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