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# -*- coding: utf-8 -*-
import requests
import time
import json
from StringIO import StringIO
import re
import sys
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import logging
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(level=logging.INFO)
MACKEREL_APIKEY = os.environ.get("MACKEREL_APIKEY")
BASEURL="https://mackerel.io"
epoch_time = int(time.time())
#duration = 60 * 60 * 24
duration = 60 * 60 * 24 * 7
THRESHOLD = 1
def fetch_hosts():
headers = {'X-Api-Key': MACKEREL_APIKEY}
payload = {}
r = requests.get(BASEURL+"/api/v0/hosts", headers=headers, params=payload)
time.sleep(2.0) # wait
hosts = json.load(StringIO(r.content))
return hosts['hosts']
def fetch_metrics(hostid, name, time_from, time_to):
payload = {'name': name, 'from': time_from, 'to': time_to}
headers = {'X-Api-Key': MACKEREL_APIKEY}
r = requests.get(BASEURL+"/api/v0/hosts/"+hostid+"/metrics", params=payload, headers=headers)
time.sleep(2.0) # wait
metrics = json.load(StringIO(r.content))
if metrics.has_key('metrics'):
return metrics['metrics']
else:
return []
def import_df(metrics, size):
# {u'metrics': [{u'value': 6.85, u'time': 1450575540}, {u'value': 7.03, u'time': 1450575600}]}
times = []
values = []
for metric in metrics:
times.append(metric['time'] - (epoch_time - duration))
values.append(metric['value'])
df = pd.DataFrame({'time': times, 'value': values})
#plt.plot(times, values, 'bo')
model = pd.ols(y=df['value'], x=df['time'], intercept=True)
#print model
#plt.plot(model.x['x'], model.y_fitted, 'g-')
#plt.hlines([size, size * 0.8], times[0], times[-1], linestyles="dashed")
#plt.show()
return model
def prepare_targets():
dat = {}
hosts = fetch_hosts()
for host in hosts:
if not dat.has_key(host['name']):
dat[host['name']] = {'id': host['id']}
meta = host['meta']
if meta.has_key('memory'):
dat[host['name']]['memory'] = meta['memory'].keys()
return dat
def human_readable_size(s):
s = float(s)
if s > 10**12:
return "%.2f TB" % (s / 10**12)
if s > 10**9:
return "%.2f GB" % (s / 10**9)
if s > 10**6:
return "%.2f MB" % (s / 10**6)
if s > 10**3:
return "%.2f MB" % (s / 10**3)
return "%d B" % s
def human_readable_time(s):
r = ""
unit = [("d", 60*60*24),("h", 60*60),("m", 60)]
for u in unit:
if s >= u[1]:
i = int(s / u[1])
r = r + "%s%s" % (i, u[0])
s = s - i * u[1]
return "%s%fs" % (r, s)
def process_targets(dat):
res = []
num = 0
for hostname in dat.keys():
num = num + 1
logger.info('fetching %s (%d/%d)', hostname, num, len(dat))
record = {'hostname': hostname, 'mem': [], 'nearest': sys.maxint}
if not dat[hostname].has_key('memory'):
continue
mem = dat[hostname]['memory']
metric_name = "memory.used"
metrics_size = fetch_metrics(dat[hostname]['id'], "memory.total", epoch_time, epoch_time - 60 * 10)
if len(metrics_size) == 0:
logger.info("skip the mem: %s" % (metric_name))
continue
metrics = fetch_metrics(dat[hostname]['id'], metric_name, epoch_time, epoch_time - duration)
size = 0
for m in metrics_size:
size = max(size, m['value'])
latest = 0
for m in metrics[::-1]:
if m['value'] > 0:
latest = m['value']
break
model = import_df(metrics, size)
x = model.beta.x
i = model.beta.intercept
logger.debug("metric:%s, x: %f, intercept: %f, size: %s, current: %s" % (metric_name, x, i, size, latest))
if x > 0:
target = size * THRESHOLD
e = (target-i)/x
record['mem'].append({'name': metric_name, 'latest': latest, 'size': size, 'x': x, 'i': i, 'fullin': e})
record['nearest'] = min(record['nearest'], e)
logger.debug("mem: %s, size: %s/%s, estimated %s" % (
mem,
human_readable_size(latest),
human_readable_size(size),
human_readable_time(e)))
else:
record['mem'].append({'name': metric_name, 'latest': latest, 'size': size, 'x': x, 'i': i})
logger.debug("mem: %s, used size is reducing." % (mem))
res.append(record)
return res
def output_results(res):
res = sorted(res, key=lambda x: x['nearest'])
for host in res:
for mem in sorted(host['mem'], key=lambda x: x['fullin'] if x.has_key('fullin') else sys.maxint):
if mem.has_key('fullin'):
print "%s:%s, size: %s/%s, full in %s" % (
host['hostname'], mem['name'],
human_readable_size(mem['latest']),
human_readable_size(mem['size']),
human_readable_time(mem['fullin']))
else:
print "%s:%s, size: %s/%s, never full" % (
host['hostname'], mem['name'],
human_readable_size(mem['latest']),
human_readable_size(mem['size']))
if __name__ == "__main__":
dat = prepare_targets()
res = process_targets(dat)
output_results(res)
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