def reload_read_request_queues(job_description_file, job_ids, redis_host,
redis_port, redis_db, skip_phase_zero,
skip_phase_one, phase_zero_sample_size):
with open(job_description_file, 'r') as fp:
job_description = json.load(fp)
coordinator_db = redis_utils.CoordinatorDB(redis_host, redis_port,
redis_db)
input_files = input_file_utils.gather_input_file_paths(
coordinator_db, job_description["input_directory"])
phases = []
if not skip_phase_zero:
phases.append(0)
if not skip_phase_one:
phases.append(1)
read_requests = input_file_utils.generate_read_requests(
input_files, phase_zero_sample_size, job_ids, phases)
input_file_utils.load_read_requests(coordinator_db, read_requests)
def reload_read_request_queues(
job_description_file, job_ids, redis_host, redis_port, redis_db,
skip_phase_zero, skip_phase_one, phase_zero_sample_size):
with open(job_description_file, 'r') as fp:
job_description = json.load(fp)
coordinator_db = redis_utils.CoordinatorDB(redis_host, redis_port, redis_db)
input_files = input_file_utils.gather_input_file_paths(
coordinator_db, job_description["input_directory"])
phases = []
if not skip_phase_zero:
phases.append(0)
if not skip_phase_one:
phases.append(1)
read_requests = input_file_utils.generate_read_requests(
input_files, phase_zero_sample_size, job_ids, phases)
input_file_utils.load_read_requests(coordinator_db, read_requests)
def run_batch(self, batch_jobs, batch_inputs):
batch_id = self.coordinator_db.next_batch_id
log.info("Running batch %d with the following job(s): %s" %
(batch_id, ', '.join(map(str, batch_jobs))))
# Create log directory for the current batch
batch_logs = create_batch_directory(self.log_directory, batch_id)
# Copy description files to the log directory
description_dir = os.path.join(
os.path.dirname(__file__), os.pardir, os.pardir, os.pardir,
"tritonsort", "mapreduce", "description")
shutil.copy(os.path.join(description_dir, "stages.json"), batch_logs)
shutil.copy(os.path.join(description_dir, "structure.json"), batch_logs)
# Copy config file to log directory
shutil.copy(self.config_file, batch_logs)
self.ready_for_next_batch = False
# Pull out relevant phase zero parameters
phase_zero_sample_rate = 1 # Sample 100% by default
if "SAMPLE_RATE" in self.config:
phase_zero_sample_rate = float(self.config["SAMPLE_RATE"])
phase_zero_sample_points_per_file = 1 # Sample prefixes by default
if "SAMPLES_PER_FILE" in self.config:
phase_zero_sample_points_per_file = \
int(self.config["SAMPLES_PER_FILE"])
fixed_key_length = None
if "MAP_INPUT_FIXED_KEY_LENGTH" in self.config:
fixed_key_length = int(self.config["MAP_INPUT_FIXED_KEY_LENGTH"])
fixed_value_length = None
if "MAP_INPUT_FIXED_VALUE_LENGTH" in self.config:
fixed_value_length = \
int(self.config["MAP_INPUT_FIXED_VALUE_LENGTH"])
# If the application config file (yaml) or the job spec file (json)
# skips a phase, we should not load read requests for that phase. The
# job spec file should override the application config file.
skip_phase_zero = 0
skip_phase_one = 0
skip_phase_two = 0
skip_phase_three = 0
if "SKIP_PHASE_ZERO" in self.config and self.config["SKIP_PHASE_ZERO"]:
skip_phase_zero = 1
if "SKIP_PHASE_ONE" in self.config and self.config["SKIP_PHASE_ONE"]:
skip_phase_one = 1
if "SKIP_PHASE_TWO" in self.config and self.config["SKIP_PHASE_TWO"]:
skip_phase_two = 1
if "SKIP_PHASE_THREE" in self.config and \
self.config["SKIP_PHASE_THREE"]:
skip_phase_three = 1
# The run_job.py script verifies that all jobs in the batch have the
# same value of these skip parameters in the job specs, so we can just
# check the first one.
for key, value in (
self.coordinator_db.job_params(batch_jobs[0]).items()):
if key == "SKIP_PHASE_ZERO":
skip_phase_zero = value
if key == "SKIP_PHASE_ONE":
skip_phase_one = value
if key == "SKIP_PHASE_TWO":
skip_phase_two = value
if key == "SKIP_PHASE_THREE":
skip_phase_three = value
if key == "MAP_INPUT_FIXED_KEY_LENGTH":
fixed_key_length = int(value)
if key == "MAP_INPUT_FIXED_VALUE_LENGTH":
fixed_value_length = int(value)
fixed_tuple_length = None
if fixed_key_length != None and fixed_value_length != None:
fixed_tuple_length = fixed_key_length + fixed_value_length
use_replication = False
if "OUTPUT_REPLICATION_LEVEL" in self.config and \
int(self.config["OUTPUT_REPLICATION_LEVEL"]) > 1:
use_replication = True
phases = []
if not skip_phase_zero:
phases.append(0)
if not skip_phase_one:
phases.append(1)
if not skip_phase_two and use_replication:
# If we're using replication, phase two will have network transfer,
# use barriers to guarantee sockets are connected.
phases.append(2)
if not skip_phase_three and use_replication:
# If we're using replication, phase three will have network
# transfer, use barriers to guarantee sockets are connected.
phases.append(3)
# Setup barriers
self.coordinator_db.create_barriers(phases, batch_id, batch_jobs)
# Generate read requests for the jobs in the batch
read_requests = generate_read_requests(
job_inputs = batch_inputs,
phase_zero_sample_rate = phase_zero_sample_rate,
phase_zero_sample_points_per_file =\
phase_zero_sample_points_per_file,
tuple_start_offset = fixed_tuple_length,
job_ids = batch_jobs, phases=phases)
# Load read requests into read request queue for each worker
load_read_requests(self.coordinator_db, read_requests)
start_time = time.time()
# Mark phase zero as starting now.
self.coordinator_db.begin_phase(batch_id, "phase_zero")
self.batch_phase_info[batch_id] = ("phase_zero", 0, start_time)
log.info("Running phase_zero...")
print_keyboard_commands()
for job_id in batch_jobs:
self.coordinator_db.update_job_status(
job_id, { "start_time" : str(start_time),
"batch_id" : batch_id,
"date" : time.asctime()})
self.coordinator_db.add_jobs_to_batch(batch_id, batch_jobs)
self.coordinator_db.mark_batch_incomplete(batch_id)
# Setting current_batch will cause all node coordinators to start work
# on that batch
self.coordinator_db.add_batch_to_node_coordinator_batch_queues(batch_id)
def run_batch(self, batch_jobs, batch_inputs):
batch_id = self.coordinator_db.next_batch_id
log.info("Running batch %d with the following job(s): %s" %
(batch_id, ', '.join(map(str, batch_jobs))))
# Create log directory for the current batch
batch_logs = create_batch_directory(self.log_directory, batch_id)
# Copy description files to the log directory
description_dir = os.path.join(os.path.dirname(__file__), os.pardir,
os.pardir, os.pardir, "tritonsort",
"mapreduce", "description")
shutil.copy(os.path.join(description_dir, "stages.json"), batch_logs)
shutil.copy(os.path.join(description_dir, "structure.json"),
batch_logs)
# Copy config file to log directory
shutil.copy(self.config_file, batch_logs)
self.ready_for_next_batch = False
# Pull out relevant phase zero parameters
phase_zero_sample_rate = 1 # Sample 100% by default
if "SAMPLE_RATE" in self.config:
phase_zero_sample_rate = float(self.config["SAMPLE_RATE"])
phase_zero_sample_points_per_file = 1 # Sample prefixes by default
if "SAMPLES_PER_FILE" in self.config:
phase_zero_sample_points_per_file = \
int(self.config["SAMPLES_PER_FILE"])
fixed_key_length = None
if "MAP_INPUT_FIXED_KEY_LENGTH" in self.config:
fixed_key_length = int(self.config["MAP_INPUT_FIXED_KEY_LENGTH"])
fixed_value_length = None
if "MAP_INPUT_FIXED_VALUE_LENGTH" in self.config:
fixed_value_length = \
int(self.config["MAP_INPUT_FIXED_VALUE_LENGTH"])
# If the application config file (yaml) or the job spec file (json)
# skips a phase, we should not load read requests for that phase. The
# job spec file should override the application config file.
skip_phase_zero = 0
skip_phase_one = 0
skip_phase_two = 0
skip_phase_three = 0
if "SKIP_PHASE_ZERO" in self.config and self.config["SKIP_PHASE_ZERO"]:
skip_phase_zero = 1
if "SKIP_PHASE_ONE" in self.config and self.config["SKIP_PHASE_ONE"]:
skip_phase_one = 1
if "SKIP_PHASE_TWO" in self.config and self.config["SKIP_PHASE_TWO"]:
skip_phase_two = 1
if "SKIP_PHASE_THREE" in self.config and \
self.config["SKIP_PHASE_THREE"]:
skip_phase_three = 1
# The run_job.py script verifies that all jobs in the batch have the
# same value of these skip parameters in the job specs, so we can just
# check the first one.
for key, value in (self.coordinator_db.job_params(
batch_jobs[0]).items()):
if key == "SKIP_PHASE_ZERO":
skip_phase_zero = value
if key == "SKIP_PHASE_ONE":
skip_phase_one = value
if key == "SKIP_PHASE_TWO":
skip_phase_two = value
if key == "SKIP_PHASE_THREE":
skip_phase_three = value
if key == "MAP_INPUT_FIXED_KEY_LENGTH":
fixed_key_length = int(value)
if key == "MAP_INPUT_FIXED_VALUE_LENGTH":
fixed_value_length = int(value)
fixed_tuple_length = None
if fixed_key_length != None and fixed_value_length != None:
fixed_tuple_length = fixed_key_length + fixed_value_length
use_replication = False
if "OUTPUT_REPLICATION_LEVEL" in self.config and \
int(self.config["OUTPUT_REPLICATION_LEVEL"]) > 1:
use_replication = True
phases = []
if not skip_phase_zero:
phases.append(0)
if not skip_phase_one:
phases.append(1)
if not skip_phase_two and use_replication:
# If we're using replication, phase two will have network transfer,
# use barriers to guarantee sockets are connected.
phases.append(2)
if not skip_phase_three and use_replication:
# If we're using replication, phase three will have network
# transfer, use barriers to guarantee sockets are connected.
phases.append(3)
# Setup barriers
self.coordinator_db.create_barriers(phases, batch_id, batch_jobs)
# Generate read requests for the jobs in the batch
read_requests = generate_read_requests(
job_inputs = batch_inputs,
phase_zero_sample_rate = phase_zero_sample_rate,
phase_zero_sample_points_per_file =\
phase_zero_sample_points_per_file,
tuple_start_offset = fixed_tuple_length,
job_ids = batch_jobs, phases=phases)
# Load read requests into read request queue for each worker
load_read_requests(self.coordinator_db, read_requests)
start_time = time.time()
# Mark phase zero as starting now.
self.coordinator_db.begin_phase(batch_id, "phase_zero")
self.batch_phase_info[batch_id] = ("phase_zero", 0, start_time)
log.info("Running phase_zero...")
print_keyboard_commands()
for job_id in batch_jobs:
self.coordinator_db.update_job_status(
job_id, {
"start_time": str(start_time),
"batch_id": batch_id,
"date": time.asctime()
})
self.coordinator_db.add_jobs_to_batch(batch_id, batch_jobs)
self.coordinator_db.mark_batch_incomplete(batch_id)
# Setting current_batch will cause all node coordinators to start work
# on that batch
self.coordinator_db.add_batch_to_node_coordinator_batch_queues(
batch_id)
