class Job:
"""A class for orchestrating a Telemetry MapReduce job"""
# 1. read input filter
# 2. generate filtered list of local input files
# 2a. generate filtered list of remote input files
# 3. load mapper
# 4. spawn N processes
# 5. distribute files among processes
# 6. map(key, value, dims) each line in the file
# 7. combine map output for each file
# 8. reduce combine output overall
def __init__(self, config):
# Sanity check args.
if config.num_mappers <= 0:
raise ValueError("Number of mappers must be greater than zero")
if config.num_reducers <= 0:
raise ValueError("Number of reducers must be greater than zero")
if not os.path.isdir(config.data_dir):
raise ValueError("Data dir must be a valid directory")
if not os.path.isdir(config.work_dir):
raise ValueError("Work dir must be a valid directory")
if not os.path.isfile(config.job_script):
raise ValueError("Job script must be a valid python file")
if not os.path.isfile(config.input_filter):
raise ValueError("Input filter must be a valid json file")
self._input_dir = config.data_dir
if self._input_dir[-1] == os.path.sep:
self._input_dir = self._input_dir[0:-1]
self._work_dir = config.work_dir
self._input_filter = TelemetrySchema(json.load(open(config.input_filter)))
self._allowed_values = self._input_filter.sanitize_allowed_values()
self._output_file = config.output
self._num_mappers = config.num_mappers
self._num_reducers = config.num_reducers
self._local_only = config.local_only
self._bucket_name = config.bucket
self._aws_key = config.aws_key
self._aws_secret_key = config.aws_secret_key
modulefd = open(config.job_script)
# let the job script import additional modules under its path
sys.path.append(os.path.dirname(config.job_script))
## Lifted from FileDriver.py in jydoop.
self._job_module = imp.load_module("telemetry_job", modulefd, config.job_script, ('.py', 'U', 1))
def dump_stats(self, partitions):
total = sum(partitions)
avg = total / len(partitions)
for i in range(len(partitions)):
print "Partition %d contained %d (%+d)" % (i, partitions[i], float(partitions[i]) - avg)
def fetch_remotes(self, remotes):
# TODO: fetch remotes inside Mappers, and process each one as it becomes available.
remote_names = [ r["name"] for r in remotes if r["type"] == "remote" ]
# TODO: check cache first.
result = 0
if len(remote_names) == 0:
return result
fetch_cwd = os.path.join(self._work_dir, "cache")
if not os.path.isdir(fetch_cwd):
os.makedirs(fetch_cwd)
loader = s3util.Loader(fetch_cwd, self._bucket_name, aws_key=self._aws_key, aws_secret_key=self._aws_secret_key)
start = datetime.now()
downloaded_bytes = 0
for local, remote, err in loader.get_list(remote_names):
if err is None:
print "Downloaded", remote
downloaded_bytes += os.path.getsize(local)
else:
print "Failed to download", remote
result += 1
duration_sec = timer.delta_sec(start)
downloaded_mb = float(downloaded_bytes) / 1024.0 / 1024.0
print "Downloaded %.2fMB in %.2fs (%.2fMB/s)" % (downloaded_mb, duration_sec, downloaded_mb / duration_sec)
return result
def dedupe_remotes(self, remote_files, local_files):
return [ r for r in remote_files if os.path.join(self._input_dir, r.name) not in local_files ]
def mapreduce(self):
# Find files matching specified input filter
files = self.get_filtered_files(self._input_dir)
remote_files = self.get_filtered_files_s3()
# If we're using the cache dir as the data dir, we will end up reading
# each already-downloaded file twice, so we should skip any remote files
# that exist in the data dir.
remote_files = self.dedupe_remotes(remote_files, files)
file_count = len(files) + len(remote_files)
if file_count == 0:
print "Filter didn't match any files... nothing to do"
return
# Not useful to have more mappers than input files.
if file_count < self._num_mappers:
print "Filter matched only %s input files (%s local in %s and %s " \
"remote from %s). Reducing number of mappers accordingly." \
% (file_count, len(files), self._input_dir, len(remote_files),
self._bucket_name)
self._num_mappers = file_count
# Partition files into reasonably equal groups for use by mappers
print "Partitioning input data..."
partitions = self.partition(files, remote_files)
print "Done"
def checkExitCode(proc):
# If process was terminated by a signal, exitcode is the negative signal value
if proc.exitcode == -signal.SIGKILL:
# SIGKILL is most likely an OOM kill
raise MemoryError("%s ran out of memory" % proc.name)
elif proc.exitcode:
raise OSError("%s exited with code %d" % (proc.name, proc.exitcode))
# Partitions are ready. Map.
mappers = []
for i in range(self._num_mappers):
if len(partitions[i]) > 0:
# Fetch the files we need for each mapper
print "Fetching remotes for partition", i
fetch_result = self.fetch_remotes(partitions[i])
if fetch_result == 0:
print "Remote files fetched successfully"
else:
print "ERROR: Failed to fetch", fetch_result, "files."
# TODO: Bail, since results will be unreliable?
p = Process(
target=Mapper,
name=("Mapper-%d" % i),
args=(i, partitions[i], self._work_dir, self._job_module, self._num_reducers))
mappers.append(p)
p.start()
else:
print "Skipping mapper", i, "- no input files to process"
for m in mappers:
m.join()
checkExitCode(m)
# Mappers are done. Reduce.
reducers = []
for i in range(self._num_reducers):
p = Process(
target=Reducer,
name=("Reducer-%d" % i),
args=(i, self._work_dir, self._job_module, self._num_mappers))
reducers.append(p)
p.start()
for r in reducers:
r.join()
checkExitCode(r)
# Reducers are done. Output results.
to_combine = 1
try:
os.rename(os.path.join(self._work_dir, "reducer_0"), self._output_file)
except OSError, e:
if e.errno != errno.EXDEV:
raise
else:
# OSError: [Errno 18] Invalid cross-device link (EXDEV == 18)
# We can't rename across devices :( Copy / delete instead.
to_combine = 0
# TODO: If _output_file ends with a compressed suffix (.gz, .xz, .bz2, etc),
# try to compress it after writing.
if self._num_reducers > to_combine:
out = open(self._output_file, "a")
for i in range(to_combine, self._num_reducers):
# FIXME: this reads the entire reducer output into memory
reducer_filename = os.path.join(self._work_dir, "reducer_" + str(i))
reducer_output = open(reducer_filename, "r")
out.write(reducer_output.read())
reducer_output.close()
os.remove(reducer_filename)
# TODO: clean up downloaded files?
# Clean up mapper outputs
for m in range(self._num_mappers):
for r in range(self._num_reducers):
mfile = os.path.join(self._work_dir, "mapper_%d_%d" % (m, r))
if os.path.exists(mfile):
os.remove(mfile)
else:
print "Warning: Could not find", mfile
class Job:
"""A class for orchestrating a Telemetry MapReduce job"""
# 1. read input filter
# 2. generate filtered list of local input files
# 2a. generate filtered list of remote input files
# 3. load mapper
# 4. spawn N processes
# 5. distribute files among processes
# 6. map(key, value, dims) each line in the file
# 7. combine map output for each file
# 8. reduce combine output overall
def __init__(self, config):
# Sanity check args.
if config.get("num_mappers") <= 0:
raise ValueError("Number of mappers must be greater than zero")
if config.get("num_reducers") <= 0:
raise ValueError("Number of reducers must be greater than zero")
if not os.path.isdir(config.get("data_dir")):
raise ValueError("Data dir must be a valid directory")
if not os.path.isdir(config.get("work_dir")):
raise ValueError("Work dir must be a valid directory")
if not os.path.isfile(config.get("job_script", "")):
raise ValueError("Job script must be a valid python file")
if not os.path.isfile(config.get("input_filter")):
raise ValueError("Input filter must be a valid json file")
self._input_dir = config.get("data_dir")
if self._input_dir[-1] == os.path.sep:
self._input_dir = self._input_dir[0:-1]
self._work_dir = config.get("work_dir")
self._input_filter = TelemetrySchema(
json.load(open(config.get("input_filter"))))
self._allowed_values = self._input_filter.sanitize_allowed_values()
self._output_file = config.get("output")
self._num_mappers = config.get("num_mappers")
self._num_reducers = config.get("num_reducers")
self._local_only = config.get("local_only")
self._bucket_name = config.get("bucket")
self._aws_key = config.get("aws_key")
self._aws_secret_key = config.get("aws_secret_key")
self._profile = config.get("profile")
modulefd = open(config.get("job_script"))
# let the job script import additional modules under its path
sys.path.append(os.path.dirname(config.get("job_script")))
## Lifted from FileDriver.py in jydoop.
self._job_module = imp.load_module("telemetry_job", modulefd,
config.get("job_script"),
('.py', 'U', 1))
def dump_stats(self, partitions):
total = sum(partitions)
avg = total / len(partitions)
for i in range(len(partitions)):
print "Partition %d contained %d (%+d)" % (
i, partitions[i], float(partitions[i]) - avg)
def fetch_remotes(self, remotes):
# TODO: fetch remotes inside Mappers, and process each one as it becomes available.
remote_names = [r["name"] for r in remotes if r["type"] == "remote"]
# TODO: check cache first.
result = 0
if len(remote_names) == 0:
return result
fetch_cwd = os.path.join(self._work_dir, "cache")
if not os.path.isdir(fetch_cwd):
os.makedirs(fetch_cwd)
loader = s3util.Loader(fetch_cwd,
self._bucket_name,
aws_key=self._aws_key,
aws_secret_key=self._aws_secret_key)
start = datetime.now()
downloaded_bytes = 0
for local, remote, err in loader.get_list(remote_names):
if err is None:
print "Downloaded", remote
downloaded_bytes += os.path.getsize(local)
else:
print "Failed to download", remote
result += 1
duration_sec = timer.delta_sec(start)
downloaded_mb = float(downloaded_bytes) / 1024.0 / 1024.0
print "Downloaded %.2fMB in %.2fs (%.2fMB/s)" % (
downloaded_mb, duration_sec, downloaded_mb / duration_sec)
return result
def dedupe_remotes(self, remote_files, local_files):
return [
r for r in remote_files
if os.path.join(self._input_dir, r.name) not in local_files
]
def mapreduce(self):
# Find files matching specified input filter
files = self.get_filtered_files(self._input_dir)
remote_files = self.get_filtered_files_s3()
# If we're using the cache dir as the data dir, we will end up reading
# each already-downloaded file twice, so we should skip any remote files
# that exist in the data dir.
remote_files = self.dedupe_remotes(remote_files, files)
file_count = len(files) + len(remote_files)
if file_count == 0:
print "Filter didn't match any files... nothing to do"
return
# Not useful to have more mappers than input files.
if file_count < self._num_mappers:
print "Filter matched only %s input files (%s local in %s and %s " \
"remote from %s). Reducing number of mappers accordingly." \
% (file_count, len(files), self._input_dir, len(remote_files),
self._bucket_name)
self._num_mappers = file_count
# Partition files into reasonably equal groups for use by mappers
print "Partitioning input data..."
partitions = self.partition(files, remote_files)
print "Done"
def checkExitCode(proc):
# If process was terminated by a signal, exitcode is the negative signal value
if proc.exitcode == -signal.SIGKILL:
# SIGKILL is most likely an OOM kill
raise MemoryError("%s ran out of memory" % proc.name)
elif proc.exitcode:
raise OSError("%s exited with code %d" %
(proc.name, proc.exitcode))
# Partitions are ready. Map.
mappers = []
for i in range(self._num_mappers):
if len(partitions[i]) > 0:
# Fetch the files we need for each mapper
print "Fetching remotes for partition", i
fetch_result = self.fetch_remotes(partitions[i])
if fetch_result == 0:
print "Remote files fetched successfully"
else:
print "ERROR: Failed to fetch", fetch_result, "files."
# TODO: Bail, since results will be unreliable?
p = Process(target=Mapper,
name=("Mapper-%d" % i),
args=(i, self._profile, partitions[i],
self._work_dir, self._job_module,
self._num_reducers))
mappers.append(p)
p.start()
else:
print "Skipping mapper", i, "- no input files to process"
for m in mappers:
m.join()
checkExitCode(m)
# Mappers are done. Reduce.
reducers = []
for i in range(self._num_reducers):
p = Process(target=Reducer,
name=("Reducer-%d" % i),
args=(i, self._profile, self._work_dir,
self._job_module, self._num_mappers))
reducers.append(p)
p.start()
for r in reducers:
r.join()
checkExitCode(r)
# Reducers are done. Output results.
to_combine = 1
try:
os.rename(os.path.join(self._work_dir, "reducer_0"),
self._output_file)
except OSError, e:
if e.errno != errno.EXDEV:
raise
else:
# OSError: [Errno 18] Invalid cross-device link (EXDEV == 18)
# We can't rename across devices :( Copy / delete instead.
to_combine = 0
# TODO: If _output_file ends with a compressed suffix (.gz, .xz, .bz2, etc),
# try to compress it after writing.
if self._num_reducers > to_combine:
out = open(self._output_file, "a")
for i in range(to_combine, self._num_reducers):
# FIXME: this reads the entire reducer output into memory
reducer_filename = os.path.join(self._work_dir,
"reducer_" + str(i))
reducer_output = open(reducer_filename, "r")
out.write(reducer_output.read())
reducer_output.close()
os.remove(reducer_filename)
# TODO: clean up downloaded files?
# Clean up mapper outputs
for m in range(self._num_mappers):
for r in range(self._num_reducers):
mfile = os.path.join(self._work_dir, "mapper_%d_%d" % (m, r))
if os.path.exists(mfile):
os.remove(mfile)
else:
print "Warning: Could not find", mfile
class AnalysisJob:
def __init__(self, cfg):
self.job_bundle = cfg.job_bundle
if cfg.input_filter:
self.input_filter = TelemetrySchema(
json.load(open(cfg.input_filter)))
else:
self.input_filter = None
if cfg.input_list_file:
self.input_list = cfg.input_list_file
else:
self.input_list = None
self.job_id = str(uuid4())
self.target_queue = cfg.target_queue
self.aws_key = cfg.aws_key
self.aws_secret_key = cfg.aws_secret_key
self.input_bucket = "telemetry-published-v1"
self.job_name = cfg.name
self.job_owner = cfg.owner
self.date_limit = cfg.date_limit
# Bucket with intermediate data for this analysis job
self.analysis_bucket = "jonasfj-telemetry-analysis"
self.s3_code_path = "batch-jobs/" + self.job_id + ".tar.gz"
# S3 region of operation
self.aws_region = "us-west-2"
self.task_size_limit = 400 * 1024 * 1024
self.sqs_input_name = cfg.sqs_queue
def get_filtered_files(self):
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.input_bucket)
# date_limit is a hack that makes it easy to launch everything before
# a given date... say to back process all we have in the bucket...
if self.date_limit != None:
print "Launching limiting to before " + self.date_limit
for k, s in self.list_partitions(bucket):
if k.split('/')[-1].split('.')[1] < self.date_limit:
yield (k, s)
else:
for k, s in self.list_partitions(bucket):
yield (k, s)
def get_filtered_files_old(self):
""" Get tuples of name and size for all input files """
# Setup some auxiliary functions
allowed_values = self.input_filter.sanitize_allowed_values()
nb_dims = len(allowed_values)
def filter_includes(level, value):
return self.input_filter.is_allowed(value, allowed_values[level])
# iterate over all files in bucket, this is very slow and we should be
# be able to something much smarter using prefix listing and ordering
# to break listing.
count = 0
selected = 0
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.input_bucket)
for f in bucket.list():
count += 1
dims = self.input_filter.get_dimensions(".", f.key)
include = True
for i in xrange(nb_dims):
if not filter_includes(i, dims[i]):
include = False
break
if include:
selected += 1
yield (f.key, f.size)
if count % 5000 == 0:
print "%i files listed with %i selected" % (count, selected)
conn.close()
def list_partitions(self, bucket, prefix='', level=0):
if self.input_filter:
#print "Listing...", prefix, level
allowed_values = self.input_filter.sanitize_allowed_values()
delimiter = '/'
if level > 3:
delimiter = '.'
for k in bucket.list(prefix=prefix, delimiter=delimiter):
partitions = k.name.split("/")
if level > 3:
# split the last couple of partition components by "." instead of "/"
partitions.extend(partitions.pop().split(".", 2))
if self.input_filter.is_allowed(partitions[level],
allowed_values[level]):
if level >= 5:
for f in bucket.list(prefix=k.name):
yield (f.key, f.size)
else:
for k, s in self.list_partitions(
bucket, k.name, level + 1):
yield (k, s)
elif self.input_list:
print "Using input list..."
for line in self.input_list:
key_name = line.strip()
k = bucket.get_key(key_name)
yield (k.key, k.size)
else:
print "Don't know how to list partitions without a filter or list :("
raise ValueError("Missing both input_filter and input_list")
def generate_tasks(self):
""" Generates SQS tasks, we batch small files into a single task """
taskid = 1
taskfiles = []
tasksize = 0
total_size_of_all = 0
for key, size in self.get_filtered_files():
# If the task have reached desired size we yield it
# Note, as SQS messages are limited to 65 KiB we limit tasks to
# 100 filenames, for simplicity
# boto only uses signature version 4, hence, we're limited to 65 KiB
if 0 < len(taskfiles) and (tasksize + size > self.task_size_limit
or len(taskfiles) > 200):
# Reduce to only filenames, sort by size... smallest first they are
# faster to download when handling the job
taskfiles = [
f for f, s in sorted(taskfiles, key=lambda (f, s): s)
]
yield {
'id': self.job_id + "/" + str(taskid),
'name': self.job_name,
'owner': self.job_owner,
'code': self.s3_code_path,
'target-queue': self.target_queue,
'files': taskfiles,
'size': tasksize
}
total_size_of_all += tasksize
print "%i tasks created acc. size: %s" % (taskid,
total_size_of_all)
taskid += 1
taskfiles = []
tasksize = 0
tasksize += size
taskfiles.append((key, size))
if len(taskfiles) > 0:
taskfiles = [f for f, s in sorted(taskfiles, key=lambda (f, s): s)]
yield {
'id': self.job_id + "/" + str(taskid),
'name': self.job_name,
'owner': self.job_owner,
'code': self.s3_code_path,
'target-queue': self.target_queue,
'files': taskfiles,
'size': tasksize
}
print "Finished:"
print "%i tasks created total size: %s" % (taskid, total_size_of_all +
tasksize)
def put_sqs_tasks(self):
""" Create an SQS tasks for this analysis job """
print "Populate SQS input queue with tasks"
# Connect to SQS is desired region
conn = sqs.connect_to_region(self.aws_region,
aws_access_key_id=self.aws_key,
aws_secret_access_key=self.aws_secret_key)
# Create queue
queue = conn.get_queue(self.sqs_input_name)
queue.set_message_class(JSONMessage)
# Populate queue with tasks
for task in self.generate_tasks():
#print "enqueueing", task["id"], "size:", task["size"]
msg = queue.new_message(body=task)
queue.write(msg)
conn.close()
def setup(self):
self.upload_job_bundle()
self.put_sqs_tasks()
print "Uploaded with job_id: %s" % self.job_id
def upload_job_bundle(self):
""" Upload job bundle to S3 """
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.analysis_bucket)
k = Key(bucket)
k.key = self.s3_code_path
k.set_contents_from_filename(self.job_bundle)
conn.close()
class AnalysisJob:
def __init__(self, cfg):
self.job_bundle = cfg.job_bundle
if cfg.input_filter:
self.input_filter = TelemetrySchema(json.load(open(cfg.input_filter)))
else:
self.input_filter = None
if cfg.input_list_file:
self.input_list = cfg.input_list_file
else:
self.input_list = None
self.job_id = str(uuid4())
self.target_queue = cfg.target_queue
self.aws_key = cfg.aws_key
self.aws_secret_key = cfg.aws_secret_key
self.input_bucket = "telemetry-published-v1"
self.job_name = cfg.name
self.job_owner = cfg.owner
self.date_limit = cfg.date_limit
# Bucket with intermediate data for this analysis job
self.analysis_bucket = "jonasfj-telemetry-analysis"
self.s3_code_path = "batch-jobs/" + self.job_id + ".tar.gz"
# S3 region of operation
self.aws_region = "us-west-2"
self.task_size_limit = 400 * 1024 * 1024
self.sqs_input_name = cfg.sqs_queue
def get_filtered_files(self):
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.input_bucket)
# date_limit is a hack that makes it easy to launch everything before
# a given date... say to back process all we have in the bucket...
if self.date_limit != None:
print "Launching limiting to before " + self.date_limit
for k,s in self.list_partitions(bucket):
if k.split('/')[-1].split('.')[1] < self.date_limit:
yield (k, s)
else:
for k,s in self.list_partitions(bucket):
yield (k, s)
def get_filtered_files_old(self):
""" Get tuples of name and size for all input files """
# Setup some auxiliary functions
allowed_values = self.input_filter.sanitize_allowed_values()
nb_dims = len(allowed_values)
def filter_includes(level, value):
return self.input_filter.is_allowed(value, allowed_values[level])
# iterate over all files in bucket, this is very slow and we should be
# be able to something much smarter using prefix listing and ordering
# to break listing.
count = 0
selected = 0
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.input_bucket)
for f in bucket.list():
count += 1
dims = self.input_filter.get_dimensions(".", f.key)
include = True
for i in xrange(nb_dims):
if not filter_includes(i, dims[i]):
include = False
break
if include:
selected += 1
yield (f.key, f.size)
if count % 5000 == 0:
print "%i files listed with %i selected" % (count, selected)
conn.close()
def list_partitions(self, bucket, prefix='', level=0):
if self.input_filter:
#print "Listing...", prefix, level
allowed_values = self.input_filter.sanitize_allowed_values()
delimiter = '/'
if level > 3:
delimiter = '.'
for k in bucket.list(prefix=prefix, delimiter=delimiter):
partitions = k.name.split("/")
if level > 3:
# split the last couple of partition components by "." instead of "/"
partitions.extend(partitions.pop().split(".", 2))
if self.input_filter.is_allowed(partitions[level], allowed_values[level]):
if level >= 5:
for f in bucket.list(prefix=k.name):
yield (f.key, f.size)
else:
for k, s in self.list_partitions(bucket, k.name, level + 1):
yield (k, s)
elif self.input_list:
print "Using input list..."
for line in self.input_list:
key_name = line.strip()
k = bucket.get_key(key_name)
yield (k.key, k.size)
else:
print "Don't know how to list partitions without a filter or list :("
raise ValueError("Missing both input_filter and input_list")
def generate_tasks(self):
""" Generates SQS tasks, we batch small files into a single task """
taskid = 1
taskfiles = []
tasksize = 0
total_size_of_all = 0
for key, size in self.get_filtered_files():
# If the task have reached desired size we yield it
# Note, as SQS messages are limited to 65 KiB we limit tasks to
# 100 filenames, for simplicity
# boto only uses signature version 4, hence, we're limited to 65 KiB
if 0 < len(taskfiles) and (tasksize + size > self.task_size_limit or
len(taskfiles) > 200):
# Reduce to only filenames, sort by size... smallest first they are
# faster to download when handling the job
taskfiles = [f for f,s in sorted(taskfiles, key=lambda (f,s): s)]
yield {
'id': self.job_id + "/" + str(taskid),
'name': self.job_name,
'owner': self.job_owner,
'code': self.s3_code_path,
'target-queue': self.target_queue,
'files': taskfiles,
'size': tasksize
}
total_size_of_all += tasksize
print "%i tasks created acc. size: %s" % (taskid, total_size_of_all)
taskid += 1
taskfiles = []
tasksize = 0
tasksize += size
taskfiles.append((key, size))
if len(taskfiles) > 0:
taskfiles = [f for f,s in sorted(taskfiles, key=lambda (f,s): s)]
yield {
'id': self.job_id + "/" + str(taskid),
'name': self.job_name,
'owner': self.job_owner,
'code': self.s3_code_path,
'target-queue': self.target_queue,
'files': taskfiles,
'size': tasksize
}
print "Finished:"
print "%i tasks created total size: %s" % (taskid, total_size_of_all + tasksize)
def put_sqs_tasks(self):
""" Create an SQS tasks for this analysis job """
print "Populate SQS input queue with tasks"
# Connect to SQS is desired region
conn = sqs.connect_to_region(
self.aws_region,
aws_access_key_id = self.aws_key,
aws_secret_access_key = self.aws_secret_key
)
# Create queue
queue = conn.get_queue(self.sqs_input_name)
queue.set_message_class(JSONMessage)
# Populate queue with tasks
for task in self.generate_tasks():
#print "enqueueing", task["id"], "size:", task["size"]
msg = queue.new_message(body = task)
queue.write(msg)
conn.close()
def setup(self):
self.upload_job_bundle()
self.put_sqs_tasks()
print "Uploaded with job_id: %s" % self.job_id
def upload_job_bundle(self):
""" Upload job bundle to S3 """
conn = S3Connection(self.aws_key, self.aws_secret_key)
bucket = conn.get_bucket(self.analysis_bucket)
k = Key(bucket)
k.key = self.s3_code_path
k.set_contents_from_filename(self.job_bundle)
conn.close()
class Job:
"""A class for orchestrating a Heka MapReduce job"""
# 1. read input filter
# 2. generate filtered list of local input files
# 2a. generate filtered list of remote input files
# 3. load mapper
# 4. spawn N processes
# 5. distribute files among processes
# 6. map(key, value, dims) each line in the file
# 7. combine map output for each file
# 8. reduce combine output overall
def __init__(self, config):
# Sanity check args.
if config.get("num_mappers") <= 0:
raise ValueError("Number of mappers must be greater than zero")
if config.get("num_reducers") <= 0:
raise ValueError("Number of reducers must be greater than zero")
if not os.path.isdir(config.get("data_dir")):
raise ValueError("Data dir must be a valid directory")
if not os.path.isdir(config.get("work_dir")):
raise ValueError("Work dir must be a valid directory")
if not os.path.isfile(config.get("job_script", "")):
raise ValueError("Job script must be a valid python file")
if not os.path.isfile(config.get("input_filter")):
raise ValueError("Input filter must be a valid json file")
self._input_dir = config.get("data_dir")
if self._input_dir[-1] == os.path.sep:
self._input_dir = self._input_dir[0:-1]
self._work_dir = config.get("work_dir")
with open(config.get("input_filter")) as filter_file:
self._input_filter = TelemetrySchema(json.load(filter_file))
self._allowed_values = self._input_filter.sanitize_allowed_values()
self._output_file = config.get("output")
self._num_mappers = config.get("num_mappers")
self._num_reducers = config.get("num_reducers")
self._local_only = config.get("local_only")
self._bucket_name = config.get("bucket")
self._aws_key = config.get("aws_key")
self._aws_secret_key = config.get("aws_secret_key")
self._profile = config.get("profile")
self._delete_data = config.get("delete_data")
with open(config.get("job_script")) as modulefd:
# let the job script import additional modules under its path
sys.path.append(os.path.dirname(config.get("job_script")))
## Lifted from FileDriver.py in jydoop.
self._job_module = imp.load_module("telemetry_job", modulefd, config.get("job_script"), (".py", "U", 1))
def dump_stats(self, partitions):
total = sum(partitions)
avg = total / len(partitions)
for i in range(len(partitions)):
print "Partition %d contained %d (%+d)" % (i, partitions[i], float(partitions[i]) - avg)
def dedupe_remotes(self, remote_files, local_files):
return (r for r in remote_files if os.path.join(self._input_dir, r.name) not in local_files)
def mapreduce(self):
# Find files matching specified input filter
files = set(self.get_filtered_files(self._input_dir))
remote_files = self.get_filtered_files_s3()
# If we're using the cache dir as the data dir, we will end up reading
# each already-downloaded file twice, so we should skip any remote files
# that exist in the data dir.
remote_files = self.dedupe_remotes(remote_files, files)
# Partition files into reasonably equal groups for use by mappers
print "Partitioning input data..."
partitions = self.partition(files, remote_files)
print "Done"
if not any(part for part in partitions):
print "Filter didn't match any files... nothing to do"
return
partitions = [part for part in partitions if part]
# Not useful to have more mappers than partitions.
if len(partitions) < self._num_mappers:
print "Filter matched only %d input files. Reducing number of mappers accordingly." % (len(partitions),)
self._num_mappers = len(partitions)
# Free up our set of names. We want to minimize
# our memory usage prior to forking map jobs.
# files = None
gc.collect()
def checkExitCode(proc):
# If process was terminated by a signal, exitcode is the negative signal value
if proc.exitcode == -signal.SIGKILL:
# SIGKILL is most likely an OOM kill
raise MemoryError("%s ran out of memory" % proc.name)
elif proc.exitcode:
raise OSError("%s exited with code %d" % (proc.name, proc.exitcode))
# Partitions are ready. Map.
mappers = []
for i in range(self._num_mappers):
if len(partitions[i]) > 0:
p = Process(
target=Mapper,
name=("Mapper-%d" % i),
args=(
i,
self._profile,
partitions[i],
self._work_dir,
self._job_module,
self._num_reducers,
self._delete_data,
self._aws_key,
self._aws_secret_key,
self._bucket_name,
),
)
mappers.append(p)
p.start()
else:
print "Skipping mapper", i, "- no input files to process"
for m in mappers:
m.join()
checkExitCode(m)
# Mappers are done. Reduce.
reducers = []
for i in range(self._num_reducers):
p = Process(
target=Reducer,
name=("Reducer-%d" % i),
args=(i, self._profile, self._work_dir, self._job_module, self._num_mappers),
)
reducers.append(p)
p.start()
for r in reducers:
r.join()
checkExitCode(r)
# Reducers are done. Output results.
to_combine = 1
try:
os.rename(os.path.join(self._work_dir, "reducer_0"), self._output_file)
except OSError, e:
if e.errno != errno.EXDEV:
raise
else:
# OSError: [Errno 18] Invalid cross-device link (EXDEV == 18)
# We can't rename across devices :( Copy / delete instead.
to_combine = 0
# TODO: If _output_file ends with a compressed suffix (.gz, .xz, .bz2, etc),
# try to compress it after writing.
if self._num_reducers > to_combine:
with open(self._output_file, "a") as out:
for i in range(to_combine, self._num_reducers):
# FIXME: this reads the entire reducer output into memory
reducer_filename = os.path.join(self._work_dir, "reducer_" + str(i))
with open(reducer_filename, "r") as reducer_output:
out.write(reducer_output.read())
os.remove(reducer_filename)
# Clean up mapper outputs
for m in range(self._num_mappers):
for r in range(self._num_reducers):
mfile = os.path.join(self._work_dir, "mapper_%d_%d" % (m, r))
if os.path.exists(mfile):
os.remove(mfile)
else:
print "Warning: Could not find", mfile
class Job:
"""A class for orchestrating a Heka MapReduce job"""
# 1. read input filter
# 2. generate filtered list of local input files
# 2a. generate filtered list of remote input files
# 3. load mapper
# 4. spawn N processes
# 5. distribute files among processes
# 6. map(key, value, dims) each line in the file
# 7. combine map output for each file
# 8. reduce combine output overall
def __init__(self, config):
# Sanity check args.
if config.get("num_mappers") <= 0:
raise ValueError("Number of mappers must be greater than zero")
if config.get("num_reducers") <= 0:
raise ValueError("Number of reducers must be greater than zero")
if not os.path.isdir(config.get("data_dir")):
raise ValueError("Data dir must be a valid directory")
if not os.path.isdir(config.get("work_dir")):
raise ValueError("Work dir must be a valid directory")
if not os.path.isfile(config.get("job_script", "")):
raise ValueError("Job script must be a valid python file")
if not os.path.isfile(config.get("input_filter")):
raise ValueError("Input filter must be a valid json file")
self._input_dir = config.get("data_dir")
if self._input_dir[-1] == os.path.sep:
self._input_dir = self._input_dir[0:-1]
self._work_dir = config.get("work_dir")
with open(config.get("input_filter")) as filter_file:
self._input_filter = TelemetrySchema(json.load(filter_file))
self._allowed_values = self._input_filter.sanitize_allowed_values()
self._output_file = config.get("output")
self._num_mappers = config.get("num_mappers")
self._num_reducers = config.get("num_reducers")
self._local_only = config.get("local_only")
self._bucket_name = config.get("bucket")
self._aws_key = config.get("aws_key")
self._aws_secret_key = config.get("aws_secret_key")
self._profile = config.get("profile")
self._delete_data = config.get("delete_data")
with open(config.get("job_script")) as modulefd:
# let the job script import additional modules under its path
sys.path.append(os.path.dirname(config.get("job_script")))
## Lifted from FileDriver.py in jydoop.
self._job_module = imp.load_module(
"telemetry_job", modulefd, config.get("job_script"), ('.py', 'U', 1))
def dump_stats(self, partitions):
total = sum(partitions)
avg = total / len(partitions)
for i in range(len(partitions)):
print "Partition %d contained %d (%+d)" % (i, partitions[i], float(partitions[i]) - avg)
def dedupe_remotes(self, remote_files, local_files):
return ( r for r in remote_files
if os.path.join(self._input_dir, r.name) not in local_files )
def mapreduce(self):
# Find files matching specified input filter
files = set(self.get_filtered_files(self._input_dir))
remote_files = self.get_filtered_files_s3()
# If we're using the cache dir as the data dir, we will end up reading
# each already-downloaded file twice, so we should skip any remote files
# that exist in the data dir.
remote_files = self.dedupe_remotes(remote_files, files)
# Partition files into reasonably equal groups for use by mappers
print "Partitioning input data..."
partitions = self.partition(files, remote_files)
print "Done"
if not any(part for part in partitions):
print "Filter didn't match any files... nothing to do"
return
partitions = [part for part in partitions if part]
# Not useful to have more mappers than partitions.
if len(partitions) < self._num_mappers:
print "Filter matched only %d input files. Reducing number of mappers accordingly." % (
len(partitions),)
self._num_mappers = len(partitions)
# Free up our set of names. We want to minimize
# our memory usage prior to forking map jobs.
#files = None
gc.collect()
def checkExitCode(proc):
# If process was terminated by a signal, exitcode is the negative signal value
if proc.exitcode == -signal.SIGKILL:
# SIGKILL is most likely an OOM kill
raise MemoryError("%s ran out of memory" % proc.name)
elif proc.exitcode:
raise OSError("%s exited with code %d" % (proc.name, proc.exitcode))
# Partitions are ready. Map.
mappers = []
for i in range(self._num_mappers):
if len(partitions[i]) > 0:
p = Process(
target=Mapper,
name=("Mapper-%d" % i),
args=(i, self._profile, partitions[i], self._work_dir, self._job_module, self._num_reducers, self._delete_data, self._aws_key, self._aws_secret_key, self._bucket_name))
mappers.append(p)
p.start()
else:
print "Skipping mapper", i, "- no input files to process"
for m in mappers:
m.join()
checkExitCode(m)
# Mappers are done. Reduce.
reducers = []
for i in range(self._num_reducers):
p = Process(
target=Reducer,
name=("Reducer-%d" % i),
args=(i, self._profile, self._work_dir, self._job_module, self._num_mappers))
reducers.append(p)
p.start()
for r in reducers:
r.join()
checkExitCode(r)
# Reducers are done. Output results.
to_combine = 1
try:
os.rename(os.path.join(self._work_dir, "reducer_0"), self._output_file)
except OSError, e:
if e.errno != errno.EXDEV:
raise
else:
# OSError: [Errno 18] Invalid cross-device link (EXDEV == 18)
# We can't rename across devices :( Copy / delete instead.
to_combine = 0
# TODO: If _output_file ends with a compressed suffix (.gz, .xz, .bz2, etc),
# try to compress it after writing.
if self._num_reducers > to_combine:
with open(self._output_file, "a") as out:
for i in range(to_combine, self._num_reducers):
# FIXME: this reads the entire reducer output into memory
reducer_filename = os.path.join(self._work_dir, "reducer_" + str(i))
with open(reducer_filename, "r") as reducer_output:
out.write(reducer_output.read())
os.remove(reducer_filename)
# Clean up mapper outputs
for m in range(self._num_mappers):
for r in range(self._num_reducers):
mfile = os.path.join(self._work_dir, "mapper_%d_%d" % (m, r))
if os.path.exists(mfile):
os.remove(mfile)
else:
print "Warning: Could not find", mfile