@ -12,8 +12,8 @@ from sets import Set
import re
import datetime
import Queue
from threading import Thread
import time
class Analysis :
def __init__ ( self ) :
self . logs = [ ]
@ -96,7 +96,8 @@ class Sandbox(Analysis):
n = len ( Set ( required_modules ) - Set ( sandbox_modules ) )
value = 1 - ( n / N )
missing = list ( Set ( required_modules ) - Set ( sandbox_modules ) )
return { " value " : value , " missing " : missing }
return dict ( self . now , * * { " value " : value , " missing " : missing } )
"""
This class performs the analysis of a list of processes and determines
@ -121,7 +122,7 @@ class ProcessCounter(Analysis):
#N = len(r)
#n = sum(r)
#return n/N
return r
return dict ( self . now , * * r )
"""
This class returns an application ' s both memory and cpu usage
"""
@ -170,7 +171,7 @@ class DetailProcess(Analysis):
r = { " memory_usage " : row [ 0 ] , " cpu_usage " : row [ 1 ] , " memory_available " : row [ 2 ] / 1000 , " label " : row [ 3 ] }
status = self . status ( r )
r [ ' status ' ] = status
return r
return dict ( self . now , * * r )
def composite ( self ) :
#value = self.evaluate(self.name)
#row= {"memory_usage":value[0],"cpu_usage":value[1]}
@ -178,17 +179,84 @@ class DetailProcess(Analysis):
#ma = [self.evaluate(name) for name in self.names]
ma = [ ]
for name in self . names :
matrix = self . evaluate ( name )
ma + = [ self . format ( row ) for row in matrix ]
#return [{"memory_usage":row[0],"cpu_usage":row[1],"memory_available":row[2]/1000,"label":row[3]} for row in ma]
return ma
class QListener ( Thread )
def __init__ ( self , handlers ) :
self . handlers = handlers
self . queue = Queue . LifoQueue ( )
def post ( self ) :
for handler in self . handlers :
self . queue . put ( handler . )
class Monitor ( Thread ) :
def __init__ ( self , pConfig , pQueue , id = ' processes ' ) :
Thread . __init__ ( self )
self . config = pConfig [ id ]
self . queue = pQueue ;
self . logs = [ ]
self . handler = self . config [ ' class ' ]
self . mconfig = self . config [ ' config ' ]
def run ( self ) :
r = { }
while True :
for label in self . mconfig :
self . handler . init ( self . mconfig [ label ] )
r [ label ] = self . handler . composite ( )
self . logs . append ( r )
self . queue . put ( r )
self . prune ( )
self . queue . task_done ( )
time . sleep ( 10 )
def prune ( self ) :
MAX_ENTRIES = 1000
if len ( self . logs ) > MAX_ENTRIES :
BEG = len ( self . logs ) - MAX_SIZE - 1
self . logs = self . logs [ BEG : ]
class mapreducer :
def __init__ ( self ) :
self . store = { }
def filter ( self , key , dataset ) :
return [ row [ key ] for row in dataset if key in row ]
def run ( self , dataset , mapper , reducer ) :
r = None
if mapper is not None :
if isinstance ( dataset , list ) :
[ mapper ( row , self . emit ) for row in dataset ]
if reducer is not None :
r = [ reducer ( self . store [ key ] ) for key in self . store ]
else :
r = self . store
return r
def mapper ( self , row , emit ) :
[ emit ( item [ ' label ' ] , item ) for item in row ]
def reducer ( self , values ) :
return value
def emit ( self , key , content ) :
if key not in self . store :
self . store [ key ] = [ ]
self . store [ key ] . append ( content )
# #
# # We need to generate the appropriate dataset here
# # map/reduce is a well documented technique for generating datasets
# #
# def map(self,key,id,rows):
# #r = [row[key] for row in rows if key in row]
# for row in rows:
# if key in row :
# for xr in row[key]:
# self.emit(xr['label'],xr)
# def reduce(keys,values):
# print values[0]
# return r
Steve L. Nyemba
8 years ago