bug fixes

src/api/index.py

@@ -196,7 +196,7 @@ def anomalies_status():
 
 if __name__== '__main__':
 	
-	#ThreadManager.start(CONFIG)	
+	ThreadManager.start(CONFIG)	
 	app.run(host='0.0.0.0',debug=True,threaded=True)
 
 	

src/api/static/js/dashboard.js

@@ -86,7 +86,7 @@ monitor.processes.render = function(label,data) {
 		{ name: 'label', type: 'text', title: "Process", headercss: "small bold", css: "small"},
 		{ name: "cpu_usage", type: "number", title: "CPU", headercss: "small bold" , width:'64px'},
 		{ name: "memory_usage", type: "text", title: "Mem. Used", type: "number", headercss: "small bold" },
-		{ name: "memory_available", type: "number", title: "Mem. Avail", headercss: "small bold" },
+		{ name: "proc_count", type: "number", title: "Proc Count", headercss: "small bold" },
 		{name:"status",type:"text",title:"Status",headercss:"small bold",align:"center", width:'64px'}
 	]
 	var grid = $('#latest_processes').jsGrid(options) ;
@@ -127,7 +127,10 @@ monitor.processes.trend.render = function (logs, key,label) {
 	conf.data = {}
 	conf.options = { legend: { position: 'bottom' } }
 	conf.options.scales = {}
-	conf.options.scales.yAxes = [  {scaleLabel:{display:true,labelString:'CPU & MEMORY USAGE'},ticks:{min:0,beginAtZero:true},gridLines: {display:false}}]
+	conf.options.scales.yAxes = [  
+		{id:'0',scaleLabel:{display:true,labelString:'CPU & MEMORY USAGE'},ticks:{min:0,beginAtZero:true},gridLines: {display:false}},
+		{id:'1',position:'right',scaleLabel:{display:true,labelString:'PROCESS COUNT'},ticks:{min:0,stepSize:1,beginAtZero:true},gridLines: {display:false}}
+	]
 	conf.options.scales.xAxes = [
 		{
 			
@@ -145,8 +148,9 @@ monitor.processes.trend.render = function (logs, key,label) {
 	var x_axis = []
 	var _x = {}
 	// var _y = {}
-	var cpu = { label: 'CPU Usage (%)', data: [] ,backgroundColor:'transparent',borderColor:COLORS[187],fill:false,borderWidth:1}
-	var mem = {label : 'Memory Usage(%)',data:[],backgroundColor:'transparent',borderColor:COLORS[32],fill:false,borderWidth:1}
+	var cpu = {yAxisID:'0', label: 'CPU Usage (%)', data: [] ,backgroundColor:'transparent',borderColor:COLORS[187],fill:false,borderWidth:1}
+	var mem = {yAxisID:'0',label : 'Memory Usage(%)',data:[],backgroundColor:'transparent',borderColor:COLORS[32],fill:false,borderWidth:1}
+	var proc= {yAxisID:'1',label : 'Proc Count',data:[],backgroundColor:'transparent',borderColor:COLORS[42],fill:false,borderWidth:1}
 	jx.utils.patterns.visitor(logs,function(item){
 		x = new Date(item.year,item.month-1,item.day,item.hour,item.minute)
 		y = item[key]
@@ -156,6 +160,7 @@ monitor.processes.trend.render = function (logs, key,label) {
 			x_axis.push(x)	
 			cpu.data.push({ x: x, y: item.cpu_usage })
 			mem.data.push({x:x,y:item.memory_usage})
+			proc.data.push({x:x,y:item.proc_count})
 			// return {x:x,y:y}
 			
 		}
@@ -164,7 +169,7 @@ monitor.processes.trend.render = function (logs, key,label) {
 	
 	
 	
-	conf.data.datasets = [cpu,mem]
+	conf.data.datasets = [cpu,mem,proc]
 	x_axis = jx.utils.unique(x_axis)
 	conf.data.labels = x_axis
 	// console.log(conf)
@@ -337,4 +342,4 @@ monitor.sandbox.render = function (logs) {
 
 /**
  * Socket handler, check for learning status
- */
+ */

src/monitor.py

@@ -146,17 +146,17 @@ class DetailProcess(Analysis):
 		pattern = "(\d+.{0,1}\d*)\x20*(\d+.{0,1}\d*)\x20*(\d+.{0,1}\d*)".replace(":name",name).strip()
 		g = re.match(pattern,stream.strip())
 		if g :
-			return list(g.groups())+[name]
+			return list(g.groups())+['1']+[name]
 		else:
 			return ''
 	def evaluate(self,name) :
-		cmd	= "ps -eo pmem,pcpu,vsize,comm|grep -E \":app\""
+		cmd	= "ps -eo pmem,pcpu,vsize,command|grep -E \":app\""
 		handler = subprocess.Popen(cmd.replace(":app",name),shell=True,stdout=subprocess.PIPE)
 		ostream = handler.communicate()[0].split('\n')
-		
-		ostream = [ self.split(name,row) for row in ostream if row != '']
+		#xstr = ostream	
+		ostream = [ self.split(name,row) for row in ostream if row != '' and 'grep' not in row]
 		if len(ostream) == 0 or len(ostream[0]) < 4 :
-			ostream = [['0','0','0',name]]
+			ostream = [['0','0','0','0',name]]
 		r = []
 		for row in ostream :
 			#
@@ -164,8 +164,26 @@ class DetailProcess(Analysis):
 			# On OSX it has been observed that the fully qualified path is sometimes returned (go figure)
 			#
 			row =  [float(value) for value in row if value.strip() != '' and name not in value ] +[re.sub('\$|^','',name)]
-			
 			r.append(row)
+		#
+		# At this point we should aggregate results
+		# The aggregation is intended for applications with several processes (e.g: apache2)
+		#
+		if len(r) > 1:
+			m = None
+			for row in r:
+				if m is None:
+					m = row
+				else:
+					m[3] += row[3]
+					m[0] += row[0]
+					m[1] += row[1]
+					m[2] += row[2]
+			m[0] = round((m[0] / m[3]),2)
+			m[1] = round((m[1] / m[3]),2)
+			m[2] = round((m[2] / m[3]),2)
+
+			r = [m]
 		return r
 	def status(self,row):
 		x = row['memory_usage']
@@ -178,7 +196,7 @@ class DetailProcess(Analysis):
 		else:
 			return "crash"
 	def format(self,row):
-		r= {"memory_usage":row[0],"cpu_usage":row[1],"memory_available":row[2]/1000,"label":row[3]}
+		r= {"memory_usage":row[0],"cpu_usage":row[1],"memory_available":row[2]/1000,"proc_count":row[3],"label":row[4]}
 		status = self.status(r)
 		r['status'] = status
 		return r
@@ -201,7 +219,32 @@ class DetailProcess(Analysis):
 		#return [{"memory_usage":row[0],"cpu_usage":row[1],"memory_available":row[2]/1000,"label":row[3]} for row in ma]
 		
 		return ma
+class FileWatch(Analysis):
+	def __init__(self,conf):
+		pass
+	def split(self,row):
+		x = row.split(' ')
+		r = {}
+		months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
+		if 'K' in x[0]:
+			size = x[0].replace('K','').replace('KB','') / 1000
+		elif 'MB' in x[0] :
+			size = x[0].replace('MB','')
+		elif 'GB' in x[0] :
+			size = x[0].replace('GB','') * 1000
+		month	= months.index(m[1]) + 1
+		day	= x[2]
+		hour,minute	= x[3].split(':')
+		year	= x[4]
+		return {"size":size,"age":age}	
 
+	def evaluate(self,path):
+		cmd = "find  :path|xargs ls -lh |awk '{print $5,$6,$7,$8,$9}'".replace(":path",path)
+		handler = subprocess.Popen(cmd,shell=True,stdout=subprocess.PIPE)
+		ostream = handler.communicate()[0].split('\n')
+		[self.split(stream) for stream in ostream if stream.strip() != '']
+		pass
+	
 class Monitor (Thread):
 	def __init__(self,pConfig,pWriter,id='processes') :
 		Thread.__init__(self)
@@ -232,8 +275,8 @@ class Monitor (Thread):
 			lock.release()		
 			
 			self.prune()
-			HALF_HOUR = 60*25
-			time.sleep(HALF_HOUR)
+			TIME_LAPSE = 60*2
+			time.sleep(TIME_LAPSE)
 		print "Stopped ..."
 	def prune(self) :
 		

src/utils/ml.py

@@ -17,7 +17,8 @@ class ML:
 		# We may have a potential issue of how the data is stored ... it may not scale
 		#
 		
-		return [item[0] for item in data if item and attr in item[0] and item[0][attr] == value]
+		#return [item[0] for item in data if item and attr in item[0] and item[0][attr] == value]
+		return [[item for item in row if item[attr] == value] for row in data]
 	@staticmethod
 	def Extract(lattr,data):
 		if isinstance(lattr,basestring):
@@ -41,6 +42,7 @@ class AnomalyDetection:
 		test	= data[end:]
 		
 		return {"train":train,"test":test}
+	
 	"""
 
 		@param key 	field name by which the data will be filtered
@@ -51,8 +53,9 @@ class AnomalyDetection:
 	"""
 	def learn(self,data,key,value,features,label):
 		xo = ML.Filter(key,value,data)
+		print key,value, len(xo)
 		
-		if not xo :
+		if not xo or len(xo) < 100:
 			return None
 		
 		#if len(xo) < 100 :
@@ -69,18 +72,20 @@ class AnomalyDetection:
 
 		if xo['train'] :
 			E = 0.01
+			fscore = 0
 			for i in range(0,10):
 				Epsilon = E + (2*E*i)
 				p = self.gParameters(xo['train'])
-				
+				if p is None :
+					return None
 				px =  self.gPx(p['mean'],p['cov'],xo['test'],Epsilon)
 				
 				perf = self.gPerformance(px,yo['test'])
-				if perf['fscore'] > 0 :
+				if fscore == 0 :
+					fscore = perf['fscore']
+				elif perf['fscore'] > fscore and perf['fscore'] > 0.5 :
 					
 					perf['epsilon'] = Epsilon
-					
-					break
 			
 			return {"label":value,"parameters":p,"performance":perf}
 		return None
@@ -157,6 +162,8 @@ class AnomalyDetection:
 		m = np.transpose(np.array(train))
 		
 		u = np.array([ np.mean(m[i][:]) for i in range(0,n)])		
+		if np.sum(u) == 0:
+			return None
 		r = np.array([ np.sqrt(np.var(m[i,:])) for i in range(0,n)])
 		#
 		#-- Normalizing the matrix then we will compute covariance matrix
@@ -174,4 +181,4 @@ class Regression:
 		pass
 	
 	def __init__(self,config):
-		pass
+		pass

src/utils/transport.py

@@ -251,7 +251,6 @@ class MessageQueue:
 		self.close()
 		return resp
 	def close(self):
-		print "closing ..."
 		self.channel.close()
 		self.connection.close()
 """

test/TestServerMonitor.py

@@ -27,8 +27,10 @@ class TestMonitorServer(unittest.TestCase):
 		self.assertTrue(p.evaluate('PATH') == 0)
 	def test_RunningProcess(self):
 		p = DetailProcess()
-		p.init(['kate','firefox']) #['rabbitmq-server','python','apache2','firefox'])
+		p.init(['kate','rabbitmq-server','python','apache2','firefox'])
 		r = p.composite()
+		for row in r:
+			print row['label'],row['status'],row['proc_count']
 		self.assertTrue(r)
 	
 	def test_ProcessCount(self):