def histogram_plot(experiment_log_dir, plot_spec_string, output_filename,
has_legend, x_limit, verbose):
queries = [
plot_utils.plot_spec_string_to_query(plot_spec_string, 0, "HIST")]
plot_data = metaprogram_utils.process_queries(
queries, experiment_log_dir, verbose)
if "plot_points" not in plot_data:
warnings.warn("No data to plot!")
return
histogram_data = plot_data["plot_points"][0]
cumulative_histogram = {}
layout = PlotLayout()
layout.dpi = 250
for stat_name in histogram_data:
plot = Plot()
plot.setTitle(stat_name)
if has_legend:
plot.hasLegend(labelSize=8)
if x_limit is not None:
plot.setXLimits(0, x_limit)
style_plot(plot, stat_name)
for key, points in sorted(histogram_data[stat_name].items()):
for size, count in itertools.izip(points["bin"], points["count"]):
if size not in cumulative_histogram:
cumulative_histogram[size] = 0
cumulative_histogram[size] += count
line = Line()
line.stepFunction("pre")
line.label = str(key)
line.xValues = points["bin"]
line.yValues = points["count"]
plot.add(line)
layout.addPlot(plot)
cumulative_plot = Plot()
if x_limit is not None:
cumulative_plot.setXLimits(0, x_limit)
cumulative_plot.setTitle("Cumulative Histogram for " + stat_name)
style_plot(cumulative_plot, stat_name)
line = Line()
line.stepFunction("pre")
line.xValues = sorted(cumulative_histogram.keys())
line.yValues = [cumulative_histogram[key] for key in line.xValues]
cumulative_plot.add(line)
layout.addPlot(cumulative_plot)
layout.save(output_filename)
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 list_time_series(
experiment_log_dir, plot_spec_strings, output_filename, verbose):
queries = []
queries.extend(get_list_time_series_queries())
time_series_data = utils.process_queries(
queries, experiment_log_dir, verbose)
time_series_keys = time_series_data["time_series_keys"]
output_fp = open(output_filename, 'w')
for time_series_key in sorted(time_series_keys, key=itemgetter(0,1,3,2)):
print >> output_fp, time_series_tuple_to_str(time_series_key)
output_fp.close()
def list_time_series(experiment_log_dir, plot_spec_strings, output_filename,
verbose):
queries = []
queries.extend(get_list_time_series_queries())
time_series_data = utils.process_queries(queries, experiment_log_dir,
verbose)
time_series_keys = time_series_data["time_series_keys"]
output_fp = open(output_filename, 'w')
for time_series_key in sorted(time_series_keys, key=itemgetter(0, 1, 3,
2)):
print >> output_fp, time_series_tuple_to_str(time_series_key)
output_fp.close()
def time_series_plot(experiment_log_dir, plot_spec_strings, make_legend,
split_by_host, group_by_query, verbose):
queries = []
for i, plot_spec_string in enumerate(plot_spec_strings):
queries.append(
plot_utils.plot_spec_string_to_query(plot_spec_string, i, "COLL"))
plot_data = metaprogram_utils.process_queries(queries, experiment_log_dir,
verbose)
plots = make_plots(plot_data, make_legend, split_by_host, group_by_query)
if len(plots) == 0:
warnings.warn("No data to plot!")
return
return plots
def time_series_plot(experiment_log_dir, plot_spec_strings,
make_legend, split_by_host, group_by_query, verbose):
queries = []
for i, plot_spec_string in enumerate(plot_spec_strings):
queries.append(
plot_utils.plot_spec_string_to_query(
plot_spec_string, i, "COLL"))
plot_data = metaprogram_utils.process_queries(
queries, experiment_log_dir, verbose)
plots = make_plots(plot_data, make_legend, split_by_host, group_by_query)
if len(plots) == 0:
warnings.warn("No data to plot!")
return
return plots
def histogram_plot(experiment_log_dir, plot_spec_string, output_filename,
has_legend, x_limit, verbose):
queries = [
plot_utils.plot_spec_string_to_query(plot_spec_string, 0, "HIST")
]
plot_data = metaprogram_utils.process_queries(queries, experiment_log_dir,
verbose)
if "plot_points" not in plot_data:
warnings.warn("No data to plot!")
return
histogram_data = plot_data["plot_points"][0]
cumulative_histogram = {}
layout = PlotLayout()
layout.dpi = 250
for stat_name in histogram_data:
plot = Plot()
plot.setTitle(stat_name)
if has_legend:
plot.hasLegend(labelSize=8)
if x_limit is not None:
plot.setXLimits(0, x_limit)
style_plot(plot, stat_name)
for key, points in sorted(histogram_data[stat_name].items()):
for size, count in itertools.izip(points["bin"], points["count"]):
if size not in cumulative_histogram:
cumulative_histogram[size] = 0
cumulative_histogram[size] += count
line = Line()
line.stepFunction("pre")
line.label = str(key)
line.xValues = points["bin"]
line.yValues = points["count"]
plot.add(line)
layout.addPlot(plot)
cumulative_plot = Plot()
if x_limit is not None:
cumulative_plot.setXLimits(0, x_limit)
cumulative_plot.setTitle("Cumulative Histogram for " + stat_name)
style_plot(cumulative_plot, stat_name)
line = Line()
line.stepFunction("pre")
line.xValues = sorted(cumulative_histogram.keys())
line.yValues = [cumulative_histogram[key] for key in line.xValues]
cumulative_plot.add(line)
layout.addPlot(cumulative_plot)
layout.save(output_filename)
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"
