HiBench成长笔记——(9) 分析源码monitor.py
monitor.py 是主监控程序,将监控数据写入日志,并统计监控数据生成HTML统计展示页面:
#!/usr/bin/env python2
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import threading, subprocess, re, os, sys, signal, socket
from time import sleep, time
from contextlib import closing
import traceback, thread
from datetime import datetime
from collections import namedtuple
from pprint import pprint
from itertools import groupby
# Probe intervals, in seconds.
# Warning: a value too short may get wrong results due to lack of data when system load goes high.
# and must be float!
PROBE_INTERVAL=float(5)
#FIXME: use log helper later
#log_lock = threading.Lock()
def log(*s):
if len(s)==1: s=s[0]
else: s= " ".join([str(x) for x in s])
# with log_lock:
# with open("/home/zhihui/monitor_proc.log", 'a') as f:
log_str = str(thread.get_ident())+":"+str(s) +'\n'
# f.write( log_str )
sys.stderr.write(log_str)
entered=False
def sig_term_handler(signo, stack):
global entered
global log_path
global report_path
global workload_title
global bench_log_path
global na
if not entered:
entered=True # FIXME: Not atomic
else: return
na.stop()
generate_report(workload_title, log_path, bench_log_path, report_path)
sys.exit(0)
def samedir(fn):
"""
return abspath of fn in the same directory where this python file stores
"""
return os.path.abspath(os.path.join(os.path.dirname(__file__), fn))
class PatchedNameTuple(object):
def __sub__(self, other):
assert isinstance(other, self.__class__)
assert self[0] == other[0]
cls = self.__class__
return cls(self[0], *[a-b for a, b in zip(self[1:], other[1:])])
def __div__(self, other):
return self.__class__(self[0], *[a/other for a in self[1:]])
def _add(self, other, override_title=None):
if other == None: return self
assert isinstance(other, self.__class__)
cls = self.__class__
title = self[0] if not override_title else override_title
return cls(title, *[a+b for a, b in zip(self[1:], other[1:])])
def ident(size, s):
return "\n".join((" "*size + x for x in s.split("\n")))
class RemoteProc(threading.Thread):
SEP="----SEP----"
template_debug=r"""exec('
import time, os, sys, socket, traceback
socket.setdefaulttimeout(1)
def log(*x, **kw):
with open("/home/zhihui/probe.log", kw.get("mode","a")) as f:
f.write(repr(x)+chr(10))
try:
log("create socket", mode="w")
s=socket.socket(socket.AF_INET, socket.SOCK_STREAM)
log("bind socket")
s.bind(("0.0.0.0",0))
log("listen socket")
s.listen(5)
log("bind socket to:", s.getsockname())
while True:
log("accepting")
try:
print s.getsockname()[1]
s2,peer=s.accept()
break
except socket.timeout:
log("accept timeout, retry")
log("accepted, peer:",peer)
except Exception as e:
import traceback
log(traceback.format_exc())
{func_template}
while True:
s2.send(("{SEP}+%s" % time.time())+chr(10))
{call_template}
s2.send("{SEP}#end"+chr(10))
time.sleep({interval})
')"""
template=r"""exec('
import time, os, sys, socket, traceback
s=socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind(("0.0.0.0",0))
s.listen(5)
print s.getsockname()[1]
s2,peer=s.accept()
{func_template}
while True:
s2.send(("{SEP}+%s" % time.time())+chr(10))
{call_template}
s2.send("{SEP}#end"+chr(10))
time.sleep({interval})
')"""
def __init__(self, host, interval=1):
self.host = host
self.cmds = []
self.interval = interval
self.monitor_ins = {}
self.local_aggr_container={}
self._running=True
super(RemoteProc, self).__init__()
def register(self, monitor_ins, cmds):
assert isinstance(monitor_ins, BaseMonitor)
self.monitor_ins[len(self.cmds)] = monitor_ins # monitor command seq id => monitor instance
self.cmds.append(cmds)
def run(self):
func_template = "\n".join(["def func_{id}():\n{func}"\
.format(id=id,
func=ident(2,
func+'\ns2.send("{SEP}={id}"+chr(10))'\
.format(SEP=self.SEP, id=id))) \
for id, func in enumerate(self.cmds)])
call_template="\n".join([" func_{id}()"\
.format(id=id) for id in range(len(self.cmds))]
)
script = self.template.format(func_template=func_template,
call_template=call_template,
interval = self.interval,
SEP = self.SEP)
s = script.replace('"', r'\"').replace("\n", r"\n")
container=[]
# log("ssh client to:", self.host)
with self.ssh_client(self.host, "python -u -c \"{script}\"".format(script=s)) as f:
# log("ssh client %s connected" % self.host)
try:
port_line = f.readline()
# log("host:", self.host, "got port,", port_line)
port = int(port_line.rstrip())
s=socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.settimeout(0.5)
for i in range(30): # try to connect 30 times maximum
try:
# log("try to connect:", self.host, port)
s.connect((self.host, port))
# log("connectted to:", self.host, port)
break
except socket.timeout:
# log("connecting to:", self.host, port, "timedout")
pass
else: # not connectted after 30 times trying
# log("cann't connectted to:", self.host, port)
s.shutdown(socket.SHUT_RDWR)
self.ssh_close()
return
s.settimeout(None)
except Exception as e:
log(traceback.format_exc())
with closing(s.makefile()) as f2:
while self._running:
try:
l = f2.readline()
except KeyboardInterrupt:
break
if not l: break
if l.startswith(self.SEP):
tail = l.lstrip(self.SEP)
if tail[0]=='+': # timestamp
remote_timestamp = float(tail[1:])
cur_timestamp = time()
elif tail.startswith('#end'): # end sign
# log("na push, timestamp:", cur_timestamp)
self.na_push(cur_timestamp)
else:
id = int(tail[1:])
if self.monitor_ins[id]:
self.monitor_ins[id].feed(container, cur_timestamp)
container = []
else:
container.append(l.rstrip())
s.shutdown(socket.SHUT_RDWR)
self.ssh_close()
def stop(self):
self._running=False
def aggregate(self, timestamp, data):
if not self.local_aggr_container:
self.local_aggr_container['timestamp']=timestamp
assert timestamp == self.local_aggr_container['timestamp']
assert type(data) is dict
self.local_aggr_container.update(data)
self.local_aggr_container['timestamp'] = timestamp
def na_register(self, na):
assert isinstance(na, NodeAggregator)
self.node_aggr_parent = na
def na_push(self, timestamp):
if self.local_aggr_container:
assert self.local_aggr_container.get('timestamp', -1) == timestamp
self.node_aggr_parent.commit_aggregate(self.host, self.local_aggr_container)
self.local_aggr_container={}
class BaseMonitor(object):
IGNORE_KEYS=[]
def __init__(self, rproc):
self.rproc = rproc
self._last = None
def feed(self, container, timestamp): # override to parse pulled data files
raise NotImplementedError()
def ssh_client(self, host, shell): # override for opening ssh client
raise NotImplementedError()
def ssh_close(self): # override for clear up ssh client
raise NotImplementedError()
def commit(self, timestamp, header, stat):
if self._last is None: self._last = stat
else:
stat_delta = dict([(header+'/'+k, stat[k] - self._last[k]) \
for k in set(self._last.keys()).union(set(stat.keys()))\
if k in stat and k in self._last and k not in self.IGNORE_KEYS
])
self._last = stat
# if header.startswith("net"):
# print stat_delta
stat_delta[header+'/total'] = reduce_patched(lambda a,b: a._add(b, 'total'), stat_delta.values())
self.rproc.aggregate(timestamp, stat_delta)
class BashSSHClientMixin(object):
ssh_lock = threading.Lock()
def ssh_client(self, host, shell):
with open(os.devnull, 'rb', 0) as DEVNULL:
with BashSSHClientMixin.ssh_lock:
self.proc = subprocess.Popen(["ssh", host, shell], bufsize=1,
stdin=DEVNULL, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
return self.proc.stdout
def ssh_close(self):
assert self.proc
self.proc.terminate()
self.proc.wait()
return self.proc.returncode
_CPU=namedtuple("CPU", ['label', 'user', 'nice', 'system', 'idle', 'iowait', 'irq', 'softirq'])
class CPU(_CPU, PatchedNameTuple):
def percentage(self):
total = sum(self[1:])
return CPU(self[0], *[x*100.0 / total for x in self[1:]]) if total>0 else self
class CPUMonitor(BaseMonitor):
def __init__(self, rproc):
super(CPUMonitor, self).__init__(rproc)
rproc.register(self, """with open("/proc/stat") as f:
s2.send("".join([x for x in f.readlines() if x.startswith("cpu")]))
""")
def feed(self, container, timestamp):
"parse /proc/stat"
self.commit(timestamp, dict([self._parse_stat(line) for line in container]))
def _parse_stat(self, line):
"parse one line of /proc/stat"
assert line.strip(), "BUG! empty line in /proc/stat"
fields = line.split()
if fields[0]=='cpu':
fields[0]='total'
return (fields[0], CPU(fields[0], *[int(x) for x in fields[1:8]]))
def commit(self, timestamp, cpu_stat):
if self._last is None:
self._last = cpu_stat
else:
cpu_usage = dict([("cpu/"+k, (cpu_stat[k] - self._last[k]).percentage()) for k in self._last])
self._last = cpu_stat
self.rproc.aggregate(timestamp, cpu_usage)
_Network=namedtuple("Network", ['label', "recv_bytes", "recv_packets", "recv_errs", "recv_drop",
"send_bytes", "send_packets", "send_errs", "send_drop"])
class Network(_Network, PatchedNameTuple): pass
class NetworkMonitor(BaseMonitor):
IGNORE_KEYS=["lo"]
def __init__(self, rproc):
rproc.register(self, """with open("/proc/net/dev") as f:
s2.send("".join([x for x in f.readlines()]))
""")
self._filter = re.compile('^\s*(.+):\s*(\d+)\s+(\d+)\s+(\d+)\s+(\d+)\s+\d+\s+\d+\s+\d+\s+\d+\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+).*$')
super(NetworkMonitor, self).__init__(rproc)
def feed(self, container, timestamp):
"parse /proc/net/dev"
self.commit(timestamp, "net", dict(filter(lambda x:x, [self._parse_net_dev(line) for line in container])))
def _parse_net_dev(self, line):
matched = self._filter.match(line)
if matched:
obj = Network(matched.groups()[0], *[int(x) for x in matched.groups()[1:]])
if not (obj.recv_bytes==0 and obj.send_bytes==0):
return (obj[0], obj)
_Disk=namedtuple("Disk", ["label", "io_read", "bytes_read", "time_spent_read", "io_write", "bytes_write", "time_spent_write"])
class Disk(_Disk, PatchedNameTuple): pass
class DiskMonitor(BaseMonitor):
def __init__(self, rproc):
super(DiskMonitor, self).__init__(rproc)
rproc.register(self, """with open("/proc/diskstats") as f:
blocks = os.listdir("/sys/block")
s2.send("".join([x for x in f.readlines() if x.split()[2] in blocks and not x.split()[2].startswith("loop") and x.split()[3]!="0"]))
""")
def feed(self, container, timestamp):
"parse /proc/diskstats"
self.commit(timestamp, "disk", dict([self._parse_disk_stat(line) for line in container]))
def _parse_disk_stat(self, line):
fields = line.split()[2:]
obj = Disk(fields[0],
io_read=int(fields[1]), bytes_read=int(fields[3])*512, time_spent_read=int(fields[4])/1000.0,
io_write=int(fields[5]), bytes_write=int(fields[7])*512, time_spent_write=int(fields[8])/1000.0)
return (obj[0], obj)
_Memory=namedtuple("Memory", ["label", "total", "used", "buffer_cache", "free", "map"])
class Memory(_Memory, PatchedNameTuple): pass
class MemoryMonitor(BaseMonitor):
def __init__(self, rproc):
super(MemoryMonitor, self).__init__(rproc)
rproc.register(self, """with open("/proc/meminfo") as f:
mem = dict([(a, b.split()[0].strip()) for a, b in [x.split(":") for x in f.readlines()]])
s2.send(":".join([mem[field] for field in ["MemTotal", "Buffers", "Cached", "MemFree", "Mapped"]])+chr(10))
""")
def feed(self, memory_status, timestamp):
"parse /proc/meminfo"
total, buffers, cached, free, mapped= [int(x) for x in memory_status[0].split(":")]
self.rproc.aggregate(timestamp, {"memory/total":Memory(label="total", total=total,
used=total - free - buffers-cached,
buffer_cache=buffers + cached,
free=free, map=mapped)})
_Proc=namedtuple("Proc", ["label", "load5", "load10", "load15", "running", "procs"])
class Proc(_Proc, PatchedNameTuple): pass
class ProcMonitor(BaseMonitor):
def __init__(self, rproc):
super(ProcMonitor, self).__init__(rproc)
rproc.register(self, """with open("/proc/loadavg") as f:
s2.send(f.read())
""")
def feed(self, load_status, timestamp):
"parse /proc/meminfo"
load5, load10, load15, running_procs= load_status[0].split()[:4]
running, procs = running_procs.split('/')
self.rproc.aggregate(timestamp, {"proc":Proc(label="total", load5=float(load5), load10=float(load10),
load15=float(load15), running=int(running), procs=int(procs))})
class NodeAggregator(object):
def __init__(self, log_name):
self.node_pool = {}
self.log_name = log_name
self.log_lock = threading.Lock()
try:
os.unlink(self.log_name)
except OSError:
pass
def append(self, node):
assert isinstance(node, RemoteProc)
self.node_pool[node.host] = node
node.na_register(self)
def commit_aggregate(self, node, datas):
datas['hostname'] = node
with self.log_lock:
with file(self.log_name, "a") as f:
f.write(repr(datas) + "\n")
def run(self):
for v in self.node_pool.values():
v.start()
def stop(self):
for v in self.node_pool.values():
v.stop()
for v in self.node_pool.values():
v.join()
def round_to_base(v, b):
"""
>>> round_to_base(0.1, 0.3)
0.0
>>> round_to_base(0.3, 0.3)
0.3
>>> round_to_base(0.0, 0.3)
0.0
>>> round_to_base(0.5, 0.3)
0.3
>>> round_to_base(0.51, 0.3)
0.3
"""
for i in range(10):
base = int(b * 10**i)
if abs(base - b * 10**i) < 0.001: break
assert base>0
return float(int(v * 10**i) / base * base) / (10**i)
def filter_dict_with_prefix(d, prefix, sort=True):
keys = sorted(d.keys()) if sort else d.keys()
if prefix[0]=='!':
return dict([(x, d[x]) for x in keys if not x.startswith(prefix[1:])])
else:
return dict([(x, d[x]) for x in keys if x.startswith(prefix)])
def reduce_patched(func, data):
if len(data)==1:
return data[0]
elif len(data)==0:
return data
else:
return reduce(func, data)
def filter_dict_with_prefixes(d, *prefixes):
if len(prefixes)==1:
return filter_dict_with_prefix(d, prefixes[0])
else:
return reduce_patched(lambda a,b: filter_dict_with_prefix(filter_dict_with_prefix(d, a),b),
prefixes)
def test():
p = BashSSHClientMixin()
script=r"""exec('
import time, os, sys
while 1:
with open("/proc/stat") as f: print f.read(),
print "---hello---"
time.sleep(1)
')"""
s = script.replace('"', r'\"').replace("\n", r"\n")
with p.ssh_client("localhost", "python -u -c \"{s}\"".format(s=s)) as f:
while 1:
l = f.readline()
print l.rstrip()
if not l: break
p.ssh_close()
def test2():
class P(RemoteProc, BashSSHClientMixin): pass
p = P("localhost", 0.3)
CPUMonitor(p)
NetworkMonitor(p)
DiskMonitor(p)
MemoryMonitor(p)
p.run()
def start_monitor(log_filename, nodes):
class P(RemoteProc, BashSSHClientMixin):
def __init__(self, *args):
RemoteProc.__init__(self, *args)
CPUMonitor(self)
NetworkMonitor(self)
DiskMonitor(self)
MemoryMonitor(self)
ProcMonitor(self)
global na
na = NodeAggregator(log_filename)
nodes = sorted(list(set(nodes)))
for node in nodes:
na.append(P(node, PROBE_INTERVAL))
na.run()
def parse_bench_log(benchlog_fn):
events=["x,event"]
_spark_stage_submit = re.compile("^(\d{2}\/\d{2}\/\d{2} \d{2}:\d{2}:\d{2}) INFO [a-zA-Z0-9_\.]*DAGScheduler: Submitting (Stage \d+) \((.*)\).+$") # submit spark stage
_spark_stage_finish = re.compile("^(\d{2}\/\d{2}\/\d{2} \d{2}:\d{2}:\d{2}) INFO [a-zA-Z0-9_\.]*DAGScheduler: (Stage \d+) \((.*)\) finished.+$") # spark stage finish
_hadoop_run_job = re.compile("^(\d{2}\/\d{2}\/\d{2} \d{2}:\d{2}:\d{2}) INFO mapred.*\.Job.*: Running job: job_([\d_]+)$") # hadoop run job
_hadoop_map_reduce_progress = re.compile("^(\d{2}\/\d{2}\/\d{2} \d{2}:\d{2}:\d{2}) INFO mapred.*\.Job.*:\s+map (\d{1,2})% reduce (\d{1,2})%$") # hadoop reduce progress
_hadoop_job_complete_mr1 = re.compile("^(\d{2}\/\d{2}\/\d{2} \d{2}:\d{2}:\d{2}) INFO mapred.JobClient: Job complete: job_([\d_]+)$")
_hadoop_job_complete_mr2 = re.compile("^(\d{2}\/\d{2}\/\d{2} \d{2}:\d{2}:\d{2}) INFO mapreduce.Job: Job job_([\d_]+) completed successfully$")
"""
# MR1 sample
14/06/24 11:18:39 INFO mapred.JobClient: Running job: job_201406241116_0001
14/06/24 11:18:40 INFO mapred.JobClient: map 0% reduce 0%
...
13/11/21 14:38:55 INFO mapred.JobClient: Job complete: job_201311150128_0050
# MR2 sample
15/04/10 17:20:01 INFO mapreduce.Job: Running job: job_1427781540447_0448
15/04/10 17:20:07 INFO mapreduce.Job: Job job_1427781540447_0448 running in uber mode : false
15/04/10 17:20:07 INFO mapreduce.Job: map 0% reduce 0%
...
15/04/10 17:20:25 INFO mapreduce.Job: Job job_1427781540447_0448 completed successfully
"""
flag={}
with open(benchlog_fn) as f:
while True:
line = f.readline().rstrip()
if not line: break
for rule in [_spark_stage_submit, _spark_stage_finish, _hadoop_run_job, _hadoop_map_reduce_progress, _hadoop_job_complete_mr1, _hadoop_job_complete_mr2]:
matched = rule.match(line)
if matched:
result = matched.groups()
timestamp = datetime.strptime(result[0], r" # convert to millsec for js
if rule is _spark_stage_submit:
events.append("{t},Start {v1} ({v2})".format(t=timestamp, v1=result[1], v2=result[2]))
elif rule is _spark_stage_finish:
events.append("{t},Finish {v1} ({v2})".format(t=timestamp, v1=result[1], v2=result[2]))
elif rule is _hadoop_run_job:
events.append("{t},Start Job {v1}".format(t=timestamp, v1=result[1]))
flag={}
elif rule is _hadoop_map_reduce_progress:
map_progress,reduce_progress = int(result[1]), int(result[2])
op={'map':False, 'reduce':False}
if map_progress == 100:
if not "map" in flag:
op['map'] = True
flag['map'] = True
elif reduce_progress>0:
if not 'reduce' in flag:
op['reduce'] = True
flag['reduce'] = True
if op['map'] and op['reduce']:
events.append("{t},Map finish and Reduce start".format(t=timestamp))
elif op['map']:
events.append("{t},Map finish".format(t=timestamp))
elif op['reduce']:
events.append("{t},Reduce start".format(t=timestamp))
elif rule is _hadoop_job_complete_mr1 or rule is _hadoop_job_complete_mr2:
events.append("{t},Finsih Job {v1}".format(t=timestamp, v1=result[1]))
else:
assert 0, "should never reach here"
# limit maximum string length of events
for i in range(len(events)):
event_time, event_str = re.split(',', events[i], 1)
if len(event_str) > 45:
event_str = event_str[:21]+ '...' + event_str[-21:]
events[i]="%s,%s" % (event_time, event_str)
# merge events occurred at sametime:
i = 1
while i < len(events)-1:
cur = events[i].split(',')[0]
next = events[i+1].split(',')[0]
if abs(int(cur)/1000 - int(next)/1000) < 1:
events[i] = events[i] + "<br>" + re.split(',', events[i+1], 1)[1]
del events[i+1]
continue
i += 1
return events
def generate_report(workload_title, log_fn, benchlog_fn, report_fn):
c =- 1
with open(log_fn) as f:
datas=[eval(x) for x in f.readlines()]
all_hosts = sorted(list(set([x['hostname'] for x in datas])))
data_slices = groupby(datas, lambda x:round_to_base(x['timestamp'], PROBE_INTERVAL)) # round to time interval and groupby
# Generating CSVs
cpu_heatmap = ["x,y,value,hostname,coreid"]
cpu_overall = ["x,idle,user,system,iowait,others"]
network_heatmap = ["x,y,value,hostname,adapterid"]
network_overall = ["x,recv_bytes,send_bytes,|recv_packets,send_packets,errors"]
diskio_heatmap = ["x,y,value,hostname,diskid"]
diskio_overall = ["x,read_bytes,write_bytes,|read_io,write_io"]
memory_heatmap = ["x,y,value,hostname"]
memory_overall = ["x,free,buffer_cache,used"]
procload_heatmap = ["x,y,value,hostname"]
procload_overall = ["x,load5,load10,load15,|running,procs"]
events = parse_bench_log(benchlog_fn)
cpu_count={}
network_count={}
diskio_count={}
memory_count={}
proc_count={}
for t, sub_data in data_slices:
classed_by_host = dict([(x['hostname'], x) for x in sub_data])
# total cpus, plot user/sys/iowait/other
data_by_all_hosts = [classed_by_host.get(h, {}) for h in all_hosts]
# all cpu cores, total cluster
summed1 = [x['cpu/total'] for x in data_by_all_hosts if x.has_key('cpu/total')]
if summed1:
summed = reduce_patched(lambda a,b: a._add(b), summed1) / len(summed1)
for x in data_by_all_hosts:
cpu = x.get('cpu/total', None)
if not cpu: continue
# user, system, io, idle, others
# print t, x['hostname'], cpu.user, cpu.system, cpu.iowait, cpu.idle, cpu.nice+cpu.irq+cpu.softirq
# print t, summed
cpu_overall.append("{time},{idle},{user},{system},{iowait},{others}" \
.format(time = int(t*1000), user = summed.user, system = summed.system,
iowait = summed.iowait, idle = summed.idle,
others = summed.nice + summed.irq + summed.softirq))
# all cpu cores, plot heatmap according to cpus/time/usage(100%-idle)
for idx, x in enumerate(data_by_all_hosts):
for idy, y in enumerate(filter_dict_with_prefixes(x, "cpu", "!cpu/total").values()):
try:
pos = cpu_count[(idx, idy, x['hostname'])]
except:
pos = len(cpu_count)
cpu_count[(idx, idy, x['hostname'])] = pos
# print t, pos, 100-y.idle, x['hostname'], y.label
cpu_heatmap.append("{time},{pos},{value},{host},{cpuid}" \
.format(time = int(t*1000), pos = pos, value = 100-y.idle,
host = x['hostname'], cpuid = y.label))
# all disk of each node, total cluster
summed1=[x['disk/total'] for x in data_by_all_hosts if x.has_key('disk/total')]
if summed1:
summed = reduce_patched(lambda a,b: a._add(b), summed1)
for x in data_by_all_hosts:
disk = x.get('disk/total', None)
if not disk: continue
# io-read, io-write, bytes-read, bytes-write
# print t, x['hostname'], disk.io_read, disk.io_write, disk.bytes_read, disk.bytes_write
# print t, summed
diskio_overall.append("{time},{bytes_read},{bytes_write},{io_read},{io_write}" \
.format(time = int(t*1000),
bytes_read = summed.bytes_read / PROBE_INTERVAL,
bytes_write = summed.bytes_write / PROBE_INTERVAL,
io_read = summed.io_read / PROBE_INTERVAL,
io_write = summed.io_write / PROBE_INTERVAL))
# all disks, plot heatmap according to disks/bytes_read+bytes_write
for idx, x in enumerate(data_by_all_hosts):
for idy, y in enumerate(filter_dict_with_prefixes(x, "disk", "!disk/total").values()):
try:
pos = diskio_count[(idx, idy, x['hostname'])]
except:
pos = len(diskio_count)
diskio_count[(idx, idy, x['hostname'])] = pos
# print t, pos, 100-y.idle, x['hostname'], y.label
diskio_heatmap.append("{time},{pos},{value},{host},{diskid}" \
.format(time = int(t*1000),
pos = pos,
value = (y.bytes_read + y.bytes_write) / PROBE_INTERVAL,
host = x['hostname'],
diskid = y.label))
# memory of each node, total cluster
summed1 = [x['memory/total'] for x in data_by_all_hosts if x.has_key('memory/total')]
if summed1:
summed = reduce_patched(lambda a,b: a._add(b), summed1)
for x in data_by_all_hosts:
mem = x.get("memory/total", None)
if not mem: continue
# mem-total, mem-used, mem-buffer&cache, mem-free, KB
# print t, x['hostname'], mem.total, mem.used, mem.buffer_cache, mem.free
#print t, summed
memory_overall.append("{time},{free},{buffer_cache},{used}" \
.format(time = int(t*1000),
free = summed.free,
used = summed.used,
buffer_cache = summed.buffer_cache))
# all memory, plot heatmap according to memory/total - free
for idx, x in enumerate(data_by_all_hosts):
for idy, y in enumerate(filter_dict_with_prefixes(x, "memory/total").values()):
try:
pos = memory_count[(idx, idy, x['hostname'])]
except:
pos = len(memory_count)
memory_count[(idx, idy, x['hostname'])] = pos
# print t, pos, 100-y.idle, x['hostname'], y.label
memory_heatmap.append("{time},{pos},{value},{host}" \
.format(time = int(t*1000),
pos = pos,
value = (y.total - y.free)*1000,
host = x['hostname']))
# proc of each node, total cluster
summed1 = [x['proc'] for x in data_by_all_hosts if x.has_key('proc')]
if summed1:
summed = reduce_patched(lambda a,b: a._add(b), summed1)
for x in data_by_all_hosts:
procs = x.get("proc", None)
if not procs: continue
procload_overall.append("{time},{load5},{load10},{load15},{running},{procs}"\
.format(time = int(t*1000),
load5 = summed.load5,load10=summed.load10,
load15 = summed.load15,running=summed.running,
procs = summed.procs))
# all nodes' proc, plot heatmap according to proc/proc.procs
for idx, x in enumerate(data_by_all_hosts):
for idy, y in enumerate(filter_dict_with_prefixes(x, "proc").values()):
try:
pos = proc_count[(idx, idy, x['hostname'])]
except:
pos = len(proc_count)
proc_count[(idx, idy, x['hostname'])] = pos
# print t, pos, 100-y.idle, x['hostname'], y.label
procload_heatmap.append("{time},{pos},{value},{host}" \
.format(time = int(t*1000), pos = pos, value = y.procs,
host = x['hostname']))
# all network interface, total cluster
summed1 = [x['net/total'] for x in data_by_all_hosts if x.has_key('net/total')]
if summed1:
summed = reduce_patched(lambda a,b: a._add(b), summed1)
for x in data_by_all_hosts:
net = x.get("net/total", None)
if not net: continue
# recv-byte, send-byte, recv-packet, send-packet, errors
# print t, x['hostname'], net.recv_bytes, net.send_bytes, net.recv_packets, net.send_packets, net.recv_errs+net.send_errs+net.recv_drop+net.send_drop
# print t, summed
network_overall.append("{time},{recv_bytes},{send_bytes},{recv_packets},{send_packets},{errors}" \
.format(time = int(t*1000),
recv_bytes = summed.recv_bytes / PROBE_INTERVAL,
send_bytes = summed.send_bytes / PROBE_INTERVAL,
recv_packets = summed.recv_packets / PROBE_INTERVAL,
send_packets = summed.send_packets / PROBE_INTERVAL,
errors = (summed.recv_errs + summed.send_errs + \
summed.recv_drop + summed.send_drop) / PROBE_INTERVAL)
)
# all network adapters, plot heatmap according to net/recv_bytes + send_bytes
for idx, x in enumerate(data_by_all_hosts):
for idy, y in enumerate(filter_dict_with_prefixes(x, "net", "!net/total").values()):
try:
pos = network_count[(idx, idy, x['hostname'])]
except:
pos = len(network_count)
network_count[(idx, idy, x['hostname'])] = pos
network_heatmap.append("{time},{pos},{value},{host},{networkid}" \
.format(time = int(t*1000),
pos = pos*2,
value = y.recv_bytes / PROBE_INTERVAL,
host = x['hostname'],
networkid = y.label+".recv"))
network_heatmap.append("{time},{pos},{value},{host},{networkid}" \
.format(time = int(t*1000),
pos = pos*2+1,
value = y.send_bytes / PROBE_INTERVAL,
host = x['hostname'],
networkid = y.label+".send"))
with open(samedir("chart-template.html")) as f:
template = f.read()
variables = locals()
def my_replace(match):
match = match.group()[1:-1]
if match.endswith('heatmap') or match.endswith('overall'):
return "\n".join(variables[match])
elif match =='events':
return "\n".join(events)
elif match == 'probe_interval':
return str(PROBE_INTERVAL * 1000)
elif match == 'workload_name':
return workload_title
else:
return '{%s}' % match
with open(report_fn, 'w') as f:
f.write(re.sub(r'{\w+}', my_replace, template))
def show_usage():
log("""Usage:
monitor.py <workload_title> <parent_pid> <log_path.log> <benchlog_fn.log> <report_path.html> <monitor_node_name1> ... <monitor_node_nameN>
""")
if __name__=="__main__":
if len(sys.argv)<6:
log(sys.argv)
show_usage()
sys.exit(1)
# log(sys.argv)
global log_path
global report_path
global workload_title
global bench_log_path
global na
workload_title = sys.argv[1]
parent_pid = sys.argv[2]
log_path = sys.argv[3]
bench_log_path = sys.argv[4]
report_path = sys.argv[5]
nodes_to_monitor = sys.argv[6:]
pid=os.fork()
if pid: #parent
print pid
else: #child
os.close(0)
os.close(1)
os.close(2)
# log("child process start")
signal.signal(signal.SIGTERM, sig_term_handler)
start_monitor(log_path, nodes_to_monitor)
while os.path.exists("/proc/%s" % parent_pid):
sleep(1)
# parent lost, stop!
signal.signal(signal.SIGTERM, signal.SIG_IGN)
na.stop()
generate_report(workload_title, log_path, bench_log_path, report_path)
HiBench成长笔记——(9) 分析源码monitor.py的更多相关文章
- HiBench成长笔记——(10) 分析源码execute_with_log.py
#!/usr/bin/env python2 # Licensed to the Apache Software Foundation (ASF) under one or more # contri ...
- HiBench成长笔记——(8) 分析源码workload_functions.sh
workload_functions.sh 是测试程序的入口,粘连了监控程序 monitor.py 和 主运行程序: #!/bin/bash # Licensed to the Apache Soft ...
- HiBench成长笔记——(11) 分析源码run.sh
#!/bin/bash # Licensed to the Apache Software Foundation (ASF) under one or more # contributor licen ...
- HiBench成长笔记——(5) HiBench-Spark-SQL-Scan源码分析
run.sh #!/bin/bash # Licensed to the Apache Software Foundation (ASF) under one or more # contributo ...
- memcached学习笔记——存储命令源码分析下篇
上一篇回顾:<memcached学习笔记——存储命令源码分析上篇>通过分析memcached的存储命令源码的过程,了解了memcached如何解析文本命令和mencached的内存管理机制 ...
- memcached学习笔记——存储命令源码分析上篇
原创文章,转载请标明,谢谢. 上一篇分析过memcached的连接模型,了解memcached是如何高效处理客户端连接,这一篇分析memcached源码中的process_update_command ...
- [转]【安卓笔记】AsyncTask源码剖析
[转][安卓笔记]AsyncTask源码剖析 http://blog.csdn.net/chdjj/article/details/39122547 前言: 初学AsyncTask时,就想研究下它的实 ...
- Hadoop学习笔记(10) ——搭建源码学习环境
Hadoop学习笔记(10) ——搭建源码学习环境 上一章中,我们对整个hadoop的目录及源码目录有了一个初步的了解,接下来计划深入学习一下这头神象作品了.但是看代码用什么,难不成gedit?,单步 ...
- 【Azure 应用服务】Azure Function App使用SendGrid发送邮件遇见异常消息The operation was canceled,分析源码逐步最终源端
问题描述 在使用Azure Function App的SendGrid Binging功能,调用SendGrid服务器发送邮件功能时,有时候遇见间歇性,偶发性异常.在重新触发SendGrid部分的Fu ...
随机推荐
- [原]Greenplum failed segment的恢复方法
当在使用greenplum过程中有不当的操作时,可能会出现segment节点宕掉的情况(比如在greenplum运行的过程中停掉其中几台segment节点的服务器),通过下面的方法可以恢复segmen ...
- mongodb的remove操作
今天学习mongodb时,打算用db.user.remove()函数把user中的数据都删了,结果没闪成功,提示:remove needs a query.上网查了一下,是因为没有给remove函数传 ...
- Java的进制转换
十进制转其它进制 其它进制转十进制 A进制转B进制可以将十进制作为中间媒介 Integer.toString(int i, int radix) 返回用第二个参数指定基数表示的第一个参数的字符串表示形 ...
- JVM类的加载顺序
前阵子看到阿里巴巴的一提面试题是关于java类的加载顺序 package com.mikey.demo.Test; class FatherVariable{ static { System.out. ...
- nginx 变量相关的map模块与split_clients模块及geo模块和geoip模块及keepalive介绍
map 模块指令默认编译进nginx的 Syntax: map string $variable { ... } # 表示string匹配到{}里的值或变量赋值给$variable Default: ...
- Python学习笔记004
变量 变量的命名规则1. 要具有描述性2. 变量名只能_,数字,字母组成,不可以是空格或特殊字符(#?<.,¥$*!~)3. 不能以中文为变量名4. 不能以数字开头,下划线或者小写字母开头,驼峰 ...
- 南京江行智能获得百度和松禾资本的A+轮融资
导读 据公司情报专家<财经涂鸦>消息,南京江行联加智能科技有限公司(江行智能)获得百度 和松禾资本的A+ 轮融资. 天眼查信息显示,12 月 8 日,公司工商信息发生变更,股东新增了广州百 ...
- Linux Kernel 5.5 最终删除 SYSCTL 系统调用
导读 Linux Kernel 5.5 最终消除了支持sysctl系统调用的代码,该代码已被弃用了大约十年,目前对任何体系结构的现代系统都没有影响. 长期以来,Linux sysctl系统调用都不建议 ...
- java获取指定月份有几个星期x,获取指定月份跨了多少个星期
例如获取2020年5月一共有多少个星期二,一共跨了多少个星期 public class MainTest { public static void main(String[] args) throws ...
- 三种方式安装mariadb-10.3.18
安装环境:CentOS Linux release 7.5.1804 (Core) 一.yum安装 官方网站yum配置方法链接:https://mariadb.com/kb/en/library/yu ...