blob: 84790118d7e034e891c9f50213b026df28512ecb [file] [log] [blame]
#!/usr/bin/env python
#
# Copyright 2011 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.
"""Pipelines for mapreduce library."""
from __future__ import with_statement
__all__ = [
"MapPipeline",
"MapperPipeline",
"MapreducePipeline",
"ReducePipeline",
"ShufflePipeline",
]
from mapreduce.lib import pipeline
from mapreduce.lib.pipeline import common as pipeline_common
from mapreduce.lib import files
from mapreduce.lib.files import file_service_pb
from mapreduce import base_handler
from mapreduce import context
from mapreduce import errors
from mapreduce import input_readers
from mapreduce import mapper_pipeline
from mapreduce import operation
from mapreduce import output_writers
from mapreduce import shuffler
from mapreduce import util
# Mapper pipeline is extracted only to remove dependency cycle with shuffler.py
# Reimport it back.
MapperPipeline = mapper_pipeline.MapperPipeline
ShufflePipeline = shuffler.ShufflePipeline
class MapPipeline(base_handler.PipelineBase):
"""Runs the map stage of MapReduce.
Iterates over input reader and outputs data into key/value format
for shuffler consumption.
Args:
job_name: mapreduce job name as string.
mapper_spec: specification of map handler function as string.
input_reader_spec: input reader specification as string.
params: mapper and input reader parameters as dict.
shards: number of shards to start as int.
Returns:
list of filenames written to by this mapper, one for each shard.
"""
def run(self,
job_name,
mapper_spec,
input_reader_spec,
params,
shards=None):
yield MapperPipeline(
job_name + "-map",
mapper_spec,
input_reader_spec,
output_writer_spec=
output_writers.__name__ + ".KeyValueBlobstoreOutputWriter",
params=params,
shards=shards)
class _ReducerReader(input_readers.RecordsReader):
"""Reader to read KeyValues records files from Files API."""
expand_parameters = True
def __init__(self, filenames, position):
super(_ReducerReader, self).__init__(filenames, position)
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)
self.current_key = None
self.current_values = None
for binary_record in super(_ReducerReader, self).__iter__():
proto = file_service_pb.KeyValues()
proto.ParseFromString(binary_record)
if self.current_key is None:
self.current_key = proto.key()
self.current_values = []
else:
assert proto.key() == self.current_key, (
"inconsistent key sequence. Expected %s but got %s" %
(self.current_key, proto.key()))
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:
# with combiner current values always come from combiner
self.current_values.append(value)
else:
# without combiner we just accumulate values.
self.current_values.extend(proto.value_list())
if not proto.partial():
key = self.current_key
values = self.current_values
# This is final value, don't try to serialize it.
self.current_key = None
self.current_values = None
yield (key, values)
else:
yield input_readers.ALLOW_CHECKPOINT
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.current_key
result["current_values"] = 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 = json["current_key"]
result.current_values = json["current_values"]
return result
class ReducePipeline(base_handler.PipelineBase):
"""Runs the reduce stage of MapReduce.
Merge-reads input files and runs reducer function on them.
Args:
job_name: mapreduce job name as string.
reader_spec: specification of reduce function.
output_writer_spec: specification of output write to use with reduce
function.
params: mapper parameters to use as dict.
filenames: list of filenames to reduce.
combiner_spec: Optional. Specification of a combine function. If not
supplied, no combine step will take place. The combine function takes a
key, list of values and list of previously combined results. It yields
combined values that might be processed by another combiner call, but will
eventually end up in reducer. The combiner output key is assumed to be the
same as the input key.
Returns:
filenames from output writer.
"""
def run(self,
job_name,
reducer_spec,
output_writer_spec,
params,
filenames,
combiner_spec=None):
new_params = dict(params or {})
new_params.update({
"files": filenames
})
if combiner_spec:
new_params.update({
"combiner_spec": combiner_spec,
})
yield mapper_pipeline.MapperPipeline(
job_name + "-reduce",
reducer_spec,
__name__ + "._ReducerReader",
output_writer_spec,
new_params)
class MapreducePipeline(base_handler.PipelineBase):
"""Pipeline to execute MapReduce jobs.
Args:
job_name: job name as string.
mapper_spec: specification of mapper to use.
reducer_spec: specification of reducer to use.
input_reader_spec: specification of input reader to read data from.
output_writer_spec: specification of output writer to save reduce output to.
mapper_params: parameters to use for mapper phase.
reducer_params: parameters to use for reduce phase.
shards: number of shards to use as int.
combiner_spec: Optional. Specification of a combine function. If not
supplied, no combine step will take place. The combine function takes a
key, list of values and list of previously combined results. It yields
combined values that might be processed by another combiner call, but will
eventually end up in reducer. The combiner output key is assumed to be the
same as the input key.
Returns:
filenames from output writer.
"""
def run(self,
job_name,
mapper_spec,
reducer_spec,
input_reader_spec,
output_writer_spec=None,
mapper_params=None,
reducer_params=None,
shards=None,
combiner_spec=None):
map_pipeline = yield MapPipeline(job_name,
mapper_spec,
input_reader_spec,
params=mapper_params,
shards=shards)
shuffler_pipeline = yield ShufflePipeline(
job_name, map_pipeline)
reducer_pipeline = yield ReducePipeline(
job_name,
reducer_spec,
output_writer_spec,
reducer_params,
shuffler_pipeline,
combiner_spec=combiner_spec)
with pipeline.After(reducer_pipeline):
all_temp_files = yield pipeline_common.Extend(
map_pipeline, shuffler_pipeline)
yield mapper_pipeline._CleanupPipeline(all_temp_files)
yield pipeline_common.Return(reducer_pipeline)