, self).initialize(request, response)
# Check request is from taskqueue.
if "X-AppEngine-QueueName" not in self.request.headers:
logging.error(self.request.headers)
logging.error("Task queue handler received non-task queue request")
self.response.set_status(
403, message="Task queue handler received non-task queue request")
return
# Check task has not been retried too many times.
if self.task_retry_count() + 1 > parameters.config.TASK_MAX_ATTEMPTS:
logging.error(
"Task %s has been attempted %s times. Dropping it permanently.",
self.request.headers["X-AppEngine-TaskName"],
self.task_retry_count() + 1)
self._drop_gracefully()
return
try:
self._preprocess()
self._preprocess_success = True
# pylint: disable=bare-except
except:
self._preprocess_success = False
logging.error(
"Preprocess task %s failed. Dropping it permanently.",
self.request.headers["X-AppEngine-TaskName"])
self._drop_gracefully() | python | def initialize(self, request, response):
"""Initialize.
1. call webapp init.
2. check request is indeed from taskqueue.
3. check the task has not been retried too many times.
4. run handler specific processing logic.
5. run error handling logic if precessing failed.
Args:
request: a webapp.Request instance.
response: a webapp.Response instance.
"""
super(TaskQueueHandler, self).initialize(request, response)
# Check request is from taskqueue.
if "X-AppEngine-QueueName" not in self.request.headers:
logging.error(self.request.headers)
logging.error("Task queue handler received non-task queue request")
self.response.set_status(
403, message="Task queue handler received non-task queue request")
return
# Check task has not been retried too many times.
if self.task_retry_count() + 1 > parameters.config.TASK_MAX_ATTEMPTS:
logging.error(
"Task %s has been attempted %s times. Dropping it permanently.",
self.request.headers["X-AppEngine-TaskName"],
self.task_retry_count() + 1)
self._drop_gracefully()
return
try:
self._preprocess()
self._preprocess_success = True
# pylint: disable=bare-except
except:
self._preprocess_success = False
logging.error(
"Preprocess task %s failed. Dropping it permanently.",
self.request.headers["X-AppEngine-TaskName"])
self._drop_gracefully() | Initialize.
1. call webapp init.
2. check request is indeed from taskqueue.
3. check the task has not been retried too many times.
4. run handler specific processing logic.
5. run error handling logic if precessing failed.
Args:
request: a webapp.Request instance.
response: a webapp.Response instance. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/base_handler.py#L93-L134 |
GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/base_handler.py | TaskQueueHandler.retry_task | def retry_task(self):
"""Ask taskqueue to retry this task.
Even though raising an exception can cause a task retry, it
will flood logs with highly visible ERROR logs. Handlers should uses
this method to perform controlled task retries. Only raise exceptions
for those deserve ERROR log entries.
"""
self.response.set_status(httplib.SERVICE_UNAVAILABLE, "Retry task")
self.response.clear() | python | def retry_task(self):
"""Ask taskqueue to retry this task.
Even though raising an exception can cause a task retry, it
will flood logs with highly visible ERROR logs. Handlers should uses
this method to perform controlled task retries. Only raise exceptions
for those deserve ERROR log entries.
"""
self.response.set_status(httplib.SERVICE_UNAVAILABLE, "Retry task")
self.response.clear() | Ask taskqueue to retry this task.
Even though raising an exception can cause a task retry, it
will flood logs with highly visible ERROR logs. Handlers should uses
this method to perform controlled task retries. Only raise exceptions
for those deserve ERROR log entries. | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/base_handler.py#L163-L172 |
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