def cpuprofile_generate_flame_graph(file_path, range_start, range_end):
f = get_file(file_path)
chrome_profile = json.load(f)
f.close()
cpuprofiles = get_cpuprofiles(chrome_profile)
# a chrome profile can contain multiple cpu profiles
# using only the first one for now
# TODO: add support for multiple cpu profiles
profile = cpuprofiles[0]
root_id = profile['nodes'][0]['id']
nodes = parse_nodes(profile)
ignore_ids = get_meta_ids(nodes)
start_time = profile['startTime']
if range_start is not None:
adjusted_range_start = (math.floor(start_time / 1000000) +
range_start) * 1000000
if range_end is not None:
adjusted_range_end = (math.floor(start_time / 1000000) +
range_end) * 1000000
return generate_flame_graph(nodes, root_id, profile['samples'],
profile['timeDeltas'], profile['startTime'],
adjusted_range_start, adjusted_range_end,
ignore_ids)
def nflxprofile_generate_differential_flame_graph(file_path, range_start, range_end):
try:
f = get_file(file_path)
profile = nflxprofile_pb2.Profile()
profile.ParseFromString(f.read())
except TypeError:
abort(500, 'Failed to parse profile.')
finally:
f.close()
start_time = profile.start_time
if range_start is not None:
range_start = (math.floor(start_time) + range_start)
if range_end is not None:
range_end = (math.floor(start_time) + range_end)
return generate_flame_graph([profile], [0], [None], range_start, range_end)
def nflxprofile_generate_flame_graph(file_path, range_start, range_end):
try:
f = get_file(file_path)
profile = nflxprofile_pb2.Profile()
profile.ParseFromString(f.read())
except TypeError:
abort(500, 'Failed to parse profile.')
finally:
f.close()
start_time = profile.start_time
if range_start is not None:
adjusted_range_start = (math.floor(start_time) + range_start)
if range_end is not None:
adjusted_range_end = (math.floor(start_time) + range_end)
return generate_flame_graph(profile.nodes, 0, profile.samples, profile.time_deltas, start_time, adjusted_range_start, adjusted_range_end, None)
def cpuprofile_generate_flame_graph(file_path, range_start, range_end):
f = get_file(file_path)
chrome_profile = json.load(f)
f.close()
profiles = get_cpuprofiles(chrome_profile)
root_ids = []
ignore_ids = []
start_time = None
for profile in profiles:
root_ids.append(profile['nodes'][0]['id'])
parsed_nodes = parse_nodes(profile['nodes'])
ignore_ids.append(get_meta_ids(parsed_nodes))
profile['nodes'] = parsed_nodes
if start_time is None or profile['startTime'] < start_time:
start_time = profile['startTime']
if range_start is not None:
adjusted_range_start = (math.floor(start_time / 1000000) + range_start) * 1000000
if range_end is not None:
adjusted_range_end = (math.floor(start_time / 1000000) + range_end) * 1000000
return generate_flame_graph(profiles, root_ids, ignore_ids, adjusted_range_start, adjusted_range_end)
def cpuprofile_generate_flame_graph(filename,
range_start,
range_end,
profile=None):
if not profile:
file_path = join(config.PROFILE_DIR, filename)
(f, mime) = get_file(file_path)
profile = json.load(f)
f.close()
nodes = parse_nodes(profile)
ignore_ids = get_meta_ids(nodes)
start_time = profile['startTime']
if range_start is not None:
adjusted_range_start = (math.floor(start_time / 1000000) +
range_start) * 1000000
if range_end is not None:
adjusted_range_end = (math.floor(start_time / 1000000) +
range_end) * 1000000
return generate_flame_graph(nodes, profile['samples'],
profile['timeDeltas'], profile['startTime'],
adjusted_range_start, adjusted_range_end,
ignore_ids)