blob: 1acec331fcfe7e30dcfc614d659d99336869e069 [file] [log] [blame]
#!/usr/bin/env python
#
# Copyright 2007 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""Defines input readers for MapReduce."""
__all__ = [
"AbstractDatastoreInputReader",
"ALLOW_CHECKPOINT",
"BadReaderParamsError",
"BlobstoreLineInputReader",
"BlobstoreZipInputReader",
"BlobstoreZipLineInputReader",
"COUNTER_IO_READ_BYTES",
"COUNTER_IO_READ_MSEC",
"DatastoreEntityInputReader",
"DatastoreInputReader",
"DatastoreKeyInputReader",
"FileInputReader",
"RandomStringInputReader",
"RawDatastoreInputReader",
"Error",
"InputReader",
"LogInputReader",
"NamespaceInputReader",
"RecordsReader",
]
import base64
import copy
import logging
import pickle
import random
import string
import StringIO
import time
import zipfile
from google.net.proto import ProtocolBuffer
from google.appengine.ext import ndb
from google.appengine.api import datastore
from google.appengine.api import files
from google.appengine.api import logservice
from google.appengine.api.files import file_service_pb
from google.appengine.api.logservice import log_service_pb
from google.appengine.ext import blobstore
from google.appengine.ext import db
from google.appengine.ext import key_range
from google.appengine.ext.db import metadata
from google.appengine.ext.mapreduce import context
from google.appengine.ext.mapreduce import datastore_range_iterators as db_iters
from google.appengine.ext.mapreduce import errors
from google.appengine.ext.mapreduce import file_format_parser
from google.appengine.ext.mapreduce import file_format_root
from google.appengine.ext.mapreduce import json_util
from google.appengine.ext.mapreduce import key_ranges
from google.appengine.ext.mapreduce import model
from google.appengine.ext.mapreduce import namespace_range
from google.appengine.ext.mapreduce import operation
from google.appengine.ext.mapreduce import property_range
from google.appengine.ext.mapreduce import records
from google.appengine.ext.mapreduce import util
try:
from google.appengine.ext import cloudstorage
if hasattr(cloudstorage, "_STUB"):
cloudstorage = None
except ImportError:
pass
Error = errors.Error
BadReaderParamsError = errors.BadReaderParamsError
COUNTER_IO_READ_BYTES = "io-read-bytes"
COUNTER_IO_READ_MSEC = "io-read-msec"
ALLOW_CHECKPOINT = object()
class InputReader(json_util.JsonMixin):
"""Abstract base class for input readers.
InputReaders have the following properties:
* They are created by using the split_input method to generate a set of
InputReaders from a MapperSpec.
* They generate inputs to the mapper via the iterator interface.
* After creation, they can be serialized and resumed using the JsonMixin
interface.
* They are cast to string for a user-readable description; it may be
valuable to implement __str__.
"""
expand_parameters = False
_APP_PARAM = "_app"
NAMESPACE_PARAM = "namespace"
NAMESPACES_PARAM = "namespaces"
def __iter__(self):
return self
def next(self):
"""Returns the next input from this input reader as a key, value pair.
Returns:
The next input from this input reader.
"""
raise NotImplementedError("next() not implemented in %s" % self.__class__)
@classmethod
def from_json(cls, input_shard_state):
"""Creates an instance of the InputReader for the given input shard state.
Args:
input_shard_state: The InputReader state as a dict-like object.
Returns:
An instance of the InputReader configured using the values of json.
"""
raise NotImplementedError("from_json() not implemented in %s" % cls)
def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader.
"""
raise NotImplementedError("to_json() not implemented in %s" %
self.__class__)
@classmethod
def split_input(cls, mapper_spec):
"""Returns a list of input readers.
This method creates a list of input readers, each for one shard.
It attempts to split inputs among readers evenly.
Args:
mapper_spec: model.MapperSpec specifies the inputs and additional
parameters to define the behavior of input readers.
Returns:
A list of InputReaders. None or [] when no input data can be found.
"""
raise NotImplementedError("split_input() not implemented in %s" % cls)
@classmethod
def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Input reader parameters are expected to be passed as "input_reader"
subdictionary in mapper_spec.params.
Pre 1.6.4 API mixes input reader parameters with all other parameters. Thus
to be compatible, input reader check mapper_spec.params as well and
issue a warning if "input_reader" subdicationary is not present.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Input reader class mismatch")
def _get_params(mapper_spec, allowed_keys=None, allow_old=True):
"""Obtain input reader parameters.
Utility function for input readers implementation. Fetches parameters
from mapreduce specification giving appropriate usage warnings.
Args:
mapper_spec: The MapperSpec for the job
allowed_keys: set of all allowed keys in parameters as strings. If it is not
None, then parameters are expected to be in a separate "input_reader"
subdictionary of mapper_spec parameters.
allow_old: Allow parameters to exist outside of the input_reader
subdictionary for compatability.
Returns:
mapper parameters as dict
Raises:
BadReaderParamsError: if parameters are invalid/missing or not allowed.
"""
if "input_reader" not in mapper_spec.params:
message = ("Input reader's parameters should be specified in "
"input_reader subdictionary.")
if not allow_old or allowed_keys:
raise errors.BadReaderParamsError(message)
params = mapper_spec.params
params = dict((str(n), v) for n, v in params.iteritems())
else:
if not isinstance(mapper_spec.params.get("input_reader"), dict):
raise errors.BadReaderParamsError(
"Input reader parameters should be a dictionary")
params = mapper_spec.params.get("input_reader")
params = dict((str(n), v) for n, v in params.iteritems())
if allowed_keys:
params_diff = set(params.keys()) - allowed_keys
if params_diff:
raise errors.BadReaderParamsError(
"Invalid input_reader parameters: %s" % ",".join(params_diff))
return params
class FileInputReader(InputReader):
"""Reader to read Files API files of user specified format.
This class currently only supports Google Storage files. It will be extended
to support blobstore files in the future.
Reader Parameters:
files: a list of filenames or filename patterns.
filename must be of format '/gs/bucket/filename'.
filename pattern has format '/gs/bucket/prefix*'.
filename pattern will be expanded to filenames with the given prefix.
Please see parseGlob in the file api.files.gs.py which is included in the
App Engine SDK for supported patterns.
Example:
["/gs/bucket1/file1", "/gs/bucket2/*", "/gs/bucket3/p*"]
includes "file1", all files under bucket2, and files under bucket3 with
a prefix "p" in its name.
format: format string determines what your map function gets as its input.
format string can be "lines", "bytes", "zip", or a cascade of them plus
optional parameters. See file_formats.FORMATS for all supported formats.
See file_format_parser._FileFormatParser for format string syntax.
Example:
"lines": your map function gets files' contents line by line.
"bytes": your map function gets files' contents entirely.
"zip": InputReader unzips files and feeds your map function each of
the archive's member files as a whole.
"zip[bytes]: same as above.
"zip[lines]": InputReader unzips files and feeds your map function
files' contents line by line.
"zip[lines(encoding=utf32)]": InputReader unzips files, reads each
file with utf32 encoding and feeds your map function line by line.
"base64[zip[lines(encoding=utf32)]]: InputReader decodes files with
base64 encoding, unzips each file, reads each of them with utf32
encoding and feeds your map function line by line.
Note that "encoding" only teaches InputReader how to interpret files.
The input your map function gets is always a Python str.
"""
FILES_PARAM = "files"
FORMAT_PARAM = "format"
def __init__(self, format_root):
"""Initialize input reader.
Args:
format_root: a FileFormatRoot instance.
"""
self._file_format_root = format_root
def __iter__(self):
"""Inherit docs."""
return self
def next(self):
"""Inherit docs."""
ctx = context.get()
start_time = time.time()
content = self._file_format_root.next().read()
if ctx:
operation.counters.Increment(
COUNTER_IO_READ_MSEC, int((time.time() - start_time) * 1000))(ctx)
operation.counters.Increment(COUNTER_IO_READ_BYTES, len(content))(ctx)
return content
@classmethod
def split_input(cls, mapper_spec):
"""Inherit docs."""
params = _get_params(mapper_spec)
filenames = []
for f in params[cls.FILES_PARAM]:
parsedName = files.gs.parseGlob(f)
if isinstance(parsedName, tuple):
filenames.extend(files.gs.listdir(parsedName[0],
{"prefix": parsedName[1]}))
else:
filenames.append(parsedName)
file_format_roots = file_format_root.split(filenames,
params[cls.FORMAT_PARAM],
mapper_spec.shard_count)
if file_format_roots is None:
return []
return [cls(root) for root in file_format_roots]
@classmethod
def validate(cls, mapper_spec):
"""Inherit docs."""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Mapper input reader class mismatch")
params = _get_params(mapper_spec)
if cls.FILES_PARAM not in params:
raise BadReaderParamsError("Must specify %s" % cls.FILES_PARAM)
if cls.FORMAT_PARAM not in params:
raise BadReaderParamsError("Must specify %s" % cls.FORMAT_PARAM)
format_string = params[cls.FORMAT_PARAM]
if not isinstance(format_string, basestring):
raise BadReaderParamsError("format should be string but is %s" %
cls.FORMAT_PARAM)
try:
file_format_parser.parse(format_string)
except ValueError, e:
raise BadReaderParamsError(e)
paths = params[cls.FILES_PARAM]
if not (paths and isinstance(paths, list)):
raise BadReaderParamsError("files should be a list of filenames.")
try:
for path in paths:
files.gs.parseGlob(path)
except files.InvalidFileNameError:
raise BadReaderParamsError("Invalid filename %s." % path)
@classmethod
def from_json(cls, json):
"""Inherit docs."""
return cls(
file_format_root.FileFormatRoot.from_json(json["file_format_root"]))
def to_json(self):
"""Inherit docs."""
return {"file_format_root": self._file_format_root.to_json()}
class AbstractDatastoreInputReader(InputReader):
"""Abstract class for datastore input readers."""
_BATCH_SIZE = 50
_MAX_SHARD_COUNT = 256
MAX_NAMESPACES_FOR_KEY_SHARD = 10
ENTITY_KIND_PARAM = "entity_kind"
KEYS_ONLY_PARAM = "keys_only"
BATCH_SIZE_PARAM = "batch_size"
KEY_RANGE_PARAM = "key_range"
FILTERS_PARAM = "filters"
_KEY_RANGE_ITER_CLS = db_iters.AbstractKeyRangeIterator
def __init__(self, iterator):
"""Create new DatastoreInputReader object.
This is internal constructor. Use split_input to create readers instead.
Args:
iterator: an iterator that generates objects for this input reader.
"""
self._iter = iterator
def __iter__(self):
"""Yields whatever internal iterator yields."""
for o in self._iter:
yield o
def __str__(self):
"""Returns the string representation of this InputReader."""
return repr(self._iter)
def to_json(self):
"""Serializes input reader to json compatible format.
Returns:
all the data in json-compatible map.
"""
return self._iter.to_json()
@classmethod
def from_json(cls, json):
"""Create new DatastoreInputReader from json, encoded by to_json.
Args:
json: json representation of DatastoreInputReader.
Returns:
an instance of DatastoreInputReader with all data deserialized from json.
"""
return cls(db_iters.RangeIteratorFactory.from_json(json))
@classmethod
def _get_query_spec(cls, mapper_spec):
"""Construct a model.QuerySpec from model.MapperSpec."""
params = _get_params(mapper_spec)
entity_kind = params[cls.ENTITY_KIND_PARAM]
filters = params.get(cls.FILTERS_PARAM)
app = params.get(cls._APP_PARAM)
ns = params.get(cls.NAMESPACE_PARAM)
return model.QuerySpec(
entity_kind=cls._get_raw_entity_kind(entity_kind),
keys_only=bool(params.get(cls.KEYS_ONLY_PARAM, False)),
filters=filters,
batch_size=int(params.get(cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE)),
model_class_path=entity_kind,
app=app,
ns=ns)
@classmethod
def split_input(cls, mapper_spec):
"""Inherit doc."""
shard_count = mapper_spec.shard_count
query_spec = cls._get_query_spec(mapper_spec)
namespaces = None
if query_spec.ns is not None:
k_ranges = cls._to_key_ranges_by_shard(
query_spec.app, [query_spec.ns], shard_count, query_spec)
else:
ns_keys = namespace_range.get_namespace_keys(
query_spec.app, cls.MAX_NAMESPACES_FOR_KEY_SHARD+1)
if not ns_keys:
return
elif len(ns_keys) <= cls.MAX_NAMESPACES_FOR_KEY_SHARD:
namespaces = [ns_key.name() or "" for ns_key in ns_keys]
k_ranges = cls._to_key_ranges_by_shard(
query_spec.app, namespaces, shard_count, query_spec)
else:
ns_ranges = namespace_range.NamespaceRange.split(n=shard_count,
contiguous=False,
can_query=lambda: True,
_app=query_spec.app)
k_ranges = [key_ranges.KeyRangesFactory.create_from_ns_range(ns_range)
for ns_range in ns_ranges]
iters = [db_iters.RangeIteratorFactory.create_key_ranges_iterator(
r, query_spec, cls._KEY_RANGE_ITER_CLS) for r in k_ranges]
return [cls(i) for i in iters]
@classmethod
def _to_key_ranges_by_shard(cls, app, namespaces, shard_count, query_spec):
"""Get a list of key_ranges.KeyRanges objects, one for each shard.
This method uses scatter index to split each namespace into pieces
and assign those pieces to shards.
Args:
app: app_id in str.
namespaces: a list of namespaces in str.
shard_count: number of shards to split.
query_spec: model.QuerySpec.
Returns:
a list of key_ranges.KeyRanges objects.
"""
key_ranges_by_ns = []
for namespace in namespaces:
ranges = cls._split_ns_by_scatter(
shard_count,
namespace,
query_spec.entity_kind,
app)
random.shuffle(ranges)
key_ranges_by_ns.append(ranges)
ranges_by_shard = [[] for _ in range(shard_count)]
for ranges in key_ranges_by_ns:
for i, k_range in enumerate(ranges):
if k_range:
ranges_by_shard[i].append(k_range)
key_ranges_by_shard = []
for ranges in ranges_by_shard:
if ranges:
key_ranges_by_shard.append(key_ranges.KeyRangesFactory.create_from_list(
ranges))
return key_ranges_by_shard
@classmethod
def _split_ns_by_scatter(cls,
shard_count,
namespace,
raw_entity_kind,
app):
"""Split a namespace by scatter index into key_range.KeyRange.
TODO: Power this with key_range.KeyRange.compute_split_points.
Args:
shard_count: number of shards.
namespace: namespace name to split. str.
raw_entity_kind: low level datastore API entity kind.
app: app id in str.
Returns:
A list of key_range.KeyRange objects. If there are not enough entities to
splits into requested shards, the returned list will contain KeyRanges
ordered lexicographically with any Nones appearing at the end.
"""
if shard_count == 1:
return [key_range.KeyRange(namespace=namespace, _app=app)]
ds_query = datastore.Query(kind=raw_entity_kind,
namespace=namespace,
_app=app,
keys_only=True)
ds_query.Order("__scatter__")
oversampling_factor = 32
random_keys = ds_query.Get(shard_count * oversampling_factor)
if not random_keys:
return ([key_range.KeyRange(namespace=namespace, _app=app)] +
[None] * (shard_count - 1))
random_keys.sort()
if len(random_keys) >= shard_count:
random_keys = cls._choose_split_points(random_keys, shard_count)
k_ranges = []
k_ranges.append(key_range.KeyRange(
key_start=None,
key_end=random_keys[0],
direction=key_range.KeyRange.ASC,
include_start=False,
include_end=False,
namespace=namespace,
_app=app))
for i in range(0, len(random_keys) - 1):
k_ranges.append(key_range.KeyRange(
key_start=random_keys[i],
key_end=random_keys[i+1],
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
k_ranges.append(key_range.KeyRange(
key_start=random_keys[-1],
key_end=None,
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
if len(k_ranges) < shard_count:
k_ranges += [None] * (shard_count - len(k_ranges))
return k_ranges
@classmethod
def _choose_split_points(cls, sorted_keys, shard_count):
"""Returns the best split points given a random set of datastore.Keys."""
assert len(sorted_keys) >= shard_count
index_stride = len(sorted_keys) / float(shard_count)
return [sorted_keys[int(round(index_stride * i))]
for i in range(1, shard_count)]
@classmethod
def validate(cls, mapper_spec):
"""Inherit docs."""
params = _get_params(mapper_spec)
if cls.ENTITY_KIND_PARAM not in params:
raise BadReaderParamsError("Missing input reader parameter 'entity_kind'")
if cls.BATCH_SIZE_PARAM in params:
try:
batch_size = int(params[cls.BATCH_SIZE_PARAM])
if batch_size < 1:
raise BadReaderParamsError("Bad batch size: %s" % batch_size)
except ValueError, e:
raise BadReaderParamsError("Bad batch size: %s" % e)
try:
bool(params.get(cls.KEYS_ONLY_PARAM, False))
except:
raise BadReaderParamsError("keys_only expects a boolean value but got %s",
params[cls.KEYS_ONLY_PARAM])
if cls.NAMESPACE_PARAM in params:
if not isinstance(params[cls.NAMESPACE_PARAM],
(str, unicode, type(None))):
raise BadReaderParamsError(
"Expected a single namespace string")
if cls.NAMESPACES_PARAM in params:
raise BadReaderParamsError("Multiple namespaces are no longer supported")
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
if not isinstance(filters, list):
raise BadReaderParamsError("Expected list for filters parameter")
for f in filters:
if not isinstance(f, (tuple, list)):
raise BadReaderParamsError("Filter should be a tuple or list: %s", f)
if len(f) != 3:
raise BadReaderParamsError("Filter should be a 3-tuple: %s", f)
prop, op, _ = f
if not isinstance(prop, basestring):
raise BadReaderParamsError("Property should be string: %s", prop)
if not isinstance(op, basestring):
raise BadReaderParamsError("Operator should be string: %s", op)
@classmethod
def _get_raw_entity_kind(cls, entity_kind_or_model_classpath):
"""Returns the entity kind to use with low level datastore calls.
Args:
entity_kind_or_model_classpath: user specified entity kind or model
classpath.
Returns:
the entity kind in str to use with low level datastore calls.
"""
return entity_kind_or_model_classpath
class RawDatastoreInputReader(AbstractDatastoreInputReader):
"""Iterates over an entity kind and yields datastore.Entity."""
_KEY_RANGE_ITER_CLS = db_iters.KeyRangeEntityIterator
@classmethod
def validate(cls, mapper_spec):
"""Inherit docs."""
super(RawDatastoreInputReader, cls).validate(mapper_spec)
params = _get_params(mapper_spec)
entity_kind = params[cls.ENTITY_KIND_PARAM]
if "." in entity_kind:
logging.warning(
". detected in entity kind %s specified for reader %s."
"Assuming entity kind contains the dot.",
entity_kind, cls.__name__)
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
for f in filters:
if f[1] != "=":
raise BadReaderParamsError(
"Only equality filters are supported: %s", f)
class DatastoreInputReader(AbstractDatastoreInputReader):
"""Iterates over a Model and yields model instances.
Supports both db.model and ndb.model.
"""
_KEY_RANGE_ITER_CLS = db_iters.KeyRangeModelIterator
@classmethod
def _get_raw_entity_kind(cls, model_classpath):
entity_type = util.for_name(model_classpath)
if isinstance(entity_type, db.Model):
return entity_type.kind()
elif isinstance(entity_type, (ndb.Model, ndb.MetaModel)):
return entity_type._get_kind()
else:
return util.get_short_name(model_classpath)
@classmethod
def validate(cls, mapper_spec):
"""Inherit docs."""
super(DatastoreInputReader, cls).validate(mapper_spec)
params = _get_params(mapper_spec)
entity_kind = params[cls.ENTITY_KIND_PARAM]
try:
model_class = util.for_name(entity_kind)
except ImportError, e:
raise BadReaderParamsError("Bad entity kind: %s" % e)
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
if issubclass(model_class, db.Model):
cls._validate_filters(filters, model_class)
else:
cls._validate_filters_ndb(filters, model_class)
property_range.PropertyRange(filters, entity_kind)
@classmethod
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())
try:
properties[prop].validate(val)
except db.BadValueError, e:
raise errors.BadReaderParamsError(e)
@classmethod
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())
try:
properties[prop]._do_validate(val)
except db.BadValueError, e:
raise errors.BadReaderParamsError(e)
@classmethod
def split_input(cls, mapper_spec):
"""Inherit docs."""
shard_count = mapper_spec.shard_count
query_spec = cls._get_query_spec(mapper_spec)
if not property_range.should_shard_by_property_range(query_spec.filters):
return super(DatastoreInputReader, cls).split_input(mapper_spec)
p_range = property_range.PropertyRange(query_spec.filters,
query_spec.model_class_path)
p_ranges = p_range.split(shard_count)
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
if len(ns_keys) <= cls.MAX_NAMESPACES_FOR_KEY_SHARD:
ns_ranges = [namespace_range.NamespaceRange(_app=query_spec.app)
for _ in p_ranges]
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]
class DatastoreKeyInputReader(RawDatastoreInputReader):
"""Iterate over an entity kind and yields datastore.Key."""
_KEY_RANGE_ITER_CLS = db_iters.KeyRangeKeyIterator
DatastoreEntityInputReader = RawDatastoreInputReader
class _OldAbstractDatastoreInputReader(InputReader):
"""Abstract base class for classes that iterate over datastore entities.
Concrete subclasses must implement _iter_key_range(self, k_range). See the
docstring for that method for details.
"""
_BATCH_SIZE = 50
_MAX_SHARD_COUNT = 256
_OVERSAMPLING_FACTOR = 32
MAX_NAMESPACES_FOR_KEY_SHARD = 10
ENTITY_KIND_PARAM = "entity_kind"
KEYS_ONLY_PARAM = "keys_only"
BATCH_SIZE_PARAM = "batch_size"
KEY_RANGE_PARAM = "key_range"
NAMESPACE_RANGE_PARAM = "namespace_range"
CURRENT_KEY_RANGE_PARAM = "current_key_range"
FILTERS_PARAM = "filters"
def __init__(self,
entity_kind,
key_ranges=None,
ns_range=None,
batch_size=_BATCH_SIZE,
current_key_range=None,
filters=None):
"""Create new AbstractDatastoreInputReader object.
This is internal constructor. Use split_query in a concrete class instead.
Args:
entity_kind: entity kind as string.
key_ranges: a sequence of key_range.KeyRange instances to process. Only
one of key_ranges or ns_range can be non-None.
ns_range: a namespace_range.NamespaceRange to process. Only one of
key_ranges or ns_range can be non-None.
batch_size: size of read batch as int.
current_key_range: the current key_range.KeyRange being processed.
filters: optional list of filters to apply to the query. Each filter is
a tuple: (<property_name_as_str>, <query_operation_as_str>, <value>).
User filters are applied first.
"""
assert key_ranges is not None or ns_range is not None, (
"must specify one of 'key_ranges' or 'ns_range'")
assert key_ranges is None or ns_range is None, (
"can't specify both 'key_ranges ' and 'ns_range'")
self._entity_kind = entity_kind
self._key_ranges = key_ranges and list(reversed(key_ranges))
self._ns_range = ns_range
self._batch_size = int(batch_size)
self._current_key_range = current_key_range
self._filters = filters
@classmethod
def _get_raw_entity_kind(cls, 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__)
return entity_kind
def __iter__(self):
"""Iterates over the given KeyRanges or NamespaceRange.
This method iterates over the given KeyRanges or NamespaceRange and sets
the self._current_key_range to the KeyRange currently being processed. It
then delegates to the _iter_key_range method to yield that actual
results.
Yields:
Forwards the objects yielded by the subclasses concrete _iter_key_range()
method. The caller must consume the result yielded because self.to_json()
will not include it.
"""
if self._key_ranges is not None:
for o in self._iter_key_ranges():
yield o
elif self._ns_range is not None:
for o in self._iter_ns_range():
yield o
else:
assert False, "self._key_ranges and self._ns_range are both None"
def _iter_key_ranges(self):
"""Iterates over self._key_ranges, delegating to self._iter_key_range()."""
while True:
if self._current_key_range is None:
if self._key_ranges:
self._current_key_range = self._key_ranges.pop()
continue
else:
break
for key, o in self._iter_key_range(
copy.deepcopy(self._current_key_range)):
self._current_key_range.advance(key)
yield o
self._current_key_range = None
def _iter_ns_range(self):
"""Iterates over self._ns_range, delegating to self._iter_key_range()."""
while True:
if self._current_key_range is None:
query = self._ns_range.make_datastore_query()
namespace_result = query.Get(1)
if not namespace_result:
break
namespace = namespace_result[0].name() or ""
self._current_key_range = key_range.KeyRange(
namespace=namespace, _app=self._ns_range.app)
yield ALLOW_CHECKPOINT
for key, o in self._iter_key_range(
copy.deepcopy(self._current_key_range)):
self._current_key_range.advance(key)
yield o
if (self._ns_range.is_single_namespace or
self._current_key_range.namespace == self._ns_range.namespace_end):
break
self._ns_range = self._ns_range.with_start_after(
self._current_key_range.namespace)
self._current_key_range = None
def _iter_key_range(self, k_range):
"""Yields a db.Key and the value that should be yielded by self.__iter__().
Args:
k_range: The key_range.KeyRange to iterate over.
Yields:
A 2-tuple containing the last db.Key processed and the value that should
be yielded by __iter__. The returned db.Key will be used to determine the
InputReader's current position in self._current_key_range.
"""
raise NotImplementedError("_iter_key_range() not implemented in %s" %
self.__class__)
def __str__(self):
"""Returns the string representation of this InputReader."""
if self._ns_range is None:
return repr(self._key_ranges)
else:
return repr(self._ns_range)
@classmethod
def _choose_split_points(cls, sorted_keys, shard_count):
"""Returns the best split points given a random set of db.Keys."""
assert len(sorted_keys) >= shard_count
index_stride = len(sorted_keys) / float(shard_count)
return [sorted_keys[int(round(index_stride * i))]
for i in range(1, shard_count)]
@classmethod
def _split_input_from_namespace(cls, app, namespace, entity_kind,
shard_count):
"""Helper for _split_input_from_params.
If there are not enough Entities to make all of the given shards, the
returned list of KeyRanges will include Nones. The returned list will
contain KeyRanges ordered lexographically with any Nones appearing at the
end.
Args:
app: the app.
namespace: the namespace.
entity_kind: entity kind as string.
shard_count: the number of shards.
Returns:
KeyRange objects.
"""
raw_entity_kind = cls._get_raw_entity_kind(entity_kind)
if shard_count == 1:
return [key_range.KeyRange(namespace=namespace, _app=app)]
ds_query = datastore.Query(kind=raw_entity_kind,
namespace=namespace,
_app=app,
keys_only=True)
ds_query.Order("__scatter__")
random_keys = ds_query.Get(shard_count * cls._OVERSAMPLING_FACTOR)
if not random_keys:
return ([key_range.KeyRange(namespace=namespace, _app=app)] +
[None] * (shard_count - 1))
random_keys.sort()
if len(random_keys) >= shard_count:
random_keys = cls._choose_split_points(random_keys, shard_count)
key_ranges = []
key_ranges.append(key_range.KeyRange(
key_start=None,
key_end=random_keys[0],
direction=key_range.KeyRange.ASC,
include_start=False,
include_end=False,
namespace=namespace,
_app=app))
for i in range(0, len(random_keys) - 1):
key_ranges.append(key_range.KeyRange(
key_start=random_keys[i],
key_end=random_keys[i+1],
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
key_ranges.append(key_range.KeyRange(
key_start=random_keys[-1],
key_end=None,
direction=key_range.KeyRange.ASC,
include_start=True,
include_end=False,
namespace=namespace,
_app=app))
if len(key_ranges) < shard_count:
key_ranges += [None] * (shard_count - len(key_ranges))
return key_ranges
@classmethod
def _split_input_from_params(cls, app, namespaces, entity_kind_name,
params, shard_count):
"""Return input reader objects. Helper for split_input."""
key_ranges = []
for namespace in namespaces:
key_ranges.extend(
cls._split_input_from_namespace(app,
namespace,
entity_kind_name,
shard_count))
shared_ranges = [[] for _ in range(shard_count)]
for i, k_range in enumerate(key_ranges):
shared_ranges[i % shard_count].append(k_range)
batch_size = int(params.get(cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE))
return [cls(entity_kind_name,
key_ranges=key_ranges,
ns_range=None,
batch_size=batch_size)
for key_ranges in shared_ranges if key_ranges]
@classmethod
def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Input reader class mismatch")
params = _get_params(mapper_spec)
if cls.ENTITY_KIND_PARAM not in params:
raise BadReaderParamsError("Missing mapper parameter 'entity_kind'")
if cls.BATCH_SIZE_PARAM in params:
try:
batch_size = int(params[cls.BATCH_SIZE_PARAM])
if batch_size < 1:
raise BadReaderParamsError("Bad batch size: %s" % batch_size)
except ValueError, e:
raise BadReaderParamsError("Bad batch size: %s" % e)
if cls.NAMESPACE_PARAM in params:
if not isinstance(params[cls.NAMESPACE_PARAM],
(str, unicode, type(None))):
raise BadReaderParamsError(
"Expected a single namespace string")
if cls.NAMESPACES_PARAM in params:
raise BadReaderParamsError("Multiple namespaces are no longer supported")
if cls.FILTERS_PARAM in params:
filters = params[cls.FILTERS_PARAM]
if not isinstance(filters, list):
raise BadReaderParamsError("Expected list for filters parameter")
for f in filters:
if not isinstance(f, (tuple, list)):
raise BadReaderParamsError("Filter should be a tuple or list: %s", f)
if len(f) != 3:
raise BadReaderParamsError("Filter should be a 3-tuple: %s", f)
if not isinstance(f[0], basestring):
raise BadReaderParamsError("First element should be string: %s", f)
if f[1] != "=":
raise BadReaderParamsError(
"Only equality filters are supported: %s", f)
@classmethod
def split_input(cls, mapper_spec):
"""Splits query into shards without fetching query results.
Tries as best as it can to split the whole query result set into equal
shards. Due to difficulty of making the perfect split, resulting shards'
sizes might differ significantly from each other.
Args:
mapper_spec: MapperSpec with params containing 'entity_kind'.
May have 'namespace' in the params as a string containing a single
namespace. If specified then the input reader will only yield values
in the given namespace. If 'namespace' is not given then values from
all namespaces will be yielded. May also have 'batch_size' in the params
to specify the number of entities to process in each batch.
Returns:
A list of InputReader objects. If the query results are empty then the
empty list will be returned. Otherwise, the list will always have a length
equal to number_of_shards but may be padded with Nones if there are too
few results for effective sharding.
"""
params = _get_params(mapper_spec)
entity_kind_name = params[cls.ENTITY_KIND_PARAM]
batch_size = int(params.get(cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE))
shard_count = mapper_spec.shard_count
namespace = params.get(cls.NAMESPACE_PARAM)
app = params.get(cls._APP_PARAM)
filters = params.get(cls.FILTERS_PARAM)
if namespace is None:
namespace_query = datastore.Query("__namespace__",
keys_only=True,
_app=app)
namespace_keys = namespace_query.Get(
limit=cls.MAX_NAMESPACES_FOR_KEY_SHARD+1)
if len(namespace_keys) > cls.MAX_NAMESPACES_FOR_KEY_SHARD:
ns_ranges = namespace_range.NamespaceRange.split(n=shard_count,
contiguous=True,
_app=app)
return [cls(entity_kind_name,
key_ranges=None,
ns_range=ns_range,
batch_size=batch_size,
filters=filters)
for ns_range in ns_ranges]
elif not namespace_keys:
return [cls(entity_kind_name,
key_ranges=None,
ns_range=namespace_range.NamespaceRange(_app=app),
batch_size=shard_count,
filters=filters)]
else:
namespaces = [namespace_key.name() or ""
for namespace_key in namespace_keys]
else:
namespaces = [namespace]
readers = cls._split_input_from_params(
app, namespaces, entity_kind_name, params, shard_count)
if filters:
for reader in readers:
reader._filters = filters
return readers
def to_json(self):
"""Serializes all the data in this query range into json form.
Returns:
all the data in json-compatible map.
"""
if self._key_ranges is None:
key_ranges_json = None
else:
key_ranges_json = []
for k in self._key_ranges:
if k:
key_ranges_json.append(k.to_json())
else:
key_ranges_json.append(None)
if self._ns_range is None:
namespace_range_json = None
else:
namespace_range_json = self._ns_range.to_json_object()
if self._current_key_range is None:
current_key_range_json = None
else:
current_key_range_json = self._current_key_range.to_json()
json_dict = {self.KEY_RANGE_PARAM: key_ranges_json,
self.NAMESPACE_RANGE_PARAM: namespace_range_json,
self.CURRENT_KEY_RANGE_PARAM: current_key_range_json,
self.ENTITY_KIND_PARAM: self._entity_kind,
self.BATCH_SIZE_PARAM: self._batch_size,
self.FILTERS_PARAM: self._filters}
return json_dict
@classmethod
def from_json(cls, json):
"""Create new DatastoreInputReader from the json, encoded by to_json.
Args:
json: json map representation of DatastoreInputReader.
Returns:
an instance of DatastoreInputReader with all data deserialized from json.
"""
if json[cls.KEY_RANGE_PARAM] is None:
key_ranges = None
else:
key_ranges = []
for k in json[cls.KEY_RANGE_PARAM]:
if k:
key_ranges.append(key_range.KeyRange.from_json(k))
else:
key_ranges.append(None)
if json[cls.NAMESPACE_RANGE_PARAM] is None:
ns_range = None
else:
ns_range = namespace_range.NamespaceRange.from_json_object(
json[cls.NAMESPACE_RANGE_PARAM])
if json[cls.CURRENT_KEY_RANGE_PARAM] is None:
current_key_range = None
else:
current_key_range = key_range.KeyRange.from_json(
json[cls.CURRENT_KEY_RANGE_PARAM])
return cls(
json[cls.ENTITY_KIND_PARAM],
key_ranges,
ns_range,
json[cls.BATCH_SIZE_PARAM],
current_key_range,
filters=json.get(cls.FILTERS_PARAM))
class BlobstoreLineInputReader(InputReader):
"""Input reader for a newline delimited blob in Blobstore."""
_BLOB_BUFFER_SIZE = 64000
_MAX_SHARD_COUNT = 256
_MAX_BLOB_KEYS_COUNT = 246
BLOB_KEYS_PARAM = "blob_keys"
INITIAL_POSITION_PARAM = "initial_position"
END_POSITION_PARAM = "end_position"
BLOB_KEY_PARAM = "blob_key"
def __init__(self, blob_key, start_position, end_position):
"""Initializes this instance with the given blob key and character range.
This BlobstoreInputReader will read from the first record starting after
strictly after start_position until the first record ending at or after
end_position (exclusive). As an exception, if start_position is 0, then
this InputReader starts reading at the first record.
Args:
blob_key: the BlobKey that this input reader is processing.
start_position: the position to start reading at.
end_position: a position in the last record to read.
"""
self._blob_key = blob_key
self._blob_reader = blobstore.BlobReader(blob_key,
self._BLOB_BUFFER_SIZE,
start_position)
self._end_position = end_position
self._has_iterated = False
self._read_before_start = bool(start_position)
def next(self):
"""Returns the next input from as an (offset, line) tuple."""
self._has_iterated = True
if self._read_before_start:
self._blob_reader.readline()
self._read_before_start = False
start_position = self._blob_reader.tell()
if start_position > self._end_position:
raise StopIteration()
line = self._blob_reader.readline()
if not line:
raise StopIteration()
return start_position, line.rstrip("\n")
def to_json(self):
"""Returns an json-compatible input shard spec for remaining inputs."""
new_pos = self._blob_reader.tell()
if self._has_iterated:
new_pos -= 1
return {self.BLOB_KEY_PARAM: self._blob_key,
self.INITIAL_POSITION_PARAM: new_pos,
self.END_POSITION_PARAM: self._end_position}
def __str__(self):
"""Returns the string representation of this BlobstoreLineInputReader."""
return "blobstore.BlobKey(%r):[%d, %d]" % (
self._blob_key, self._blob_reader.tell(), self._end_position)
@classmethod
def from_json(cls, json):
"""Instantiates an instance of this InputReader for the given shard spec."""
return cls(json[cls.BLOB_KEY_PARAM],
json[cls.INITIAL_POSITION_PARAM],
json[cls.END_POSITION_PARAM])
@classmethod
def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Mapper input reader class mismatch")
params = _get_params(mapper_spec)
if cls.BLOB_KEYS_PARAM not in params:
raise BadReaderParamsError("Must specify 'blob_keys' for mapper input")
blob_keys = params[cls.BLOB_KEYS_PARAM]
if isinstance(blob_keys, basestring):
blob_keys = blob_keys.split(",")
if len(blob_keys) > cls._MAX_BLOB_KEYS_COUNT:
raise BadReaderParamsError("Too many 'blob_keys' for mapper input")
if not blob_keys:
raise BadReaderParamsError("No 'blob_keys' specified for mapper input")
for blob_key in blob_keys:
blob_info = blobstore.BlobInfo.get(blobstore.BlobKey(blob_key))
if not blob_info:
raise BadReaderParamsError("Could not find blobinfo for key %s" %
blob_key)
@classmethod
def split_input(cls, mapper_spec):
"""Returns a list of shard_count input_spec_shards for input_spec.
Args:
mapper_spec: The mapper specification to split from. Must contain
'blob_keys' parameter with one or more blob keys.
Returns:
A list of BlobstoreInputReaders corresponding to the specified shards.
"""
params = _get_params(mapper_spec)
blob_keys = params[cls.BLOB_KEYS_PARAM]
if isinstance(blob_keys, basestring):
blob_keys = blob_keys.split(",")
blob_sizes = {}
for blob_key in blob_keys:
blob_info = blobstore.BlobInfo.get(blobstore.BlobKey(blob_key))
blob_sizes[blob_key] = blob_info.size
shard_count = min(cls._MAX_SHARD_COUNT, mapper_spec.shard_count)
shards_per_blob = shard_count // len(blob_keys)
if shards_per_blob == 0:
shards_per_blob = 1
chunks = []
for blob_key, blob_size in blob_sizes.items():
blob_chunk_size = blob_size // shards_per_blob
for i in xrange(shards_per_blob - 1):
chunks.append(BlobstoreLineInputReader.from_json(
{cls.BLOB_KEY_PARAM: blob_key,
cls.INITIAL_POSITION_PARAM: blob_chunk_size * i,
cls.END_POSITION_PARAM: blob_chunk_size * (i + 1)}))
chunks.append(BlobstoreLineInputReader.from_json(
{cls.BLOB_KEY_PARAM: blob_key,
cls.INITIAL_POSITION_PARAM: blob_chunk_size * (shards_per_blob - 1),
cls.END_POSITION_PARAM: blob_size}))
return chunks
class BlobstoreZipInputReader(InputReader):
"""Input reader for files from a zip archive stored in the Blobstore.
Each instance of the reader will read the TOC, from the end of the zip file,
and then only the contained files which it is responsible for.
"""
_MAX_SHARD_COUNT = 256
BLOB_KEY_PARAM = "blob_key"
START_INDEX_PARAM = "start_index"
END_INDEX_PARAM = "end_index"
def __init__(self, blob_key, start_index, end_index,
_reader=blobstore.BlobReader):
"""Initializes this instance with the given blob key and file range.
This BlobstoreZipInputReader will read from the file with index start_index
up to but not including the file with index end_index.
Args:
blob_key: the BlobKey that this input reader is processing.
start_index: the index of the first file to read.
end_index: the index of the first file that will not be read.
_reader: a callable that returns a file-like object for reading blobs.
Used for dependency injection.
"""
self._blob_key = blob_key
self._start_index = start_index
self._end_index = end_index
self._reader = _reader
self._zip = None
self._entries = None
def next(self):
"""Returns the next input from this input reader as (ZipInfo, opener) tuple.
Returns:
The next input from this input reader, in the form of a 2-tuple.
The first element of the tuple is a zipfile.ZipInfo object.
The second element of the tuple is a zero-argument function that, when
called, returns the complete body of the file.
"""
if not self._zip:
self._zip = zipfile.ZipFile(self._reader(self._blob_key))
self._entries = self._zip.infolist()[self._start_index:self._end_index]
self._entries.reverse()
if not self._entries:
raise StopIteration()
entry = self._entries.pop()
self._start_index += 1
return (entry, lambda: self._read(entry))
def _read(self, entry):
"""Read entry content.
Args:
entry: zip file entry as zipfile.ZipInfo.
Returns:
Entry content as string.
"""
start_time = time.time()
content = self._zip.read(entry.filename)
ctx = context.get()
if ctx:
operation.counters.Increment(COUNTER_IO_READ_BYTES, len(content))(ctx)
operation.counters.Increment(
COUNTER_IO_READ_MSEC, int((time.time() - start_time) * 1000))(ctx)
return content
@classmethod
def from_json(cls, json):
"""Creates an instance of the InputReader for the given input shard state.
Args:
json: The InputReader state as a dict-like object.
Returns:
An instance of the InputReader configured using the values of json.
"""
return cls(json[cls.BLOB_KEY_PARAM],
json[cls.START_INDEX_PARAM],
json[cls.END_INDEX_PARAM])
def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader.
"""
return {self.BLOB_KEY_PARAM: self._blob_key,
self.START_INDEX_PARAM: self._start_index,
self.END_INDEX_PARAM: self._end_index}
def __str__(self):
"""Returns the string representation of this BlobstoreZipInputReader."""
return "blobstore.BlobKey(%r):[%d, %d]" % (
self._blob_key, self._start_index, self._end_index)
@classmethod
def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Mapper input reader class mismatch")
params = _get_params(mapper_spec)
if cls.BLOB_KEY_PARAM not in params:
raise BadReaderParamsError("Must specify 'blob_key' for mapper input")
blob_key = params[cls.BLOB_KEY_PARAM]
blob_info = blobstore.BlobInfo.get(blobstore.BlobKey(blob_key))
if not blob_info:
raise BadReaderParamsError("Could not find blobinfo for key %s" %
blob_key)
@classmethod
def split_input(cls, mapper_spec, _reader=blobstore.BlobReader):
"""Returns a list of input shard states for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader. Must contain
'blob_key' parameter with one blob key.
_reader: a callable that returns a file-like object for reading blobs.
Used for dependency injection.
Returns:
A list of InputReaders spanning files within the zip.
"""
params = _get_params(mapper_spec)
blob_key = params[cls.BLOB_KEY_PARAM]
zip_input = zipfile.ZipFile(_reader(blob_key))
zfiles = zip_input.infolist()
total_size = sum(x.file_size for x in zfiles)
num_shards = min(mapper_spec.shard_count, cls._MAX_SHARD_COUNT)
size_per_shard = total_size // num_shards
shard_start_indexes = [0]
current_shard_size = 0
for i, fileinfo in enumerate(zfiles):
current_shard_size += fileinfo.file_size
if current_shard_size >= size_per_shard:
shard_start_indexes.append(i + 1)
current_shard_size = 0
if shard_start_indexes[-1] != len(zfiles):
shard_start_indexes.append(len(zfiles))
return [cls(blob_key, start_index, end_index, _reader)
for start_index, end_index
in zip(shard_start_indexes, shard_start_indexes[1:])]
class BlobstoreZipLineInputReader(InputReader):
"""Input reader for newline delimited files in zip archives from Blobstore.
This has the same external interface as the BlobstoreLineInputReader, in that
it takes a list of blobs as its input and yields lines to the reader.
However the blobs themselves are expected to be zip archives of line delimited
files instead of the files themselves.
This is useful as many line delimited files gain greatly from compression.
"""
_MAX_SHARD_COUNT = 256
_MAX_BLOB_KEYS_COUNT = 246
BLOB_KEYS_PARAM = "blob_keys"
BLOB_KEY_PARAM = "blob_key"
START_FILE_INDEX_PARAM = "start_file_index"
END_FILE_INDEX_PARAM = "end_file_index"
OFFSET_PARAM = "offset"
def __init__(self, blob_key, start_file_index, end_file_index, offset,
_reader=blobstore.BlobReader):
"""Initializes this instance with the given blob key and file range.
This BlobstoreZipLineInputReader will read from the file with index
start_file_index up to but not including the file with index end_file_index.
It will return lines starting at offset within file[start_file_index]
Args:
blob_key: the BlobKey that this input reader is processing.
start_file_index: the index of the first file to read within the zip.
end_file_index: the index of the first file that will not be read.
offset: the byte offset within blob_key.zip[start_file_index] to start
reading. The reader will continue to the end of the file.
_reader: a callable that returns a file-like object for reading blobs.
Used for dependency injection.
"""
self._blob_key = blob_key
self._start_file_index = start_file_index
self._end_file_index = end_file_index
self._initial_offset = offset
self._reader = _reader
self._zip = None
self._entries = None
self._filestream = None
@classmethod
def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Mapper input reader class mismatch")
params = _get_params(mapper_spec)
if cls.BLOB_KEYS_PARAM not in params:
raise BadReaderParamsError("Must specify 'blob_keys' for mapper input")
blob_keys = params[cls.BLOB_KEYS_PARAM]
if isinstance(blob_keys, basestring):
blob_keys = blob_keys.split(",")
if len(blob_keys) > cls._MAX_BLOB_KEYS_COUNT:
raise BadReaderParamsError("Too many 'blob_keys' for mapper input")
if not blob_keys:
raise BadReaderParamsError("No 'blob_keys' specified for mapper input")
for blob_key in blob_keys:
blob_info = blobstore.BlobInfo.get(blobstore.BlobKey(blob_key))
if not blob_info:
raise BadReaderParamsError("Could not find blobinfo for key %s" %
blob_key)
@classmethod
def split_input(cls, mapper_spec, _reader=blobstore.BlobReader):
"""Returns a list of input readers for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader. Must contain
'blob_keys' parameter with one or more blob keys.
_reader: a callable that returns a file-like object for reading blobs.
Used for dependency injection.
Returns:
A list of InputReaders spanning the subfiles within the blobs.
There will be at least one reader per blob, but it will otherwise
attempt to keep the expanded size even.
"""
params = _get_params(mapper_spec)
blob_keys = params[cls.BLOB_KEYS_PARAM]
if isinstance(blob_keys, basestring):
blob_keys = blob_keys.split(",")
blob_files = {}
total_size = 0
for blob_key in blob_keys:
zip_input = zipfile.ZipFile(_reader(blob_key))
blob_files[blob_key] = zip_input.infolist()
total_size += sum(x.file_size for x in blob_files[blob_key])
shard_count = min(cls._MAX_SHARD_COUNT, mapper_spec.shard_count)
size_per_shard = total_size // shard_count
readers = []
for blob_key in blob_keys:
bfiles = blob_files[blob_key]
current_shard_size = 0
start_file_index = 0
next_file_index = 0
for fileinfo in bfiles:
next_file_index += 1
current_shard_size += fileinfo.file_size
if current_shard_size >= size_per_shard:
readers.append(cls(blob_key, start_file_index, next_file_index, 0,
_reader))
current_shard_size = 0
start_file_index = next_file_index
if current_shard_size != 0:
readers.append(cls(blob_key, start_file_index, next_file_index, 0,
_reader))
return readers
def next(self):
"""Returns the next line from this input reader as (lineinfo, line) tuple.
Returns:
The next input from this input reader, in the form of a 2-tuple.
The first element of the tuple describes the source, it is itself
a tuple (blobkey, filenumber, byteoffset).
The second element of the tuple is the line found at that offset.
"""
if not self._filestream:
if not self._zip:
self._zip = zipfile.ZipFile(self._reader(self._blob_key))
self._entries = self._zip.infolist()[self._start_file_index:
self._end_file_index]
self._entries.reverse()
if not self._entries:
raise StopIteration()
entry = self._entries.pop()
value = self._zip.read(entry.filename)
self._filestream = StringIO.StringIO(value)
if self._initial_offset:
self._filestream.seek(self._initial_offset)
self._filestream.readline()
start_position = self._filestream.tell()
line = self._filestream.readline()
if not line:
self._filestream.close()
self._filestream = None
self._start_file_index += 1
self._initial_offset = 0
return self.next()
return ((self._blob_key, self._start_file_index, start_position),
line.rstrip("\n"))
def _next_offset(self):
"""Return the offset of the next line to read."""
if self._filestream:
offset = self._filestream.tell()
if offset:
offset -= 1
else:
offset = self._initial_offset
return offset
def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader.
"""
return {self.BLOB_KEY_PARAM: self._blob_key,
self.START_FILE_INDEX_PARAM: self._start_file_index,
self.END_FILE_INDEX_PARAM: self._end_file_index,
self.OFFSET_PARAM: self._next_offset()}
@classmethod
def from_json(cls, json, _reader=blobstore.BlobReader):
"""Creates an instance of the InputReader for the given input shard state.
Args:
json: The InputReader state as a dict-like object.
_reader: For dependency injection.
Returns:
An instance of the InputReader configured using the values of json.
"""
return cls(json[cls.BLOB_KEY_PARAM],
json[cls.START_FILE_INDEX_PARAM],
json[cls.END_FILE_INDEX_PARAM],
json[cls.OFFSET_PARAM],
_reader)
def __str__(self):
"""Returns the string representation of this reader.
Returns:
string blobkey:[start file num, end file num]:current offset.
"""
return "blobstore.BlobKey(%r):[%d, %d]:%d" % (
self._blob_key, self._start_file_index, self._end_file_index,
self._next_offset())
class RandomStringInputReader(InputReader):
"""RandomStringInputReader generates random strings as output.
Primary usage is to populate output with testing entries.
"""
COUNT = "count"
STRING_LENGTH = "string_length"
DEFAULT_STRING_LENGTH = 10
def __init__(self, count, string_length):
"""Initialize input reader.
Args:
count: number of entries this shard should generate.
string_length: the length of generated random strings.
"""
self._count = count
self._string_length = string_length
def __iter__(self):
ctx = context.get()
while self._count:
self._count -= 1
start_time = time.time()
content = "".join(random.choice(string.ascii_lowercase)
for _ in range(self._string_length))
if ctx:
operation.counters.Increment(
COUNTER_IO_READ_MSEC, int((time.time() - start_time) * 1000))(ctx)
operation.counters.Increment(COUNTER_IO_READ_BYTES, len(content))(ctx)
yield content
@classmethod
def split_input(cls, mapper_spec):
params = _get_params(mapper_spec)
count = params[cls.COUNT]
string_length = cls.DEFAULT_STRING_LENGTH
if cls.STRING_LENGTH in params:
string_length = params[cls.STRING_LENGTH]
shard_count = mapper_spec.shard_count
count_per_shard = count // shard_count
mr_input_readers = [
cls(count_per_shard, string_length) for _ in range(shard_count)]
left = count - count_per_shard*shard_count
if left > 0:
mr_input_readers.append(cls(left, string_length))
return mr_input_readers
@classmethod
def validate(cls, mapper_spec):
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Mapper input reader class mismatch")
params = _get_params(mapper_spec)
if cls.COUNT not in params:
raise BadReaderParamsError("Must specify %s" % cls.COUNT)
if not isinstance(params[cls.COUNT], int):
raise BadReaderParamsError("%s should be an int but is %s" %
(cls.COUNT, type(params[cls.COUNT])))
if params[cls.COUNT] <= 0:
raise BadReaderParamsError("%s should be a positive int")
if cls.STRING_LENGTH in params and not (
isinstance(params[cls.STRING_LENGTH], int) and
params[cls.STRING_LENGTH] > 0):
raise BadReaderParamsError("%s should be a positive int but is %s" %
(cls.STRING_LENGTH, params[cls.STRING_LENGTH]))
if (not isinstance(mapper_spec.shard_count, int) or
mapper_spec.shard_count <= 0):
raise BadReaderParamsError(
"shard_count should be a positive int but is %s" %
mapper_spec.shard_count)
@classmethod
def from_json(cls, json):
return cls(json[cls.COUNT], json[cls.STRING_LENGTH])
def to_json(self):
return {self.COUNT: self._count, self.STRING_LENGTH: self._string_length}
class NamespaceInputReader(InputReader):
"""An input reader to iterate over namespaces.
This reader yields namespace names as string.
It will always produce only one shard.
"""
NAMESPACE_RANGE_PARAM = "namespace_range"
BATCH_SIZE_PARAM = "batch_size"
_BATCH_SIZE = 10
def __init__(self, ns_range, batch_size=_BATCH_SIZE):
self.ns_range = ns_range
self._batch_size = batch_size
def to_json(self):
"""Serializes all the data in this query range into json form.
Returns:
all the data in json-compatible map.
"""
return {self.NAMESPACE_RANGE_PARAM: self.ns_range.to_json_object(),
self.BATCH_SIZE_PARAM: self._batch_size}
@classmethod
def from_json(cls, json):
"""Create new DatastoreInputReader from the json, encoded by to_json.
Args:
json: json map representation of DatastoreInputReader.
Returns:
an instance of DatastoreInputReader with all data deserialized from json.
"""
return cls(
namespace_range.NamespaceRange.from_json_object(
json[cls.NAMESPACE_RANGE_PARAM]),
json[cls.BATCH_SIZE_PARAM])
@classmethod
def validate(cls, mapper_spec):
"""Validates mapper spec.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise BadReaderParamsError("Input reader class mismatch")
params = _get_params(mapper_spec)
if cls.BATCH_SIZE_PARAM in params:
try:
batch_size = int(params[cls.BATCH_SIZE_PARAM])
if batch_size < 1:
raise BadReaderParamsError("Bad batch size: %s" % batch_size)
except ValueError, e:
raise BadReaderParamsError("Bad batch size: %s" % e)
@classmethod
def split_input(cls, mapper_spec):
"""Returns a list of input readers for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader.
Returns:
A list of InputReaders.
"""
batch_size = int(_get_params(mapper_spec).get(
cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE))
shard_count = mapper_spec.shard_count
namespace_ranges = namespace_range.NamespaceRange.split(shard_count,
contiguous=True)
return [NamespaceInputReader(ns_range, batch_size)
for ns_range in namespace_ranges]
def __iter__(self):
while True:
keys = self.ns_range.make_datastore_query().Get(limit=self._batch_size)
if not keys:
break
for key in keys:
namespace = metadata.Namespace.key_to_namespace(key)
self.ns_range = self.ns_range.with_start_after(namespace)
yield namespace
def __str__(self):
return repr(self.ns_range)
class RecordsReader(InputReader):
"""Reader to read a list of Files API file in records format.
The number of input shards can be specified by the SHARDS_PARAM
mapper parameter. Input files cannot be split, so there will be at most
one shard per file. Also the number of shards will not be reduced based on
the number of input files, so shards in always equals shards out.
"""
FILE_PARAM = "file"
FILES_PARAM = "files"
def __init__(self, filenames, position):
"""Constructor.
Args:
filenames: list of filenames.
position: file position to start reading from as int.
"""
self._filenames = filenames
if self._filenames:
self._reader = records.RecordsReader(
files.BufferedFile(self._filenames[0]))
self._reader.seek(position)
else:
self._reader = None
def __iter__(self):
"""Iterate over records in file.
Yields:
Records as strings.
"""
ctx = context.get()
while self._reader:
try:
start_time = time.time()
record = self._reader.read()
if ctx:
operation.counters.Increment(
COUNTER_IO_READ_MSEC, int((time.time() - start_time) * 1000))(ctx)
operation.counters.Increment(COUNTER_IO_READ_BYTES, len(record))(ctx)
yield record
except (files.ExistenceError), e:
raise errors.FailJobError("ExistenceError: %s" % e)
except (files.UnknownError), e:
raise errors.RetrySliceError("UnknownError: %s" % e)
except EOFError:
self._filenames.pop(0)
if not self._filenames:
self._reader = None
else:
self._reader = records.RecordsReader(
files.BufferedFile(self._filenames[0]))
@classmethod
def from_json(cls, json):
"""Creates an instance of the InputReader for the given input shard state.
Args:
json: The InputReader state as a dict-like object.
Returns:
An instance of the InputReader configured using the values of json.
"""
return cls(json["filenames"], json["position"])
def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader.
"""
result = {
"filenames": self._filenames,
"position": 0,
}
if self._reader:
result["position"] = self._reader.tell()
return result
@classmethod
def split_input(cls, mapper_spec):
"""Returns a list of input readers for the input spec.
Args:
mapper_spec: The MapperSpec for this InputReader.
Returns:
A list of InputReaders.
"""
params = _get_params(mapper_spec)
shard_count = mapper_spec.shard_count
if cls.FILES_PARAM in params:
filenames = params[cls.FILES_PARAM]
if isinstance(filenames, basestring):
filenames = filenames.split(",")
else:
filenames = [params[cls.FILE_PARAM]]
batch_list = [[] for _ in xrange(shard_count)]
for index, _ in enumerate(filenames):
batch_list[index % shard_count].append(filenames[index])
batch_list.sort(reverse=True, key=len)
return [cls(batch, 0) for batch in batch_list]
@classmethod
def validate(cls, mapper_spec):
"""Validates mapper spec and all mapper parameters.
Args:
mapper_spec: The MapperSpec for this InputReader.
Raises:
BadReaderParamsError: required parameters are missing or invalid.
"""
if mapper_spec.input_reader_class() != cls:
raise errors.BadReaderParamsError("Input reader class mismatch")
params = _get_params(mapper_spec)
if (cls.FILES_PARAM not in params and
cls.FILE_PARAM not in params):
raise BadReaderParamsError(
"Must specify '%s' or '%s' parameter for mapper input" %
(cls.FILES_PARAM, cls.FILE_PARAM))
def __str__(self):
position = 0
if self._reader:
position = self._reader.tell()
return "%s:%s" % (self._filenames, position)
class LogInputReader(InputReader):
"""Input reader for a time range of logs via the Logs Reader API.
The number of input shards may be specified by the SHARDS_PARAM mapper
parameter. A starting and ending time (in seconds since the Unix epoch) are
required to generate time ranges over which to shard the input.
"""
START_TIME_PARAM = "start_time"
END_TIME_PARAM = "end_time"
MINIMUM_LOG_LEVEL_PARAM = "minimum_log_level"
INCLUDE_INCOMPLETE_PARAM = "include_incomplete"
INCLUDE_APP_LOGS_PARAM = "include_app_logs"
VERSION_IDS_PARAM = "version_ids"
MODULE_VERSIONS_PARAM = "module_versions"
_OFFSET_PARAM = "offset"
_PROTOTYPE_REQUEST_PARAM = "prototype_request"
_PARAMS = frozenset([START_TIME_PARAM, END_TIME_PARAM, _OFFSET_PARAM,
MINIMUM_LOG_LEVEL_PARAM, INCLUDE_INCOMPLETE_PARAM,
INCLUDE_APP_LOGS_PARAM, VERSION_IDS_PARAM,
MODULE_VERSIONS_PARAM, _PROTOTYPE_REQUEST_PARAM])
_KWARGS = frozenset([_OFFSET_PARAM, _PROTOTYPE_REQUEST_PARAM])
def __init__(self,
start_time=None,
end_time=None,
minimum_log_level=None,
include_incomplete=False,
include_app_logs=False,
version_ids=None,
module_versions=None,
**kwargs):
"""Constructor.
Args:
start_time: The earliest request completion or last-update time of logs
that should be mapped over, in seconds since the Unix epoch.
end_time: The latest request completion or last-update time that logs
should be mapped over, in seconds since the Unix epoch.
minimum_log_level: An application log level which serves as a filter on
the requests mapped over--requests with no application log at or above
the specified level will be omitted, even if include_app_logs is False.
include_incomplete: Whether or not to include requests that have started
but not yet finished, as a boolean. Defaults to False.
include_app_logs: Whether or not to include application level logs in the
mapped logs, as a boolean. Defaults to False.
version_ids: A list of version ids whose logs should be read. This can not
be used with module_versions
module_versions: A list of tuples containing a module and version id
whose logs should be read. This can not be used with version_ids
**kwargs: A dictionary of keywords associated with this input reader.
"""
InputReader.__init__(self)
self.__params = dict(kwargs)
if start_time is not None:
self.__params[self.START_TIME_PARAM] = start_time
if end_time is not None:
self.__params[self.END_TIME_PARAM] = end_time
if minimum_log_level is not None:
self.__params[self.MINIMUM_LOG_LEVEL_PARAM] = minimum_log_level
if include_incomplete is not None:
self.__params[self.INCLUDE_INCOMPLETE_PARAM] = include_incomplete
if include_app_logs is not None:
self.__params[self.INCLUDE_APP_LOGS_PARAM] = include_app_logs
if version_ids:
self.__params[self.VERSION_IDS_PARAM] = version_ids
if module_versions:
self.__params[self.MODULE_VERSIONS_PARAM] = module_versions
if self._PROTOTYPE_REQUEST_PARAM in self.__params:
prototype_request = log_service_pb.LogReadRequest(
self.__params[self._PROTOTYPE_REQUEST_PARAM])
self.__params[self._PROTOTYPE_REQUEST_PARAM] = prototype_request
def __iter__(self):
"""Iterates over logs in a given range of time.
Yields:
A RequestLog containing all the information for a single request.
"""
for log in logservice.fetch(**self.__params):
self.__params[self._OFFSET_PARAM] = log.offset
yield log
@classmethod
def from_json(cls, json):
"""Creates an instance of the InputReader for the given input shard's state.
Args:
json: The InputReader state as a dict-like object.
Returns:
An instance of the InputReader configured using the given JSON parameters.
"""
params = dict((str(k), v) for k, v in json.iteritems()
if k in cls._PARAMS)
if cls._OFFSET_PARAM in params:
params[cls._OFFSET_PARAM] = base64.b64decode(params[cls._OFFSET_PARAM])
return cls(**params)
def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A JSON serializable version of the remaining input to read.
"""
params = dict(self.__params)
if self._PROTOTYPE_REQUEST_PARAM in params:
prototype_request = params[self._PROTOTYPE_REQUEST_PARAM]
params[self._PROTOTYPE_REQUEST_PARAM] = prototype_request.Encode()
if self._OFFSET_PARAM in params:
params[self._OFFSET_PARAM] = base64.b64encode(params[self._OFFSET_PARAM])
return params
@classmethod
def split_input(cls, mapper_spec):
"""Returns a list of input readers for the given input specification.
Args:
mapper_spec: The MapperSpec for this InputReader.
Returns:
A list of InputReaders.
"""
params = _get_params(mapper_spec)
shard_count = mapper_spec.shard_count
start_time = params[cls.START_TIME_PARAM]
end_time = params[cls.END_TIME_PARAM]
seconds_per_shard = (end_time - start_time) / shard_count
shards = []
for _ in xrange(shard_count - 1):
params[cls.END_TIME_PARAM] = (params[cls.START_TIME_PARAM] +
seconds_per_shard)
shards.append(LogInputReader(**params))
params[cls.START_TIME_PARAM] = params[cls.END_TIME_PARAM]
params[cls.END_TIME_PARAM] = end_time
return shards + [LogInputReader(**params)]
@classmethod
def validate(cls, mapper_spec):
"""Validates the mapper's specification and all necessary parameters.
Args:
mapper_spec: The MapperSpec to be used with this InputReader.
Raises:
BadReaderParamsError: If the user fails to specify both a starting time
and an ending time, or if the starting time is later than the ending
time.
"""
if mapper_spec.input_reader_class() != cls:
raise errors.BadReaderParamsError("Input reader class mismatch")
params = _get_params(mapper_spec, allowed_keys=cls._PARAMS)
if (cls.VERSION_IDS_PARAM not in params and
cls.MODULE_VERSIONS_PARAM not in params):
raise errors.BadReaderParamsError("Must specify a list of version ids or "
"module/version ids for mapper input")
if (cls.VERSION_IDS_PARAM in params and
cls.MODULE_VERSIONS_PARAM in params):
raise errors.BadReaderParamsError("Can not supply both version ids or "
"module/version ids. Use only one.")
if (cls.START_TIME_PARAM not in params or
params[cls.START_TIME_PARAM] is None):
raise errors.BadReaderParamsError("Must specify a starting time for "
"mapper input")
if cls.END_TIME_PARAM not in params or params[cls.END_TIME_PARAM] is None:
params[cls.END_TIME_PARAM] = time.time()
if params[cls.START_TIME_PARAM] >= params[cls.END_TIME_PARAM]:
raise errors.BadReaderParamsError("The starting time cannot be later "
"than or the same as the ending time.")
if cls._PROTOTYPE_REQUEST_PARAM in params:
try:
params[cls._PROTOTYPE_REQUEST_PARAM] = log_service_pb.LogReadRequest(
params[cls._PROTOTYPE_REQUEST_PARAM])
except (TypeError, ProtocolBuffer.ProtocolBufferDecodeError):
raise errors.BadReaderParamsError("The prototype request must be "
"parseable as a LogReadRequest.")
try:
logservice.fetch(**params)
except logservice.InvalidArgumentError, e:
raise errors.BadReaderParamsError("One or more parameters are not valid "
"inputs to logservice.fetch(): %s" % e)
def __str__(self):
"""Returns the string representation of this LogInputReader."""
params = []
for key in sorted(self.__params.keys()):
value = self.__params[key]
if key is self._PROTOTYPE_REQUEST_PARAM:
params.append("%s='%s'" % (key, value))
elif key is self._OFFSET_PARAM:
params.append("%s='%s'" % (key, value))
else:
params.append("%s=%s" % (key, value))
return "LogInputReader(%s)" % ", ".join(params)
class _GoogleCloudStorageInputReader(InputReader):
"""Input reader from Google Cloud Storage using the cloudstorage library.
This class is expected to be subclassed with a reader that understands
user-level records.
Required configuration in the mapper_spec.input_reader dictionary.
BUCKET_NAME_PARAM: name of the bucket to use (with no extra delimiters or
suffixed such as directories.
OBJECT_NAMES_PARAM: a list of object names or prefixes. All objects must be
in the BUCKET_NAME_PARAM bucket. If the name ends with a * it will be
treated as prefix and all objects with matching names will be read.
Entries should not start with a slash unless that is part of the object's
name. An example list could be:
["my-1st-input-file", "directory/my-2nd-file", "some/other/dir/input-*"]
To retrieve all files "*" will match every object in the bucket. If a file
is listed twice or is covered by multiple prefixes it will be read twice,
there is no deduplication.
Optional configuration in the mapper_sec.input_reader dictionary.
BUFFER_SIZE_PARAM: the size of the read buffer for each file handle.
DELIMITER_PARAM: if specified, turn on the shallow splitting mode.
The delimiter is used as a path separator to designate directory
hierarchy. Matching of prefixes from OBJECT_NAME_PARAM
will stop at the first directory instead of matching
all files under the directory. This allows MR to process bucket with
hundreds of thousands of files.
"""
BUCKET_NAME_PARAM = "bucket_name"
OBJECT_NAMES_PARAM = "objects"
BUFFER_SIZE_PARAM = "buffer_size"
DELIMITER_PARAM = "delimiter"
_ACCOUNT_ID_PARAM = "account_id"
_JSON_PICKLE = "pickle"
_STRING_MAX_FILES_LISTED = 10
def __init__(self, filenames, index=0, buffer_size=None, _account_id=None,
delimiter=None):
"""Initialize a GoogleCloudStorageInputReader instance.
Args:
filenames: A list of Google Cloud Storage filenames of the form
'/bucket/objectname'.
index: Index of the next filename to read.
buffer_size: The size of the read buffer, None to use default.
_account_id: Internal use only. See cloudstorage documentation.
delimiter: Delimiter used as path separator. See class doc for details.
"""
self._filenames = filenames
self._index = index
self._buffer_size = buffer_size
self._account_id = _account_id
self._delimiter = delimiter
self._bucket = None
self._bucket_iter = None
def _next_file(self):
"""Find next filename.
self._filenames may need to be expanded via listbucket.
Returns:
None if no more file is left. Filename otherwise.
"""
while True:
if self._bucket_iter:
try:
return self._bucket_iter.next().filename
except StopIteration:
self._bucket_iter = None
self._bucket = None
if self._index >= len(self._filenames):
return
filename = self._filenames[self._index]
self._index += 1
if self._delimiter is None or not filename.endswith(self._delimiter):
return filename
self._bucket = cloudstorage.listbucket(filename,
delimiter=self._delimiter)
self._bucket_iter = iter(self._bucket)
@classmethod
def get_params(cls, mapper_spec, allowed_keys=None, allow_old=True):
params = _get_params(mapper_spec, allowed_keys, allow_old)
if (mapper_spec.params.get(cls.BUCKET_NAME_PARAM) is not None and
params.get(cls.BUCKET_NAME_PARAM) is None):
params[cls.BUCKET_NAME_PARAM] = mapper_spec.params[cls.BUCKET_NAME_PARAM]
return params
@classmethod
def validate(cls, mapper_spec):
"""Validate mapper specification.
Args:
mapper_spec: an instance of model.MapperSpec
Raises:
BadReaderParamsError: if the specification is invalid for any reason such
as missing the bucket name or providing an invalid bucket name.
"""
reader_spec = cls.get_params(mapper_spec, allow_old=False)
if cls.BUCKET_NAME_PARAM not in reader_spec:
raise errors.BadReaderParamsError(
"%s is required for Google Cloud Storage" %
cls.BUCKET_NAME_PARAM)
try:
cloudstorage.validate_bucket_name(
reader_spec[cls.BUCKET_NAME_PARAM])
except ValueError, error:
raise errors.BadReaderParamsError("Bad bucket name, %s" % (error))
if cls.OBJECT_NAMES_PARAM not in reader_spec:
raise errors.BadReaderParamsError(
"%s is required for Google Cloud Storage" %
cls.OBJECT_NAMES_PARAM)
filenames = reader_spec[cls.OBJECT_NAMES_PARAM]
if not isinstance(filenames, list):
raise errors.BadReaderParamsError(
"Object name list is not a list but a %s" %
filenames.__class__.__name__)
for filename in filenames:
if not isinstance(filename, basestring):
raise errors.BadReaderParamsError(
"Object name is not a string but a %s" %
filename.__class__.__name__)
if cls.DELIMITER_PARAM in reader_spec:
delimiter = reader_spec[cls.DELIMITER_PARAM]
if not isinstance(delimiter, basestring):
raise errors.BadReaderParamsError(
"%s is not a string but a %s" %
(cls.DELIMITER_PARAM, type(delimiter)))
@classmethod
def split_input(cls, mapper_spec):
"""Returns a list of input readers.
An equal number of input files are assigned to each shard (+/- 1). If there
are fewer files than shards, fewer than the requested number of shards will
be used. Input files are currently never split (although for some formats
could be and may be split in a future implementation).
Args:
mapper_spec: an instance of model.MapperSpec.
Returns:
A list of InputReaders. None when no input data can be found.
"""
reader_spec = cls.get_params(mapper_spec, allow_old=False)
bucket = reader_spec[cls.BUCKET_NAME_PARAM]
filenames = reader_spec[cls.OBJECT_NAMES_PARAM]
delimiter = reader_spec.get(cls.DELIMITER_PARAM)
account_id = reader_spec.get(cls._ACCOUNT_ID_PARAM)
buffer_size = reader_spec.get(cls.BUFFER_SIZE_PARAM)
all_filenames = []
for filename in filenames:
if filename.endswith("*"):
all_filenames.extend(
[file_stat.filename for file_stat in cloudstorage.listbucket(
"/" + bucket + "/" + filename[:-1], delimiter=delimiter,
_account_id=account_id)])
else:
all_filenames.append("/%s/%s" % (bucket, filename))
readers = []
for shard in range(0, mapper_spec.shard_count):
shard_filenames = all_filenames[shard::mapper_spec.shard_count]
if shard_filenames:
readers.append(cls(
shard_filenames, buffer_size=buffer_size, _account_id=account_id,
delimiter=delimiter))
return readers
@classmethod
def from_json(cls, state):
obj = pickle.loads(state[cls._JSON_PICKLE])
if obj._bucket:
obj._bucket_iter = iter(obj._bucket)
return obj
def to_json(self):
before_iter = self._bucket_iter
self._bucket_iter = None
try:
return {self._JSON_PICKLE: pickle.dumps(self)}
finally:
self._bucket_itr = before_iter
def next(self):
"""Returns the next input from this input reader, a block of bytes.
Non existent files will be logged and skipped. The file might have been
removed after input splitting.
Returns:
The next input from this input reader in the form of a cloudstorage
ReadBuffer that supports a File-like interface (read, readline, seek,
tell, and close). An error may be raised if the file can not be opened.
Raises:
StopIteration: The list of files has been exhausted.
"""
options = {}
if self._buffer_size:
options["read_buffer_size"] = self._buffer_size
if self._account_id:
options["_account_id"] = self._account_id
while True:
filename = self._next_file()
if filename is None:
raise StopIteration()
try:
start_time = time.time()
handle = cloudstorage.open(filename, **options)
ctx = context.get()
if ctx:
operation.counters.Increment(
COUNTER_IO_READ_MSEC, int((time.time() - start_time) * 1000))(ctx)
return handle
except cloudstorage.NotFoundError:
logging.warning("File %s may have been removed. Skipping file.",
filename)
def __str__(self):
num_files = len(self._filenames)
if num_files > self._STRING_MAX_FILES_LISTED:
names = "%s...%s + %d not shown" % (
",".join(self._filenames[0:self._STRING_MAX_FILES_LISTED-1]),
self._filenames[-1],
num_files - self._STRING_MAX_FILES_LISTED)
else:
names = ",".join(self._filenames)
if self._index > num_files:
status = "EOF"
else:
status = "Next %s (%d of %d)" % (
self._filenames[self._index],
self._index + 1,
num_files)
return "CloudStorage [%s, %s]" % (status, names)
class _GoogleCloudStorageRecordInputReader(_GoogleCloudStorageInputReader):
"""Read data from a Google Cloud Storage file using LevelDB format.
See the _GoogleCloudStorageOutputWriter for additional configuration options.
"""
def __getstate__(self):
result = self.__dict__.copy()
if "_record_reader" in result:
result.pop("_record_reader")
return result
def next(self):
"""Returns the next input from this input reader, a record.
Returns:
The next input from this input reader in the form of a record read from
an LevelDB file.
Raises:
StopIteration: The ordered set records has been exhausted.
"""
while True:
if not hasattr(self, "_cur_handle") or self._cur_handle is None:
self._cur_handle = super(_GoogleCloudStorageRecordInputReader,
self).next()
if not hasattr(self, "_record_reader") or self._record_reader is None:
self._record_reader = records.RecordsReader(self._cur_handle)
try:
start_time = time.time()
content = self._record_reader.read()
ctx = context.get()
if ctx:
operation.counters.Increment(COUNTER_IO_READ_BYTES, len(content))(ctx)
operation.counters.Increment(
COUNTER_IO_READ_MSEC, int((time.time() - start_time) * 1000))(ctx)
return content
except EOFError:
self._cur_handle = None
self._record_reader = None
class _ReducerReader(_GoogleCloudStorageRecordInputReader):
"""Reader to read KeyValues records from GCS."""
expand_parameters = True
def __init__(self, filenames, index=0, buffer_size=None, _account_id=None,
delimiter=None):
super(_ReducerReader, self).__init__(filenames, index, buffer_size,
_account_id, delimiter)
self.current_key = None
self.current_values = None
def __iter__(self):
ctx = context.get()
combiner = None
if ctx:
combiner_spec = ctx.mapreduce_spec.mapper.params.get("combiner_spec")
if combiner_spec:
combiner = util.handler_for_name(combiner_spec)
try:
while True:
binary_record = super(_ReducerReader, self).next()
proto = file_service_pb.KeyValues()
proto.ParseFromString(binary_record)
to_yield = None
if self.current_key is not None and self.current_key != proto.key():
to_yield = (self.current_key, self.current_values)
self.current_key = None
self.current_values = None
if self.current_key is None:
self.current_key = proto.key()
self.current_values = []
if combiner:
combiner_result = combiner(
self.current_key, proto.value_list(), self.current_values)
if not util.is_generator(combiner_result):
raise errors.BadCombinerOutputError(
"Combiner %s should yield values instead of returning them "
"(%s)" % (combiner, combiner_result))
self.current_values = []
for value in combiner_result:
if isinstance(value, operation.Operation):
value(ctx)
else:
self.current_values.append(value)
if not to_yield:
yield ALLOW_CHECKPOINT
else:
self.current_values.extend(proto.value_list())
if to_yield:
yield to_yield
yield ALLOW_CHECKPOINT
except StopIteration:
pass
if self.current_key is not None:
to_yield = (self.current_key, self.current_values)
self.current_key = None
self.current_values = None
yield to_yield
@staticmethod
def encode_data(data):
"""Encodes the given data, which may have include raw bytes.
Works around limitations in JSON encoding, which cannot handle raw bytes.
Args:
data: the data to encode.
Returns:
The data encoded.
"""
return base64.b64encode(pickle.dumps(data))
@staticmethod
def decode_data(data):
"""Decodes data encoded with the encode_data function."""
return pickle.loads(base64.b64decode(data))
def to_json(self):
"""Returns an input shard state for the remaining inputs.