#!/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", "GoogleCloudStorageInputReader", "GoogleCloudStorageRecordInputReader", "RandomStringInputReader", "RawDatastoreInputReader", "Error", "InputReader", "LogInputReader", "NamespaceInputReader", ] 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 logservice 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 json_util from google.appengine.ext.mapreduce import key_ranges from google.appengine.ext.mapreduce import kv_pb 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: cloudstorage = None from google.appengine._internal import cloudstorage if hasattr(cloudstorage, "_STUB"): cloudstorage = None except ImportError: pass if cloudstorage is None: try: import cloudstorage 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 = util.ALLOW_CHECKPOINT 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 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" WHOLE_EG_PARAM = "whole_eg" _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) params = _get_params(mapper_spec) shard_whole_eg = params.get(cls.WHOLE_EG_PARAM, False) 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, shard_whole_eg=shard_whole_eg) 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, shard_whole_eg=shard_whole_eg) 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, shard_whole_eg=False): """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. shard_whole_eg: Whether to align shard ranges to entity-group boundaries. 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, shard_whole_eg=shard_whole_eg) 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, shard_whole_eg=False): """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. shard_whole_eg: Whether to align shard ranges to entity-group boundaries. 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 shard_whole_eg: random_keys = cls.align_to_entity_groups(random_keys) 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 align_to_entity_groups(cls, keys): """Aligns the specified keys to entity group boundaries. This entails converting each key to the key of its entity group and removing duplicates. Args: keys: The sorted list of entity keys that should be aligned. If the list contains Nones, they must all be at the end. Returns: The sorted list of entity group keys, with Nones truncated from the end. """ result = [] for key in keys: if key is None: break eg_key = key.entity_group() if not result or result[-1] != eg_key: result.append(eg_key) return result @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) 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 (, , ). 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: (, , ). 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 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. FAIL_ON_MISSING_INPUT: if specified and True, the MR will fail if any of the input files are missing. Missing files will be skipped otherwise. """ BUCKET_NAME_PARAM = "bucket_name" OBJECT_NAMES_PARAM = "objects" BUFFER_SIZE_PARAM = "buffer_size" DELIMITER_PARAM = "delimiter" FAIL_ON_MISSING_INPUT = "fail_on_missing_input" _ACCOUNT_ID_PARAM = "account_id" _JSON_PICKLE = "pickle" _JSON_FAIL_ON_MISSING_INPUT = "fail_on_missing_input" _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 self._fail_on_missing_input = 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) fail_on_missing_input = reader_spec.get(cls.FAIL_ON_MISSING_INPUT) 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: reader = cls( shard_filenames, buffer_size=buffer_size, _account_id=account_id, delimiter=delimiter) reader._fail_on_missing_input = fail_on_missing_input readers.append(reader) return readers @classmethod def from_json(cls, state): obj = pickle.loads(state[cls._JSON_PICKLE]) obj._fail_on_missing_input = state.get( cls._JSON_FAIL_ON_MISSING_INPUT, False) 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), self._JSON_FAIL_ON_MISSING_INPUT: getattr(self, "_fail_on_missing_input", False) } 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: self._on_missing_input_file(filename) if getattr(self, "_fail_on_missing_input", False): raise errors.FailJobError( "File missing in GCS, aborting: %s" % filename) logging.warning("File %s may have been removed. Skipping file.", filename) def _on_missing_input_file(self, filename): """Hook which is called when an input file is missing. This implementation is a no-op. Subclasses can override it to add error handling. Note that this method should not raise exceptions. Instead, use the FAIL_ON_MISSING_INPUT param to control that. Args: filename: The file that is missing. """ pass 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) GoogleCloudStorageInputReader = _GoogleCloudStorageInputReader 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 GoogleCloudStorageRecordInputReader = _GoogleCloudStorageRecordInputReader 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 = kv_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. Returns: A json-izable version of the remaining InputReader. """ result = super(_ReducerReader, self).to_json() result["current_key"] = self.encode_data(self.current_key) result["current_values"] = self.encode_data(self.current_values) return result @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. """ result = super(_ReducerReader, cls).from_json(json) result.current_key = _ReducerReader.decode_data(json["current_key"]) result.current_values = _ReducerReader.decode_data(json["current_values"]) return result