def worker_completion_plot(log_directory, verbose):
queries = []
# Get each runtime for each worker in the cluster
query = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "worker_runtime"),
("start_time", None))
query.match_processor_function = handle_worker_runtime
queries.append(query)
worker_runtime_data = log_utils.process_queries(
queries, log_directory, verbose)
for (job, phase) in worker_runtime_data:
phase_data = worker_runtime_data[(job, phase)]
plot = plot_timeline_for_phase(
log_directory, job, phase, phase_data)
plot.dpi = 250
print "Saving plot for job %s phase %s" % (job, phase)
plot.save("%s_%s_completion_plot.png" % (job, phase))
return 0
def gen_worker_query(
query_number, stat_type, hostname, phase, stage, worker_id, stat):
"""
This function generates a worker stat query. Worker stat queries operate
over statistics logged by individual workers. As such, they specify either
a particular worker ID or all worker IDs. If the query applies to all
worker IDs, worker_id is None.
"""
if worker_id is not None:
worker_id = int(worker_id)
query_parts = [stat_type, ("phase_name", phase), ("epoch", None),
("stage_name", stage), ("id", worker_id)]
if stat_type == "COLL":
query_parts.append(("collection_stat_name", stat))
elif stat_type == "HIST":
query_parts.append(("stat_name", stat))
query = StatQuery(*query_parts)
if hostname is not None:
hostname_regex = re.compile(hostname)
else:
hostname_regex = None
def match_function(match, data):
match_hostname = match["hostname"].split('.')[0]
phase = match["phase_name"]
stage = match["stage_name"]
worker_id = match["id"]
if (hostname is not None and hostname_regex is not None and
hostname_regex.match(match_hostname) is None):
return
if stat_type == "COLL":
stat_type_matcher_function = gather_timestamped_points_matcher
elif stat_type == "HIST":
stat_type_matcher_function = gather_histogram_points_matcher
stat_type_matcher_function(
query_number, match, data,
(match_hostname, phase, stage, worker_id))
query.match_processor_function = match_function
return query
def gen_logger_query(
query_number, stat_type, hostname, phase, logger_name, stat):
"""
This function generates general time-series log queries. In general,
time-series logs can be logged by anything as long as the logger provides a
logger name that distinguishes it from other loggers. In particular,
queries of this form cover stats logged by memory allocators and stage
trackers.
"""
query_parts = [stat_type, ("phase_name", phase), ("epoch", None),
("logger_name", logger_name)]
if stat_type == "COLL":
query_parts.append(("collection_stat_name", stat))
elif stat_type == "HIST":
query_parts.append(("stat_name", stat))
query = StatQuery(*query_parts)
if hostname is not None:
hostname_regex = re.compile(hostname)
else:
hostname_regex = None
def match_function(match, data):
match_hostname = match["hostname"].split('.')[0]
phase = match["phase_name"]
logger_name = match["logger_name"]
if (hostname is not None and hostname_regex is not None and
hostname_regex.match(match_hostname) is None):
return
if stat_type == "COLL":
stat_type_matcher_function = gather_timestamped_points_matcher
elif stat_type == "HIST":
stat_type_matcher_function = gather_histogram_points_matcher
stat_type_matcher_function(
query_number, match, data, (match_hostname, phase, logger_name))
query.match_processor_function = match_function
return query
def get_list_time_series_queries():
worker_query = StatQuery(
"COLL",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("collection_stat_name", None))
def worker_match_function(match, data):
phase = match["phase_name"]
stage = match["stage_name"]
worker_id = str(match["id"])
stat_name = match["collection_stat_name"]
if "time_series_keys" not in data:
data["time_series_keys"] = set()
time_series_key = (phase, stage, worker_id, stat_name)
data["time_series_keys"].add(time_series_key)
worker_query.match_processor_function = worker_match_function
tracker_query = StatQuery(
"COLL",
("phase_name", None),
("epoch", None),
("logger_name", None),
("collection_stat_name", None))
def tracker_match_function(match, data):
phase = match["phase_name"]
stage = match["logger_name"]
stat_name = match["collection_stat_name"]
if "time_series_keys" not in data:
data["time_series_keys"] = set()
time_series_key = (phase, stage, None, stat_name)
data["time_series_keys"].add(time_series_key)
tracker_query.match_processor_function = tracker_match_function
return (worker_query, tracker_query)
def gen_worker_query(query_number, stat_type, hostname, phase, stage, worker_id, stat):
"""
This function generates a worker stat query. Worker stat queries operate
over statistics logged by individual workers. As such, they specify either
a particular worker ID or all worker IDs. If the query applies to all
worker IDs, worker_id is None.
"""
if worker_id is not None:
worker_id = int(worker_id)
query_parts = [stat_type, ("phase_name", phase), ("epoch", None), ("stage_name", stage), ("id", worker_id)]
if stat_type == "COLL":
query_parts.append(("collection_stat_name", stat))
elif stat_type == "HIST":
query_parts.append(("stat_name", stat))
query = StatQuery(*query_parts)
if hostname is not None:
hostname_regex = re.compile(hostname)
else:
hostname_regex = None
def match_function(match, data):
match_hostname = match["hostname"].split(".")[0]
phase = match["phase_name"]
stage = match["stage_name"]
worker_id = match["id"]
if hostname is not None and hostname_regex is not None and hostname_regex.match(match_hostname) is None:
return
if stat_type == "COLL":
stat_type_matcher_function = gather_timestamped_points_matcher
elif stat_type == "HIST":
stat_type_matcher_function = gather_histogram_points_matcher
stat_type_matcher_function(query_number, match, data, (match_hostname, phase, stage, worker_id))
query.match_processor_function = match_function
return query
def worker_completion_plot(log_directory, verbose):
queries = []
# Get each runtime for each worker in the cluster
query = StatQuery("DATM", ("phase_name", None), ("epoch", None),
("stage_name", None), ("id", None),
("stat_name", "worker_runtime"), ("start_time", None))
query.match_processor_function = handle_worker_runtime
queries.append(query)
worker_runtime_data = log_utils.process_queries(queries, log_directory,
verbose)
for (job, phase) in worker_runtime_data:
phase_data = worker_runtime_data[(job, phase)]
plot = plot_timeline_for_phase(log_directory, job, phase, phase_data)
plot.dpi = 250
print "Saving plot for job %s phase %s" % (job, phase)
plot.save("%s_%s_completion_plot.png" % (job, phase))
return 0
def gen_logger_query(query_number, stat_type, hostname, phase, logger_name, stat):
"""
This function generates general time-series log queries. In general,
time-series logs can be logged by anything as long as the logger provides a
logger name that distinguishes it from other loggers. In particular,
queries of this form cover stats logged by memory allocators and stage
trackers.
"""
query_parts = [stat_type, ("phase_name", phase), ("epoch", None), ("logger_name", logger_name)]
if stat_type == "COLL":
query_parts.append(("collection_stat_name", stat))
elif stat_type == "HIST":
query_parts.append(("stat_name", stat))
query = StatQuery(*query_parts)
if hostname is not None:
hostname_regex = re.compile(hostname)
else:
hostname_regex = None
def match_function(match, data):
match_hostname = match["hostname"].split(".")[0]
phase = match["phase_name"]
logger_name = match["logger_name"]
if hostname is not None and hostname_regex is not None and hostname_regex.match(match_hostname) is None:
return
if stat_type == "COLL":
stat_type_matcher_function = gather_timestamped_points_matcher
elif stat_type == "HIST":
stat_type_matcher_function = gather_histogram_points_matcher
stat_type_matcher_function(query_number, match, data, (match_hostname, phase, logger_name))
query.match_processor_function = match_function
return query
def get_list_time_series_queries():
worker_query = StatQuery("COLL", ("phase_name", None), ("epoch", None),
("stage_name", None), ("id", None),
("collection_stat_name", None))
def worker_match_function(match, data):
phase = match["phase_name"]
stage = match["stage_name"]
worker_id = str(match["id"])
stat_name = match["collection_stat_name"]
if "time_series_keys" not in data:
data["time_series_keys"] = set()
time_series_key = (phase, stage, worker_id, stat_name)
data["time_series_keys"].add(time_series_key)
worker_query.match_processor_function = worker_match_function
tracker_query = StatQuery("COLL", ("phase_name", None), ("epoch", None),
("logger_name", None),
("collection_stat_name", None))
def tracker_match_function(match, data):
phase = match["phase_name"]
stage = match["logger_name"]
stat_name = match["collection_stat_name"]
if "time_series_keys" not in data:
data["time_series_keys"] = set()
time_series_key = (phase, stage, None, stat_name)
data["time_series_keys"].add(time_series_key)
tracker_query.match_processor_function = tracker_match_function
return (worker_query, tracker_query)
def calculate_rate(input_directory, skip_phase_zero, skip_phase_one,
skip_phase_two, verbose):
phaseTimesQuery = StatQuery("DATM", ("phase_name", None), ("epoch", None),
("logger_name", None),
("stat_name", "phase_runtime"),
("start_time", None))
phaseTimesQuery.match_processor_function = handleTimestampQueryMatch
diskCountQuery = StatQuery(
"DATM", ("phase_name", None), ("epoch", None),
("logger_name", "mapreduce"),
("stat_name", ["num_input_disks", "num_intermediate_disks"]),
("uint_value", None))
diskCountQuery.match_processor_function = handleDiskCountMatch
inputSizeQuery = StatQuery("DATM", ("phase_name", None), ("epoch", None),
("stage_name", "reader"), ("id", None),
("stat_name", "bytes_produced"),
("uint_value", None))
inputSizeQuery.match_processor_function = handleReaderInputMatch
writerOutputQuery = StatQuery("DATM", ("phase_name", None),
("epoch", None), ("stage_name", "writer"),
("id", None),
("stat_name", "bytes_consumed"),
("uint_value", None))
writerOutputQuery.match_processor_function = handleWriterOutputMatch
queries = [
phaseTimesQuery, diskCountQuery, inputSizeQuery, writerOutputQuery
]
skipped_phases = []
if skip_phase_zero:
skipped_phases.append("phase_zero")
if skip_phase_one:
skipped_phases.append("phase_one")
if skip_phase_two:
skipped_phases.append("phase_two")
output_data = utils.process_queries(queries, input_directory, verbose,
skipped_phases)
data_for_display = postprocess_rate_data(output_data)
for key in sorted(data_for_display.keys()):
env = jinja2.Environment(loader=jinja2.FileSystemLoader(
os.path.dirname(__file__)),
trim_blocks=True)
template = env.get_template('rate_summary_template.jinja')
rendered_template = template.render(**data_for_display[key])
print rendered_template.strip() + "\n"
def gather_runtime_info(experiment_directory, verbose, skipped_phases=[]):
total_runtime_query = StatQuery("SUMM", ("phase_name", None),
("epoch", None), ("stage_name", None),
("id", None), ("stat_name", "runtime"),
("summary_stat_name", "sum"))
total_runtime_query.match_processor_function = \
stat_container_append_matcher("total_runtime", "value")
total_idle_time_query = StatQuery("SUMM", ("phase_name", None),
("epoch", None), ("stage_name", None),
("id", None), ("stat_name", "wait"),
("summary_stat_name", "sum"))
total_idle_time_query.match_processor_function = \
stat_container_append_matcher("total_idle_time", "value")
pipeline_saturation_time_query = StatQuery(
"SUMM", ("phase_name", None), ("epoch", None), ("stage_name", None),
("id", None), ("stat_name", "pipeline_saturation_wait"),
("summary_stat_name", "sum"))
pipeline_saturation_time_query.match_processor_function = (
stat_container_append_matcher("pipeline_saturation_time", "value"))
num_workers_query = StatQuery("DATM", ("phase_name", None),
("epoch", None), ("logger_name", None),
("stat_name", "num_workers"),
("uint_value", None))
num_workers_query.match_processor_function = stat_container_append_matcher(
"num_workers", "uint_value")
teardown_time_query = StatQuery("DATM", ("phase_name", None),
("epoch", None), ("stage_name", None),
("id", None), ("stat_name", "teardown"),
("start_time", None))
teardown_time_query.match_processor_function = \
stat_container_append_matcher("total_teardown_time", "elapsed_time")
stage_runtime_query = StatQuery("DATM", ("phase_name", None),
("epoch", None), ("logger_name", None),
("stat_name", "stage_runtime"),
("start_time", None))
stage_runtime_query.match_processor_function = \
stat_container_append_matcher("stage_runtime", "elapsed_time")
input_size_query = StatQuery("DATM", ("phase_name", None), ("epoch", None),
("stage_name", None), ("id", None),
("stat_name", "bytes_consumed"),
("uint_value", None))
input_size_query.match_processor_function = stat_container_append_matcher(
"total_bytes_in", "uint_value")
output_size_query = StatQuery("DATM", ("phase_name", None),
("epoch", None), ("stage_name", None),
("id", None),
("stat_name", "bytes_produced"),
("uint_value", None))
output_size_query.match_processor_function = stat_container_append_matcher(
"total_bytes_out", "uint_value")
allocation_time_query = StatQuery("SUMM", ("phase_name", None),
("epoch", None), ("stage_name", None),
("id", None),
("stat_name", "allocation_wait_time"),
("summary_stat_name", "sum"))
allocation_time_query.match_processor_function = \
stat_container_append_matcher("total_mem_wait_time", "value")
enqueue_block_time_query = StatQuery(
"SUMM", ("phase_name", None), ("epoch", None), ("stage_name", None),
("id", None), ("stat_name", "queue_saturation_block_time"),
("summary_stat_name", "sum"))
enqueue_block_time_query.match_processor_function = \
stat_container_append_matcher(
"total_enqueue_block_time", "value")
worker_type_query = StatQuery("DATM", ("phase_name", None),
("epoch", None), ("stage_name", None),
("id", None), ("stat_name", "worker_type"),
("str_value", None))
worker_type_query.match_processor_function = set_stage_value_matcher
would_have_blocked_query = StatQuery("DATM", ("phase_name", None),
("epoch", None), ("stage_name", None),
("id", None),
("stat_name", "would_have_blocked"),
("uint_value", None))
would_have_blocked_query.match_processor_function = \
stat_container_append_matcher("would_have_blocked", "uint_value")
total_ios_query = StatQuery("DATM", ("phase_name", None), ("epoch", None),
("stage_name", None), ("id", None),
("stat_name", "total_ios"),
("uint_value", None))
total_ios_query.match_processor_function = stat_container_append_matcher(
"total_ios", "uint_value")
worker_start_time_query = StatQuery("DATM", ("phase_name", None),
("epoch", None), ("stage_name", None),
("id", None),
("stat_name", "worker_start_time"),
("uint_value", None))
worker_start_time_query.match_processor_function = \
stat_container_append_matcher("worker_start_time", "uint_value")
worker_stop_time_query = StatQuery("DATM", ("phase_name", None),
("epoch", None), ("stage_name", None),
("id", None),
("stat_name", "worker_stop_time"),
("uint_value", None))
worker_stop_time_query.match_processor_function = \
stat_container_append_matcher("worker_stop_time", "uint_value")
queries = [
total_runtime_query, total_idle_time_query,
pipeline_saturation_time_query, num_workers_query, teardown_time_query,
stage_runtime_query, input_size_query, output_size_query,
allocation_time_query, enqueue_block_time_query, worker_type_query,
would_have_blocked_query, total_ios_query, worker_start_time_query,
worker_stop_time_query
]
runtime_info = utils.process_queries(queries, experiment_directory,
verbose, skipped_phases)
runtime_info = postprocess(runtime_info, experiment_directory)
return runtime_info
def gather_runtime_info(experiment_directory, verbose, skipped_phases=[]):
total_runtime_query = StatQuery(
"SUMM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "runtime"),
("summary_stat_name", "sum"))
total_runtime_query.match_processor_function = \
stat_container_append_matcher("total_runtime", "value")
total_idle_time_query = StatQuery(
"SUMM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "wait"),
("summary_stat_name", "sum"))
total_idle_time_query.match_processor_function = \
stat_container_append_matcher("total_idle_time", "value")
pipeline_saturation_time_query = StatQuery(
"SUMM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "pipeline_saturation_wait"),
("summary_stat_name", "sum"))
pipeline_saturation_time_query.match_processor_function = (
stat_container_append_matcher("pipeline_saturation_time", "value"))
num_workers_query = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("logger_name", None),
("stat_name", "num_workers"),
("uint_value", None))
num_workers_query.match_processor_function = stat_container_append_matcher(
"num_workers", "uint_value")
teardown_time_query = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "teardown"),
("start_time", None))
teardown_time_query.match_processor_function = \
stat_container_append_matcher("total_teardown_time", "elapsed_time")
stage_runtime_query = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("logger_name", None),
("stat_name", "stage_runtime"),
("start_time", None))
stage_runtime_query.match_processor_function = \
stat_container_append_matcher("stage_runtime", "elapsed_time")
input_size_query = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "bytes_consumed"),
("uint_value", None))
input_size_query.match_processor_function = stat_container_append_matcher(
"total_bytes_in", "uint_value")
output_size_query = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "bytes_produced"),
("uint_value", None))
output_size_query.match_processor_function = stat_container_append_matcher(
"total_bytes_out", "uint_value")
allocation_time_query = StatQuery(
"SUMM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "allocation_wait_time"),
("summary_stat_name", "sum"))
allocation_time_query.match_processor_function = \
stat_container_append_matcher("total_mem_wait_time", "value")
enqueue_block_time_query = StatQuery(
"SUMM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "queue_saturation_block_time"),
("summary_stat_name", "sum"))
enqueue_block_time_query.match_processor_function = \
stat_container_append_matcher(
"total_enqueue_block_time", "value")
worker_type_query = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "worker_type"),
("str_value", None))
worker_type_query.match_processor_function = set_stage_value_matcher
would_have_blocked_query = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "would_have_blocked"),
("uint_value", None))
would_have_blocked_query.match_processor_function = \
stat_container_append_matcher("would_have_blocked", "uint_value")
total_ios_query = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "total_ios"),
("uint_value", None))
total_ios_query.match_processor_function = stat_container_append_matcher(
"total_ios", "uint_value")
worker_start_time_query = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "worker_start_time"),
("uint_value", None))
worker_start_time_query.match_processor_function = \
stat_container_append_matcher("worker_start_time", "uint_value")
worker_stop_time_query = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("stage_name", None),
("id", None),
("stat_name", "worker_stop_time"),
("uint_value", None))
worker_stop_time_query.match_processor_function = \
stat_container_append_matcher("worker_stop_time", "uint_value")
queries = [total_runtime_query, total_idle_time_query,
pipeline_saturation_time_query, num_workers_query,
teardown_time_query, stage_runtime_query, input_size_query,
output_size_query, allocation_time_query,
enqueue_block_time_query, worker_type_query,
would_have_blocked_query, total_ios_query,
worker_start_time_query, worker_stop_time_query]
runtime_info = utils.process_queries(
queries, experiment_directory, verbose, skipped_phases)
runtime_info = postprocess(runtime_info, experiment_directory)
return runtime_info
def calculate_rate(
input_directory, skip_phase_zero, skip_phase_one, skip_phase_two, verbose):
phaseTimesQuery = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("logger_name", None),
("stat_name", "phase_runtime"),
("start_time", None))
phaseTimesQuery.match_processor_function = handleTimestampQueryMatch
diskCountQuery = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("logger_name", "mapreduce"),
("stat_name", ["num_input_disks", "num_intermediate_disks"]),
("uint_value", None))
diskCountQuery.match_processor_function = handleDiskCountMatch
inputSizeQuery = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("stage_name", "reader"),
("id", None),
("stat_name", "bytes_produced"),
("uint_value", None)
)
inputSizeQuery.match_processor_function = handleReaderInputMatch
writerOutputQuery = StatQuery(
"DATM",
("phase_name", None),
("epoch", None),
("stage_name", "writer"),
("id", None),
("stat_name", "bytes_consumed"),
("uint_value", None)
)
writerOutputQuery.match_processor_function = handleWriterOutputMatch
queries = [phaseTimesQuery, diskCountQuery, inputSizeQuery,
writerOutputQuery]
skipped_phases = []
if skip_phase_zero:
skipped_phases.append("phase_zero")
if skip_phase_one:
skipped_phases.append("phase_one")
if skip_phase_two:
skipped_phases.append("phase_two")
output_data = utils.process_queries(
queries, input_directory, verbose, skipped_phases)
data_for_display = postprocess_rate_data(output_data)
for key in sorted(data_for_display.keys()):
env = jinja2.Environment(
loader = jinja2.FileSystemLoader(os.path.dirname(__file__)),
trim_blocks = True)
template = env.get_template('rate_summary_template.jinja')
rendered_template = template.render(**data_for_display[key])
print rendered_template.strip() + "\n"