def run(self, job_name, bucket_name, filenames):
sort_mappers = []
for i in range(len(filenames)):
filenames_only = util.strip_prefix_from_items("/%s/" % bucket_name,
filenames[i])
sort_mapper = yield mapper_pipeline.MapperPipeline(
"%s-shuffle-sort-%s" % (job_name, str(i)),
__name__ + "._sort_records_map",
__name__ + "._BatchGCSRecordsReader",
None,
{
"input_reader": {
"bucket_name": bucket_name,
"objects": filenames_only,
},
},
shards=1)
sort_mappers.append(sort_mapper)
with pipeline.After(*sort_mappers):
job_ids = yield pipeline_common.Append(*[mapper.job_id for mapper in
sort_mappers])
result = yield _CollectOutputFiles(job_ids)
with pipeline.After(result):
yield _CleanupOutputFiles(job_ids)
yield pipeline_common.Return(result)
def run(self, shards):
result = []
shuffled_shards = [[] for _ in shards[0]]
for shard in shards:
for i, filename in enumerate(shard):
shuffled_shards[i].append(filename)
for filenames in shuffled_shards:
sorted_files = yield shuffler.SortPipeline(filenames)
result.append(sorted_files)
yield pipeline_common.Append(*result)
def run(self, job_name, filenames):
sort_mappers = []
for i in range(len(filenames)):
filename = filenames[i]
sort_mapper = yield mapper_pipeline.MapperPipeline(
"%s-shuffle-sort-%s" % (job_name, str(i)),
__name__ + "._sort_records_map",
__name__ + "._BatchRecordsReader",
None, {
"files": [filename],
"processing_rate": 1000000,
},
shards=1)
sort_mappers.append(sort_mapper)
with pipeline.After(*sort_mappers):
job_ids = yield pipeline_common.Append(
*[mapper.job_id for mapper in sort_mappers])
result = yield _CollectOutputFiles(job_ids)
with pipeline.After(result):
yield _CleanupOutputFiles(job_ids)
yield pipeline_common.Return(result)
