def initialize(self, arg="all"):
"""Clear all instance data and user data on the memory"""
self.search = FGSearch()
self.chart = FGCharts()
self.instances = FGInstances()
self.instances.db.conf()
self.instances.db.connect()
class FGMetricAPI:
""" FG Metric Python API
This API supports usage statistics in FG Metric way, but rely on database query.
In a nutshell,
FG Metric retrieves all records on the database and collects matching records on Python programmatically.
However, FG Metric API retrieves records on the database with search condition, especially 'ownerid' is required field to search.
Mainly, this API calls database with a query look like " select * from instance where start >= date and end <= date and ownerid = id "
Other than that, rest of processes are same as FG Metric.
return value is python 'dict' type
Description
===========
FG Metric Python API to provide usage data like FG Metric's cmd tool but through python API.
Requirement
^^^^^^^^^^^
set_user(ownerid) should be set
get_stats() returns selected statistics
Sample Usage
^^^^^^^^^^^^
api = FGMetricAPI()
api.set_user('hrlee')
api.set_date('2012-01-01', '2012-12-31')
api.set_metric('count')
res = api.get_stats()
print res
Development Status :: 2 - Pre-Alpha
"""
def __init__(self):
self.search = FGSearch()
self.instances = FGInstances()
self.init_db()
self.init_vars()
def init_db(self):
self.instances.db.conf()
self.instances.db.connect()
def init_vars(self):
self.start_date = None
self.end_date = None
self.metric = None
self.cloud = None
self.hostname = None
self.period = None
self.project = None
self.userinfo = None
self.projectinfo = None
def set_date(self, *dates):
self.start_date = dates[0]
self.end_date = dates[1]
def set_metric(self, name):
self.metric = name
def set_user(self, name):
self.username = name
def set_project(self, name):
self.project = name
def set_cloud(self, name):
self.cloud = name
def set_hostname(self, name):
self.hostname = name
def get_metric(self):
# TBD
return
def get_cloud(self):
# TBD
return
def set_period(self, name):
self.period = name
def get_period(self):
# TBD
return
def set_groupby(self, name):
self.groupby = name
def get_groupby(self):
return self.groupby
def get_stats(self):
ownerids = self._get_ownerids()
self._get_instances(ownerids)
self.search.init_stats()
self._set_search_vars()
# pprint(vars(self.search.get_filter()))
self._calculate_stats()
return self.search.get_metric()
def get_realtime(self):
return
def get_series(self):
return self.search.get_series()
def _set_search_vars(self):
self.search.set_date([self.start_date, self.end_date])
self.search.set_metric(self.metric)
self.search.set_platform(self.cloud)
self.search.set_nodename(self.hostname)
self.search.set_period(self.period)
self.search.set_groupby(self.groupby)
def _calculate_stats(self):
for i in range(0, self.instances.count()):
instance = self.instances.get_data(
i, self.search._is_userinfo_needed())[0]
if not self.search._is_in_date(instance):
continue
if not self.search._is_filtered(instance):
continue
res = self.search.collect(instance)
def _get_ownerids(self):
try:
self.instances.read_userinfo({"username": self.username})
userinfo = self.instances.userinfo
ownerids = [element['ownerid'] for element in userinfo]
return ownerids
except:
return None
def _get_instances(self, ownerids=None):
if ownerids:
whereclause = " and ownerid in " + str(tuple(ownerids)) + ""
else:
whereclause = ""
self.instances.read_instances({}, whereclause)
def get_instances(self):
return self.instances.instance
def get_userinfo(self):
"""Return all users"""
if self.userinfo:
return self.userinfo
try:
self.instances.read_userinfo_detail()#({}, " group by username ")
self.userinfo = self.instances.get_userinfo()
return self.userinfo
except:
print "failed to read userinfo %s" % sys.exc_info()
return None
def get_projectinfo(self):
"""Return all projects"""
if self.projectinfo:
return self.projectinfo
try:
prj_info = self.instances.get_projectinfo()
self.projectinfo = prj_info
return self.projectinfo
#self.instances.read_projectinfo()
#self.projectinfo = self.instances.projectinfo
except:
print "failed to read project info %s" % sys.exc_info()
return None
def _set_dict_vars(self):
self.result = {
"start_date": self.start_date,
"end_date": self.end_date,
"ownerid": self.username,
"metric": self.metric,
"period": self.period or "All",
"clouds": self.cloud or "All",
"hostname": self.hostname or "All"
}
return self.result
class FGMetricShell(Cmd):
instances = None
search = None
def __init__(self):
Cmd.__init__(self)
self.prompt = "fg-metric> "
self.intro = "Welcome to FutureGrid Cloud Metrics!"
def initialize(self, arg="all"):
"""Clear all instance data and user data on the memory"""
self.search = FGSearch()
self.chart = FGCharts()
self.instances = FGInstances()
self.instances.db.conf()
self.instances.db.connect()
def init_objects(self):
self.search.__init__()
self.chart.__init__()
def load_db(self, option=None):
"""Read the statistical data from database (MySQL, etc)"""
print "\rloading database ..."
# Get data from the database
self.instances.read_from_db()
# Get also userinfo data from the database
self.instances.read_userinfo_from_db()
self.instances.read_projectinfo_from_db()
print "\r... loaded"
def show_dbinfo(self, param=None):
pprint(vars(self.instances.db), indent=2)
def show_filter_setting(self, param=None):
pprint(vars(self.search.get_filter()))
# res = vars(self.search.get_filter()).copy()
# del res["selected"]
# pprint(res)
def measure(self):
total_counts = self.instances.count()
print "Calculating metrics in " + str(total_counts) + " records...\n"
cnt = cnt2 = cnt3 = 0
for i in range(0, total_counts):
try:
instance = self.instances.get_data(
i, self.search._is_userinfo_needed())[0]
cnt += 1
if not self.search._is_in_date(instance):
continue
cnt2 += 1
if not self.search._is_filtered(instance):
continue
cnt3 += 1
res = self.search.collect(instance)
except:
# print sys.exc_info()
pass # raise
print self.search.get_metric()
# print cnt, cnt2, cnt3
'''
I am where to create a dict/list for data of charts.
what I need to do is
1) choose which column that I need to collect. This should be done by the 'metric' filter
2) get value from the instance
3) create data structure for the result
4) if it has a groupby(s), create multi-dimentional dict/list to save the value in a depth
e.g. res[groupby1][groupby2] =
e.g. res = { groupby1 : { groupby2: val1, ... } }
5) fill missing date? for chart format? this should be done by in a chart module
6) convert the result data structure to chart formatted data
'''
def set_configfile(self, filename):
self.instances.db.set_conf(filename)
self.instances.db.update_conf()
print filename + " loaded."
print "refresh db may required."
def create_csvfile(self, data, dirname="./", filename="default.csv"):
try:
writer = csv.writer(open(dirname + filename, 'wb'), delimiter=",",
quotechar="\"", quoting=csv.QUOTE_NONNUMERIC) # QUOTE_MINIMAL)
for row in data:
writer.writerow(row)
msg = filename + " is created"
except:
msg = filename + " is not created"
print sys.exc_info()
pass
print msg
@options([
make_option('-f', '--start_date', type="string",
help="start time of the interval (type. YYYY-MM-DDThh:mm:ss)"),
make_option('-t', '--end_date', type="string",
help="end time of the interval (type. YYYY-MM-DDThh:mm:ss)"),
make_option('-M', '--month', type="int",
help="month to analyze (type. MM)"),
make_option('-Y', '--year', type="int",
help="year to analyze (type. YYYY)"),
make_option('-m', '--metric', dest="metric", type="string",
help="item name to measure (e.g. runtime, count)"),
make_option('-P', '--period', dest="period",
type="string", help="search period (monthly, daily)")
])
def do_analyze(self, line, opts=None):
"""Run analysis for cloud usage data.
Typically, set platform ***, set nodename ***, set date *** *** are required prior to this command
Once analysis is finised, 'chart' command is usually following to generate results in a chart html file.
Args:
line(str): input line
Returns:
n/a
Raises:
n/a
"""
try:
self.set_parameters(opts)
self.search.check_vailidity()
self.search.init_stats()
self.show_filter_setting()
self.measure()
except ValueError as e:
print e
except:
print sys.exc_info()
@options([
make_option('-o', '--output', type="string",
dest="filepath", help="filepath to export a csv file")
])
def do_csv(self, line, opts=None):
"""Export statistics as a csv file"""
try:
data = self.search.get_csv()
if not opts.filepath:
filedir = "./"
filename = self.search.get_filename() + "." + "csv"
else:
filedir, filename = split(opts.filepath)
self.create_csvfile(data, filedir, filename)
except:
print "no dataset is available to export."
print "please perform 'analyze' first to export data"
print
@options([
make_option('-o', '--directory', type="string",
dest="DIR", help="change to directory DIR"),
make_option('-t', '--type', type="string", dest="ctype",
default="column", help="chart e.g. bar, line, column, pie, and motion"),
make_option(
'-a', '--api', type="string", dest="api", default="highcharts",
help="chart api e.g. highchart, google, jquery sparkline")
])
def do_chart(self, line, opts=None):
''' Generate html typed chart file based on the statistics from analyze command '''
self.chart.set_chart_api(opts.api)
self.chart.set_type(opts.ctype)
self.chart.set_output_path(opts.DIR)
self.chart.set_filename(
self.search.get_filename() + "." + self.chart.output_type)
for key, data in self.search.get_metric().iteritems():
# self.chart.set_xaxis(key) TBD
if key == "All":
self.chart.set_data_beta(
data, self.search.metric, self.search.period, self.search.groupby)
else:
new_key = self.search.adjust_stats_keys(key)
self.chart.set_data_beta2(new_key, data, ''.join(
self.search.metric), self.search.period or "Total")
# self.chart.set_series_beta(data)
self.chart.set_series(self.search.get_series())
self.chart.set_title_beta(', '.join(
self.search.metric), self.search.period, self.search.groupby)
self.chart.set_subtitle("source: " + str(
self.search.get_platform_names()) + " on " + str(self.search.get_node_names()))
self.chart.set_yaxis(self.search.timetype or "")
self.chart.display()
@options([
make_option('-u', '--user', type="string",
help="Show only image numbers owned by the userid specified."),
make_option('-d', '--detail', action="store_true",
default=False, help="Show details about images"),
make_option('-s', '--summary', action="store_true",
default=False, help="Show summary values about images")
])
def count_images(self, arg, opts=None):
"""Count bucket images per user (development level)
It is virtual machine image counts grouped by users or accounts based on euca2ools.
It shows that which user or account currently owns how many virtual machine images on the system.
This metric is based on the euca2ool command .euca-describe-images. that a eucalyptus user can see
a list of machine images.
"""
bucket_dict = {}
details = {}
detail = {}
max_user = ["", 0]
bin_path = subprocess.check_output(["which", "euca-describe-images"])
eucabin = bin_path.split("\n")
output = subprocess.check_output(["python2.7", eucabin[0]])
# Split the output by end-of-line chars.
lines = output.split("\n")
chart_labels = []
# Loop through lines. The image path is the third item.
# Split by "/" to get bucket and key.
for line in lines:
if line:
try:
values = line.split()
bucket, key = values[2].split("/")
# replace bucket with accountId - hrlee
# No reason to gather bucket name. Instead, accountid would
# be meaningful.
bucket = values[3] + "(" + values[3] + ")"
count = bucket_dict.get(bucket, 0)
detail[count] = line
details[bucket] = detail
bucket_dict[bucket] = count + 1
if bucket_dict[bucket] > max_user[1]:
max_user[0] = bucket
max_user[1] = bucket_dict[bucket]
except:
continue
for key, value in bucket_dict.items():
if opts.user:
if opts.user != key:
continue
print("\t".join([key, str(value)]))
chart_labels.append(key + ":" + str(value))
# show detail information of image owned by a specific user from -u,
# --user option
if opts.user and opts.detail:
for key, value in details[opts.user].items():
print (value)
# Show summary of images. i.e. number of total images, number of users,
# average numbers of images, and maximum numbers of images.
if opts.summary:
total_image_count = str(len(
lines) - 1) # Except (-1) last \n line count
total_user_count = str(len(bucket_dict))
print ""
print "= Summary ="
print "Total image counts:\t" + total_image_count
print "Total user counts:\t" + total_user_count
print "Average image counts per user:\t" + str(float(total_image_count) / float(total_user_count))
print "Maximum image counts and userid:\t" + max_user[0] + " has " + str(max_user[1])
print "=========="
def do_refresh(self, line, opts=None):
"""Refresh component (same as 'load')
Usage example:
fg-metric] refresh db"""
self.do_load(line, opts)
def do_load(self, line, opts=None):
"""Load component
Usage example:
fg-metric] load db"""
self.call_attr(line, "load_")
def do_showconf(self, line, opts=None):
"""Display current settings
Usage example:
fg-metric] showconf dbinfo
fg-metric] showconf filter_setting"""
self.call_attr(line, "show_")
def do_show(self, line, opts=None):
'''show search options set by a user'''
self.call_attr(line, "show_", "self.search")
def do_get(self, line, opts=None):
"""Show current settings
Usage example:
fg-metric] get filter"""
self.call_attr(line, "get_", "self.search")
def do_setconf(self, line, opts=None):
"""Set a configuration"""
self.call_attr(line, "set_")
def do_set(self, line, opts=None):
"""Set a function with parameter(s)"""
self.call_attr(line, "set_", "self.search")
def do_count(self, line, opts=None):
"""Set a function with parameter(s)"""
self.call_attr(line, "count_")
def call_attr(self, line, prefix="_", obj_name="self"):
try:
args = line.split()
cmd = args[0]
if len(args) == 1:
params = ""
elif len(args) == 2:
params = args[1]
else:
params = args[1:]
except:
cmd = None
params = ""
function = prefix + str(cmd)
try:
func = getattr(eval(obj_name), function)
if callable(func):
func(params)
print function + " is called .(" + "".join(params) + ")"
except:
print sys.exc_info()
pass
def set_parameters(self, opts):
"""Set search options from opt parse variables
What variables are set:
a. dates
b. metric
c. period
Setting prioirity
1. start_date, end_date
2. year, month
3. set date $from $to (set by prior to analyze command)
For example,
if opts.start_date and opts.end_date are given, opts.year and opts.month will be ignored.
Args:
opts.start_date
opts.end_date
opts.year
opts.month
opts.period
opts.metric
"""
if opts.year or opts.month:
now = datetime.now()
from_date = datetime(opts.year or now.year, opts.month or 1, 1)
to_date = datetime(opts.year or now.year, opts.month or 12, monthrange(
opts.year or now.year, opts.month or 12)[1])
self.search.set_date([from_date, to_date])
if opts.start_date and opts.end_date:
self.search.set_date([opts.start_date, opts.end_date])
if opts.period:
self.search.set_period(opts.period)
if opts.metric:
self.search.set_metric(opts.metric)
def help_analyze(self):
print "Run analysis for cloud usage data"
def do_clear(self, line):
"""Clear settings for analysis. (e.g. nodename, platform, date will be cleared)"""
self.init_objects()
def preloop(self):
self.initialize()
self.load_db()
def postloop(self):
print "Bye ..."
def __init__(self):
self.search = FGSearch()
self.instances = FGInstances()
self.init_db()
self.init_vars()
