blob: 9b32035c8cf46b3d414f95c2f003ee9f0c13ec50 [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."""
__all__ = [
"MapperPipeline",
]
from mapreduce.lib import files
from mapreduce import base_handler
from mapreduce import control
from mapreduce import model
class MapperPipeline(base_handler.PipelineBase):
"""Pipeline wrapper for mapper job.
Args:
job_name: mapper job name as string
handler_spec: mapper handler specification as string.
input_reader_spec: input reader specification as string.
output_writer_spec: output writer specification as string.
params: mapper parameters for input reader and output writer as dict.
shards: number of shards in the job as int.
Returns:
The list of filenames mapper was outputting to.
"""
async = True
# TODO(user): we probably want to output counters too.
# Might also need to double filenames as named output.
output_names = [
# Job ID. MapreduceState.get_by_job_id can be used to load
# mapreduce state. Is filled immediately after job starts up.
"job_id",
# Dictionary of final counter values. Filled when job is completed.
"counters",
]
def run(self,
job_name,
handler_spec,
input_reader_spec,
output_writer_spec=None,
params=None,
shards=None):
mapreduce_id = control.start_map(
job_name,
handler_spec,
input_reader_spec,
params or {},
mapreduce_parameters={
"done_callback": self.get_callback_url(),
"done_callback_method": "GET",
"pipeline_id": self.pipeline_id,
},
shard_count=shards,
output_writer_spec=output_writer_spec,
)
self.fill(self.outputs.job_id, mapreduce_id)
self.set_status(console_url="%s/detail?job_id=%s" % (
(base_handler._DEFAULT_BASE_PATH, mapreduce_id)))
def callback(self):
mapreduce_id = self.outputs.job_id.value
mapreduce_state = model.MapreduceState.get_by_job_id(mapreduce_id)
mapper_spec = mapreduce_state.mapreduce_spec.mapper
files = None
output_writer_class = mapper_spec.output_writer_class()
if output_writer_class:
files = output_writer_class.get_filenames(mapreduce_state)
self.fill(self.outputs.counters, mapreduce_state.counters_map.to_dict())
self.complete(files)
class _CleanupPipeline(base_handler.PipelineBase):
"""A pipeline to do a cleanup for mapreduce jobs.
Args:
filename_or_list: list of files or file lists to delete.
"""
def delete_file_or_list(self, filename_or_list):
if isinstance(filename_or_list, list):
for filename in filename_or_list:
self.delete_file_or_list(filename)
else:
filename = filename_or_list
for _ in range(10):
try:
files.delete(filename)
break
except:
pass
def run(self, temp_files):
self.delete_file_or_list(temp_files)